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fundamentals_of_database_systems_6th_edition.pdf
FUNDAMENTALS OF
Database Systems SIXTH EDITION
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FUNDAMENTALS OF
Database Systems SIXTH EDITION
Ramez Elmasri Department of Computer Science and Engineering The University of Texas at Arlington
Shamkant B. Navathe College of Computing Georgia Institute of Technology
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Library of Congress Cataloging-in-Publication Data
Elmasri, Ramez. Fundamentals of database systems / Ramez Elmasri, Shamkant B. Navathe.—6th ed.
p. cm. Includes bibliographical references and index. ISBN-13: 978-0-136-08620-8
1. Database management. I. Navathe, Sham. II. Title.
QA76.9.D3E57 2010 005.74—dc22Addison-Wesley
is an imprint of
10 9 8 7 6 5 4 3 2 1—CW—14 13 12 11 10 ISBN 10: 0-136-08620-9 ISBN 13: 978-0-136-08620-8
To Katrina, Thomas, and Dora (and also to Ficky)
R. E.
To my wife Aruna, mother Vijaya, and to my entire family
for their love and support
S.B.N.
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vii
This book introduces the fundamental concepts nec-essary for designing, using, and implementing database systems and database applications. Our presentation stresses the funda- mentals of database modeling and design, the languages and models provided by the database management systems, and database system implementation tech- niques. The book is meant to be used as a textbook for a one- or two-semester course in database systems at the junior, senior, or graduate level, and as a reference book. Our goal is to provide an in-depth and up-to-date presentation of the most important aspects of database systems and applications, and related technologies. We assume that readers are familiar with elementary programming and data- structuring concepts and that they have had some exposure to the basics of com- puter organization.
New to This Edition The following key features have been added in the sixth edition:
■ A reorganization of the chapter ordering to allow instructors to start with projects and laboratory exercises very early in the course
■ The material on SQL, the relational database standard, has been moved early in the book to Chapters 4 and 5 to allow instructors to focus on this impor- tant topic at the beginning of a course
■ The material on object-relational and object-oriented databases has been updated to conform to the latest SQL and ODMG standards, and consoli- dated into a single chapter (Chapter 11)
■ The presentation of XML has been expanded and updated, and moved ear- lier in the book to Chapter 12
■ The chapters on normalization theory have been reorganized so that the first chapter (Chapter 15) focuses on intuitive normalization concepts, while the second chapter (Chapter 16) focuses on the formal theories and normaliza- tion algorithms
■ The presentation of database security threats has been updated with a dis- cussion on SQL injection attacks and prevention techniques in Chapter 24, and an overview of label-based security with examples
Preface
■ Our presentation on spatial databases and multimedia databases has been expanded and updated in Chapter 26
■ A new Chapter 27 on information retrieval techniques has been added, which discusses models and techniques for retrieval, querying, browsing, and indexing of information from Web documents; we present the typical processing steps in an information retrieval system, the evaluation metrics, and how information retrieval techniques are related to databases and to Web search
The following are key features of the book:
■ A self-contained, flexible organization that can be tailored to individual needs
■ A Companion Website (http://www.aw.com/elmasri) includes data to be loaded into various types of relational databases for more realistic student laboratory exercises
■ A simple relational algebra and calculus interpreter
■ A collection of supplements, including a robust set of materials for instruc- tors and students, such as PowerPoint slides, figures from the text, and an instructor’s guide with solutions
Organization of the Sixth Edition There are significant organizational changes in the sixth edition, as well as improve- ment to the individual chapters. The book is now divided into eleven parts as follows:
■ Part 1 (Chapters 1 and 2) includes the introductory chapters
■ The presentation on relational databases and SQL has been moved to Part 2 (Chapters 3 through 6) of the book; Chapter 3 presents the formal relational model and relational database constraints; the material on SQL (Chapters 4 and 5) is now presented before our presentation on relational algebra and cal- culus in Chapter 6 to allow instructors to start SQL projects early in a course if they wish (this reordering is also based on a study that suggests students master SQL better when it is taught before the formal relational languages)
■ The presentation on entity-relationship modeling and database design is now in Part 3 (Chapters 7 through 10), but it can still be covered before Part 2 if the focus of a course is on database design
■ Part 4 covers the updated material on object-relational and object-oriented databases (Chapter 11) and XML (Chapter 12)
■ Part 5 includes the chapters on database programming techniques (Chapter 13) and Web database programming using PHP (Chapter 14, which was moved earlier in the book)
■ Part 6 (Chapters 15 and 16) are the normalization and design theory chapters (we moved all the formal aspects of normalization algorithms to Chapter 16)
viii Preface
Preface ix
■ Part 7 (Chapters 17 and 18) contains the chapters on file organizations, indexing, and hashing
■ Part 8 includes the chapters on query processing and optimization tech- niques (Chapter 19) and database tuning (Chapter 20)
■ Part 9 includes Chapter 21 on transaction processing concepts; Chapter 22 on concurrency control; and Chapter 23 on database recovery from failures
■ Part 10 on additional database topics includes Chapter 24 on database secu- rity and Chapter 25 on distributed databases
■ Part 11 on advanced database models and applications includes Chapter 26 on advanced data models (active, temporal, spatial, multimedia, and deduc- tive databases); the new Chapter 27 on information retrieval and Web search; and the chapters on data mining (Chapter 28) and data warehousing (Chapter 29)
Contents of the Sixth Edition Part 1 describes the basic introductory concepts necessary for a good understanding of database models, systems, and languages. Chapters 1 and 2 introduce databases, typical users, and DBMS concepts, terminology, and architecture.
Part 2 describes the relational data model, the SQL standard, and the formal rela- tional languages. Chapter 3 describes the basic relational model, its integrity con- straints, and update operations. Chapter 4 describes some of the basic parts of the SQL standard for relational databases, including data definition, data modification operations, and simple SQL queries. Chapter 5 presents more complex SQL queries, as well as the SQL concepts of triggers, assertions, views, and schema modification. Chapter 6 describes the operations of the relational algebra and introduces the rela- tional calculus.
Part 3 covers several topics related to conceptual database modeling and database design. In Chapter 7, the concepts of the Entity-Relationship (ER) model and ER diagrams are presented and used to illustrate conceptual database design. Chapter 8 focuses on data abstraction and semantic data modeling concepts and shows how the ER model can be extended to incorporate these ideas, leading to the enhanced- ER (EER) data model and EER diagrams. The concepts presented in Chapter 8 include subclasses, specialization, generalization, and union types (categories). The notation for the class diagrams of UML is also introduced in Chapters 7 and 8. Chapter 9 discusses relational database design using ER- and EER-to-relational mapping. We end Part 3 with Chapter 10, which presents an overview of the differ- ent phases of the database design process in enterprises for medium-sized and large database applications.
Part 4 covers the object-oriented, object-relational, and XML data models, and their affiliated languages and standards. Chapter 11 first introduces the concepts for object databases, and then shows how they have been incorporated into the SQL standard in order to add object capabilities to relational database systems. It then
x Preface
covers the ODMG object model standard, and its object definition and query lan- guages. Chapter 12 covers the XML (eXtensible Markup Language) model and lan- guages, and discusses how XML is related to database systems. It presents XML concepts and languages, and compares the XML model to traditional database models. We also show how data can be converted between the XML and relational representations.
Part 5 is on database programming techniques. Chapter 13 covers SQL program- ming topics, such as embedded SQL, dynamic SQL, ODBC, SQLJ, JDBC, and SQL/CLI. Chapter 14 introduces Web database programming, using the PHP script- ing language in our examples.
Part 6 covers normalization theory. Chapters 15 and 16 cover the formalisms, theo- ries, and algorithms developed for relational database design by normalization. This material includes functional and other types of dependencies and normal forms of relations. Step-by-step intuitive normalization is presented in Chapter 15, which also defines multivalued and join dependencies. Relational design algorithms based on normalization, along with the theoretical materials that the algorithms are based on, are presented in Chapter 16.
Part 7 describes the physical file structures and access methods used in database sys- tems. Chapter 17 describes primary methods of organizing files of records on disk, including static and dynamic hashing. Chapter 18 describes indexing techniques for files, including B-tree and B+-tree data structures and grid files.
Part 8 focuses on query processing and database performance tuning. Chapter 19 introduces the basics of query processing and optimization, and Chapter 20 dis- cusses physical database design and tuning.
Part 9 discusses transaction processing, concurrency control, and recovery tech- niques, including discussions of how these concepts are realized in SQL. Chapter 21 introduces the techniques needed for transaction processing systems, and defines the concepts of recoverability and serializability of schedules. Chapter 22 gives an overview of the various types of concurrency control protocols, with a focus on two-phase locking. We also discuss timestamp ordering and optimistic concurrency control techniques, as well as multiple-granularity locking. Finally, Chapter 23 focuses on database recovery protocols, and gives an overview of the concepts and techniques that are used in recovery.
Parts 10 and 11 cover a number of advanced topics. Chapter 24 gives an overview of database security including the discretionary access control model with SQL com- mands to GRANT and REVOKE privileges, the mandatory access control model with user categories and polyinstantiation, a discussion of data privacy and its rela- tionship to security, and an overview of SQL injection attacks. Chapter 25 gives an introduction to distributed databases and discusses the three-tier client/server architecture. Chapter 26 introduces several enhanced database models for advanced applications. These include active databases and triggers, as well as temporal, spa- tial, multimedia, and deductive databases. Chapter 27 is a new chapter on informa- tion retrieval techniques, and how they are related to database systems and to Web
search methods. Chapter 28 on data mining gives an overview of the process of data mining and knowledge discovery, discusses algorithms for association rule mining, classification, and clustering, and briefly covers other approaches and commercial tools. Chapter 29 introduces data warehousing and OLAP concepts.
Appendix A gives a number of alternative diagrammatic notations for displaying a conceptual ER or EER schema. These may be substituted for the notation we use, if the instructor prefers. Appendix B gives some important physical parameters of disks. Appendix C gives an overview of the QBE graphical query language. Appen- dixes D and E (available on the book’s Companion Website located at http://www.aw.com/elmasri) cover legacy database systems, based on the hierar- chical and network database models. They have been used for more than thirty years as a basis for many commercial database applications and transaction- processing systems. We consider it important to expose database management stu- dents to these legacy approaches so they can gain a better insight of how database technology has progressed.
Guidelines for Using This Book There are many different ways to teach a database course. The chapters in Parts 1 through 7 can be used in an introductory course on database systems in the order that they are given or in the preferred order of individual instructors. Selected chap- ters and sections may be left out, and the instructor can add other chapters from the rest of the book, depending on the emphasis of the course. At the end of the open- ing section of many of the book’s chapters, we list sections that are candidates for being left out whenever a less-detailed discussion of the topic is desired. We suggest covering up to Chapter 15 in an introductory database course and including selected parts of other chapters, depending on the background of the students and the desired coverage. For an emphasis on system implementation techniques, chap- ters from Parts 7, 8, and 9 should replace some of the earlier chapters.
Chapters 7 and 8, which cover conceptual modeling using the ER and EER models, are important for a good conceptual understanding of databases. However, they may be partially covered, covered later in a course, or even left out if the emphasis is on DBMS implementation. Chapters 17 and 18 on file organizations and indexing may also be covered early, later, or even left out if the emphasis is on database mod- els and languages. For students who have completed a course on file organization, parts of these chapters can be assigned as reading material or some exercises can be assigned as a review for these concepts.
If the emphasis of a course is on database design, then the instructor should cover Chapters 7 and 8 early on, followed by the presentation of relational databases. A total life-cycle database design and implementation project would cover conceptual design (Chapters 7 and 8), relational databases (Chapters 3, 4, and 5), data model mapping (Chapter 9), normalization (Chapter 15), and application programs implementation with SQL (Chapter 13). Chapter 14 also should be covered if the emphasis is on Web database programming and applications. Additional documen- tation on the specific programming languages and RDBMS used would be required.
Preface xi
The book is written so that it is possible to cover topics in various sequences. The chapter dependency chart below shows the major dependencies among chapters. As the diagram illustrates, it is possible to start with several different topics following the first two introductory chapters. Although the chart may seem complex, it is important to note that if the chapters are covered in order, the dependencies are not lost. The chart can be consulted by instructors wishing to use an alternative order of presentation.
For a one-semester course based on this book, selected chapters can be assigned as reading material. The book also can be used for a two-semester course sequence. The first course, Introduction to Database Design and Database Systems, at the soph- omore, junior, or senior level, can cover most of Chapters 1 through 15. The second course, Database Models and Implementation Techniques, at the senior or first-year graduate level, can cover most of Chapters 16 through 29. The two-semester sequence can also been designed in various other ways, depending on the prefer- ences of the instructors.
xii Preface
1, 2 Introductory
7, 8 ER, EER Models
3 Relational
Model
6 Relational Algebra 13, 14
DB, Web Programming
9 ER--, EER-to-
Relational
17, 18 File Organization,
Indexing
28, 29 Data Mining, Warehousing
24, 25 Security,
DDB
10 DB Design,
UML
21, 22, 23 Transactions, CC, Recovery
11, 12 ODB, ORDB,
XML
4, 5 SQL
26, 27 Advanced Models,
IR
15, 16 FD, MVD,
Normalization
19, 20 Query Processing,
Optimization, DB Tuning
Supplemental Materials Support material is available to all users of this book and additional material is available to qualified instructors.
■ PowerPoint lecture notes and figures are available at the Computer Science support Website at http://www.aw.com/cssupport.
■ A lab manual for the sixth edition is available through the Companion Web- site (http://www.aw.com/elmasri). The lab manual contains coverage of popular data modeling tools, a relational algebra and calculus interpreter, and examples from the book implemented using two widely available data- base management systems. Select end-of-chapter laboratory problems in the book are correlated to the lab manual.
■ A solutions manual is available to qualified instructors. Visit Addison- Wesley’s instructor resource center (http://www.aw.com/irc), contact your local Addison-Wesley sales representative, or e-mail [email protected] for information about how to access the solutions.
Additional Support Material Gradiance, an online homework and tutorial system that provides additional prac- tice and tests comprehension of important concepts, is available to U.S. adopters of this book. For more information, please e-mail [email protected] or contact your local Pearson representative.
Acknowledgments It is a great pleasure to acknowledge the assistance and contributions of many indi- viduals to this effort. First, we would like to thank our editor, Matt Goldstein, for his guidance, encouragement, and support. We would like to acknowledge the excellent work of Gillian Hall for production management and Rebecca Greenberg for a thorough copy editing of the book. We thank the following persons from Pearson who have contributed to the sixth edition: Jeff Holcomb, Marilyn Lloyd, Margaret Waples, and Chelsea Bell.
Sham Navathe would like to acknowledge the significant contribution of Saurav Sahay to Chapter 27. Several current and former students also contributed to vari- ous chapters in this edition: Rafi Ahmed, Liora Sahar, Fariborz Farahmand, Nalini Polavarapu, and Wanxia Xie (former students); and Bharath Rengarajan, Narsi Srinivasan, Parimala R. Pranesh, Neha Deodhar, Balaji Palanisamy and Hariprasad Kumar (current students). Discussions with his colleagues Ed Omiecinski and Leo Mark at Georgia Tech and Venu Dasigi at SPSU, Atlanta have also contributed to the revision of the material.
We would like to repeat our thanks to those who have reviewed and contributed to previous editions of Fundamentals of Database Systems.
■ First edition. Alan Apt (editor), Don Batory, Scott Downing, Dennis Heimbinger, Julia Hodges, Yannis Ioannidis, Jim Larson, Per-Ake Larson,
Preface xiii
Dennis McLeod, Rahul Patel, Nicholas Roussopoulos, David Stemple, Michael Stonebraker, Frank Tompa, and Kyu-Young Whang.
■ Second edition. Dan Joraanstad (editor), Rafi Ahmed, Antonio Albano, David Beech, Jose Blakeley, Panos Chrysanthis, Suzanne Dietrich, Vic Ghor- padey, Goetz Graefe, Eric Hanson, Junguk L. Kim, Roger King, Vram Kouramajian, Vijay Kumar, John Lowther, Sanjay Manchanda, Toshimi Minoura, Inderpal Mumick, Ed Omiecinski, Girish Pathak, Raghu Ramakr- ishnan, Ed Robertson, Eugene Sheng, David Stotts, Marianne Winslett, and Stan Zdonick.
■ Third edition. Maite Suarez-Rivas and Katherine Harutunian (editors); Suzanne Dietrich, Ed Omiecinski, Rafi Ahmed, Francois Bancilhon, Jose Blakeley, Rick Cattell, Ann Chervenak, David W. Embley, Henry A. Etlinger, Leonidas Fegaras, Dan Forsyth, Farshad Fotouhi, Michael Franklin, Sreejith Gopinath, Goetz Craefe, Richard Hull, Sushil Jajodia, Ramesh K. Karne, Harish Kotbagi, Vijay Kumar, Tarcisio Lima, Ramon A. Mata-Toledo, Jack McCaw, Dennis McLeod, Rokia Missaoui, Magdi Morsi, M. Narayanaswamy, Carlos Ordonez, Joan Peckham, Betty Salzberg, Ming-Chien Shan, Junping Sun, Rajshekhar Sunderraman, Aravindan Veerasamy, and Emilia E. Villareal.
■ Fourth edition. Maite Suarez-Rivas, Katherine Harutunian, Daniel Rausch, and Juliet Silveri (editors); Phil Bernhard, Zhengxin Chen, Jan Chomicki, Hakan Ferhatosmanoglu, Len Fisk, William Hankley, Ali R. Hurson, Vijay Kumar, Peretz Shoval, Jason T. L. Wang (reviewers); Ed Omiecinski (who contributed to Chapter 27). Contributors from the University of Texas at Arlington are Jack Fu, Hyoil Han, Babak Hojabri, Charley Li, Ande Swathi, and Steven Wu; Contributors from Georgia Tech are Weimin Feng, Dan Forsythe, Angshuman Guin, Abrar Ul-Haque, Bin Liu, Ying Liu, Wanxia Xie, and Waigen Yee.
■ Fifth edition. Matt Goldstein and Katherine Harutunian (editors); Michelle Brown, Gillian Hall, Patty Mahtani, Maite Suarez-Rivas, Bethany Tidd, and Joyce Cosentino Wells (from Addison-Wesley); Hani Abu-Salem, Jamal R. Alsabbagh, Ramzi Bualuan, Soon Chung, Sumali Conlon, Hasan Davulcu, James Geller, Le Gruenwald, Latifur Khan, Herman Lam, Byung S. Lee, Donald Sanderson, Jamil Saquer, Costas Tsatsoulis, and Jack C. Wileden (reviewers); Raj Sunderraman (who contributed the laboratory projects); Salman Azar (who contributed some new exercises); Gaurav Bhatia, Fariborz Farahmand, Ying Liu, Ed Omiecinski, Nalini Polavarapu, Liora Sahar, Saurav Sahay, and Wanxia Xie (from Georgia Tech).
Last, but not least, we gratefully acknowledge the support, encouragement, and patience of our families.
R. E.
S.B.N.
xiv Preface
Contents
■ part 1 Introduction to Databases ■
chapter 1 Databases and Database Users 3 1.1 Introduction 4 1.2 An Example 6 1.3 Characteristics of the Database Approach 9 1.4 Actors on the Scene 14 1.5 Workers behind the Scene 16 1.6 Advantages of Using the DBMS Approach 17 1.7 A Brief History of Database Applications 23 1.8 When Not to Use a DBMS 26 1.9 Summary 27 Review Questions 27 Exercises 28 Selected Bibliography 28
chapter 2 Database System Concepts and Architecture 29
2.1 Data Models, Schemas, and Instances 30 2.2 Three-Schema Architecture and Data Independence 33 2.3 Database Languages and Interfaces 36 2.4 The Database System Environment 40 2.5 Centralized and Client/Server Architectures for DBMSs 44 2.6 Classification of Database Management Systems 49 2.7 Summary 52 Review Questions 53 Exercises 54 Selected Bibliography 55
xv
xvi Contents
■ part 2 The Relational Data Model and SQL ■
chapter 3 The Relational Data Model and Relational Database Constraints 59
3.1 Relational Model Concepts 60 3.2 Relational Model Constraints and Relational Database Schemas 67 3.3 Update Operations, Transactions, and Dealing
with Constraint Violations 75 3.4 Summary 79 Review Questions 80 Exercises 80 Selected Bibliography 85
chapter 4 Basic SQL 87 4.1 SQL Data Definition and Data Types 89 4.2 Specifying Constraints in SQL 94 4.3 Basic Retrieval Queries in SQL 97 4.4 INSERT, DELETE, and UPDATE Statements in SQL 107 4.5 Additional Features of SQL 110 4.6 Summary 111 Review Questions 112 Exercises 112 Selected Bibliography 114
chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification 115
5.1 More Complex SQL Retrieval Queries 115 5.2 Specifying Constraints as Assertions and Actions as Triggers 131 5.3 Views (Virtual Tables) in SQL 133 5.4 Schema Change Statements in SQL 137 5.5 Summary 139 Review Questions 141 Exercises 141 Selected Bibliography 143
chapter 6 The Relational Algebra and Relational Calculus 145
6.1 Unary Relational Operations: SELECT and PROJECT 147 6.2 Relational Algebra Operations from Set Theory 152 6.3 Binary Relational Operations: JOIN and DIVISION 157 6.4 Additional Relational Operations 165 6.5 Examples of Queries in Relational Algebra 171 6.6 The Tuple Relational Calculus 174 6.7 The Domain Relational Calculus 183 6.8 Summary 185 Review Questions 186 Exercises 187 Laboratory Exercises 192 Selected Bibliography 194
■ part 3 Conceptual Modeling and Database Design ■
chapter 7 Data Modeling Using the Entity-Relationship (ER) Model 199
7.1 Using High-Level Conceptual Data Models for Database Design 200 7.2 A Sample Database Application 202 7.3 Entity Types, Entity Sets, Attributes, and Keys 203 7.4 Relationship Types, Relationship Sets, Roles,
and Structural Constraints 212 7.5 Weak Entity Types 219 7.6 Refining the ER Design for the COMPANY Database 220 7.7 ER Diagrams, Naming Conventions, and Design Issues 221 7.8 Example of Other Notation: UML Class Diagrams 226 7.9 Relationship Types of Degree Higher than Two 228 7.10 Summary 232 Review Questions 234 Exercises 234 Laboratory Exercises 241 Selected Bibliography 243
Contents xvii
xviii Contents
chapter 8 The Enhanced Entity-Relationship (EER) Model 245
8.1 Subclasses, Superclasses, and Inheritance 246 8.2 Specialization and Generalization 248 8.3 Constraints and Characteristics of Specialization
and Generalization Hierarchies 251 8.4 Modeling of UNION Types Using Categories 258 8.5 A Sample UNIVERSITY EER Schema, Design Choices,
and Formal Definitions 260 8.6 Example of Other Notation: Representing Specialization
and Generalization in UML Class Diagrams 265 8.7 Data Abstraction, Knowledge Representation,
and Ontology Concepts 267 8.8 Summary 273 Review Questions 273 Exercises 274 Laboratory Exercises 281 Selected Bibliography 284
chapter 9 Relational Database Design by ER- and EER-to-Relational Mapping 285
9.1 Relational Database Design Using ER-to-Relational Mapping 286 9.2 Mapping EER Model Constructs to Relations 294 9.3 Summary 299 Review Questions 299 Exercises 299 Laboratory Exercises 301 Selected Bibliography 302
chapter 10 Practical Database Design Methodology and Use of UML Diagrams 303
10.1 The Role of Information Systems in Organizations 304 10.2 The Database Design and Implementation Process 309 10.3 Use of UML Diagrams as an Aid to Database
Design Specification 328 10.4 Rational Rose: A UML-Based Design Tool 337 10.5 Automated Database Design Tools 342
Contents xix
10.6 Summary 345 Review Questions 347 Selected Bibliography 348
■ part 4 Object, Object-Relational, and XML: Concepts, Models, Languages, and Standards ■
chapter 11 Object and Object-Relational Databases 353 11.1 Overview of Object Database Concepts 355 11.2 Object-Relational Features: Object Database Extensions
to SQL 369 11.3 The ODMG Object Model and the Object Definition
Language ODL 376 11.4 Object Database Conceptual Design 395 11.5 The Object Query Language OQL 398 11.6 Overview of the C++ Language Binding in the ODMG Standard 407 11.7 Summary 408 Review Questions 409 Exercises 411 Selected Bibliography 412
chapter 12 XML: Extensible Markup Language 415 12.1 Structured, Semistructured, and Unstructured Data 416 12.2 XML Hierarchical (Tree) Data Model 420 12.3 XML Documents, DTD, and XML Schema 423 12.4 Storing and Extracting XML Documents from Databases 431 12.5 XML Languages 432 12.6 Extracting XML Documents from Relational Databases 436 12.7 Summary 442 Review Questions 442 Exercises 443 Selected Bibliography 443
■ part 5 Database Programming Techniques ■
chapter 13 Introduction to SQL Programming Techniques 447
13.1 Database Programming: Techniques and Issues 448 13.2 Embedded SQL, Dynamic SQL, and SQLJ 451 13.3 Database Programming with Function Calls: SQL/CLI and JDBC
464 13.4 Database Stored Procedures and SQL/PSM 473 13.5 Comparing the Three Approaches 476 13.6 Summary 477 Review Questions 478 Exercises 478 Selected Bibliography 479
chapter 14 Web Database Programming Using PHP 481 14.1 A Simple PHP Example 482 14.2 Overview of Basic Features of PHP 484 14.3 Overview of PHP Database Programming 491 14.4 Summary 496 Review Questions 496 Exercises 497 Selected Bibliography 497
■ part 6 Database Design Theory and Normalization ■
chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases 501
15.1 Informal Design Guidelines for Relation Schemas 503 15.2 Functional Dependencies 513 15.3 Normal Forms Based on Primary Keys 516 15.4 General Definitions of Second and Third Normal Forms 525 15.5 Boyce-Codd Normal Form 529
xx Contents
15.6 Multivalued Dependency and Fourth Normal Form 531 15.7 Join Dependencies and Fifth Normal Form 534 15.8 Summary 535 Review Questions 536 Exercises 537 Laboratory Exercises 542 Selected Bibliography 542
chapter 16 Relational Database Design Algorithms and Further Dependencies 543
16.1 Further Topics in Functional Dependencies: Inference Rules, Equivalence, and Minimal Cover 545
16.2 Properties of Relational Decompositions 551 16.3 Algorithms for Relational Database Schema Design 557 16.4 About Nulls, Dangling Tuples, and Alternative Relational
Designs 563 16.5 Further Discussion of Multivalued Dependencies and 4NF 567 16.6 Other Dependencies and Normal Forms 571 16.7 Summary 575 Review Questions 576 Exercises 576 Laboratory Exercises 578 Selected Bibliography 579
■ part 7 File Structures, Indexing, and Hashing ■
chapter 17 Disk Storage, Basic File Structures, and Hashing 583
17.1 Introduction 584 17.2 Secondary Storage Devices 587 17.3 Buffering of Blocks 593 17.4 Placing File Records on Disk 594 17.5 Operations on Files 599 17.6 Files of Unordered Records (Heap Files) 601 17.7 Files of Ordered Records (Sorted Files) 603 17.8 Hashing Techniques 606
Contents xxi
17.9 Other Primary File Organizations 616 17.10 Parallelizing Disk Access Using RAID Technology 617 17.11 New Storage Systems 621 17.12 Summary 624 Review Questions 625 Exercises 626 Selected Bibliography 630
chapter 18 Indexing Structures for Files 631 18.1 Types of Single-Level Ordered Indexes 632 18.2 Multilevel Indexes 643 18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 646 18.4 Indexes on Multiple Keys 660 18.5 Other Types of Indexes 663 18.6 Some General Issues Concerning Indexing 668 18.7 Summary 670 Review Questions 671 Exercises 672 Selected Bibliography 674
■ part 8 Query Processing and Optimization, and Database Tuning ■
chapter 19 Algorithms for Query Processing and Optimization 679
19.1 Translating SQL Queries into Relational Algebra 681 19.2 Algorithms for External Sorting 682 19.3 Algorithms for SELECT and JOIN Operations 685 19.4 Algorithms for PROJECT and Set Operations 696 19.5 Implementing Aggregate Operations and OUTER JOINs 698 19.6 Combining Operations Using Pipelining 700 19.7 Using Heuristics in Query Optimization 700 19.8 Using Selectivity and Cost Estimates in Query Optimization 710 19.9 Overview of Query Optimization in Oracle 721 19.10 Semantic Query Optimization 722 19.11 Summary 723
xxii Contents
Review Questions 723 Exercises 724 Selected Bibliography 725
chapter 20 Physical Database Design and Tuning 727 20.1 Physical Database Design in Relational Databases 727 20.2 An Overview of Database Tuning in Relational Systems 733 20.3 Summary 739 Review Questions 739 Selected Bibliography 740
■ part 9 Transaction Processing, Concurrency Control, and Recovery ■
chapter 21 Introduction to Transaction Processing Concepts and Theory 743
21.1 Introduction to Transaction Processing 744 21.2 Transaction and System Concepts 751 21.3 Desirable Properties of Transactions 754 21.4 Characterizing Schedules Based on Recoverability 755 21.5 Characterizing Schedules Based on Serializability 759 21.6 Transaction Support in SQL 770 21.7 Summary 772 Review Questions 772 Exercises 773 Selected Bibliography 775
chapter 22 Concurrency Control Techniques 777 22.1 Two-Phase Locking Techniques for Concurrency Control 778 22.2 Concurrency Control Based on Timestamp Ordering 788 22.3 Multiversion Concurrency Control Techniques 791 22.4 Validation (Optimistic) Concurrency Control Techniques 794 22.5 Granularity of Data Items and Multiple Granularity Locking 795 22.6 Using Locks for Concurrency Control in Indexes 798 22.7 Other Concurrency Control Issues 800
Contents xxiii
xxiv Contents
22.8 Summary 802 Review Questions 803 Exercises 804 Selected Bibliography 804
chapter 23 Database Recovery Techniques 807 23.1 Recovery Concepts 808 23.2 NO-UNDO/REDO Recovery Based on Deferred Update 815 23.3 Recovery Techniques Based on Immediate Update 817 23.4 Shadow Paging 820 23.5 The ARIES Recovery Algorithm 821 23.6 Recovery in Multidatabase Systems 825 23.7 Database Backup and Recovery from Catastrophic Failures 826 23.8 Summary 827 Review Questions 828 Exercises 829 Selected Bibliography 832
■ part 10 Additional Database Topics: Security and Distribution ■
chapter 24 Database Security 835 24.1 Introduction to Database Security Issues 836 24.2 Discretionary Access Control Based on Granting
and Revoking Privileges 842 24.3 Mandatory Access Control and Role-Based Access Control
for Multilevel Security 847 24.4 SQL Injection 855 24.5 Introduction to Statistical Database Security 859 24.6 Introduction to Flow Control 860 24.7 Encryption and Public Key Infrastructures 862 24.8 Privacy Issues and Preservation 866 24.9 Challenges of Database Security 867 24.10 Oracle Label-Based Security 868 24.11 Summary 870
Contents xxv
Review Questions 872 Exercises 873 Selected Bibliography 874
chapter 25 Distributed Databases 877 25.1 Distributed Database Concepts 878 25.2 Types of Distributed Database Systems 883 25.3 Distributed Database Architectures 887 25.4 Data Fragmentation, Replication, and Allocation Techniques for
Distributed Database Design 894 25.5 Query Processing and Optimization in Distributed Databases 901 25.6 Overview of Transaction Management in Distributed Databases 907 25.7 Overview of Concurrency Control and Recovery in Distributed
Databases 909 25.8 Distributed Catalog Management 913 25.9 Current Trends in Distributed Databases 914 25.10 Distributed Databases in Oracle 915 25.11 Summary 919 Review Questions 921 Exercises 922 Selected Bibliography 924
■ part 11 Advanced Database Models, Systems, and Applications ■
chapter 26 Enhanced Data Models for Advanced Applications 931
26.1 Active Database Concepts and Triggers 933 26.2 Temporal Database Concepts 943 26.3 Spatial Database Concepts 957 26.4 Multimedia Database Concepts 965 26.5 Introduction to Deductive Databases 970 26.6 Summary 983 Review Questions 985 Exercises 986 Selected Bibliography 989
xxvi Contents
chapter 27 Introduction to Information Retrieval and Web Search 993
27.1 Information Retrieval (IR) Concepts 994 27.2 Retrieval Models 1001 27.3 Types of Queries in IR Systems 1007 27.4 Text Preprocessing 1009 27.5 Inverted Indexing 1012 27.6 Evaluation Measures of Search Relevance 1014 27.7 Web Search and Analysis 1018 27.8 Trends in Information Retrieval 1028 27.9 Summary 1030 Review Questions 1031 Selected Bibliography 1033
chapter 28 Data Mining Concepts 1035 28.1 Overview of Data Mining Technology 1036 28.2 Association Rules 1039 28.3 Classification 1051 28.4 Clustering 1054 28.5 Approaches to Other Data Mining Problems 1057 28.6 Applications of Data Mining 1060 28.7 Commercial Data Mining Tools 1060 28.8 Summary 1063 Review Questions 1063 Exercises 1064 Selected Bibliography 1065
chapter 29 Overview of Data Warehousing and OLAP 1067
29.1 Introduction, Definitions, and Terminology 1067 29.2 Characteristics of Data Warehouses 1069 29.3 Data Modeling for Data Warehouses 1070 29.4 Building a Data Warehouse 1075 29.5 Typical Functionality of a Data Warehouse 1078 29.6 Data Warehouse versus Views 1079 29.7 Difficulties of Implementing Data Warehouses 1080
29.8 Summary 1081 Review Questions 1081 Selected Bibliography 1082
appendix A Alternative Diagrammatic Notations for ER Models 1083
appendix B Parameters of Disks 1087
appendix C Overview of the QBE Language 1091 C.1 Basic Retrievals in QBE 1091 C.2 Grouping, Aggregation, and Database
Modification in QBE 1095
appendix D Overview of the Hierarchical Data Model (located on the Companion Website at http://www.aw.com/elmasri)
appendix E Overview of the Network Data Model (located on the Companion Website at http://www.aw.com/elmasri)
Selected Bibliography 1099
Index 1133
Contents xxvii
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part 1 Introduction
to Databases
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3
Databases and Database Users
Databases and database systems are an essentialcomponent of life in modern society: most of us encounter several activities every day that involve some interaction with a database. For example, if we go to the bank to deposit or withdraw funds, if we make a hotel or airline reservation, if we access a computerized library catalog to search for a bib- liographic item, or if we purchase something online—such as a book, toy, or com- puter—chances are that our activities will involve someone or some computer program accessing a database. Even purchasing items at a supermarket often auto- matically updates the database that holds the inventory of grocery items.
These interactions are examples of what we may call traditional database applica- tions, in which most of the information that is stored and accessed is either textual or numeric. In the past few years, advances in technology have led to exciting new applications of database systems. New media technology has made it possible to store images, audio clips, and video streams digitally. These types of files are becom- ing an important component of multimedia databases. Geographic information systems (GIS) can store and analyze maps, weather data, and satellite images. Data warehouses and online analytical processing (OLAP) systems are used in many companies to extract and analyze useful business information from very large data- bases to support decision making. Real-time and active database technology is used to control industrial and manufacturing processes. And database search tech- niques are being applied to the World Wide Web to improve the search for informa- tion that is needed by users browsing the Internet.
To understand the fundamentals of database technology, however, we must start from the basics of traditional database applications. In Section 1.1 we start by defin- ing a database, and then we explain other basic terms. In Section 1.2, we provide a
1chapter 1
4 Chapter 1 Databases and Database Users
simple UNIVERSITY database example to illustrate our discussion. Section 1.3 describes some of the main characteristics of database systems, and Sections 1.4 and 1.5 categorize the types of personnel whose jobs involve using and interacting with database systems. Sections 1.6, 1.7, and 1.8 offer a more thorough discussion of the various capabilities provided by database systems and discuss some typical database applications. Section 1.9 summarizes the chapter.
The reader who desires a quick introduction to database systems can study Sections 1.1 through 1.5, then skip or browse through Sections 1.6 through 1.8 and go on to Chapter 2.
1.1 Introduction Databases and database technology have a major impact on the growing use of computers. It is fair to say that databases play a critical role in almost all areas where computers are used, including business, electronic commerce, engineering, medi- cine, genetics, law, education, and library science. The word database is so com- monly used that we must begin by defining what a database is. Our initial definition is quite general.
A database is a collection of related data.1 By data, we mean known facts that can be recorded and that have implicit meaning. For example, consider the names, tele- phone numbers, and addresses of the people you know. You may have recorded this data in an indexed address book or you may have stored it on a hard drive, using a personal computer and software such as Microsoft Access or Excel. This collection of related data with an implicit meaning is a database.
The preceding definition of database is quite general; for example, we may consider the collection of words that make up this page of text to be related data and hence to constitute a database. However, the common use of the term database is usually more restricted. A database has the following implicit properties:
■ A database represents some aspect of the real world, sometimes called the miniworld or the universe of discourse (UoD). Changes to the miniworld are reflected in the database.
■ A database is a logically coherent collection of data with some inherent meaning. A random assortment of data cannot correctly be referred to as a database.
■ A database is designed, built, and populated with data for a specific purpose. It has an intended group of users and some preconceived applications in which these users are interested.
In other words, a database has some source from which data is derived, some degree of interaction with events in the real world, and an audience that is actively inter-
1We will use the word data as both singular and plural, as is common in database literature; the context will determine whether it is singular or plural. In standard English, data is used for plural and datum for singular.
1.1 Introduction 5
ested in its contents. The end users of a database may perform business transactions (for example, a customer buys a camera) or events may happen (for example, an employee has a baby) that cause the information in the database to change. In order for a database to be accurate and reliable at all times, it must be a true reflection of the miniworld that it represents; therefore, changes must be reflected in the database as soon as possible.
A database can be of any size and complexity. For example, the list of names and addresses referred to earlier may consist of only a few hundred records, each with a simple structure. On the other hand, the computerized catalog of a large library may contain half a million entries organized under different categories—by pri- mary author’s last name, by subject, by book title—with each category organized alphabetically. A database of even greater size and complexity is maintained by the Internal Revenue Service (IRS) to monitor tax forms filed by U.S. taxpayers. If we assume that there are 100 million taxpayers and each taxpayer files an average of five forms with approximately 400 characters of information per form, we would have a database of 100 × 106 × 400 × 5 characters (bytes) of information. If the IRS keeps the past three returns of each taxpayer in addition to the current return, we would have a database of 8 × 1011 bytes (800 gigabytes). This huge amount of information must be organized and managed so that users can search for, retrieve, and update the data as needed.
An example of a large commercial database is Amazon.com. It contains data for over 20 million books, CDs, videos, DVDs, games, electronics, apparel, and other items. The database occupies over 2 terabytes (a terabyte is 1012 bytes worth of stor- age) and is stored on 200 different computers (called servers). About 15 million vis- itors access Amazon.com each day and use the database to make purchases. The database is continually updated as new books and other items are added to the inventory and stock quantities are updated as purchases are transacted. About 100 people are responsible for keeping the Amazon database up-to-date.
A database may be generated and maintained manually or it may be computerized. For example, a library card catalog is a database that may be created and maintained manually. A computerized database may be created and maintained either by a group of application programs written specifically for that task or by a database management system. We are only concerned with computerized databases in this book.
A database management system (DBMS) is a collection of programs that enables users to create and maintain a database. The DBMS is a general-purpose software sys- tem that facilitates the processes of defining, constructing, manipulating, and sharing databases among various users and applications. Defining a database involves spec- ifying the data types, structures, and constraints of the data to be stored in the data- base. The database definition or descriptive information is also stored by the DBMS in the form of a database catalog or dictionary; it is called meta-data. Constructing the database is the process of storing the data on some storage medium that is con- trolled by the DBMS. Manipulating a database includes functions such as querying the database to retrieve specific data, updating the database to reflect changes in the
6 Chapter 1 Databases and Database Users
miniworld, and generating reports from the data. Sharing a database allows multi- ple users and programs to access the database simultaneously.
An application program accesses the database by sending queries or requests for data to the DBMS. A query2 typically causes some data to be retrieved; a transaction may cause some data to be read and some data to be written into the database.
Other important functions provided by the DBMS include protecting the database and maintaining it over a long period of time. Protection includes system protection against hardware or software malfunction (or crashes) and security protection against unauthorized or malicious access. A typical large database may have a life cycle of many years, so the DBMS must be able to maintain the database system by allowing the system to evolve as requirements change over time.
It is not absolutely necessary to use general-purpose DBMS software to implement a computerized database. We could write our own set of programs to create and maintain the database, in effect creating our own special-purpose DBMS software. In either case—whether we use a general-purpose DBMS or not—we usually have to deploy a considerable amount of complex software. In fact, most DBMSs are very complex software systems.
To complete our initial definitions, we will call the database and DBMS software together a database system. Figure 1.1 illustrates some of the concepts we have dis- cussed so far.
1.2 An Example Let us consider a simple example that most readers may be familiar with: a UNIVERSITY database for maintaining information concerning students, courses, and grades in a university environment. Figure 1.2 shows the database structure and a few sample data for such a database. The database is organized as five files, each of which stores data records of the same type.3 The STUDENT file stores data on each student, the COURSE file stores data on each course, the SECTION file stores data on each section of a course, the GRADE_REPORT file stores the grades that students receive in the various sections they have completed, and the PREREQUISITE file stores the prerequisites of each course.
To define this database, we must specify the structure of the records of each file by specifying the different types of data elements to be stored in each record. In Figure 1.2, each STUDENT record includes data to represent the student’s Name, Student_number, Class (such as freshman or ‘1’, sophomore or ‘2’, and so forth), and
2The term query, originally meaning a question or an inquiry, is loosely used for all types of interactions with databases, including modifying the data. 3We use the term file informally here. At a conceptual level, a file is a collection of records that may or may not be ordered.
1.2 An Example 7
Database System
Users/Programmers
Application Programs/Queries
Software to Process Queries/Programs
Software to Access Stored Data
Stored Database Stored Database
Definition (Meta-Data)
DBMS Software
Figure 1.1 A simplified database system environment.
Major (such as mathematics or ‘MATH’ and computer science or ‘CS’); each COURSE record includes data to represent the Course_name, Course_number, Credit_hours, and Department (the department that offers the course); and so on. We must also specify a data type for each data element within a record. For example, we can specify that Name of STUDENT is a string of alphabetic characters, Student_number of STUDENT is an integer, and Grade of GRADE_REPORT is a single character from the set {‘A’, ‘B’, ‘C’, ‘D’, ‘F’, ‘I’}. We may also use a coding scheme to rep- resent the values of a data item. For example, in Figure 1.2 we represent the Class of a STUDENT as 1 for freshman, 2 for sophomore, 3 for junior, 4 for senior, and 5 for graduate student.
To construct the UNIVERSITY database, we store data to represent each student, course, section, grade report, and prerequisite as a record in the appropriate file. Notice that records in the various files may be related. For example, the record for Smith in the STUDENT file is related to two records in the GRADE_REPORT file that specify Smith’s grades in two sections. Similarly, each record in the PREREQUISITE file relates two course records: one representing the course and the other represent- ing the prerequisite. Most medium-size and large databases include many types of records and have many relationships among the records.
8 Chapter 1 Databases and Database Users
Name Student_number Class Major
Smith 17 1 CS
Brown 8 2 CS
STUDENT
Course_name Course_number Credit_hours Department
Intro to Computer Science CS1310 4 CS
Data Structures CS3320 4 CS
Discrete Mathematics MATH2410 3 MATH
Database CS3380 3 CS
COURSE
Section_identifier Course_number Semester Year Instructor
85 MATH2410 Fall 07 King
92 CS1310 Fall 07 Anderson
102 CS3320 Spring 08 Knuth
112 MATH2410 Fall 08 Chang
119 CS1310 Fall 08 Anderson
135 CS3380 Fall 08 Stone
SECTION
Student_number Section_identifier Grade
17 112 B
17 119 C
8 85 A
8 92 A
8 102 B
8 135 A
GRADE_REPORT
Course_number Prerequisite_number
CS3380 CS3320
CS3380 MATH2410
CS3320 CS1310
PREREQUISITE
Figure 1.2 A database that stores student and course information.
1.3 Characteristics of the Database Approach 9
Database manipulation involves querying and updating. Examples of queries are as follows:
■ Retrieve the transcript—a list of all courses and grades—of ‘Smith’
■ List the names of students who took the section of the ‘Database’ course offered in fall 2008 and their grades in that section
■ List the prerequisites of the ‘Database’ course
Examples of updates include the following:
■ Change the class of ‘Smith’ to sophomore
■ Create a new section for the ‘Database’ course for this semester
■ Enter a grade of ‘A’ for ‘Smith’ in the ‘Database’ section of last semester
These informal queries and updates must be specified precisely in the query lan- guage of the DBMS before they can be processed.
At this stage, it is useful to describe the database as a part of a larger undertaking known as an information system within any organization. The Information Technology (IT) department within a company designs and maintains an informa- tion system consisting of various computers, storage systems, application software, and databases. Design of a new application for an existing database or design of a brand new database starts off with a phase called requirements specification and analysis. These requirements are documented in detail and transformed into a conceptual design that can be represented and manipulated using some computer- ized tools so that it can be easily maintained, modified, and transformed into a data- base implementation. (We will introduce a model called the Entity-Relationship model in Chapter 7 that is used for this purpose.) The design is then translated to a logical design that can be expressed in a data model implemented in a commercial DBMS. (In this book we will emphasize a data model known as the Relational Data Model from Chapter 3 onward. This is currently the most popular approach for designing and implementing databases using relational DBMSs.) The final stage is physical design, during which further specifications are provided for storing and accessing the database. The database design is implemented, populated with actual data, and continuously maintained to reflect the state of the miniworld.
1.3 Characteristics of the Database Approach A number of characteristics distinguish the database approach from the much older approach of programming with files. In traditional file processing, each user defines and implements the files needed for a specific software application as part of programming the application. For example, one user, the grade reporting office, may keep files on students and their grades. Programs to print a student’s transcript and to enter new grades are implemented as part of the application. A second user, the accounting office, may keep track of students’ fees and their payments. Although both users are interested in data about students, each user maintains separate files— and programs to manipulate these files—because each requires some data not avail-
10 Chapter 1 Databases and Database Users
able from the other user’s files. This redundancy in defining and storing data results in wasted storage space and in redundant efforts to maintain common up-to-date data.
In the database approach, a single repository maintains data that is defined once and then accessed by various users. In file systems, each application is free to name data elements independently. In contrast, in a database, the names or labels of data are defined once, and used repeatedly by queries, transactions, and applications. The main characteristics of the database approach versus the file-processing approach are the following:
■ Self-describing nature of a database system
■ Insulation between programs and data, and data abstraction
■ Support of multiple views of the data
■ Sharing of data and multiuser transaction processing
We describe each of these characteristics in a separate section. We will discuss addi- tional characteristics of database systems in Sections 1.6 through 1.8.
1.3.1 Self-Describing Nature of a Database System A fundamental characteristic of the database approach is that the database system contains not only the database itself but also a complete definition or description of the database structure and constraints. This definition is stored in the DBMS cata- log, which contains information such as the structure of each file, the type and stor- age format of each data item, and various constraints on the data. The information stored in the catalog is called meta-data, and it describes the structure of the pri- mary database (Figure 1.1).
The catalog is used by the DBMS software and also by database users who need information about the database structure. A general-purpose DBMS software pack- age is not written for a specific database application. Therefore, it must refer to the catalog to know the structure of the files in a specific database, such as the type and format of data it will access. The DBMS software must work equally well with any number of database applications—for example, a university database, a banking database, or a company database—as long as the database definition is stored in the catalog.
In traditional file processing, data definition is typically part of the application pro- grams themselves. Hence, these programs are constrained to work with only one specific database, whose structure is declared in the application programs. For example, an application program written in C++ may have struct or class declara- tions, and a COBOL program has data division statements to define its files. Whereas file-processing software can access only specific databases, DBMS software can access diverse databases by extracting the database definitions from the catalog and using these definitions.
For the example shown in Figure 1.2, the DBMS catalog will store the definitions of all the files shown. Figure 1.3 shows some sample entries in a database catalog.
Relation_name No_of_columns
STUDENT 4
COURSE 4
SECTION 5
GRADE_REPORT 3
PREREQUISITE 2
Column_name Data_type Belongs_to_relation
Name Character (30) STUDENT
Student_number Character (4) STUDENT
Class Integer (1) STUDENT
Major Major_type STUDENT
Course_name Character (10) COURSE
Course_number XXXXNNNN COURSE
…. …. …..
…. …. …..
…. …. …..
Prerequisite_number XXXXNNNN PREREQUISITE
RELATIONS
COLUMNS
1.3 Characteristics of the Database Approach 11
Figure 1.3 An example of a database catalog for the database in Figure 1.2.
Note: Major_type is defined as an enumerated type with all known majors. XXXXNNNN is used to define a type with four alpha characters followed by four digits.
These definitions are specified by the database designer prior to creating the actual database and are stored in the catalog. Whenever a request is made to access, say, the Name of a STUDENT record, the DBMS software refers to the catalog to determine the structure of the STUDENT file and the position and size of the Name data item within a STUDENT record. By contrast, in a typical file-processing application, the file structure and, in the extreme case, the exact location of Name within a STUDENT record are already coded within each program that accesses this data item.
1.3.2 Insulation between Programs and Data, and Data Abstraction
In traditional file processing, the structure of data files is embedded in the applica- tion programs, so any changes to the structure of a file may require changing all pro- grams that access that file. By contrast, DBMS access programs do not require such changes in most cases. The structure of data files is stored in the DBMS catalog sepa- rately from the access programs. We call this property program-data independence.
12 Chapter 1 Databases and Database Users
For example, a file access program may be written in such a way that it can access only STUDENT records of the structure shown in Figure 1.4. If we want to add another piece of data to each STUDENT record, say the Birth_date, such a program will no longer work and must be changed. By contrast, in a DBMS environment, we only need to change the description of STUDENT records in the catalog (Figure 1.3) to reflect the inclusion of the new data item Birth_date; no programs are changed. The next time a DBMS program refers to the catalog, the new structure of STUDENT records will be accessed and used.
In some types of database systems, such as object-oriented and object-relational systems (see Chapter 11), users can define operations on data as part of the database definitions. An operation (also called a function or method) is specified in two parts. The interface (or signature) of an operation includes the operation name and the data types of its arguments (or parameters). The implementation (or method) of the operation is specified separately and can be changed without affecting the interface. User application programs can operate on the data by invoking these operations through their names and arguments, regardless of how the operations are imple- mented. This may be termed program-operation independence.
The characteristic that allows program-data independence and program-operation independence is called data abstraction. A DBMS provides users with a conceptual representation of data that does not include many of the details of how the data is stored or how the operations are implemented. Informally, a data model is a type of data abstraction that is used to provide this conceptual representation. The data model uses logical concepts, such as objects, their properties, and their interrela- tionships, that may be easier for most users to understand than computer storage concepts. Hence, the data model hides storage and implementation details that are not of interest to most database users.
For example, reconsider Figures 1.2 and 1.3. The internal implementation of a file may be defined by its record length—the number of characters (bytes) in each record—and each data item may be specified by its starting byte within a record and its length in bytes. The STUDENT record would thus be represented as shown in Figure 1.4. But a typical database user is not concerned with the location of each data item within a record or its length; rather, the user is concerned that when a ref- erence is made to Name of STUDENT, the correct value is returned. A conceptual rep- resentation of the STUDENT records is shown in Figure 1.2. Many other details of file storage organization—such as the access paths specified on a file—can be hidden from database users by the DBMS; we discuss storage details in Chapters 17 and 18.
Data Item Name Starting Position in Record Length in Characters (bytes)
Name 1 30
Student_number 31 4
Class 35 1
Major 36 4
Figure 1.4 Internal storage format for a STUDENT record, based on the database catalog in Figure 1.3.
1.3 Characteristics of the Database Approach 13
In the database approach, the detailed structure and organization of each file are stored in the catalog. Database users and application programs refer to the concep- tual representation of the files, and the DBMS extracts the details of file storage from the catalog when these are needed by the DBMS file access modules. Many data models can be used to provide this data abstraction to database users. A major part of this book is devoted to presenting various data models and the concepts they use to abstract the representation of data.
In object-oriented and object-relational databases, the abstraction process includes not only the data structure but also the operations on the data. These operations provide an abstraction of miniworld activities commonly understood by the users. For example, an operation CALCULATE_GPA can be applied to a STUDENT object to calculate the grade point average. Such operations can be invoked by the user queries or application programs without having to know the details of how the operations are implemented. In that sense, an abstraction of the miniworld activity is made available to the user as an abstract operation.
1.3.3 Support of Multiple Views of the Data A database typically has many users, each of whom may require a different perspec- tive or view of the database. A view may be a subset of the database or it may con- tain virtual data that is derived from the database files but is not explicitly stored. Some users may not need to be aware of whether the data they refer to is stored or derived. A multiuser DBMS whose users have a variety of distinct applications must provide facilities for defining multiple views. For example, one user of the database of Figure 1.2 may be interested only in accessing and printing the transcript of each student; the view for this user is shown in Figure 1.5(a). A second user, who is inter- ested only in checking that students have taken all the prerequisites of each course for which they register, may require the view shown in Figure 1.5(b).
1.3.4 Sharing of Data and Multiuser Transaction Processing A multiuser DBMS, as its name implies, must allow multiple users to access the data- base at the same time. This is essential if data for multiple applications is to be inte- grated and maintained in a single database. The DBMS must include concurrency control software to ensure that several users trying to update the same data do so in a controlled manner so that the result of the updates is correct. For example, when several reservation agents try to assign a seat on an airline flight, the DBMS should ensure that each seat can be accessed by only one agent at a time for assignment to a passenger. These types of applications are generally called online transaction pro- cessing (OLTP) applications. A fundamental role of multiuser DBMS software is to ensure that concurrent transactions operate correctly and efficiently.
The concept of a transaction has become central to many database applications. A transaction is an executing program or process that includes one or more database accesses, such as reading or updating of database records. Each transaction is sup- posed to execute a logically correct database access if executed in its entirety without interference from other transactions. The DBMS must enforce several transaction
14 Chapter 1 Databases and Database Users
properties. The isolation property ensures that each transaction appears to execute in isolation from other transactions, even though hundreds of transactions may be executing concurrently. The atomicity property ensures that either all the database operations in a transaction are executed or none are. We discuss transactions in detail in Part 9.
The preceding characteristics are important in distinguishing a DBMS from tradi- tional file-processing software. In Section 1.6 we discuss additional features that characterize a DBMS. First, however, we categorize the different types of people who work in a database system environment.
1.4 Actors on the Scene For a small personal database, such as the list of addresses discussed in Section 1.1, one person typically defines, constructs, and manipulates the database, and there is no sharing. However, in large organizations, many people are involved in the design, use, and maintenance of a large database with hundreds of users. In this section we identify the people whose jobs involve the day-to-day use of a large database; we call them the actors on the scene. In Section 1.5 we consider people who may be called workers behind the scene—those who work to maintain the database system envi- ronment but who are not actively interested in the database contents as part of their daily job.
Student_name Student_transcript
Course_number Grade Semester Year Section_id
Smith CS1310 C Fall 08 119
MATH2410 B Fall 08 112
Brown
MATH2410 A Fall 07 85
CS1310 A Fall 07 92
CS3320 B Spring 08 102
CS3380 A Fall 08 135
TRANSCRIPT
Course_name Course_number Prerequisites
Database CS3380 CS3320
MATH2410
Data Structures CS3320 CS1310
COURSE_PREREQUISITES
(a)
(b)
Figure 1.5 Two views derived from the database in Figure 1.2. (a) The TRANSCRIPT view. (b) The COURSE_PREREQUISITES view.
1.4 Actors on the Scene 15
1.4.1 Database Administrators In any organization where many people use the same resources, there is a need for a chief administrator to oversee and manage these resources. In a database environ- ment, the primary resource is the database itself, and the secondary resource is the DBMS and related software. Administering these resources is the responsibility of the database administrator (DBA). The DBA is responsible for authorizing access to the database, coordinating and monitoring its use, and acquiring software and hardware resources as needed. The DBA is accountable for problems such as secu- rity breaches and poor system response time. In large organizations, the DBA is assisted by a staff that carries out these functions.
1.4.2 Database Designers Database designers are responsible for identifying the data to be stored in the data- base and for choosing appropriate structures to represent and store this data. These tasks are mostly undertaken before the database is actually implemented and popu- lated with data. It is the responsibility of database designers to communicate with all prospective database users in order to understand their requirements and to cre- ate a design that meets these requirements. In many cases, the designers are on the staff of the DBA and may be assigned other staff responsibilities after the database design is completed. Database designers typically interact with each potential group of users and develop views of the database that meet the data and processing requirements of these groups. Each view is then analyzed and integrated with the views of other user groups. The final database design must be capable of supporting the requirements of all user groups.
1.4.3 End Users End users are the people whose jobs require access to the database for querying, updating, and generating reports; the database primarily exists for their use. There are several categories of end users:
■ Casual end users occasionally access the database, but they may need differ- ent information each time. They use a sophisticated database query language to specify their requests and are typically middle- or high-level managers or other occasional browsers.
■ Naive or parametric end users make up a sizable portion of database end users. Their main job function revolves around constantly querying and updating the database, using standard types of queries and updates—called canned transactions—that have been carefully programmed and tested. The tasks that such users perform are varied:
� Bank tellers check account balances and post withdrawals and deposits.
� Reservation agents for airlines, hotels, and car rental companies check availability for a given request and make reservations.
16 Chapter 1 Databases and Database Users
� Employees at receiving stations for shipping companies enter package identifications via bar codes and descriptive information through buttons to update a central database of received and in-transit packages.
■ Sophisticated end users include engineers, scientists, business analysts, and others who thoroughly familiarize themselves with the facilities of the DBMS in order to implement their own applications to meet their complex requirements.
■ Standalone users maintain personal databases by using ready-made pro- gram packages that provide easy-to-use menu-based or graphics-based interfaces. An example is the user of a tax package that stores a variety of per- sonal financial data for tax purposes.
A typical DBMS provides multiple facilities to access a database. Naive end users need to learn very little about the facilities provided by the DBMS; they simply have to understand the user interfaces of the standard transactions designed and imple- mented for their use. Casual users learn only a few facilities that they may use repeatedly. Sophisticated users try to learn most of the DBMS facilities in order to achieve their complex requirements. Standalone users typically become very profi- cient in using a specific software package.
1.4.4 System Analysts and Application Programmers (Software Engineers)
System analysts determine the requirements of end users, especially naive and parametric end users, and develop specifications for standard canned transactions that meet these requirements. Application programmers implement these specifi- cations as programs; then they test, debug, document, and maintain these canned transactions. Such analysts and programmers—commonly referred to as software developers or software engineers—should be familiar with the full range of capabilities provided by the DBMS to accomplish their tasks.
1.5 Workers behind the Scene In addition to those who design, use, and administer a database, others are associ- ated with the design, development, and operation of the DBMS software and system environment. These persons are typically not interested in the database content itself. We call them the workers behind the scene, and they include the following cat- egories:
■ DBMS system designers and implementers design and implement the DBMS modules and interfaces as a software package. A DBMS is a very com- plex software system that consists of many components, or modules, includ- ing modules for implementing the catalog, query language processing, interface processing, accessing and buffering data, controlling concurrency, and handling data recovery and security. The DBMS must interface with other system software such as the operating system and compilers for vari- ous programming languages.
1.6 Advantages of Using the DBMS Approach 17
■ Tool developers design and implement tools—the software packages that facilitate database modeling and design, database system design, and improved performance. Tools are optional packages that are often purchased separately. They include packages for database design, performance moni- toring, natural language or graphical interfaces, prototyping, simulation, and test data generation. In many cases, independent software vendors develop and market these tools.
■ Operators and maintenance personnel (system administration personnel) are responsible for the actual running and maintenance of the hardware and software environment for the database system.
Although these categories of workers behind the scene are instrumental in making the database system available to end users, they typically do not use the database contents for their own purposes.
1.6 Advantages of Using the DBMS Approach In this section we discuss some of the advantages of using a DBMS and the capabil- ities that a good DBMS should possess. These capabilities are in addition to the four main characteristics discussed in Section 1.3. The DBA must utilize these capabili- ties to accomplish a variety of objectives related to the design, administration, and use of a large multiuser database.
1.6.1 Controlling Redundancy In traditional software development utilizing file processing, every user group maintains its own files for handling its data-processing applications. For example, consider the UNIVERSITY database example of Section 1.2; here, two groups of users might be the course registration personnel and the accounting office. In the tradi- tional approach, each group independently keeps files on students. The accounting office keeps data on registration and related billing information, whereas the regis- tration office keeps track of student courses and grades. Other groups may further duplicate some or all of the same data in their own files.
This redundancy in storing the same data multiple times leads to several problems. First, there is the need to perform a single logical update—such as entering data on a new student—multiple times: once for each file where student data is recorded. This leads to duplication of effort. Second, storage space is wasted when the same data is stored repeatedly, and this problem may be serious for large databases. Third, files that represent the same data may become inconsistent. This may happen because an update is applied to some of the files but not to others. Even if an update—such as adding a new student—is applied to all the appropriate files, the data concerning the student may still be inconsistent because the updates are applied independently by each user group. For example, one user group may enter a student’s birth date erroneously as ‘JAN-19-1988’, whereas the other user groups may enter the correct value of ‘JAN-29-1988’.
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Student_number Student_name Section_identifier Course_number Grade
17 Smith 112 MATH2410 B
17 Smith 119 CS1310 C
8 Brown 85 MATH2410 A
8 Brown 92 CS1310 A
8 Brown 102 CS3320 B
8 Brown 135 CS3380 A
GRADE_REPORT
Student_number Student_name Section_identifier Course_number Grade
17 Brown 112 MATH2410 B
GRADE_REPORT
(a)
(b)
Figure 1.6 Redundant storage of Student_name and Course_name in GRADE_REPORT. (a) Consistent data. (b) Inconsistent record.
In the database approach, the views of different user groups are integrated during database design. Ideally, we should have a database design that stores each logical data item—such as a student’s name or birth date—in only one place in the database. This is known as data normalization, and it ensures consistency and saves storage space (data normalization is described in Part 6 of the book). However, in practice, it is sometimes necessary to use controlled redundancy to improve the performance of queries. For example, we may store Student_name and Course_number redundantly in a GRADE_REPORT file (Figure 1.6(a)) because whenever we retrieve a GRADE_REPORT record, we want to retrieve the student name and course number along with the grade, student number, and section identifier. By placing all the data together, we do not have to search multiple files to collect this data. This is known as denormalization. In such cases, the DBMS should have the capability to control this redundancy in order to prohibit inconsistencies among the files. This may be done by automatically checking that the Student_name–Student_number values in any GRADE_REPORT record in Figure 1.6(a) match one of the Name–Student_number val- ues of a STUDENT record (Figure 1.2). Similarly, the Section_identifier–Course_number values in GRADE_REPORT can be checked against SECTION records. Such checks can be specified to the DBMS during database design and automatically enforced by the DBMS whenever the GRADE_REPORT file is updated. Figure 1.6(b) shows a GRADE_REPORT record that is inconsistent with the STUDENT file in Figure 1.2; this kind of error may be entered if the redundancy is not controlled. Can you tell which part is inconsistent?
1.6.2 Restricting Unauthorized Access When multiple users share a large database, it is likely that most users will not be authorized to access all information in the database. For example, financial data is often considered confidential, and only authorized persons are allowed to access such data. In addition, some users may only be permitted to retrieve data, whereas
1.6 Advantages of Using the DBMS Approach 19
others are allowed to retrieve and update. Hence, the type of access operation— retrieval or update—must also be controlled. Typically, users or user groups are given account numbers protected by passwords, which they can use to gain access to the database. A DBMS should provide a security and authorization subsystem, which the DBA uses to create accounts and to specify account restrictions. Then, the DBMS should enforce these restrictions automatically. Notice that we can apply similar controls to the DBMS software. For example, only the dba’s staff may be allowed to use certain privileged software, such as the software for creating new accounts. Similarly, parametric users may be allowed to access the database only through the predefined canned transactions developed for their use.
1.6.3 Providing Persistent Storage for Program Objects Databases can be used to provide persistent storage for program objects and data structures. This is one of the main reasons for object-oriented database systems. Programming languages typically have complex data structures, such as record types in Pascal or class definitions in C++ or Java. The values of program variables or objects are discarded once a program terminates, unless the programmer explic- itly stores them in permanent files, which often involves converting these complex structures into a format suitable for file storage. When the need arises to read this data once more, the programmer must convert from the file format to the pro- gram variable or object structure. Object-oriented database systems are compatible with programming languages such as C++ and Java, and the DBMS software auto- matically performs any necessary conversions. Hence, a complex object in C++ can be stored permanently in an object-oriented DBMS. Such an object is said to be persistent, since it survives the termination of program execution and can later be directly retrieved by another C++ program.
The persistent storage of program objects and data structures is an important func- tion of database systems. Traditional database systems often suffered from the so- called impedance mismatch problem, since the data structures provided by the DBMS were incompatible with the programming language’s data structures. Object-oriented database systems typically offer data structure compatibility with one or more object-oriented programming languages.
1.6.4 Providing Storage Structures and Search Techniques for Efficient Query Processing
Database systems must provide capabilities for efficiently executing queries and updates. Because the database is typically stored on disk, the DBMS must provide specialized data structures and search techniques to speed up disk search for the desired records. Auxiliary files called indexes are used for this purpose. Indexes are typically based on tree data structures or hash data structures that are suitably mod- ified for disk search. In order to process the database records needed by a particular query, those records must be copied from disk to main memory. Therefore, the DBMS often has a buffering or caching module that maintains parts of the data- base in main memory buffers. In general, the operating system is responsible for
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disk-to-memory buffering. However, because data buffering is crucial to the DBMS performance, most DBMSs do their own data buffering.
The query processing and optimization module of the DBMS is responsible for choosing an efficient query execution plan for each query based on the existing stor- age structures. The choice of which indexes to create and maintain is part of physical database design and tuning, which is one of the responsibilities of the DBA staff. We discuss the query processing, optimization, and tuning in Part 8 of the book.
1.6.5 Providing Backup and Recovery A DBMS must provide facilities for recovering from hardware or software failures. The backup and recovery subsystem of the DBMS is responsible for recovery. For example, if the computer system fails in the middle of a complex update transac- tion, the recovery subsystem is responsible for making sure that the database is restored to the state it was in before the transaction started executing. Alternatively, the recovery subsystem could ensure that the transaction is resumed from the point at which it was interrupted so that its full effect is recorded in the database. Disk backup is also necessary in case of a catastrophic disk failure. We discuss recovery and backup in Chapter 23.
1.6.6 Providing Multiple User Interfaces Because many types of users with varying levels of technical knowledge use a data- base, a DBMS should provide a variety of user interfaces. These include query lan- guages for casual users, programming language interfaces for application programmers, forms and command codes for parametric users, and menu-driven interfaces and natural language interfaces for standalone users. Both forms-style interfaces and menu-driven interfaces are commonly known as graphical user interfaces (GUIs). Many specialized languages and environments exist for specify- ing GUIs. Capabilities for providing Web GUI interfaces to a database—or Web- enabling a database—are also quite common.
1.6.7 Representing Complex Relationships among Data A database may include numerous varieties of data that are interrelated in many ways. Consider the example shown in Figure 1.2. The record for ‘Brown’ in the STUDENT file is related to four records in the GRADE_REPORT file. Similarly, each section record is related to one course record and to a number of GRADE_REPORT records—one for each student who completed that section. A DBMS must have the capability to represent a variety of complex relationships among the data, to define new relationships as they arise, and to retrieve and update related data easily and efficiently.
1.6.8 Enforcing Integrity Constraints Most database applications have certain integrity constraints that must hold for the data. A DBMS should provide capabilities for defining and enforcing these con-
1.6 Advantages of Using the DBMS Approach 21
straints. The simplest type of integrity constraint involves specifying a data type for each data item. For example, in Figure 1.3, we specified that the value of the Class data item within each STUDENT record must be a one digit integer and that the value of Name must be a string of no more than 30 alphabetic characters. To restrict the value of Class between 1 and 5 would be an additional constraint that is not shown in the current catalog. A more complex type of constraint that frequently occurs involves specifying that a record in one file must be related to records in other files. For example, in Figure 1.2, we can specify that every section record must be related to a course record. This is known as a referential integrity constraint. Another type of constraint specifies uniqueness on data item values, such as every course record must have a unique value for Course_number. This is known as a key or uniqueness constraint. These constraints are derived from the meaning or semantics of the data and of the miniworld it represents. It is the responsibility of the database designers to identify integrity constraints during database design. Some constraints can be specified to the DBMS and automatically enforced. Other constraints may have to be checked by update programs or at the time of data entry. For typical large applications, it is customary to call such constraints business rules.
A data item may be entered erroneously and still satisfy the specified integrity con- straints. For example, if a student receives a grade of ‘A’ but a grade of ‘C’ is entered in the database, the DBMS cannot discover this error automatically because ‘C’ is a valid value for the Grade data type. Such data entry errors can only be discovered manually (when the student receives the grade and complains) and corrected later by updating the database. However, a grade of ‘Z’ would be rejected automatically by the DBMS because ‘Z’ is not a valid value for the Grade data type. When we dis- cuss each data model in subsequent chapters, we will introduce rules that pertain to that model implicitly. For example, in the Entity-Relationship model in Chapter 7, a relationship must involve at least two entities. Such rules are inherent rules of the data model and are automatically assumed to guarantee the validity of the model.
1.6.9 Permitting Inferencing and Actions Using Rules Some database systems provide capabilities for defining deduction rules for inferencing new information from the stored database facts. Such systems are called deductive database systems. For example, there may be complex rules in the mini- world application for determining when a student is on probation. These can be specified declaratively as rules, which when compiled and maintained by the DBMS can determine all students on probation. In a traditional DBMS, an explicit procedural program code would have to be written to support such applications. But if the miniworld rules change, it is generally more convenient to change the declared deduction rules than to recode procedural programs. In today’s relational database systems, it is possible to associate triggers with tables. A trigger is a form of a rule activated by updates to the table, which results in performing some additional oper- ations to some other tables, sending messages, and so on. More involved procedures to enforce rules are popularly called stored procedures; they become a part of the overall database definition and are invoked appropriately when certain conditions are met. More powerful functionality is provided by active database systems, which
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provide active rules that can automatically initiate actions when certain events and conditions occur.
1.6.10 Additional Implications of Using the Database Approach
This section discusses some additional implications of using the database approach that can benefit most organizations.
Potential for Enforcing Standards. The database approach permits the DBA to define and enforce standards among database users in a large organization. This facil- itates communication and cooperation among various departments, projects, and users within the organization. Standards can be defined for names and formats of data elements, display formats, report structures, terminology, and so on. The DBA can enforce standards in a centralized database environment more easily than in an environment where each user group has control of its own data files and software.
Reduced Application Development Time. A prime selling feature of the data- base approach is that developing a new application—such as the retrieval of certain data from the database for printing a new report—takes very little time. Designing and implementing a large multiuser database from scratch may take more time than writing a single specialized file application. However, once a database is up and run- ning, substantially less time is generally required to create new applications using DBMS facilities. Development time using a DBMS is estimated to be one-sixth to one-fourth of that for a traditional file system.
Flexibility. It may be necessary to change the structure of a database as require- ments change. For example, a new user group may emerge that needs information not currently in the database. In response, it may be necessary to add a file to the database or to extend the data elements in an existing file. Modern DBMSs allow certain types of evolutionary changes to the structure of the database without affecting the stored data and the existing application programs.
Availability of Up-to-Date Information. A DBMS makes the database available to all users. As soon as one user’s update is applied to the database, all other users can immediately see this update. This availability of up-to-date information is essential for many transaction-processing applications, such as reservation systems or banking databases, and it is made possible by the concurrency control and recov- ery subsystems of a DBMS.
Economies of Scale. The DBMS approach permits consolidation of data and applications, thus reducing the amount of wasteful overlap between activities of data-processing personnel in different projects or departments as well as redundan- cies among applications. This enables the whole organization to invest in more powerful processors, storage devices, or communication gear, rather than having each department purchase its own (lower performance) equipment. This reduces overall costs of operation and management.
1.7 A Brief History of Database Applications 23
1.7 A Brief History of Database Applications We now give a brief historical overview of the applications that use DBMSs and how these applications provided the impetus for new types of database systems.
1.7.1 Early Database Applications Using Hierarchical and Network Systems
Many early database applications maintained records in large organizations such as corporations, universities, hospitals, and banks. In many of these applications, there were large numbers of records of similar structure. For example, in a university application, similar information would be kept for each student, each course, each grade record, and so on. There were also many types of records and many interrela- tionships among them.
One of the main problems with early database systems was the intermixing of con- ceptual relationships with the physical storage and placement of records on disk. Hence, these systems did not provide sufficient data abstraction and program-data independence capabilities. For example, the grade records of a particular student could be physically stored next to the student record. Although this provided very efficient access for the original queries and transactions that the database was designed to handle, it did not provide enough flexibility to access records efficiently when new queries and transactions were identified. In particular, new queries that required a different storage organization for efficient processing were quite difficult to implement efficiently. It was also laborious to reorganize the database when changes were made to the application’s requirements.
Another shortcoming of early systems was that they provided only programming language interfaces. This made it time-consuming and expensive to implement new queries and transactions, since new programs had to be written, tested, and debugged. Most of these database systems were implemented on large and expensive mainframe computers starting in the mid-1960s and continuing through the 1970s and 1980s. The main types of early systems were based on three main paradigms: hierarchical systems, network model based systems, and inverted file systems.
1.7.2 Providing Data Abstraction and Application Flexibility with Relational Databases
Relational databases were originally proposed to separate the physical storage of data from its conceptual representation and to provide a mathematical foundation for data representation and querying. The relational data model also introduced high-level query languages that provided an alternative to programming language interfaces, making it much faster to write new queries. Relational representation of data somewhat resembles the example we presented in Figure 1.2. Relational sys- tems were initially targeted to the same applications as earlier systems, and provided flexibility to develop new queries quickly and to reorganize the database as require- ments changed. Hence, data abstraction and program-data independence were much improved when compared to earlier systems.
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Early experimental relational systems developed in the late 1970s and the commer- cial relational database management systems (RDBMS) introduced in the early 1980s were quite slow, since they did not use physical storage pointers or record placement to access related data records. With the development of new storage and indexing techniques and better query processing and optimization, their perfor- mance improved. Eventually, relational databases became the dominant type of data- base system for traditional database applications. Relational databases now exist on almost all types of computers, from small personal computers to large servers.
1.7.3 Object-Oriented Applications and the Need for More Complex Databases
The emergence of object-oriented programming languages in the 1980s and the need to store and share complex, structured objects led to the development of object-oriented databases (OODBs). Initially, OODBs were considered a competi- tor to relational databases, since they provided more general data structures. They also incorporated many of the useful object-oriented paradigms, such as abstract data types, encapsulation of operations, inheritance, and object identity. However, the complexity of the model and the lack of an early standard contributed to their limited use. They are now mainly used in specialized applications, such as engineer- ing design, multimedia publishing, and manufacturing systems. Despite expecta- tions that they will make a big impact, their overall penetration into the database products market remains under 5% today. In addition, many object-oriented con- cepts were incorporated into the newer versions of relational DBMSs, leading to object-relational database management systems, known as ORDBMSs.
1.7.4 Interchanging Data on the Web for E-Commerce Using XML
The World Wide Web provides a large network of interconnected computers. Users can create documents using a Web publishing language, such as HyperText Markup Language (HTML), and store these documents on Web servers where other users (clients) can access them. Documents can be linked through hyperlinks, which are pointers to other documents. In the 1990s, electronic commerce (e-commerce) emerged as a major application on the Web. It quickly became apparent that parts of the information on e-commerce Web pages were often dynamically extracted data from DBMSs. A variety of techniques were developed to allow the interchange of data on the Web. Currently, eXtended Markup Language (XML) is considered to be the primary standard for interchanging data among various types of databases and Web pages. XML combines concepts from the models used in document systems with database modeling concepts. Chapter 12 is devoted to the discussion of XML.
1.7.5 Extending Database Capabilities for New Applications The success of database systems in traditional applications encouraged developers of other types of applications to attempt to use them. Such applications tradition- ally used their own specialized file and data structures. Database systems now offer
1.7 A Brief History of Database Applications 25
extensions to better support the specialized requirements for some of these applica- tions. The following are some examples of these applications:
■ Scientific applications that store large amounts of data resulting from scien- tific experiments in areas such as high-energy physics, the mapping of the human genome, and the discovery of protein structures.
■ Storage and retrieval of images, including scanned news or personal photo- graphs, satellite photographic images, and images from medical procedures such as x-rays and MRIs (magnetic resonance imaging).
■ Storage and retrieval of videos, such as movies, and video clips from news or personal digital cameras.
■ Data mining applications that analyze large amounts of data searching for the occurrences of specific patterns or relationships, and for identifying unusual patterns in areas such as credit card usage.
■ Spatial applications that store spatial locations of data, such as weather information, maps used in geographical information systems, and in auto- mobile navigational systems.
■ Time series applications that store information such as economic data at regular points in time, such as daily sales and monthly gross national prod- uct figures.
It was quickly apparent that basic relational systems were not very suitable for many of these applications, usually for one or more of the following reasons:
■ More complex data structures were needed for modeling the application than the simple relational representation.
■ New data types were needed in addition to the basic numeric and character string types.
■ New operations and query language constructs were necessary to manipu- late the new data types.
■ New storage and indexing structures were needed for efficient searching on the new data types.
This led DBMS developers to add functionality to their systems. Some functionality was general purpose, such as incorporating concepts from object-oriented data- bases into relational systems. Other functionality was special purpose, in the form of optional modules that could be used for specific applications. For example, users could buy a time series module to use with their relational DBMS for their time series application.
Many large organizations use a variety of software application packages that work closely with database back-ends. The database back-end represents one or more databases, possibly from different vendors and using different data models, that maintain data that is manipulated by these packages for supporting transactions, generating reports, and answering ad-hoc queries. One of the most commonly used systems includes Enterprise Resource Planning (ERP), which is used to consolidate a variety of functional areas within an organization, including production, sales,
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distribution, marketing, finance, human resources, and so on. Another popular type of system is Customer Relationship Management (CRM) software that spans order processing as well as marketing and customer support functions. These applications are Web-enabled in that internal and external users are given a variety of Web- portal interfaces to interact with the back-end databases.
1.7.6 Databases versus Information Retrieval Traditionally, database technology applies to structured and formatted data that arises in routine applications in government, business, and industry. Database tech- nology is heavily used in manufacturing, retail, banking, insurance, finance, and health care industries, where structured data is collected through forms, such as invoices or patient registration documents. An area related to database technology is Information Retrieval (IR), which deals with books, manuscripts, and various forms of library-based articles. Data is indexed, cataloged, and annotated using key- words. IR is concerned with searching for material based on these keywords, and with the many problems dealing with document processing and free-form text pro- cessing. There has been a considerable amount of work done on searching for text based on keywords, finding documents and ranking them based on relevance, auto- matic text categorization, classification of text documents by topics, and so on. With the advent of the Web and the proliferation of HTML pages running into the bil- lions, there is a need to apply many of the IR techniques to processing data on the Web. Data on Web pages typically contains images, text, and objects that are active and change dynamically. Retrieval of information on the Web is a new problem that requires techniques from databases and IR to be applied in a variety of novel com- binations. We discuss concepts related to information retrieval and Web search in Chapter 27.
1.8 When Not to Use a DBMS In spite of the advantages of using a DBMS, there are a few situations in which a DBMS may involve unnecessary overhead costs that would not be incurred in tradi- tional file processing. The overhead costs of using a DBMS are due to the following:
■ High initial investment in hardware, software, and training
■ The generality that a DBMS provides for defining and processing data
■ Overhead for providing security, concurrency control, recovery, and integrity functions
Therefore, it may be more desirable to use regular files under the following circum- stances:
■ Simple, well-defined database applications that are not expected to change at all
■ Stringent, real-time requirements for some application programs that may not be met because of DBMS overhead
Review Questions 27
■ Embedded systems with limited storage capacity, where a general-purpose DBMS would not fit
■ No multiple-user access to data
Certain industries and applications have elected not to use general-purpose DBMSs. For example, many computer-aided design (CAD) tools used by mechani- cal and civil engineers have proprietary file and data management software that is geared for the internal manipulations of drawings and 3D objects. Similarly, com- munication and switching systems designed by companies like AT&T were early manifestations of database software that was made to run very fast with hierarchi- cally organized data for quick access and routing of calls. Similarly, GIS implemen- tations often implement their own data organization schemes for efficiently implementing functions related to processing maps, physical contours, lines, poly- gons, and so on. General-purpose DBMSs are inadequate for their purpose.
1.9 Summary In this chapter we defined a database as a collection of related data, where data means recorded facts. A typical database represents some aspect of the real world and is used for specific purposes by one or more groups of users. A DBMS is a gen- eralized software package for implementing and maintaining a computerized data- base. The database and software together form a database system. We identified several characteristics that distinguish the database approach from traditional file- processing applications, and we discussed the main categories of database users, or the actors on the scene. We noted that in addition to database users, there are several categories of support personnel, or workers behind the scene, in a database environ- ment.
We presented a list of capabilities that should be provided by the DBMS software to the DBA, database designers, and end users to help them design, administer, and use a database. Then we gave a brief historical perspective on the evolution of database applications. We pointed out the marriage of database technology with information retrieval technology, which will play an important role due to the popularity of the Web. Finally, we discussed the overhead costs of using a DBMS and discussed some situations in which it may not be advantageous to use one.
Review Questions 1.1. Define the following terms: data, database, DBMS, database system, database
catalog, program-data independence, user view, DBA, end user, canned trans- action, deductive database system, persistent object, meta-data, and transaction-processing application.
1.2. What four main types of actions involve databases? Briefly discuss each.
1.3. Discuss the main characteristics of the database approach and how it differs from traditional file systems.
28 Chapter 1 Databases and Database Users
1.4. What are the responsibilities of the DBA and the database designers?
1.5. What are the different types of database end users? Discuss the main activi- ties of each.
1.6. Discuss the capabilities that should be provided by a DBMS.
1.7. Discuss the differences between database systems and information retrieval systems.
Exercises 1.8. Identify some informal queries and update operations that you would expect
to apply to the database shown in Figure 1.2.
1.9. What is the difference between controlled and uncontrolled redundancy? Illustrate with examples.
1.10. Specify all the relationships among the records of the database shown in Figure 1.2.
1.11. Give some additional views that may be needed by other user groups for the database shown in Figure 1.2.
1.12. Cite some examples of integrity constraints that you think can apply to the database shown in Figure 1.2.
1.13. Give examples of systems in which it may make sense to use traditional file processing instead of a database approach.
1.14. Consider Figure 1.2.
a. If the name of the ‘CS’ (Computer Science) Department changes to ‘CSSE’ (Computer Science and Software Engineering) Department and the corresponding prefix for the course number also changes, identify the columns in the database that would need to be updated.
b. Can you restructure the columns in the COURSE, SECTION, and PREREQUISITE tables so that only one column will need to be updated?
Selected Bibliography The October 1991 issue of Communications of the ACM and Kim (1995) include several articles describing next-generation DBMSs; many of the database features discussed in the former are now commercially available. The March 1976 issue of ACM Computing Surveys offers an early introduction to database systems and may provide a historical perspective for the interested reader.
29
Database System Concepts and Architecture
The architecture of DBMS packages has evolved fromthe early monolithic systems, where the whole DBMS software package was one tightly integrated system, to the modern DBMS packages that are modular in design, with a client/server system architecture. This evolution mirrors the trends in computing, where large centralized mainframe com- puters are being replaced by hundreds of distributed workstations and personal computers connected via communications networks to various types of server machines—Web servers, database servers, file servers, application servers, and so on.
In a basic client/server DBMS architecture, the system functionality is distributed between two types of modules.1 A client module is typically designed so that it will run on a user workstation or personal computer. Typically, application programs and user interfaces that access the database run in the client module. Hence, the client module handles user interaction and provides the user-friendly interfaces such as forms- or menu-based GUIs (graphical user interfaces). The other kind of module, called a server module, typically handles data storage, access, search, and other functions. We discuss client/server architectures in more detail in Section 2.5. First, we must study more basic concepts that will give us a better understanding of modern database architectures.
In this chapter we present the terminology and basic concepts that will be used throughout the book. Section 2.1 discusses data models and defines the concepts of schemas and instances, which are fundamental to the study of database systems. Then, we discuss the three-schema DBMS architecture and data independence in Section 2.2; this provides a user’s perspective on what a DBMS is supposed to do. In Section 2.3 we describe the types of interfaces and languages that are typically pro- vided by a DBMS. Section 2.4 discusses the database system software environment.
2chapter 2
1As we shall see in Section 2.5, there are variations on this simple two-tier client/server architecture.
30 Chapter 2 Database System Concepts and Architecture
Section 2.5 gives an overview of various types of client/server architectures. Finally, Section 2.6 presents a classification of the types of DBMS packages. Section 2.7 summarizes the chapter.
The material in Sections 2.4 through 2.6 provides more detailed concepts that may be considered as supplementary to the basic introductory material.
2.1 Data Models, Schemas, and Instances One fundamental characteristic of the database approach is that it provides some level of data abstraction. Data abstraction generally refers to the suppression of details of data organization and storage, and the highlighting of the essential fea- tures for an improved understanding of data. One of the main characteristics of the database approach is to support data abstraction so that different users can perceive data at their preferred level of detail. A data model—a collection of concepts that can be used to describe the structure of a database—provides the necessary means to achieve this abstraction.2 By structure of a database we mean the data types, rela- tionships, and constraints that apply to the data. Most data models also include a set of basic operations for specifying retrievals and updates on the database.
In addition to the basic operations provided by the data model, it is becoming more common to include concepts in the data model to specify the dynamic aspect or behavior of a database application. This allows the database designer to specify a set of valid user-defined operations that are allowed on the database objects.3 An exam- ple of a user-defined operation could be COMPUTE_GPA, which can be applied to a STUDENT object. On the other hand, generic operations to insert, delete, modify, or retrieve any kind of object are often included in the basic data model operations. Concepts to specify behavior are fundamental to object-oriented data models (see Chapter 11) but are also being incorporated in more traditional data models. For example, object-relational models (see Chapter 11) extend the basic relational model to include such concepts, among others. In the basic relational data model, there is a provision to attach behavior to the relations in the form of persistent stored modules, popularly known as stored procedures (see Chapter 13).
2.1.1 Categories of Data Models Many data models have been proposed, which we can categorize according to the types of concepts they use to describe the database structure. High-level or conceptual data models provide concepts that are close to the way many users per- ceive data, whereas low-level or physical data models provide concepts that describe the details of how data is stored on the computer storage media, typically
2Sometimes the word model is used to denote a specific database description, or schema—for example, the marketing data model. We will not use this interpretation. 3The inclusion of concepts to describe behavior reflects a trend whereby database design and software design activities are increasingly being combined into a single activity. Traditionally, specifying behavior is associated with software design.
2.1 Data Models, Schemas, and Instances 31
magnetic disks. Concepts provided by low-level data models are generally meant for computer specialists, not for end users. Between these two extremes is a class of representational (or implementation) data models,4 which provide concepts that may be easily understood by end users but that are not too far removed from the way data is organized in computer storage. Representational data models hide many details of data storage on disk but can be implemented on a computer system directly.
Conceptual data models use concepts such as entities, attributes, and relationships. An entity represents a real-world object or concept, such as an employee or a project from the miniworld that is described in the database. An attribute represents some property of interest that further describes an entity, such as the employee’s name or salary. A relationship among two or more entities represents an association among the entities, for example, a works-on relationship between an employee and a proj- ect. Chapter 7 presents the Entity-Relationship model—a popular high-level con- ceptual data model. Chapter 8 describes additional abstractions used for advanced modeling, such as generalization, specialization, and categories (union types).
Representational or implementation data models are the models used most fre- quently in traditional commercial DBMSs. These include the widely used relational data model, as well as the so-called legacy data models—the network and hierarchical models—that have been widely used in the past. Part 2 is devoted to the relational data model, and its constraints, operations and languages.5 The SQL standard for relational databases is described in Chapters 4 and 5. Representational data models represent data by using record structures and hence are sometimes called record-based data models.
We can regard the object data model as an example of a new family of higher-level implementation data models that are closer to conceptual data models. A standard for object databases called the ODMG object model has been proposed by the Object Data Management Group (ODMG). We describe the general characteristics of object databases and the object model proposed standard in Chapter 11. Object data models are also frequently utilized as high-level conceptual models, particu- larly in the software engineering domain.
Physical data models describe how data is stored as files in the computer by repre- senting information such as record formats, record orderings, and access paths. An access path is a structure that makes the search for particular database records effi- cient. We discuss physical storage techniques and access structures in Chapters 17 and 18. An index is an example of an access path that allows direct access to data using an index term or a keyword. It is similar to the index at the end of this book, except that it may be organized in a linear, hierarchical (tree-structured), or some other fashion.
4The term implementation data model is not a standard term; we have introduced it to refer to the avail- able data models in commercial database systems. 5A summary of the hierarchical and network data models is included in Appendices D and E. They are accessible from the book’s Web site.
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2.1.2 Schemas, Instances, and Database State In any data model, it is important to distinguish between the description of the data- base and the database itself. The description of a database is called the database schema, which is specified during database design and is not expected to change frequently.6 Most data models have certain conventions for displaying schemas as diagrams.7 A displayed schema is called a schema diagram. Figure 2.1 shows a schema diagram for the database shown in Figure 1.2; the diagram displays the structure of each record type but not the actual instances of records. We call each object in the schema—such as STUDENT or COURSE—a schema construct.
A schema diagram displays only some aspects of a schema, such as the names of record types and data items, and some types of constraints. Other aspects are not specified in the schema diagram; for example, Figure 2.1 shows neither the data type of each data item, nor the relationships among the various files. Many types of con- straints are not represented in schema diagrams. A constraint such as students majoring in computer science must take CS1310 before the end of their sophomore year is quite difficult to represent diagrammatically.
The actual data in a database may change quite frequently. For example, the data- base shown in Figure 1.2 changes every time we add a new student or enter a new grade. The data in the database at a particular moment in time is called a database state or snapshot. It is also called the current set of occurrences or instances in the
Section_identifier SemesterCourse_number InstructorYear
SECTION
Course_name Course_number Credit_hours Department
COURSE
Name Student_number Class Major
STUDENT
Course_number Prerequisite_number PREREQUISITE
Student_number GradeSection_identifier
GRADE_REPORT
Figure 2.1 Schema diagram for the database in Figure 1.2.
6Schema changes are usually needed as the requirements of the database applications change. Newer database systems include operations for allowing schema changes, although the schema change process is more involved than simple database updates. 7It is customary in database parlance to use schemas as the plural for schema, even though schemata is the proper plural form. The word scheme is also sometimes used to refer to a schema.
2.2 Three-Schema Architecture and Data Independence 33
database. In a given database state, each schema construct has its own current set of instances; for example, the STUDENT construct will contain the set of individual student entities (records) as its instances. Many database states can be constructed to correspond to a particular database schema. Every time we insert or delete a record or change the value of a data item in a record, we change one state of the database into another state.
The distinction between database schema and database state is very important. When we define a new database, we specify its database schema only to the DBMS. At this point, the corresponding database state is the empty state with no data. We get the initial state of the database when the database is first populated or loaded with the initial data. From then on, every time an update operation is applied to the database, we get another database state. At any point in time, the database has a current state.8 The DBMS is partly responsible for ensuring that every state of the database is a valid state—that is, a state that satisfies the structure and constraints specified in the schema. Hence, specifying a correct schema to the DBMS is extremely important and the schema must be designed with utmost care. The DBMS stores the descriptions of the schema constructs and constraints—also called the meta-data—in the DBMS catalog so that DBMS software can refer to the schema whenever it needs to. The schema is sometimes called the intension, and a database state is called an extension of the schema.
Although, as mentioned earlier, the schema is not supposed to change frequently, it is not uncommon that changes occasionally need to be applied to the schema as the application requirements change. For example, we may decide that another data item needs to be stored for each record in a file, such as adding the Date_of_birth to the STUDENT schema in Figure 2.1. This is known as schema evolution. Most mod- ern DBMSs include some operations for schema evolution that can be applied while the database is operational.
2.2 Three-Schema Architecture and Data Independence
Three of the four important characteristics of the database approach, listed in Section 1.3, are (1) use of a catalog to store the database description (schema) so as to make it self-describing, (2) insulation of programs and data (program-data and program-operation independence), and (3) support of multiple user views. In this section we specify an architecture for database systems, called the three-schema architecture,9 that was proposed to help achieve and visualize these characteristics. Then we discuss the concept of data independence further.
8The current state is also called the current snapshot of the database. It has also been called a database instance, but we prefer to use the term instance to refer to individual records. 9This is also known as the ANSI/SPARC architecture, after the committee that proposed it (Tsichritzis and Klug 1978).
34 Chapter 2 Database System Concepts and Architecture
External View
Conceptual Schema
Internal Schema
Stored Database
External View
Internal Level
Conceptual/Internal Mapping
Conceptual Level
External/Conceptual Mapping
External Level
End Users
. . .
Figure 2.2 The three-schema architecture.
2.2.1 The Three-Schema Architecture The goal of the three-schema architecture, illustrated in Figure 2.2, is to separate the user applications from the physical database. In this architecture, schemas can be defined at the following three levels:
1. The internal level has an internal schema, which describes the physical stor- age structure of the database. The internal schema uses a physical data model and describes the complete details of data storage and access paths for the database.
2. The conceptual level has a conceptual schema, which describes the struc- ture of the whole database for a community of users. The conceptual schema hides the details of physical storage structures and concentrates on describ- ing entities, data types, relationships, user operations, and constraints. Usually, a representational data model is used to describe the conceptual schema when a database system is implemented. This implementation con- ceptual schema is often based on a conceptual schema design in a high-level data model.
3. The external or view level includes a number of external schemas or user views. Each external schema describes the part of the database that a partic- ular user group is interested in and hides the rest of the database from that user group. As in the previous level, each external schema is typically imple- mented using a representational data model, possibly based on an external schema design in a high-level data model.
2.2 Three-Schema Architecture and Data Independence 35
The three-schema architecture is a convenient tool with which the user can visualize the schema levels in a database system. Most DBMSs do not separate the three levels completely and explicitly, but support the three-schema architecture to some extent. Some older DBMSs may include physical-level details in the conceptual schema. The three-level ANSI architecture has an important place in database technology development because it clearly separates the users’ external level, the database’s con- ceptual level, and the internal storage level for designing a database. It is very much applicable in the design of DBMSs, even today. In most DBMSs that support user views, external schemas are specified in the same data model that describes the conceptual-level information (for example, a relational DBMS like Oracle uses SQL for this). Some DBMSs allow different data models to be used at the conceptual and external levels. An example is Universal Data Base (UDB), a DBMS from IBM, which uses the relational model to describe the conceptual schema, but may use an object-oriented model to describe an external schema.
Notice that the three schemas are only descriptions of data; the stored data that actually exists is at the physical level only. In a DBMS based on the three-schema architecture, each user group refers to its own external schema. Hence, the DBMS must transform a request specified on an external schema into a request against the conceptual schema, and then into a request on the internal schema for processing over the stored database. If the request is a database retrieval, the data extracted from the stored database must be reformatted to match the user’s external view. The processes of transforming requests and results between levels are called mappings. These mappings may be time-consuming, so some DBMSs—especially those that are meant to support small databases—do not support external views. Even in such systems, however, a certain amount of mapping is necessary to transform requests between the conceptual and internal levels.
2.2.2 Data Independence The three-schema architecture can be used to further explain the concept of data independence, which can be defined as the capacity to change the schema at one level of a database system without having to change the schema at the next higher level. We can define two types of data independence:
1. Logical data independence is the capacity to change the conceptual schema without having to change external schemas or application programs. We may change the conceptual schema to expand the database (by adding a record type or data item), to change constraints, or to reduce the database (by removing a record type or data item). In the last case, external schemas that refer only to the remaining data should not be affected. For example, the external schema of Figure 1.5(a) should not be affected by changing the GRADE_REPORT file (or record type) shown in Figure 1.2 into the one shown in Figure 1.6(a). Only the view definition and the mappings need to be changed in a DBMS that supports logical data independence. After the conceptual schema undergoes a logical reorganization, application pro- grams that reference the external schema constructs must work as before.
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Changes to constraints can be applied to the conceptual schema without affecting the external schemas or application programs.
2. Physical data independence is the capacity to change the internal schema without having to change the conceptual schema. Hence, the external schemas need not be changed as well. Changes to the internal schema may be needed because some physical files were reorganized—for example, by creat- ing additional access structures—to improve the performance of retrieval or update. If the same data as before remains in the database, we should not have to change the conceptual schema. For example, providing an access path to improve retrieval speed of section records (Figure 1.2) by semester and year should not require a query such as list all sections offered in fall 2008 to be changed, although the query would be executed more efficiently by the DBMS by utilizing the new access path.
Generally, physical data independence exists in most databases and file environ- ments where physical details such as the exact location of data on disk, and hard- ware details of storage encoding, placement, compression, splitting, merging of records, and so on are hidden from the user. Applications remain unaware of these details. On the other hand, logical data independence is harder to achieve because it allows structural and constraint changes without affecting application programs—a much stricter requirement.
Whenever we have a multiple-level DBMS, its catalog must be expanded to include information on how to map requests and data among the various levels. The DBMS uses additional software to accomplish these mappings by referring to the mapping information in the catalog. Data independence occurs because when the schema is changed at some level, the schema at the next higher level remains unchanged; only the mapping between the two levels is changed. Hence, application programs refer- ring to the higher-level schema need not be changed.
The three-schema architecture can make it easier to achieve true data indepen- dence, both physical and logical. However, the two levels of mappings create an overhead during compilation or execution of a query or program, leading to ineffi- ciencies in the DBMS. Because of this, few DBMSs have implemented the full three- schema architecture.
2.3 Database Languages and Interfaces In Section 1.4 we discussed the variety of users supported by a DBMS. The DBMS must provide appropriate languages and interfaces for each category of users. In this section we discuss the types of languages and interfaces provided by a DBMS and the user categories targeted by each interface.
2.3.1 DBMS Languages Once the design of a database is completed and a DBMS is chosen to implement the database, the first step is to specify conceptual and internal schemas for the database
2.3 Database Languages and Interfaces 37
and any mappings between the two. In many DBMSs where no strict separation of levels is maintained, one language, called the data definition language (DDL), is used by the DBA and by database designers to define both schemas. The DBMS will have a DDL compiler whose function is to process DDL statements in order to iden- tify descriptions of the schema constructs and to store the schema description in the DBMS catalog.
In DBMSs where a clear separation is maintained between the conceptual and inter- nal levels, the DDL is used to specify the conceptual schema only. Another language, the storage definition language (SDL), is used to specify the internal schema. The mappings between the two schemas may be specified in either one of these lan- guages. In most relational DBMSs today, there is no specific language that performs the role of SDL. Instead, the internal schema is specified by a combination of func- tions, parameters, and specifications related to storage. These permit the DBA staff to control indexing choices and mapping of data to storage. For a true three-schema architecture, we would need a third language, the view definition language (VDL), to specify user views and their mappings to the conceptual schema, but in most DBMSs the DDL is used to define both conceptual and external schemas. In relational DBMSs, SQL is used in the role of VDL to define user or application views as results of predefined queries (see Chapters 4 and 5).
Once the database schemas are compiled and the database is populated with data, users must have some means to manipulate the database. Typical manipulations include retrieval, insertion, deletion, and modification of the data. The DBMS pro- vides a set of operations or a language called the data manipulation language (DML) for these purposes.
In current DBMSs, the preceding types of languages are usually not considered dis- tinct languages; rather, a comprehensive integrated language is used that includes constructs for conceptual schema definition, view definition, and data manipula- tion. Storage definition is typically kept separate, since it is used for defining physi- cal storage structures to fine-tune the performance of the database system, which is usually done by the DBA staff. A typical example of a comprehensive database lan- guage is the SQL relational database language (see Chapters 4 and 5), which repre- sents a combination of DDL, VDL, and DML, as well as statements for constraint specification, schema evolution, and other features. The SDL was a component in early versions of SQL but has been removed from the language to keep it at the con- ceptual and external levels only.
There are two main types of DMLs. A high-level or nonprocedural DML can be used on its own to specify complex database operations concisely. Many DBMSs allow high-level DML statements either to be entered interactively from a display monitor or terminal or to be embedded in a general-purpose programming lan- guage. In the latter case, DML statements must be identified within the program so that they can be extracted by a precompiler and processed by the DBMS. A low- level or procedural DML must be embedded in a general-purpose programming language. This type of DML typically retrieves individual records or objects from the database and processes each separately. Therefore, it needs to use programming
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language constructs, such as looping, to retrieve and process each record from a set of records. Low-level DMLs are also called record-at-a-time DMLs because of this property. DL/1, a DML designed for the hierarchical model, is a low-level DML that uses commands such as GET UNIQUE, GET NEXT, or GET NEXT WITHIN PARENT to navigate from record to record within a hierarchy of records in the database. High- level DMLs, such as SQL, can specify and retrieve many records in a single DML statement; therefore, they are called set-at-a-time or set-oriented DMLs. A query in a high-level DML often specifies which data to retrieve rather than how to retrieve it; therefore, such languages are also called declarative.
Whenever DML commands, whether high level or low level, are embedded in a general-purpose programming language, that language is called the host language and the DML is called the data sublanguage.10 On the other hand, a high-level DML used in a standalone interactive manner is called a query language. In general, both retrieval and update commands of a high-level DML may be used interactively and are hence considered part of the query language.11
Casual end users typically use a high-level query language to specify their requests, whereas programmers use the DML in its embedded form. For naive and paramet- ric users, there usually are user-friendly interfaces for interacting with the data- base; these can also be used by casual users or others who do not want to learn the details of a high-level query language. We discuss these types of interfaces next.
2.3.2 DBMS Interfaces User-friendly interfaces provided by a DBMS may include the following:
Menu-Based Interfaces for Web Clients or Browsing. These interfaces pre- sent the user with lists of options (called menus) that lead the user through the for- mulation of a request. Menus do away with the need to memorize the specific commands and syntax of a query language; rather, the query is composed step-by- step by picking options from a menu that is displayed by the system. Pull-down menus are a very popular technique in Web-based user interfaces. They are also often used in browsing interfaces, which allow a user to look through the contents of a database in an exploratory and unstructured manner.
Forms-Based Interfaces. A forms-based interface displays a form to each user. Users can fill out all of the form entries to insert new data, or they can fill out only certain entries, in which case the DBMS will retrieve matching data for the remain- ing entries. Forms are usually designed and programmed for naive users as inter- faces to canned transactions. Many DBMSs have forms specification languages,
10In object databases, the host and data sublanguages typically form one integrated language—for example, C++ with some extensions to support database functionality. Some relational systems also provide integrated languages—for example, Oracle’s PL/SQL. 11According to the English meaning of the word query, it should really be used to describe retrievals only, not updates.
2.3 Database Languages and Interfaces 39
which are special languages that help programmers specify such forms. SQL*Forms is a form-based language that specifies queries using a form designed in conjunc- tion with the relational database schema. Oracle Forms is a component of the Oracle product suite that provides an extensive set of features to design and build applications using forms. Some systems have utilities that define a form by letting the end user interactively construct a sample form on the screen.
Graphical User Interfaces. A GUI typically displays a schema to the user in dia- grammatic form. The user then can specify a query by manipulating the diagram. In many cases, GUIs utilize both menus and forms. Most GUIs use a pointing device, such as a mouse, to select certain parts of the displayed schema diagram.
Natural Language Interfaces. These interfaces accept requests written in English or some other language and attempt to understand them. A natural lan- guage interface usually has its own schema, which is similar to the database concep- tual schema, as well as a dictionary of important words. The natural language interface refers to the words in its schema, as well as to the set of standard words in its dictionary, to interpret the request. If the interpretation is successful, the inter- face generates a high-level query corresponding to the natural language request and submits it to the DBMS for processing; otherwise, a dialogue is started with the user to clarify the request. The capabilities of natural language interfaces have not advanced rapidly. Today, we see search engines that accept strings of natural lan- guage (like English or Spanish) words and match them with documents at specific sites (for local search engines) or Web pages on the Web at large (for engines like Google or Ask). They use predefined indexes on words and use ranking functions to retrieve and present resulting documents in a decreasing degree of match. Such “free form” textual query interfaces are not yet common in structured relational or legacy model databases, although a research area called keyword-based querying has emerged recently for relational databases.
Speech Input and Output. Limited use of speech as an input query and speech as an answer to a question or result of a request is becoming commonplace. Applications with limited vocabularies such as inquiries for telephone directory, flight arrival/departure, and credit card account information are allowing speech for input and output to enable customers to access this information. The speech input is detected using a library of predefined words and used to set up the param- eters that are supplied to the queries. For output, a similar conversion from text or numbers into speech takes place.
Interfaces for Parametric Users. Parametric users, such as bank tellers, often have a small set of operations that they must perform repeatedly. For example, a teller is able to use single function keys to invoke routine and repetitive transactions such as account deposits or withdrawals, or balance inquiries. Systems analysts and programmers design and implement a special interface for each known class of naive users. Usually a small set of abbreviated commands is included, with the goal of minimizing the number of keystrokes required for each request. For example,
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function keys in a terminal can be programmed to initiate various commands. This allows the parametric user to proceed with a minimal number of keystrokes.
Interfaces for the DBA. Most database systems contain privileged commands that can be used only by the DBA staff. These include commands for creating accounts, setting system parameters, granting account authorization, changing a schema, and reorganizing the storage structures of a database.
2.4 The Database System Environment A DBMS is a complex software system. In this section we discuss the types of soft- ware components that constitute a DBMS and the types of computer system soft- ware with which the DBMS interacts.
2.4.1 DBMS Component Modules Figure 2.3 illustrates, in a simplified form, the typical DBMS components. The fig- ure is divided into two parts. The top part of the figure refers to the various users of the database environment and their interfaces. The lower part shows the internals of the DBMS responsible for storage of data and processing of transactions.
The database and the DBMS catalog are usually stored on disk. Access to the disk is controlled primarily by the operating system (OS), which schedules disk read/write. Many DBMSs have their own buffer management module to schedule disk read/write, because this has a considerable effect on performance. Reducing disk read/write improves performance considerably. A higher-level stored data manager module of the DBMS controls access to DBMS information that is stored on disk, whether it is part of the database or the catalog.
Let us consider the top part of Figure 2.3 first. It shows interfaces for the DBA staff, casual users who work with interactive interfaces to formulate queries, application programmers who create programs using some host programming languages, and parametric users who do data entry work by supplying parameters to predefined transactions. The DBA staff works on defining the database and tuning it by making changes to its definition using the DDL and other privileged commands.
The DDL compiler processes schema definitions, specified in the DDL, and stores descriptions of the schemas (meta-data) in the DBMS catalog. The catalog includes information such as the names and sizes of files, names and data types of data items, storage details of each file, mapping information among schemas, and constraints. In addition, the catalog stores many other types of information that are needed by the DBMS modules, which can then look up the catalog information as needed.
Casual users and persons with occasional need for information from the database interact using some form of interface, which we call the interactive query interface in Figure 2.3. We have not explicitly shown any menu-based or form-based interac- tion that may be used to generate the interactive query automatically. These queries are parsed and validated for correctness of the query syntax, the names of files and
2.4 The Database System Environment 41
Query Compiler
Runtime Database Processor
Precompiler
System Catalog/
Data Dictionary
Query Optimizer
DML Compiler
Host Language Compiler
Concurrency Control/ Backup/Recovery
Subsystems
Stored Data
Manager
Compiled Transactions
Stored Database
DBA Commands, Queries, and Transactions
Input/Output from DatabaseQuery and Transaction
Execution:
DDL Compiler
DDL Statements
Privileged Commands
Interactive Query
Applicatio n Programs
DBA Staff Casual Users Application Programmers
Parametric UsersUsers:
Figure 2.3 Component modules of a DBMS and their interactions.
data elements, and so on by a query compiler that compiles them into an internal form. This internal query is subjected to query optimization (discussed in Chapters 19 and 20). Among other things, the query optimizer is concerned with the rearrangement and possible reordering of operations, elimination of redundancies, and use of correct algorithms and indexes during execution. It consults the system catalog for statistical and other physical information about the stored data and gen- erates executable code that performs the necessary operations for the query and makes calls on the runtime processor.
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Application programmers write programs in host languages such as Java, C, or C++ that are submitted to a precompiler. The precompiler extracts DML commands from an application program written in a host programming language. These com- mands are sent to the DML compiler for compilation into object code for database access. The rest of the program is sent to the host language compiler. The object codes for the DML commands and the rest of the program are linked, forming a canned transaction whose executable code includes calls to the runtime database processor. Canned transactions are executed repeatedly by parametric users, who simply supply the parameters to the transactions. Each execution is considered to be a separate transaction. An example is a bank withdrawal transaction where the account number and the amount may be supplied as parameters.
In the lower part of Figure 2.3, the runtime database processor executes (1) the priv- ileged commands, (2) the executable query plans, and (3) the canned transactions with runtime parameters. It works with the system catalog and may update it with statistics. It also works with the stored data manager, which in turn uses basic oper- ating system services for carrying out low-level input/output (read/write) operations between the disk and main memory. The runtime database processor handles other aspects of data transfer, such as management of buffers in the main memory. Some DBMSs have their own buffer management module while others depend on the OS for buffer management. We have shown concurrency control and backup and recov- ery systems separately as a module in this figure. They are integrated into the work- ing of the runtime database processor for purposes of transaction management.
It is now common to have the client program that accesses the DBMS running on a separate computer from the computer on which the database resides. The former is called the client computer running a DBMS client software and the latter is called the database server. In some cases, the client accesses a middle computer, called the application server, which in turn accesses the database server. We elaborate on this topic in Section 2.5.
Figure 2.3 is not meant to describe a specific DBMS; rather, it illustrates typical DBMS modules. The DBMS interacts with the operating system when disk accesses—to the database or to the catalog—are needed. If the computer system is shared by many users, the OS will schedule DBMS disk access requests and DBMS processing along with other processes. On the other hand, if the computer system is mainly dedicated to running the database server, the DBMS will control main mem- ory buffering of disk pages. The DBMS also interfaces with compilers for general- purpose host programming languages, and with application servers and client programs running on separate machines through the system network interface.
2.4.2 Database System Utilities In addition to possessing the software modules just described, most DBMSs have database utilities that help the DBA manage the database system. Common utilities have the following types of functions:
■ Loading. A loading utility is used to load existing data files—such as text files or sequential files—into the database. Usually, the current (source) for-
2.4 The Database System Environment 43
mat of the data file and the desired (target) database file structure are speci- fied to the utility, which then automatically reformats the data and stores it in the database. With the proliferation of DBMSs, transferring data from one DBMS to another is becoming common in many organizations. Some ven- dors are offering products that generate the appropriate loading programs, given the existing source and target database storage descriptions (internal schemas). Such tools are also called conversion tools. For the hierarchical DBMS called IMS (IBM) and for many network DBMSs including IDMS (Computer Associates), SUPRA (Cincom), and IMAGE (HP), the vendors or third-party companies are making a variety of conversion tools available (e.g., Cincom’s SUPRA Server SQL) to transform data into the relational model.
■ Backup. A backup utility creates a backup copy of the database, usually by dumping the entire database onto tape or other mass storage medium. The backup copy can be used to restore the database in case of catastrophic disk failure. Incremental backups are also often used, where only changes since the previous backup are recorded. Incremental backup is more complex, but saves storage space.
■ Database storage reorganization. This utility can be used to reorganize a set of database files into different file organizations, and create new access paths to improve performance.
■ Performance monitoring. Such a utility monitors database usage and pro- vides statistics to the DBA. The DBA uses the statistics in making decisions such as whether or not to reorganize files or whether to add or drop indexes to improve performance.
Other utilities may be available for sorting files, handling data compression, monitoring access by users, interfacing with the network, and performing other functions.
2.4.3 Tools, Application Environments, and Communications Facilities
Other tools are often available to database designers, users, and the DBMS. CASE tools12 are used in the design phase of database systems. Another tool that can be quite useful in large organizations is an expanded data dictionary (or data reposi- tory) system. In addition to storing catalog information about schemas and con- straints, the data dictionary stores other information, such as design decisions, usage standards, application program descriptions, and user information. Such a system is also called an information repository. This information can be accessed directly by users or the DBA when needed. A data dictionary utility is similar to the DBMS catalog, but it includes a wider variety of information and is accessed mainly by users rather than by the DBMS software.
12Although CASE stands for computer-aided software engineering, many CASE tools are used primarily for database design.
44 Chapter 2 Database System Concepts and Architecture
Application development environments, such as PowerBuilder (Sybase) or JBuilder (Borland), have been quite popular. These systems provide an environment for developing database applications and include facilities that help in many facets of database systems, including database design, GUI development, querying and updating, and application program development.
The DBMS also needs to interface with communications software, whose function is to allow users at locations remote from the database system site to access the data- base through computer terminals, workstations, or personal computers. These are connected to the database site through data communications hardware such as Internet routers, phone lines, long-haul networks, local networks, or satellite com- munication devices. Many commercial database systems have communication packages that work with the DBMS. The integrated DBMS and data communica- tions system is called a DB/DC system. In addition, some distributed DBMSs are physically distributed over multiple machines. In this case, communications net- works are needed to connect the machines. These are often local area networks (LANs), but they can also be other types of networks.
2.5 Centralized and Client/Server Architectures for DBMSs
2.5.1 Centralized DBMSs Architecture Architectures for DBMSs have followed trends similar to those for general computer system architectures. Earlier architectures used mainframe computers to provide the main processing for all system functions, including user application programs and user interface programs, as well as all the DBMS functionality. The reason was that most users accessed such systems via computer terminals that did not have pro- cessing power and only provided display capabilities. Therefore, all processing was performed remotely on the computer system, and only display information and controls were sent from the computer to the display terminals, which were con- nected to the central computer via various types of communications networks.
As prices of hardware declined, most users replaced their terminals with PCs and workstations. At first, database systems used these computers similarly to how they had used display terminals, so that the DBMS itself was still a centralized DBMS in which all the DBMS functionality, application program execution, and user inter- face processing were carried out on one machine. Figure 2.4 illustrates the physical components in a centralized architecture. Gradually, DBMS systems started to exploit the available processing power at the user side, which led to client/server DBMS architectures.
2.5.2 Basic Client/Server Architectures First, we discuss client/server architecture in general, then we see how it is applied to DBMSs. The client/server architecture was developed to deal with computing envi- ronments in which a large number of PCs, workstations, file servers, printers, data-
2.5 Centralized and Client/Server Architectures for DBMSs 45
Display Monitor
Display Monitor
Network
Software
Hardware/Firmware
Operating System
Display Monitor
Application Programs
DBMS
Controller
CPU
Controller
. . .
. . .
. . .
Controller
Memory Disk I/O Devices
(Printers, Tape Drives, . . .)
Compilers
Text Editors
Terminal Display Control
System Bus
Terminals . . .
. . .
Figure 2.4 A physical centralized architecture.
base servers, Web servers, e-mail servers, and other software and equipment are connected via a network. The idea is to define specialized servers with specific functionalities. For example, it is possible to connect a number of PCs or small workstations as clients to a file server that maintains the files of the client machines. Another machine can be designated as a printer server by being connected to vari- ous printers; all print requests by the clients are forwarded to this machine. Web servers or e-mail servers also fall into the specialized server category. The resources provided by specialized servers can be accessed by many client machines. The client machines provide the user with the appropriate interfaces to utilize these servers, as well as with local processing power to run local applications. This concept can be carried over to other software packages, with specialized programs—such as a CAD (computer-aided design) package—being stored on specific server machines and being made accessible to multiple clients. Figure 2.5 illustrates client/server archi- tecture at the logical level; Figure 2.6 is a simplified diagram that shows the physical architecture. Some machines would be client sites only (for example, diskless work- stations or workstations/PCs with disks that have only client software installed).
Client Client Client
Print Server
DBMS Server
File Server
. . .
. . .
Network
Figure 2.5 Logical two-tier client/server architecture.
46 Chapter 2 Database System Concepts and Architecture
Client CLIENT
Site 2
Client with Disk
Client
Site 1
Diskless Client
Server
Site 3
Server
Communication Network
Site n
Server and Client
. . .
Client
Server
Figure 2.6 Physical two-tier client/server architecture.
Other machines would be dedicated servers, and others would have both client and server functionality.
The concept of client/server architecture assumes an underlying framework that consists of many PCs and workstations as well as a smaller number of mainframe machines, connected via LANs and other types of computer networks. A client in this framework is typically a user machine that provides user interface capabilities and local processing. When a client requires access to additional functionality— such as database access—that does not exist at that machine, it connects to a server that provides the needed functionality. A server is a system containing both hard- ware and software that can provide services to the client machines, such as file access, printing, archiving, or database access. In general, some machines install only client software, others only server software, and still others may include both client and server software, as illustrated in Figure 2.6. However, it is more common that client and server software usually run on separate machines. Two main types of basic DBMS architectures were created on this underlying client/server framework: two-tier and three-tier.13 We discuss them next.
2.5.3 Two-Tier Client/Server Architectures for DBMSs In relational database management systems (RDBMSs), many of which started as centralized systems, the system components that were first moved to the client side were the user interface and application programs. Because SQL (see Chapters 4 and 5) provided a standard language for RDBMSs, this created a logical dividing point
13There are many other variations of client/server architectures. We discuss the two most basic ones here.
2.5 Centralized and Client/Server Architectures for DBMSs 47
between client and server. Hence, the query and transaction functionality related to SQL processing remained on the server side. In such an architecture, the server is often called a query server or transaction server because it provides these two functionalities. In an RDBMS, the server is also often called an SQL server.
The user interface programs and application programs can run on the client side. When DBMS access is required, the program establishes a connection to the DBMS (which is on the server side); once the connection is created, the client program can communicate with the DBMS. A standard called Open Database Connectivity (ODBC) provides an application programming interface (API), which allows client-side programs to call the DBMS, as long as both client and server machines have the necessary software installed. Most DBMS vendors provide ODBC drivers for their systems. A client program can actually connect to several RDBMSs and send query and transaction requests using the ODBC API, which are then processed at the server sites. Any query results are sent back to the client program, which can process and display the results as needed. A related standard for the Java program- ming language, called JDBC, has also been defined. This allows Java client programs to access one or more DBMSs through a standard interface.
The different approach to two-tier client/server architecture was taken by some object-oriented DBMSs, where the software modules of the DBMS were divided between client and server in a more integrated way. For example, the server level may include the part of the DBMS software responsible for handling data storage on disk pages, local concurrency control and recovery, buffering and caching of disk pages, and other such functions. Meanwhile, the client level may handle the user interface; data dictionary functions; DBMS interactions with programming lan- guage compilers; global query optimization, concurrency control, and recovery across multiple servers; structuring of complex objects from the data in the buffers; and other such functions. In this approach, the client/server interaction is more tightly coupled and is done internally by the DBMS modules—some of which reside on the client and some on the server—rather than by the users/programmers. The exact division of functionality can vary from system to system. In such a client/server architecture, the server has been called a data server because it pro- vides data in disk pages to the client. This data can then be structured into objects for the client programs by the client-side DBMS software.
The architectures described here are called two-tier architectures because the soft- ware components are distributed over two systems: client and server. The advan- tages of this architecture are its simplicity and seamless compatibility with existing systems. The emergence of the Web changed the roles of clients and servers, leading to the three-tier architecture.
2.5.4 Three-Tier and n-Tier Architectures for Web Applications
Many Web applications use an architecture called the three-tier architecture, which adds an intermediate layer between the client and the database server, as illustrated in Figure 2.7(a).
48 Chapter 2 Database System Concepts and Architecture
GUI, Web Interface
Client
Application Server or
Web Server
Database Server
Application Programs,
Web Pages
Database Management
System
Presentation Layer
Business Logic Layer
Database Services
Layer
(a) (b)
Figure 2.7 Logical three-tier client/server architecture, with a couple of commonly used nomenclatures.
This intermediate layer or middle tier is called the application server or the Web server, depending on the application. This server plays an intermediary role by run- ning application programs and storing business rules (procedures or constraints) that are used to access data from the database server. It can also improve database security by checking a client’s credentials before forwarding a request to the data- base server. Clients contain GUI interfaces and some additional application-specific business rules. The intermediate server accepts requests from the client, processes the request and sends database queries and commands to the database server, and then acts as a conduit for passing (partially) processed data from the database server to the clients, where it may be processed further and filtered to be presented to users in GUI format. Thus, the user interface, application rules, and data access act as the three tiers. Figure 2.7(b) shows another architecture used by database and other application package vendors. The presentation layer displays information to the user and allows data entry. The business logic layer handles intermediate rules and constraints before data is passed up to the user or down to the DBMS. The bottom layer includes all data management services. The middle layer can also act as a Web server, which retrieves query results from the database server and formats them into dynamic Web pages that are viewed by the Web browser at the client side.
Other architectures have also been proposed. It is possible to divide the layers between the user and the stored data further into finer components, thereby giving rise to n-tier architectures, where n may be four or five tiers. Typically, the business logic layer is divided into multiple layers. Besides distributing programming and data throughout a network, n-tier applications afford the advantage that any one tier can run on an appropriate processor or operating system platform and can be handled independently. Vendors of ERP (enterprise resource planning) and CRM (customer relationship management) packages often use a middleware layer, which accounts for the front-end modules (clients) communicating with a number of back-end databases (servers).
2.6 Classification of Database Management Systems 49
Advances in encryption and decryption technology make it safer to transfer sensi- tive data from server to client in encrypted form, where it will be decrypted. The lat- ter can be done by the hardware or by advanced software. This technology gives higher levels of data security, but the network security issues remain a major con- cern. Various technologies for data compression also help to transfer large amounts of data from servers to clients over wired and wireless networks.
2.6 Classification of Database Management Systems
Several criteria are normally used to classify DBMSs. The first is the data model on which the DBMS is based. The main data model used in many current commercial DBMSs is the relational data model. The object data model has been implemented in some commercial systems but has not had widespread use. Many legacy applica- tions still run on database systems based on the hierarchical and network data models. Examples of hierarchical DBMSs include IMS (IBM) and some other sys- tems like System 2K (SAS Inc.) and TDMS. IMS is still used at governmental and industrial installations, including hospitals and banks, although many of its users have converted to relational systems. The network data model was used by many vendors and the resulting products like IDMS (Cullinet—now Computer Associates), DMS 1100 (Univac—now Unisys), IMAGE (Hewlett-Packard), VAX- DBMS (Digital—then Compaq and now HP), and SUPRA (Cincom) still have a fol- lowing and their user groups have their own active organizations. If we add IBM’s popular VSAM file system to these, we can easily say that a reasonable percentage of worldwide-computerized data is still in these so-called legacy database systems.
The relational DBMSs are evolving continuously, and, in particular, have been incorporating many of the concepts that were developed in object databases. This has led to a new class of DBMSs called object-relational DBMSs. We can categorize DBMSs based on the data model: relational, object, object-relational, hierarchical, network, and other.
More recently, some experimental DBMSs are based on the XML (eXtended Markup Language) model, which is a tree-structured (hierarchical) data model. These have been called native XML DBMSs. Several commercial relational DBMSs have added XML interfaces and storage to their products.
The second criterion used to classify DBMSs is the number of users supported by the system. Single-user systems support only one user at a time and are mostly used with PCs. Multiuser systems, which include the majority of DBMSs, support con- current multiple users.
The third criterion is the number of sites over which the database is distributed. A DBMS is centralized if the data is stored at a single computer site. A centralized DBMS can support multiple users, but the DBMS and the database reside totally at a single computer site. A distributed DBMS (DDBMS) can have the actual database and DBMS software distributed over many sites, connected by a computer network. Homogeneous DDBMSs use the same DBMS software at all the sites, whereas
50 Chapter 2 Database System Concepts and Architecture
heterogeneous DDBMSs can use different DBMS software at each site. It is also possible to develop middleware software to access several autonomous preexisting databases stored under heterogeneousDBMSs. This leads to a federated DBMS (or multidatabase system), in which the participating DBMSs are loosely coupled and have a degree of local autonomy. Many DDBMSs use client-server architecture, as we described in Section 2.5.
The fourth criterion is cost. It is difficult to propose a classification of DBMSs based on cost. Today we have open source (free) DBMS products like MySQL and PostgreSQL that are supported by third-party vendors with additional services. The main RDBMS products are available as free examination 30-day copy versions as well as personal versions, which may cost under $100 and allow a fair amount of functionality. The giant systems are being sold in modular form with components to handle distribution, replication, parallel processing, mobile capability, and so on, and with a large number of parameters that must be defined for the configuration. Furthermore, they are sold in the form of licenses—site licenses allow unlimited use of the database system with any number of copies running at the customer site. Another type of license limits the number of concurrent users or the number of user seats at a location. Standalone single user versions of some systems like Microsoft Access are sold per copy or included in the overall configuration of a desktop or laptop. In addition, data warehousing and mining features, as well as support for additional data types, are made available at extra cost. It is possible to pay millions of dollars for the installation and maintenance of large database sys- tems annually.
We can also classify a DBMS on the basis of the types of access path options for storing files. One well-known family of DBMSs is based on inverted file structures. Finally, a DBMS can be general purpose or special purpose. When performance is a primary consideration, a special-purpose DBMS can be designed and built for a specific application; such a system cannot be used for other applications without major changes. Many airline reservations and telephone directory systems devel- oped in the past are special-purpose DBMSs. These fall into the category of online transaction processing (OLTP) systems, which must support a large number of concurrent transactions without imposing excessive delays.
Let us briefly elaborate on the main criterion for classifying DBMSs: the data model. The basic relational data model represents a database as a collection of tables, where each table can be stored as a separate file. The database in Figure 1.2 resem- bles a relational representation. Most relational databases use the high-level query language called SQL and support a limited form of user views. We discuss the rela- tional model and its languages and operations in Chapters 3 through 6, and tech- niques for programming relational applications in Chapters 13 and 14.
The object data model defines a database in terms of objects, their properties, and their operations. Objects with the same structure and behavior belong to a class, and classes are organized into hierarchies (or acyclic graphs). The operations of each class are specified in terms of predefined procedures called methods. Relational DBMSs have been extending their models to incorporate object database
2.6 Classification of Database Mangement Systems 51
concepts and other capabilities; these systems are referred to as object-relational or extended relational systems. We discuss object databases and object-relational sys- tems in Chapter 11.
The XML model has emerged as a standard for exchanging data over the Web, and has been used as a basis for implementing several prototype native XML systems. XML uses hierarchical tree structures. It combines database concepts with concepts from document representation models. Data is represented as elements; with the use of tags, data can be nested to create complex hierarchical structures. This model conceptually resembles the object model but uses different terminology. XML capa- bilities have been added to many commercial DBMS products. We present an overview of XML in Chapter 12.
Two older, historically important data models, now known as legacy data models, are the network and hierarchical models. The network model represents data as record types and also represents a limited type of 1:N relationship, called a set type. A 1:N, or one-to-many, relationship relates one instance of a record to many record instances using some pointer linking mechanism in these models. Figure 2.8 shows a network schema diagram for the database of Figure 2.1, where record types are shown as rectangles and set types are shown as labeled directed arrows.
The network model, also known as the CODASYL DBTG model,14 has an associated record-at-a-time language that must be embedded in a host programming lan- guage. The network DML was proposed in the 1971 Database Task Group (DBTG) Report as an extension of the COBOL language. It provides commands for locating records directly (e.g., FIND ANY <record-type> USING <field-list>, or FIND DUPLICATE <record-type> USING <field-list>). It has commands to support tra- versals within set-types (e.g., GET OWNER, GET {FIRST, NEXT, LAST} MEMBER WITHIN <set-type> WHERE <condition>). It also has commands to store new data
GRADE_REPORT
SECTION
COURSE_OFFERINGS
STUDENT_GRADES HAS_A
IS_A
PREREQUISITE
SECTION_GRADES
STUDENT COURSE Figure 2.8 The schema of Figure 2.1 in network model notation.
14CODASYL DBTG stands for Conference on Data Systems Languages Database Task Group, which is the committee that specified the network model and its language.
52 Chapter 2 Database System Concepts and Architecture
(e.g., STORE <record-type>) and to make it part of a set type (e.g., CONNECT <record-type> TO <set-type>). The language also handles many additional consid- erations, such as the currency of record types and set types, which are defined by the current position of the navigation process within the database. It is prominently used by IDMS, IMAGE, and SUPRA DBMSs today.
The hierarchical model represents data as hierarchical tree structures. Each hierar- chy represents a number of related records. There is no standard language for the hierarchical model. A popular hierarchical DML is DL/1 of the IMS system. It dom- inated the DBMS market for over 20 years between 1965 and 1985 and is still a widely used DBMS worldwide, holding a large percentage of data in governmental, health care, and banking and insurance databases. Its DML, called DL/1, was a de facto industry standard for a long time. DL/1 has commands to locate a record (e.g., GET { UNIQUE, NEXT} <record-type> WHERE <condition>). It has navigational facilities to navigate within hierarchies (e.g., GET NEXT WITHIN PARENT or GET {FIRST, NEXT} PATH <hierarchical-path-specification> WHERE <condition>). It has appropriate facilities to store and update records (e.g., INSERT <record-type>, REPLACE <record-type>). Currency issues during navigation are also handled with additional features in the language.15
2.7 Summary In this chapter we introduced the main concepts used in database systems. We defined a data model and we distinguished three main categories:
■ High-level or conceptual data models (based on entities and relationships)
■ Low-level or physical data models
■ Representational or implementation data models (record-based, object- oriented)
We distinguished the schema, or description of a database, from the database itself. The schema does not change very often, whereas the database state changes every time data is inserted, deleted, or modified. Then we described the three-schema DBMS architecture, which allows three schema levels:
■ An internal schema describes the physical storage structure of the database.
■ A conceptual schema is a high-level description of the whole database.
■ External schemas describe the views of different user groups.
A DBMS that cleanly separates the three levels must have mappings between the schemas to transform requests and query results from one level to the next. Most DBMSs do not separate the three levels completely. We used the three-schema archi- tecture to define the concepts of logical and physical data independence.
15The full chapters on the network and hierarchical models from the second edition of this book are available from this book’s Companion Website at http://www.aw.com/elmasri.
Review Questions 53
Then we discussed the main types of languages and interfaces that DBMSs support. A data definition language (DDL) is used to define the database conceptual schema. In most DBMSs, the DDL also defines user views and, sometimes, storage struc- tures; in other DBMSs, separate languages or functions exist for specifying storage structures. This distinction is fading away in today’s relational implementations, with SQL serving as a catchall language to perform multiple roles, including view definition. The storage definition part (SDL) was included in SQL’s early versions, but is now typically implemented as special commands for the DBA in relational DBMSs. The DBMS compiles all schema definitions and stores their descriptions in the DBMS catalog.
A data manipulation language (DML) is used for specifying database retrievals and updates. DMLs can be high level (set-oriented, nonprocedural) or low level (record- oriented, procedural). A high-level DML can be embedded in a host programming language, or it can be used as a standalone language; in the latter case it is often called a query language.
We discussed different types of interfaces provided by DBMSs, and the types of DBMS users with which each interface is associated. Then we discussed the database system environment, typical DBMS software modules, and DBMS utilities for help- ing users and the DBA staff perform their tasks. We continued with an overview of the two-tier and three-tier architectures for database applications, progressively moving toward n-tier, which are now common in many applications, particularly Web database applications.
Finally, we classified DBMSs according to several criteria: data model, number of users, number of sites, types of access paths, and cost. We discussed the availability of DBMSs and additional modules—from no cost in the form of open source soft- ware, to configurations that annually cost millions to maintain. We also pointed out the variety of licensing arrangements for DBMS and related products. The main classification of DBMSs is based on the data model. We briefly discussed the main data models used in current commercial DBMSs.
Review Questions 2.1. Define the following terms: data model, database schema, database state,
internal schema, conceptual schema, external schema, data independence, DDL, DML, SDL, VDL, query language, host language, data sublanguage, database utility, catalog, client/server architecture, three-tier architecture, and n-tier architecture.
2.2. Discuss the main categories of data models. What are the basic differences between the relational model, the object model, and the XML model?
2.3. What is the difference between a database schema and a database state?
2.4. Describe the three-schema architecture. Why do we need mappings between schema levels? How do different schema definition languages support this architecture?
54 Chapter 2 Database System Concepts and Architecture
2.5. What is the difference between logical data independence and physical data independence? Which one is harder to achieve? Why?
2.6. What is the difference between procedural and nonprocedural DMLs?
2.7. Discuss the different types of user-friendly interfaces and the types of users who typically use each.
2.8. With what other computer system software does a DBMS interact?
2.9. What is the difference between the two-tier and three-tier client/server architectures?
2.10. Discuss some types of database utilities and tools and their functions.
2.11. What is the additional functionality incorporated in n-tier architecture (n > 3)?
Exercises 2.12. Think of different users for the database shown in Figure 1.2. What types of
applications would each user need? To which user category would each belong, and what type of interface would each need?
2.13. Choose a database application with which you are familiar. Design a schema and show a sample database for that application, using the notation of Figures 1.2 and 2.1. What types of additional information and constraints would you like to represent in the schema? Think of several users of your database, and design a view for each.
2.14. If you were designing a Web-based system to make airline reservations and sell airline tickets, which DBMS architecture would you choose from Section 2.5? Why? Why would the other architectures not be a good choice?
2.15. Consider Figure 2.1. In addition to constraints relating the values of columns in one table to columns in another table, there are also constraints that impose restrictions on values in a column or a combination of columns within a table. One such constraint dictates that a column or a group of columns must be unique across all rows in the table. For example, in the STUDENT table, the Student_number column must be unique (to prevent two different students from having the same Student_number). Identify the col- umn or the group of columns in the other tables that must be unique across all rows in the table.
Selected Bibliography 55
Selected Bibliography Many database textbooks, including Date (2004), Silberschatz et al. (2006), Ramakrishnan and Gehrke (2003), Garcia-Molina et al. (2000, 2009), and Abiteboul et al. (1995), provide a discussion of the various database concepts presented here. Tsichritzis and Lochovsky (1982) is an early textbook on data models. Tsichritzis and Klug (1978) and Jardine (1977) present the three-schema architecture, which was first suggested in the DBTG CODASYL report (1971) and later in an American National Standards Institute (ANSI) report (1975). An in-depth analysis of the rela- tional data model and some of its possible extensions is given in Codd (1990). The proposed standard for object-oriented databases is described in Cattell et al. (2000). Many documents describing XML are available on the Web, such as XML (2005).
Examples of database utilities are the ETI Connect, Analyze and Transform tools (http://www.eti.com) and the database administration tool, DBArtisan, from Embarcadero Technologies (http://www.embarcadero.com).
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part 2 The Relational Data
Model and SQL
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59
The Relational Data Model and Relational Database Constraints
This chapter opens Part 2 of the book, which coversrelational databases. The relational data model was first introduced by Ted Codd of IBM Research in 1970 in a classic paper (Codd 1970), and it attracted immediate attention due to its simplicity and mathematical foundation. The model uses the concept of a mathematical relation—which looks somewhat like a table of values—as its basic building block, and has its theoretical basis in set theory and first-order predicate logic. In this chapter we discuss the basic characteristics of the model and its constraints.
The first commercial implementations of the relational model became available in the early 1980s, such as the SQL/DS system on the MVS operating system by IBM and the Oracle DBMS. Since then, the model has been implemented in a large num- ber of commercial systems. Current popular relational DBMSs (RDBMSs) include DB2 and Informix Dynamic Server (from IBM), Oracle and Rdb (from Oracle), Sybase DBMS (from Sybase) and SQLServer and Access (from Microsoft). In addi- tion, several open source systems, such as MySQL and PostgreSQL, are available.
Because of the importance of the relational model, all of Part 2 is devoted to this model and some of the languages associated with it. In Chapters 4 and 5, we describe the SQL query language, which is the standard for commercial relational DBMSs. Chapter 6 covers the operations of the relational algebra and introduces the relational calculus—these are two formal languages associated with the relational model. The relational calculus is considered to be the basis for the SQL language, and the relational algebra is used in the internals of many database implementations for query processing and optimization (see Part 8 of the book).
3chapter 3
60 Chapter 3 The Relational Data Model and Relational Database Constraints
Other aspects of the relational model are presented in subsequent parts of the book. Chapter 9 relates the relational model data structures to the constructs of the ER and EER models (presented in Chapters 7 and 8), and presents algorithms for designing a relational database schema by mapping a conceptual schema in the ER or EER model into a relational representation. These mappings are incorporated into many database design and CASE1 tools. Chapters 13 and 14 in Part 5 discuss the programming techniques used to access database systems and the notion of connecting to relational databases via ODBC and JDBC standard protocols. We also introduce the topic of Web database programming in Chapter 14. Chapters 15 and 16 in Part 6 present another aspect of the relational model, namely the formal con- straints of functional and multivalued dependencies; these dependencies are used to develop a relational database design theory based on the concept known as normalization.
Data models that preceded the relational model include the hierarchical and net- work models. They were proposed in the 1960s and were implemented in early DBMSs during the late 1960s and early 1970s. Because of their historical impor- tance and the existing user base for these DBMSs, we have included a summary of the highlights of these models in Appendices D and E, which are available on this book’s Companion Website at http://www.aw.com/elmasri. These models and sys- tems are now referred to as legacy database systems.
In this chapter, we concentrate on describing the basic principles of the relational model of data. We begin by defining the modeling concepts and notation of the relational model in Section 3.1. Section 3.2 is devoted to a discussion of relational constraints that are considered an important part of the relational model and are automatically enforced in most relational DBMSs. Section 3.3 defines the update operations of the relational model, discusses how violations of integrity constraints are handled, and introduces the concept of a transaction. Section 3.4 summarizes the chapter.
3.1 Relational Model Concepts The relational model represents the database as a collection of relations. Informally, each relation resembles a table of values or, to some extent, a flat file of records. It is called a flat file because each record has a simple linear or flat structure. For exam- ple, the database of files that was shown in Figure 1.2 is similar to the basic rela- tional model representation. However, there are important differences between relations and files, as we shall soon see.
When a relation is thought of as a table of values, each row in the table represents a collection of related data values. A row represents a fact that typically corresponds to a real-world entity or relationship. The table name and column names are used to help to interpret the meaning of the values in each row. For example, the first table of Figure 1.2 is called STUDENT because each row represents facts about a particular
1CASE stands for computer-aided software engineering.
3.1 Relational Model Concepts 61
student entity. The column names—Name, Student_number, Class, and Major—spec- ify how to interpret the data values in each row, based on the column each value is in. All values in a column are of the same data type.
In the formal relational model terminology, a row is called a tuple, a column header is called an attribute, and the table is called a relation. The data type describing the types of values that can appear in each column is represented by a domain of possi- ble values. We now define these terms—domain, tuple, attribute, and relation— formally.
3.1 Domains, Attributes, Tuples, and Relations A domain D is a set of atomic values. By atomic we mean that each value in the domain is indivisible as far as the formal relational model is concerned. A common method of specifying a domain is to specify a data type from which the data values forming the domain are drawn. It is also useful to specify a name for the domain, to help in interpreting its values. Some examples of domains follow:
■ Usa_phone_numbers. The set of ten-digit phone numbers valid in the United States.
■ Local_phone_numbers. The set of seven-digit phone numbers valid within a particular area code in the United States. The use of local phone numbers is quickly becoming obsolete, being replaced by standard ten-digit numbers.
■ Social_security_numbers. The set of valid nine-digit Social Security numbers. (This is a unique identifier assigned to each person in the United States for employment, tax, and benefits purposes.)
■ Names: The set of character strings that represent names of persons.
■ Grade_point_averages. Possible values of computed grade point averages; each must be a real (floating-point) number between 0 and 4.
■ Employee_ages. Possible ages of employees in a company; each must be an integer value between 15 and 80.
■ Academic_department_names. The set of academic department names in a university, such as Computer Science, Economics, and Physics.
■ Academic_department_codes. The set of academic department codes, such as ‘CS’, ‘ECON’, and ‘PHYS’.
The preceding are called logical definitions of domains. A data type or format is also specified for each domain. For example, the data type for the domain Usa_phone_numbers can be declared as a character string of the form (ddd)ddd- dddd, where each d is a numeric (decimal) digit and the first three digits form a valid telephone area code. The data type for Employee_ages is an integer number between 15 and 80. For Academic_department_names, the data type is the set of all character strings that represent valid department names. A domain is thus given a name, data type, and format. Additional information for interpreting the values of a domain can also be given; for example, a numeric domain such as Person_weights should have the units of measurement, such as pounds or kilograms.
62 Chapter 3 The Relational Data Model and Relational Database Constraints
A relation schema2 R, denoted by R(A1, A 2, ..., An), is made up of a relation name R
and a list of attributes, A1, A2, ..., An. Each attribute Ai is the name of a role played by some domain D in the relation schema R. D is called the domain of Ai and is denoted by dom(Ai). A relation schema is used to describe a relation; R is called the name of this relation. The degree (or arity) of a relation is the number of attributes n of its relation schema.
A relation of degree seven, which stores information about university students, would contain seven attributes describing each student. as follows:
STUDENT(Name, Ssn, Home_phone, Address, Office_phone, Age, Gpa)
Using the data type of each attribute, the definition is sometimes written as:
STUDENT(Name: string, Ssn: string, Home_phone: string, Address: string, Office_phone: string, Age: integer, Gpa: real)
For this relation schema, STUDENT is the name of the relation, which has seven attributes. In the preceding definition, we showed assignment of generic types such as string or integer to the attributes. More precisely, we can specify the following previously defined domains for some of the attributes of the STUDENT relation: dom(Name) = Names; dom(Ssn) = Social_security_numbers; dom(HomePhone) = USA_phone_numbers3, dom(Office_phone) = USA_phone_numbers, and dom(Gpa) = Grade_point_averages. It is also possible to refer to attributes of a relation schema by their position within the relation; thus, the second attribute of the STUDENT rela- tion is Ssn, whereas the fourth attribute is Address.
A relation (or relation state)4 r of the relation schema R(A1, A2, ..., An), also denoted by r(R), is a set of n-tuples r = {t1, t2, ..., tm}. Each n-tuple t is an ordered list of n values t =<v1, v2, ..., vn>, where each value vi, 1 ≤ i ≤ n, is an element of dom (Ai) or is a special NULL value. (NULL values are discussed further below and in Section 3.1.2.) The ith value in tuple t, which corresponds to the attribute Ai, is referred to as t[Ai] or t.Ai (or t[i] if we use the positional notation). The terms relation intension for the schema R and relation extension for a relation state r(R) are also commonly used.
Figure 3.1 shows an example of a STUDENT relation, which corresponds to the STUDENT schema just specified. Each tuple in the relation represents a particular student entity (or object). We display the relation as a table, where each tuple is shown as a row and each attribute corresponds to a column header indicating a role or interpretation of the values in that column. NULL values represent attributes whose values are unknown or do not exist for some individual STUDENT tuple.
2A relation schema is sometimes called a relation scheme. 3With the large increase in phone numbers caused by the proliferation of mobile phones, most metropol- itan areas in the U.S. now have multiple area codes, so seven-digit local dialing has been discontinued in most areas. We changed this domain to Usa_phone_numbers instead of Local_phone_numbers which would be a more general choice. This illustrates how database requirements can change over time. 4This has also been called a relation instance. We will not use this term because instance is also used to refer to a single tuple or row.
3.1 Relational Model Concepts 63
Relation Name
Tuples
STUDENT
Name
Benjamin Bayer
Chung-cha Kim
Dick Davidson
Rohan Panchal
Barbara Benson
Ssn
305-61-2435
381-62-1245
422-11-2320
489-22-1100
533-69-1238
Home_phone
(817)373-1616
(817)375-4409
NULL
(817)376-9821
(817)839-8461
Address
2918 Bluebonnet Lane
125 Kirby Road
3452 Elgin Road
265 Lark Lane
7384 Fontana Lane
Office_phone
NULL
NULL
(817)749-1253
(817)749-6492
NULL
Age
19
18
25
28
19
3.21
2.89
3.53
3.93
3.25
Gpa
Attributes
Figure 3.1 The attributes and tuples of a relation STUDENT.
The earlier definition of a relation can be restated more formally using set theory concepts as follows. A relation (or relation state) r(R) is a mathematical relation of degree n on the domains dom(A1), dom(A2), ..., dom(An), which is a subset of the Cartesian product (denoted by ×) of the domains that define R:
r(R) ⊆ (dom(A1) × dom(A2) × ... × dom(An))
The Cartesian product specifies all possible combinations of values from the under- lying domains. Hence, if we denote the total number of values, or cardinality, in a domain D by |D| (assuming that all domains are finite), the total number of tuples in the Cartesian product is
|dom(A1)| × |dom(A2)| × ... × |dom(An)|
This product of cardinalities of all domains represents the total number of possible instances or tuples that can ever exist in any relation state r(R). Of all these possible combinations, a relation state at a given time—the current relation state—reflects only the valid tuples that represent a particular state of the real world. In general, as the state of the real world changes, so does the relation state, by being transformed into another relation state. However, the schema R is relatively static and changes very infrequently—for example, as a result of adding an attribute to represent new information that was not originally stored in the relation.
It is possible for several attributes to have the same domain. The attribute names indicate different roles, or interpretations, for the domain. For example, in the STUDENT relation, the same domain USA_phone_numbers plays the role of Home_phone, referring to the home phone of a student, and the role of Office_phone, referring to the office phone of the student. A third possible attribute (not shown) with the same domain could be Mobile_phone.
3.1.2 Characteristics of Relations The earlier definition of relations implies certain characteristics that make a relation different from a file or a table. We now discuss some of these characteristics.
64 Chapter 3 The Relational Data Model and Relational Database Constraints
Dick Davidson
Barbara Benson
Rohan Panchal
Chung-cha Kim
422-11-2320
533-69-1238
489-22-1100
381-62-1245
NULL
(817)839-8461
(817)376-9821
(817)375-4409
3452 Elgin Road
7384 Fontana Lane
265 Lark Lane
125 Kirby Road
(817)749-1253
NULL
(817)749-6492
NULL
25
19
28
18
3.53
3.25
3.93
2.89
Benjamin Bayer 305-61-2435 (817)373-1616 2918 Bluebonnet Lane NULL 19 3.21
STUDENT Name Ssn Home_phone Address Office_phone Age Gpa
Figure 3.2 The relation STUDENT from Figure 3.1 with a different order of tuples.
Ordering of Tuples in a Relation. A relation is defined as a set of tuples. Mathematically, elements of a set have no order among them; hence, tuples in a rela- tion do not have any particular order. In other words, a relation is not sensitive to the ordering of tuples. However, in a file, records are physically stored on disk (or in memory), so there always is an order among the records. This ordering indicates first, second, ith, and last records in the file. Similarly, when we display a relation as a table, the rows are displayed in a certain order.
Tuple ordering is not part of a relation definition because a relation attempts to rep- resent facts at a logical or abstract level. Many tuple orders can be specified on the same relation. For example, tuples in the STUDENT relation in Figure 3.1 could be ordered by values of Name, Ssn, Age, or some other attribute. The definition of a rela- tion does not specify any order: There is no preference for one ordering over another. Hence, the relation displayed in Figure 3.2 is considered identical to the one shown in Figure 3.1. When a relation is implemented as a file or displayed as a table, a particu- lar ordering may be specified on the records of the file or the rows of the table.
Ordering of Values within a Tuple and an Alternative Definition of a Relation. According to the preceding definition of a relation, an n-tuple is an ordered list of n values, so the ordering of values in a tuple—and hence of attributes in a relation schema—is important. However, at a more abstract level, the order of attributes and their values is not that important as long as the correspondence between attributes and values is maintained.
An alternative definition of a relation can be given, making the ordering of values in a tuple unnecessary. In this definition, a relation schema R = {A1, A2, ..., An} is a set of attributes (instead of a list), and a relation state r(R) is a finite set of mappings r = {t1, t2, ..., tm}, where each tuple ti is a mapping from R to D, and D is the union (denoted by ∪) of the attribute domains; that is, D = dom(A1) ∪ dom(A2) ∪ ... ∪ dom(An). In this definition, t[Ai] must be in dom(Ai) for 1 ≤ i ≤ n for each mapping t in r. Each mapping ti is called a tuple.
According to this definition of tuple as a mapping, a tuple can be considered as a set of (<attribute>, <value>) pairs, where each pair gives the value of the mapping from an attribute Ai to a value vi from dom(Ai). The ordering of attributes is not
3.1 Relational Model Concepts 65
t = < (Name, Dick Davidson),(Ssn, 422-11-2320),(Home_phone, NULL),(Address, 3452 Elgin Road), (Office_phone, (817)749-1253),(Age, 25),(Gpa, 3.53)>
t = < (Address, 3452 Elgin Road),(Name, Dick Davidson),(Ssn, 422-11-2320),(Age, 25), (Office_phone, (817)749-1253),(Gpa, 3.53),(Home_phone, NULL)>
Figure 3.3 Two identical tuples when the order of attributes and values is not part of relation definition.
important, because the attribute name appears with its value. By this definition, the two tuples shown in Figure 3.3 are identical. This makes sense at an abstract level, since there really is no reason to prefer having one attribute value appear before another in a tuple.
When a relation is implemented as a file, the attributes are physically ordered as fields within a record. We will generally use the first definition of relation, where the attributes and the values within tuples are ordered, because it simplifies much of the notation. However, the alternative definition given here is more general.5
Values and NULLs in the Tuples. Each value in a tuple is an atomic value; that is, it is not divisible into components within the framework of the basic relational model. Hence, composite and multivalued attributes (see Chapter 7) are not allowed. This model is sometimes called the flat relational model. Much of the the- ory behind the relational model was developed with this assumption in mind, which is called the first normal form assumption.6 Hence, multivalued attributes must be represented by separate relations, and composite attributes are represented only by their simple component attributes in the basic relational model.7
An important concept is that of NULL values, which are used to represent the values of attributes that may be unknown or may not apply to a tuple. A special value, called NULL, is used in these cases. For example, in Figure 3.1, some STUDENT tuples have NULL for their office phones because they do not have an office (that is, office phone does not apply to these students). Another student has a NULL for home phone, presumably because either he does not have a home phone or he has one but we do not know it (value is unknown). In general, we can have several meanings for NULL values, such as value unknown, value exists but is not available, or attribute does not apply to this tuple (also known as value undefined). An example of the last type of NULL will occur if we add an attribute Visa_status to the STUDENT relation
5As we shall see, the alternative definition of relation is useful when we discuss query processing and optimization in Chapter 19. 6We discuss this assumption in more detail in Chapter 15. 7Extensions of the relational model remove these restrictions. For example, object-relational systems (Chapter 11) allow complex-structured attributes, as do the non-first normal form or nested relational models.
66 Chapter 3 The Relational Data Model and Relational Database Constraints
that applies only to tuples representing foreign students. It is possible to devise dif- ferent codes for different meanings of NULL values. Incorporating different types of NULL values into relational model operations (see Chapter 6) has proven difficult and is outside the scope of our presentation.
The exact meaning of a NULL value governs how it fares during arithmetic aggrega- tions or comparisons with other values. For example, a comparison of two NULL values leads to ambiguities—if both Customer A and B have NULL addresses, it does not mean they have the same address. During database design, it is best to avoid NULL values as much as possible. We will discuss this further in Chapters 5 and 6 in the context of operations and queries, and in Chapter 15 in the context of database design and normalization.
Interpretation (Meaning) of a Relation. The relation schema can be interpreted as a declaration or a type of assertion. For example, the schema of the STUDENT relation of Figure 3.1 asserts that, in general, a student entity has a Name, Ssn, Home_phone, Address, Office_phone, Age, and Gpa. Each tuple in the relation can then be interpreted as a fact or a particular instance of the assertion. For example, the first tuple in Figure 3.1 asserts the fact that there is a STUDENT whose Name is Benjamin Bayer, Ssn is 305-61-2435, Age is 19, and so on.
Notice that some relations may represent facts about entities, whereas other relations may represent facts about relationships. For example, a relation schema MAJORS (Student_ssn, Department_code) asserts that students major in academic disciplines. A tuple in this relation relates a student to his or her major discipline. Hence, the rela- tional model represents facts about both entities and relationships uniformly as rela- tions. This sometimes compromises understandability because one has to guess whether a relation represents an entity type or a relationship type. We introduce the Entity-Relationship (ER) model in detail in Chapter 7 where the entity and relation- ship concepts will be described in detail. The mapping procedures in Chapter 9 show how different constructs of the ER and EER (Enhanced ER model covered in Chapter 8) conceptual data models (see Part 3) get converted to relations.
An alternative interpretation of a relation schema is as a predicate; in this case, the values in each tuple are interpreted as values that satisfy the predicate. For example, the predicate STUDENT (Name, Ssn, ...) is true for the five tuples in relation STUDENT of Figure 3.1. These tuples represent five different propositions or facts in the real world. This interpretation is quite useful in the context of logical program- ming languages, such as Prolog, because it allows the relational model to be used within these languages (see Section 26.5). An assumption called the closed world assumption states that the only true facts in the universe are those present within the extension (state) of the relation(s). Any other combination of values makes the predicate false.
3.1.3 Relational Model Notation We will use the following notation in our presentation:
■ A relation schema R of degree n is denoted by R(A1, A2, ..., An).
3.2 Relational Model Constraints and Relational Database Schemas 67
■ The uppercase letters Q, R, S denote relation names.
■ The lowercase letters q, r, s denote relation states.
■ The letters t, u, v denote tuples.
■ In general, the name of a relation schema such as STUDENT also indicates the current set of tuples in that relation—the current relation state—whereas STUDENT(Name, Ssn, ...) refers only to the relation schema.
■ An attribute A can be qualified with the relation name R to which it belongs by using the dot notation R.A—for example, STUDENT.Name or STUDENT.Age. This is because the same name may be used for two attributes in different relations. However, all attribute names in a particular relation must be distinct.
■ An n-tuple t in a relation r(R) is denoted by t = <v1, v2, ..., vn>, where vi is the value corresponding to attribute Ai. The following notation refers to component values of tuples:
■ Both t[Ai] and t.Ai (and sometimes t[i]) refer to the value vi in t for attribute Ai.
■ Both t[Au, Aw, ..., Az] and t.(Au, Aw, ..., Az), where Au, Aw, ..., Az is a list of attributes from R, refer to the subtuple of values <vu, vw, ..., vz> from t cor- responding to the attributes specified in the list.
As an example, consider the tuple t = <‘Barbara Benson’, ‘533-69-1238’, ‘(817)839- 8461’, ‘7384 Fontana Lane’, NULL, 19, 3.25> from the STUDENT relation in Figure 3.1; we have t[Name] = <‘Barbara Benson’>, and t[Ssn, Gpa, Age] = <‘533-69-1238’, 3.25, 19>.
3.2 Relational Model Constraints and Relational Database Schemas
So far, we have discussed the characteristics of single relations. In a relational data- base, there will typically be many relations, and the tuples in those relations are usu- ally related in various ways. The state of the whole database will correspond to the states of all its relations at a particular point in time. There are generally many restrictions or constraints on the actual values in a database state. These constraints are derived from the rules in the miniworld that the database represents, as we dis- cussed in Section 1.6.8.
In this section, we discuss the various restrictions on data that can be specified on a relational database in the form of constraints. Constraints on databases can gener- ally be divided into three main categories:
1. Constraints that are inherent in the data model. We call these inherent model-based constraints or implicit constraints.
2. Constraints that can be directly expressed in schemas of the data model, typ- ically by specifying them in the DDL (data definition language, see Section 2.3.1). We call these schema-based constraints or explicit constraints.
68 Chapter 3 The Relational Data Model and Relational Database Constraints
3. Constraints that cannot be directly expressed in the schemas of the data model, and hence must be expressed and enforced by the application pro- grams. We call these application-based or semantic constraints or business rules.
The characteristics of relations that we discussed in Section 3.1.2 are the inherent constraints of the relational model and belong to the first category. For example, the constraint that a relation cannot have duplicate tuples is an inherent constraint. The constraints we discuss in this section are of the second category, namely, constraints that can be expressed in the schema of the relational model via the DDL. Constraints in the third category are more general, relate to the meaning as well as behavior of attributes, and are difficult to express and enforce within the data model, so they are usually checked within the application programs that perform database updates.
Another important category of constraints is data dependencies, which include functional dependencies and multivalued dependencies. They are used mainly for testing the “goodness” of the design of a relational database and are utilized in a process called normalization, which is discussed in Chapters 15 and 16.
The schema-based constraints include domain constraints, key constraints, con- straints on NULLs, entity integrity constraints, and referential integrity constraints.
3.2.1 Domain Constraints Domain constraints specify that within each tuple, the value of each attribute A must be an atomic value from the domain dom(A). We have already discussed the ways in which domains can be specified in Section 3.1.1. The data types associated with domains typically include standard numeric data types for integers (such as short integer, integer, and long integer) and real numbers (float and double- precision float). Characters, Booleans, fixed-length strings, and variable-length strings are also available, as are date, time, timestamp, and money, or other special data types. Other possible domains may be described by a subrange of values from a data type or as an enumerated data type in which all possible values are explicitly listed. Rather than describe these in detail here, we discuss the data types offered by the SQL relational standard in Section 4.1.
3.2.2 Key Constraints and Constraints on NULL Values In the formal relational model, a relation is defined as a set of tuples. By definition, all elements of a set are distinct; hence, all tuples in a relation must also be distinct. This means that no two tuples can have the same combination of values for all their attributes. Usually, there are other subsets of attributes of a relation schema R with the property that no two tuples in any relation state r of R should have the same combination of values for these attributes. Suppose that we denote one such subset of attributes by SK; then for any two distinct tuples t1 and t2 in a relation state r of R, we have the constraint that:
t1[SK] ≠ t2[SK]
3.2 Relational Model Constraints and Relational Database Schemas 69
Any such set of attributes SK is called a superkey of the relation schema R. A superkey SK specifies a uniqueness constraint that no two distinct tuples in any state r of R can have the same value for SK. Every relation has at least one default superkey—the set of all its attributes. A superkey can have redundant attributes, however, so a more useful concept is that of a key, which has no redundancy. A key K of a relation schema R is a superkey of R with the additional property that remov- ing any attribute A from K leaves a set of attributes K� that is not a superkey of R any more. Hence, a key satisfies two properties:
1. Two distinct tuples in any state of the relation cannot have identical values for (all) the attributes in the key. This first property also applies to a superkey.
2. It is a minimal superkey—that is, a superkey from which we cannot remove any attributes and still have the uniqueness constraint in condition 1 hold. This property is not required by a superkey.
Whereas the first property applies to both keys and superkeys, the second property is required only for keys. Hence, a key is also a superkey but not vice versa. Consider the STUDENT relation of Figure 3.1. The attribute set {Ssn} is a key of STUDENT because no two student tuples can have the same value for Ssn.8 Any set of attrib- utes that includes Ssn—for example, {Ssn, Name, Age}—is a superkey. However, the superkey {Ssn, Name, Age} is not a key of STUDENT because removing Name or Age or both from the set still leaves us with a superkey. In general, any superkey formed from a single attribute is also a key. A key with multiple attributes must require all its attributes together to have the uniqueness property.
The value of a key attribute can be used to identify uniquely each tuple in the rela- tion. For example, the Ssn value 305-61-2435 identifies uniquely the tuple corre- sponding to Benjamin Bayer in the STUDENT relation. Notice that a set of attributes constituting a key is a property of the relation schema; it is a constraint that should hold on every valid relation state of the schema. A key is determined from the mean- ing of the attributes, and the property is time-invariant: It must continue to hold when we insert new tuples in the relation. For example, we cannot and should not designate the Name attribute of the STUDENT relation in Figure 3.1 as a key because it is possible that two students with identical names will exist at some point in a valid state.9
In general, a relation schema may have more than one key. In this case, each of the keys is called a candidate key. For example, the CAR relation in Figure 3.4 has two candidate keys: License_number and Engine_serial_number. It is common to designate one of the candidate keys as the primary key of the relation. This is the candidate key whose values are used to identify tuples in the relation. We use the convention that the attributes that form the primary key of a relation schema are underlined, as shown in Figure 3.4. Notice that when a relation schema has several candidate keys,
8Note that Ssn is also a superkey. 9Names are sometimes used as keys, but then some artifact—such as appending an ordinal number— must be used to distinguish between identical names.
70 Chapter 3 The Relational Data Model and Relational Database Constraints
CAR
License_number Engine_serial_number Make Model Year
Texas ABC-739
Florida TVP-347
New York MPO-22
California 432-TFY
California RSK-629
Texas RSK-629
A69352
B43696
X83554
C43742
Y82935
U028365
Ford
Oldsmobile
Oldsmobile
Mercedes
Toyota
Jaguar
Mustang
Cutlass
Delta
190-D
Camry
XJS
02
05
01
99
04
04
Figure 3.4 The CAR relation, with two candidate keys: License_number and Engine_serial_number.
the choice of one to become the primary key is somewhat arbitrary; however, it is usually better to choose a primary key with a single attribute or a small number of attributes. The other candidate keys are designated as unique keys, and are not underlined.
Another constraint on attributes specifies whether NULL values are or are not per- mitted. For example, if every STUDENT tuple must have a valid, non-NULL value for the Name attribute, then Name of STUDENT is constrained to be NOT NULL.
3.2.3 Relational Databases and Relational Database Schemas
The definitions and constraints we have discussed so far apply to single relations and their attributes. A relational database usually contains many relations, with tuples in relations that are related in various ways. In this section we define a rela- tional database and a relational database schema.
A relational database schema S is a set of relation schemas S = {R1, R2, ..., Rm} and a set of integrity constraints IC. A relational database state10 DB of S is a set of relation states DB = {r1, r2, ..., rm} such that each ri is a state of Ri and such that the ri relation states satisfy the integrity constraints specified in IC. Figure 3.5 shows a relational database schema that we call COMPANY = {EMPLOYEE, DEPARTMENT, DEPT_LOCATIONS, PROJECT, WORKS_ON, DEPENDENT}. The underlined attrib- utes represent primary keys. Figure 3.6 shows a relational database state corres- ponding to the COMPANY schema. We will use this schema and database state in this chapter and in Chapters 4 through 6 for developing sample queries in different relational languages. (The data shown here is expanded and available for loading as a populated database from the Companion Website for the book, and can be used for the hands-on project exercises at the end of the chapters.)
When we refer to a relational database, we implicitly include both its schema and its current state. A database state that does not obey all the integrity constraints is
10A relational database state is sometimes called a relational database instance. However, as we men- tioned earlier, we will not use the term instance since it also applies to single tuples.
3.2 Relational Model Constraints and Relational Database Schemas 71
DEPARTMENT
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
EMPLOYEE
DEPT_LOCATIONS
Dnumber Dlocation
PROJECT
Pname Pnumber Plocation Dnum
WORKS_ON
Essn Pno Hours
DEPENDENT
Essn Dependent_name Sex Bdate Relationship
Dname Dnumber Mgr_ssn Mgr_start_date
Figure 3.5 Schema diagram for the COMPANY relational database schema.
called an invalid state, and a state that satisfies all the constraints in the defined set of integrity constraints IC is called a valid state.
In Figure 3.5, the Dnumber attribute in both DEPARTMENT and DEPT_LOCATIONS stands for the same real-world concept—the number given to a department. That same concept is called Dno in EMPLOYEE and Dnum in PROJECT. Attributes that represent the same real-world concept may or may not have identical names in dif- ferent relations. Alternatively, attributes that represent different concepts may have the same name in different relations. For example, we could have used the attribute name Name for both Pname of PROJECT and Dname of DEPARTMENT; in this case, we would have two attributes that share the same name but represent different real- world concepts—project names and department names.
In some early versions of the relational model, an assumption was made that the same real-world concept, when represented by an attribute, would have identical attribute names in all relations. This creates problems when the same real-world concept is used in different roles (meanings) in the same relation. For example, the concept of Social Security number appears twice in the EMPLOYEE relation of Figure 3.5: once in the role of the employee’s SSN, and once in the role of the super- visor’s SSN. We are required to give them distinct attribute names—Ssn and Super_ssn, respectively—because they appear in the same relation and in order to distinguish their meaning.
Each relational DBMS must have a data definition language (DDL) for defining a relational database schema. Current relational DBMSs are mostly using SQL for this purpose. We present the SQL DDL in Sections 4.1 and 4.2.
DEPT_LOCATIONS
Dnumber
Houston
Stafford
Bellaire
Sugarland
Dlocation
DEPARTMENT
Dname
Research
Administration
Headquarters 1
5
4
888665555
333445555
987654321
1981-06-19
1988-05-22
1995-01-01
Dnumber Mgr_ssn Mgr_start_date
WORKS_ON
Essn
123456789
123456789
666884444
453453453
453453453
333445555
333445555
333445555
333445555
999887777
999887777
987987987
987987987
987654321
987654321
888665555
3
1
2
2
1
2
30
30
30
10
10
3
10
20
20
20
40.0
32.5
7.5
10.0
10.0
10.0
10.0
20.0
20.0
30.0
5.0
10.0
35.0
20.0
15.0
NULL
Pno Hours
PROJECT
Pname
ProductX
ProductY
ProductZ
Computerization
Reorganization
Newbenefits
3
1
2
30
10
20
5
5
5
4
4
1
Houston
Bellaire
Sugarland
Stafford
Stafford
Houston
Pnumber Plocation Dnum
DEPENDENT
333445555
333445555
333445555
987654321
123456789
123456789
123456789
Joy
Alice F
M
F
M
M
F
F
1986-04-05
1983-10-25
1958-05-03
1942-02-28
1988-01-04
1988-12-30
1967-05-05
Theodore
Alice
Elizabeth
Abner
Michael
Spouse
Daughter
Son
Daughter
Spouse
Spouse
Son
Dependent_name Sex Bdate Relationship
EMPLOYEE
Fname
John
Franklin
Jennifer
Alicia
Ramesh
Joyce
James
Ahmad
Narayan
English
Borg
Jabbar
666884444
453453453
888665555
987987987
F
F
M
M
M
M
M
F
4
4
5
5
4
1
5
5
25000
43000
30000
40000
25000
55000
38000
25000
987654321
888665555
333445555
888665555
987654321
NULL
333445555
333445555
Zelaya
Wallace
Smith
Wong
3321 Castle, Spring, TX
291 Berry, Bellaire, TX
731 Fondren, Houston, TX
638 Voss, Houston, TX
1968-01-19
1941-06-20
1965-01-09
1955-12-08
1969-03-29
1937-11-10
1962-09-15
1972-07-31
980 Dallas, Houston, TX
450 Stone, Houston, TX
975 Fire Oak, Humble, TX
5631 Rice, Houston, TX
999887777
987654321
123456789
333445555
Minit Lname Ssn Bdate Address Sex DnoSalary Super_ssn
B
T
J
S
K
A
V
E
Houston
1
4
5
5
Essn
5
Figure 3.6 One possible database state for the COMPANY relational database schema.
72 Chapter 3 The Relational Data Model and Relational Database Constraints
Integrity constraints are specified on a database schema and are expected to hold on every valid database state of that schema. In addition to domain, key, and NOT NULL constraints, two other types of constraints are considered part of the relational model: entity integrity and referential integrity.
3.2.4 Integrity, Referential Integrity, and Foreign Keys
The entity integrity constraint states that no primary key value can be NULL. This is because the primary key value is used to identify individual tuples in a relation. Having NULL values for the primary key implies that we cannot identify some tuples. For example, if two or more tuples had NULL for their primary keys, we may not be able to distinguish them if we try to reference them from other relations.
Key constraints and entity integrity constraints are specified on individual relations. The referential integrity constraint is specified between two relations and is used to maintain the consistency among tuples in the two relations. Informally, the refer- ential integrity constraint states that a tuple in one relation that refers to another relation must refer to an existing tuple in that relation. For example, in Figure 3.6, the attribute Dno of EMPLOYEE gives the department number for which each employee works; hence, its value in every EMPLOYEE tuple must match the Dnumber value of some tuple in the DEPARTMENT relation.
To define referential integrity more formally, first we define the concept of a foreign key. The conditions for a foreign key, given below, specify a referential integrity con- straint between the two relation schemas R1 and R2. A set of attributes FK in rela- tion schema R1 is a foreign key of R1 that references relation R2 if it satisfies the following rules:
1. The attributes in FK have the same domain(s) as the primary key attributes PK of R2; the attributes FK are said to reference or refer to the relation R2.
2. A value of FK in a tuple t1 of the current state r1(R1) either occurs as a value of PK for some tuple t2 in the current state r2(R2) or is NULL. In the former case, we have t1[FK] = t2[PK], and we say that the tuple t1 references or refers to the tuple t2.
In this definition, R1 is called the referencing relation and R2 is the referenced rela- tion. If these two conditions hold, a referential integrity constraint from R1 to R2 is said to hold. In a database of many relations, there are usually many referential integrity constraints.
To specify these constraints, first we must have a clear understanding of the mean- ing or role that each attribute or set of attributes plays in the various relation schemas of the database. Referential integrity constraints typically arise from the relationships among the entities represented by the relation schemas. For example, consider the database shown in Figure 3.6. In the EMPLOYEE relation, the attribute Dno refers to the department for which an employee works; hence, we designate Dno to be a foreign key of EMPLOYEE referencing the DEPARTMENT relation. This means that a value of Dno in any tuple t1 of the EMPLOYEE relation must match a value of
3.2 Relational Model Constraints and Relational Database Schemas 73
74 Chapter 3 The Relational Data Model and Relational Database Constraints
the primary key of DEPARTMENT—the Dnumber attribute—in some tuple t2 of the DEPARTMENT relation, or the value of Dno can be NULL if the employee does not belong to a department or will be assigned to a department later. For example, in Figure 3.6 the tuple for employee ‘John Smith’ references the tuple for the ‘Research’ department, indicating that ‘John Smith’ works for this department.
Notice that a foreign key can refer to its own relation. For example, the attribute Super_ssn in EMPLOYEE refers to the supervisor of an employee; this is another employee, represented by a tuple in the EMPLOYEE relation. Hence, Super_ssn is a foreign key that references the EMPLOYEE relation itself. In Figure 3.6 the tuple for employee ‘John Smith’ references the tuple for employee ‘Franklin Wong,’ indicating that ‘Franklin Wong’ is the supervisor of ‘John Smith.’
We can diagrammatically display referential integrity constraints by drawing a directed arc from each foreign key to the relation it references. For clarity, the arrowhead may point to the primary key of the referenced relation. Figure 3.7 shows the schema in Figure 3.5 with the referential integrity constraints displayed in this manner.
All integrity constraints should be specified on the relational database schema (i.e., defined as part of its definition) if we want to enforce these constraints on the data- base states. Hence, the DDL includes provisions for specifying the various types of constraints so that the DBMS can automatically enforce them. Most relational DBMSs support key, entity integrity, and referential integrity constraints. These constraints are specified as a part of data definition in the DDL.
3.2.5 Other Types of Constraints The preceding integrity constraints are included in the data definition language because they occur in most database applications. However, they do not include a large class of general constraints, sometimes called semantic integrity constraints, which may have to be specified and enforced on a relational database. Examples of such constraints are the salary of an employee should not exceed the salary of the employee’s supervisor and the maximum number of hours an employee can work on all projects per week is 56. Such constraints can be specified and enforced within the application programs that update the database, or by using a general-purpose constraint specification language. Mechanisms called triggers and assertions can be used. In SQL, CREATE ASSERTION and CREATE TRIGGER statements can be used for this purpose (see Chapter 5). It is more common to check for these types of constraints within the application programs than to use constraint specification languages because the latter are sometimes difficult and complex to use, as we dis- cuss in Section 26.1.
Another type of constraint is the functional dependency constraint, which establishes a functional relationship among two sets of attributes X and Y. This constraint spec- ifies that the value of X determines a unique value of Y in all states of a relation; it is denoted as a functional dependency X → Y. We use functional depen-dencies and other types of dependencies in Chapters 15 and 16 as tools to analyze the quality of relational designs and to “normalize” relations to improve their quality.
3.3 Update Operations, Transactions, and Dealing with Constraint Violations 75
DEPARTMENT
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
EMPLOYEE
DEPT_LOCATIONS
Dnumber Dlocation
PROJECT
Pname Pnumber Plocation Dnum
WORKS_ON
Essn Pno Hours
DEPENDENT
Essn Dependent_name Sex Bdate Relationship
Dname Dnumber Mgr_ssn Mgr_start_date
Figure 3.7 Referential integrity constraints displayed on the COMPANY relational database schema.
The types of constraints we discussed so far may be called state constraints because they define the constraints that a valid state of the database must satisfy. Another type of constraint, called transition constraints, can be defined to deal with state changes in the database.11 An example of a transition constraint is: “the salary of an employee can only increase.” Such constraints are typically enforced by the application pro- grams or specified using active rules and triggers, as we discuss in Section 26.1.
3.3 Update Operations, Transactions, and Dealing with Constraint Violations
The operations of the relational model can be categorized into retrievals and updates. The relational algebra operations, which can be used to specify retrievals, are discussed in detail in Chapter 6. A relational algebra expression forms a new relation after applying a number of algebraic operators to an existing set of rela- tions; its main use is for querying a database to retrieve information. The user for- mulates a query that specifies the data of interest, and a new relation is formed by applying relational operators to retrieve this data. That result relation becomes the
11State constraints are sometimes called static constraints, and transition constraints are sometimes called dynamic constraints.
76 Chapter 3 The Relational Data Model and Relational Database Constraints
answer to (or result of ) the user’s query. Chapter 6 also introduces the language called relational calculus, which is used to define the new relation declaratively without giving a specific order of operations.
In this section, we concentrate on the database modification or update operations. There are three basic operations that can change the states of relations in the data- base: Insert, Delete, and Update (or Modify). They insert new data, delete old data, or modify existing data records. Insert is used to insert one or more new tuples in a relation, Delete is used to delete tuples, and Update (or Modify) is used to change the values of some attributes in existing tuples. Whenever these operations are applied, the integrity constraints specified on the relational database schema should not be violated. In this section we discuss the types of constraints that may be vio- lated by each of these operations and the types of actions that may be taken if an operation causes a violation. We use the database shown in Figure 3.6 for examples and discuss only key constraints, entity integrity constraints, and the referential integrity constraints shown in Figure 3.7. For each type of operation, we give some examples and discuss any constraints that each operation may violate.
3.3.1 The Insert Operation The Insert operation provides a list of attribute values for a new tuple t that is to be inserted into a relation R. Insert can violate any of the four types of constraints dis- cussed in the previous section. Domain constraints can be violated if an attribute value is given that does not appear in the corresponding domain or is not of the appropriate data type. Key constraints can be violated if a key value in the new tuple t already exists in another tuple in the relation r(R). Entity integrity can be violated if any part of the primary key of the new tuple t is NULL. Referential integrity can be violated if the value of any foreign key in t refers to a tuple that does not exist in the referenced relation. Here are some examples to illustrate this discussion.
■ Operation: Insert <‘Cecilia’, ‘F’, ‘Kolonsky’, NULL, ‘1960-04-05’, ‘6357 Windy Lane, Katy, TX’, F, 28000, NULL, 4> into EMPLOYEE. Result: This insertion violates the entity integrity constraint (NULL for the primary key Ssn), so it is rejected.
■ Operation: Insert <‘Alicia’, ‘J’, ‘Zelaya’, ‘999887777’, ‘1960-04-05’, ‘6357 Windy Lane, Katy, TX’, F, 28000, ‘987654321’, 4> into EMPLOYEE. Result: This insertion violates the key constraint because another tuple with the same Ssn value already exists in the EMPLOYEE relation, and so it is rejected.
■ Operation: Insert <‘Cecilia’, ‘F’, ‘Kolonsky’, ‘677678989’, ‘1960-04-05’, ‘6357 Windswept, Katy, TX’, F, 28000, ‘987654321’, 7> into EMPLOYEE. Result: This insertion violates the referential integrity constraint specified on Dno in EMPLOYEE because no corresponding referenced tuple exists in DEPARTMENT with Dnumber = 7.
3.3 Update Operations, Transactions, and Dealing with Constraint Violations 77
■ Operation: Insert <‘Cecilia’, ‘F’, ‘Kolonsky’, ‘677678989’, ‘1960-04-05’, ‘6357 Windy Lane, Katy, TX’, F, 28000, NULL, 4> into EMPLOYEE. Result: This insertion satisfies all constraints, so it is acceptable.
If an insertion violates one or more constraints, the default option is to reject the insertion. In this case, it would be useful if the DBMS could provide a reason to the user as to why the insertion was rejected. Another option is to attempt to correct the reason for rejecting the insertion, but this is typically not used for violations caused by Insert; rather, it is used more often in correcting violations for Delete and Update. In the first operation, the DBMS could ask the user to provide a value for Ssn, and could then accept the insertion if a valid Ssn value is provided. In opera- tion 3, the DBMS could either ask the user to change the value of Dno to some valid value (or set it to NULL), or it could ask the user to insert a DEPARTMENT tuple with Dnumber = 7 and could accept the original insertion only after such an operation was accepted. Notice that in the latter case the insertion violation can cascade back to the EMPLOYEE relation if the user attempts to insert a tuple for department 7 with a value for Mgr_ssn that does not exist in the EMPLOYEE relation.
3.3.2 The Delete Operation The Delete operation can violate only referential integrity. This occurs if the tuple being deleted is referenced by foreign keys from other tuples in the database. To specify deletion, a condition on the attributes of the relation selects the tuple (or tuples) to be deleted. Here are some examples.
■ Operation: Delete the WORKS_ON tuple with Essn = ‘999887777’ and Pno = 10. Result: This deletion is acceptable and deletes exactly one tuple.
■ Operation: Delete the EMPLOYEE tuple with Ssn = ‘999887777’. Result: This deletion is not acceptable, because there are tuples in WORKS_ON that refer to this tuple. Hence, if the tuple in EMPLOYEE is deleted, referential integrity violations will result.
■ Operation: Delete the EMPLOYEE tuple with Ssn = ‘333445555’. Result: This deletion will result in even worse referential integrity violations, because the tuple involved is referenced by tuples from the EMPLOYEE, DEPARTMENT, WORKS_ON, and DEPENDENT relations.
Several options are available if a deletion operation causes a violation. The first option, called restrict, is to reject the deletion. The second option, called cascade, is to attempt to cascade (or propagate) the deletion by deleting tuples that reference the tuple that is being deleted. For example, in operation 2, the DBMS could automati- cally delete the offending tuples from WORKS_ON with Essn = ‘999887777’. A third option, called set null or set default, is to modify the referencing attribute values that cause the violation; each such value is either set to NULL or changed to reference
78 Chapter 3 The Relational Data Model and Relational Database Constraints
another default valid tuple. Notice that if a referencing attribute that causes a viola- tion is part of the primary key, it cannot be set to NULL; otherwise, it would violate entity integrity.
Combinations of these three options are also possible. For example, to avoid having operation 3 cause a violation, the DBMS may automatically delete all tuples from WORKS_ON and DEPENDENT with Essn = ‘333445555’. Tuples in EMPLOYEE with Super_ssn = ‘333445555’ and the tuple in DEPARTMENT with Mgr_ssn = ‘333445555’ can have their Super_ssn and Mgr_ssn values changed to other valid values or to NULL. Although it may make sense to delete automatically the WORKS_ON and DEPENDENT tuples that refer to an EMPLOYEE tuple, it may not make sense to delete other EMPLOYEE tuples or a DEPARTMENT tuple.
In general, when a referential integrity constraint is specified in the DDL, the DBMS will allow the database designer to specify which of the options applies in case of a violation of the constraint. We discuss how to specify these options in the SQL DDL in Chapter 4.
3.3.3 The Update Operation The Update (or Modify) operation is used to change the values of one or more attributes in a tuple (or tuples) of some relation R. It is necessary to specify a condi- tion on the attributes of the relation to select the tuple (or tuples) to be modified. Here are some examples.
■ Operation: Update the salary of the EMPLOYEE tuple with Ssn = ‘999887777’ to 28000. Result: Acceptable.
■ Operation: Update the Dno of the EMPLOYEE tuple with Ssn = ‘999887777’ to 1. Result: Acceptable.
■ Operation: Update the Dno of the EMPLOYEE tuple with Ssn = ‘999887777’ to 7. Result: Unacceptable, because it violates referential integrity.
■ Operation: Update the Ssn of the EMPLOYEE tuple with Ssn = ‘999887777’ to ‘987654321’. Result: Unacceptable, because it violates primary key constraint by repeating a value that already exists as a primary key in another tuple; it violates refer- ential integrity constraints because there are other relations that refer to the existing value of Ssn.
Updating an attribute that is neither part of a primary key nor of a foreign key usually causes no problems; the DBMS need only check to confirm that the new value is of the correct data type and domain. Modifying a primary key value is similar to delet- ing one tuple and inserting another in its place because we use the primary key to identify tuples. Hence, the issues discussed earlier in both Sections 3.3.1 (Insert) and 3.3.2 (Delete) come into play. If a foreign key attribute is modified, the DBMS must
3.4 Summary 79
make sure that the new value refers to an existing tuple in the referenced relation (or is set to NULL). Similar options exist to deal with referential integrity violations caused by Update as those options discussed for the Delete operation. In fact, when a referential integrity constraint is specified in the DDL, the DBMS will allow the user to choose separate options to deal with a violation caused by Delete and a vio- lation caused by Update (see Section 4.2).
3.3.4 The Transaction Concept A database application program running against a relational database typically exe- cutes one or more transactions. A transaction is an executing program that includes some database operations, such as reading from the database, or applying inser- tions, deletions, or updates to the database. At the end of the transaction, it must leave the database in a valid or consistent state that satisfies all the constraints spec- ified on the database schema. A single transaction may involve any number of retrieval operations (to be discussed as part of relational algebra and calculus in Chapter 6, and as a part of the language SQL in Chapters 4 and 5), and any number of update operations. These retrievals and updates will together form an atomic unit of work against the database. For example, a transaction to apply a bank with- drawal will typically read the user account record, check if there is a sufficient bal- ance, and then update the record by the withdrawal amount.
A large number of commercial applications running against relational databases in online transaction processing (OLTP) systems are executing transactions at rates that reach several hundred per second. Transaction processing concepts, concurrent execution of transactions, and recovery from failures will be discussed in Chapters 21 to 23.
3.4 Summary In this chapter we presented the modeling concepts, data structures, and constraints provided by the relational model of data. We started by introducing the concepts of domains, attributes, and tuples. Then, we defined a relation schema as a list of attributes that describe the structure of a relation. A relation, or relation state, is a set of tuples that conforms to the schema.
Several characteristics differentiate relations from ordinary tables or files. The first is that a relation is not sensitive to the ordering of tuples. The second involves the ordering of attributes in a relation schema and the corresponding ordering of values within a tuple. We gave an alternative definition of relation that does not require these two orderings, but we continued to use the first definition, which requires attributes and tuple values to be ordered, for convenience. Then, we discussed val- ues in tuples and introduced NULL values to represent missing or unknown infor- mation. We emphasized that NULL values should be avoided as much as possible.
We classified database constraints into inherent model-based constraints, explicit schema-based constraints, and application-based constraints, otherwise known as semantic constraints or business rules. Then, we discussed the schema constraints
80 Chapter 3 The Relational Data Model and Relational Database Constraints
pertaining to the relational model, starting with domain constraints, then key con- straints, including the concepts of superkey, candidate key, and primary key, and the NOT NULL constraint on attributes. We defined relational databases and relational database schemas. Additional relational constraints include the entity integrity con- straint, which prohibits primary key attributes from being NULL. We described the interrelation referential integrity constraint, which is used to maintain consistency of references among tuples from different relations.
The modification operations on the relational model are Insert, Delete, and Update. Each operation may violate certain types of constraints (refer to Section 3.3). Whenever an operation is applied, the database state after the operation is executed must be checked to ensure that no constraints have been violated. Finally, we intro- duced the concept of a transaction, which is important in relational DBMSs because it allows the grouping of several database operations into a single atomic action on the database.
Review Questions 3.1. Define the following terms as they apply to the relational model of data:
domain, attribute, n-tuple, relation schema, relation state, degree of a relation, relational database schema, and relational database state.
3.2. Why are tuples in a relation not ordered?
3.3. Why are duplicate tuples not allowed in a relation?
3.4. What is the difference between a key and a superkey?
3.5. Why do we designate one of the candidate keys of a relation to be the pri- mary key?
3.6. Discuss the characteristics of relations that make them different from ordi- nary tables and files.
3.7. Discuss the various reasons that lead to the occurrence of NULL values in relations.
3.8. Discuss the entity integrity and referential integrity constraints. Why is each considered important?
3.9. Define foreign key. What is this concept used for?
3.10. What is a transaction? How does it differ from an Update operation?
Exercises 3.11. Suppose that each of the following Update operations is applied directly to
the database state shown in Figure 3.6. Discuss all integrity constraints vio- lated by each operation, if any, and the different ways of enforcing these con- straints.
Exercises 81
a. Insert <‘Robert’, ‘F’, ‘Scott’, ‘943775543’, ‘1972-06-21’, ‘2365 Newcastle Rd, Bellaire, TX’, M, 58000, ‘888665555’, 1> into EMPLOYEE.
b. Insert <‘ProductA’, 4, ‘Bellaire’, 2> into PROJECT.
c. Insert <‘Production’, 4, ‘943775543’, ‘2007-10-01’> into DEPARTMENT.
d. Insert <‘677678989’, NULL, ‘40.0’> into WORKS_ON.
e. Insert <‘453453453’, ‘John’, ‘M’, ‘1990-12-12’, ‘spouse’> into DEPENDENT.
f. Delete the WORKS_ON tuples with Essn = ‘333445555’.
g. Delete the EMPLOYEE tuple with Ssn = ‘987654321’.
h. Delete the PROJECT tuple with Pname = ‘ProductX’.
i. Modify the Mgr_ssn and Mgr_start_date of the DEPARTMENT tuple with Dnumber = 5 to ‘123456789’ and ‘2007-10-01’, respectively.
j. Modify the Super_ssn attribute of the EMPLOYEE tuple with Ssn = ‘999887777’ to ‘943775543’.
k. Modify the Hours attribute of the WORKS_ON tuple with Essn = ‘999887777’ and Pno = 10 to ‘5.0’.
3.12. Consider the AIRLINE relational database schema shown in Figure 3.8, which describes a database for airline flight information. Each FLIGHT is identified by a Flight_number, and consists of one or more FLIGHT_LEGs with Leg_numbers 1, 2, 3, and so on. Each FLIGHT_LEG has scheduled arrival and departure times, airports, and one or more LEG_INSTANCEs—one for each Date on which the flight travels. FAREs are kept for each FLIGHT. For each FLIGHT_LEG instance, SEAT_RESERVATIONs are kept, as are the AIRPLANE used on the leg and the actual arrival and departure times and airports. An AIRPLANE is identified by an Airplane_id and is of a particular AIRPLANE_TYPE. CAN_LAND relates AIRPLANE_TYPEs to the AIRPORTs at which they can land. An AIRPORT is identified by an Airport_code. Consider an update for the AIRLINE database to enter a reservation on a particular flight or flight leg on a given date.
a. Give the operations for this update.
b. What types of constraints would you expect to check?
c. Which of these constraints are key, entity integrity, and referential integrity constraints, and which are not?
d. Specify all the referential integrity constraints that hold on the schema shown in Figure 3.8.
3.13. Consider the relation CLASS(Course#, Univ_Section#, Instructor_name, Semester, Building_code, Room#, Time_period, Weekdays, Credit_hours). This represents classes taught in a university, with unique Univ_section#s. Identify what you think should be various candidate keys, and write in your own words the conditions or assumptions under which each candidate key would be valid.
82 Chapter 3 The Relational Data Model and Relational Database Constraints
AIRPORT Airport_code Name City State
Flight_number Airline Weekdays
FLIGHT
FLIGHT_LEG Flight_number Leg_number Departure_airport_code Scheduled_departure_time
Scheduled_arrival_timeArrival_airport_code
LEG_INSTANCE
Flight_number Leg_number Date Number_of_available_seats Airplane_id
FARE
Flight_number Fare_code Amount Restrictions
AIRPLANE_TYPE Airplane_type_name Max_seats Company
CAN_LAND Airplane_type_name Airport_code
AIRPLANE Airplane_id Total_number_of_seats Airplane_type
SEAT_RESERVATION Leg_number Date Seat_number Customer_name Customer_phoneFlight_number
Arrival_timeArrival_airport_codeDeparture_timeDeparture_airport_code
Figure 3.8 The AIRLINE relational database schema.
3.14. Consider the following six relations for an order-processing database appli- cation in a company:
CUSTOMER(Cust#, Cname, City) ORDER(Order#, Odate, Cust#, Ord_amt) ORDER_ITEM(Order#, Item#, Qty)
Exercises 83
ITEM(Item#, Unit_price) SHIPMENT(Order#, Warehouse#, Ship_date) WAREHOUSE(Warehouse#, City)
Here, Ord_amt refers to total dollar amount of an order; Odate is the date the order was placed; and Ship_date is the date an order (or part of an order) is shipped from the warehouse. Assume that an order can be shipped from sev- eral warehouses. Specify the foreign keys for this schema, stating any assumptions you make. What other constraints can you think of for this database?
3.15. Consider the following relations for a database that keeps track of business trips of salespersons in a sales office:
SALESPERSON(Ssn, Name, Start_year, Dept_no) TRIP(Ssn, From_city, To_city, Departure_date, Return_date, Trip_id) EXPENSE(Trip_id, Account#, Amount)
A trip can be charged to one or more accounts. Specify the foreign keys for this schema, stating any assumptions you make.
3.16. Consider the following relations for a database that keeps track of student enrollment in courses and the books adopted for each course:
STUDENT(Ssn, Name, Major, Bdate) COURSE(Course#, Cname, Dept) ENROLL(Ssn, Course#, Quarter, Grade) BOOK_ADOPTION(Course#, Quarter, Book_isbn) TEXT(Book_isbn, Book_title, Publisher, Author)
Specify the foreign keys for this schema, stating any assumptions you make.
3.17. Consider the following relations for a database that keeps track of automo- bile sales in a car dealership (OPTION refers to some optional equipment installed on an automobile):
CAR(Serial_no, Model, Manufacturer, Price) OPTION(Serial_no, Option_name, Price) SALE(Salesperson_id, Serial_no, Date, Sale_price) SALESPERSON(Salesperson_id, Name, Phone)
First, specify the foreign keys for this schema, stating any assumptions you make. Next, populate the relations with a few sample tuples, and then give an example of an insertion in the SALE and SALESPERSON relations that violates the referential integrity constraints and of another insertion that does not.
3.18. Database design often involves decisions about the storage of attributes. For example, a Social Security number can be stored as one attribute or split into three attributes (one for each of the three hyphen-delineated groups of numbers in a Social Security number—XXX-XX-XXXX). However, Social Security numbers are usually represented as just one attribute. The decision
84 Chapter 3 The Relational Data Model and Relational Database Constraints
is based on how the database will be used. This exercise asks you to think about specific situations where dividing the SSN is useful.
3.19. Consider a STUDENT relation in a UNIVERSITY database with the following attributes (Name, Ssn, Local_phone, Address, Cell_phone, Age, Gpa). Note that the cell phone may be from a different city and state (or province) from the local phone. A possible tuple of the relation is shown below:
Name Ssn Local_phone Address Cell_phone Age Gpa George Shaw 123-45-6789 555-1234 123 Main St., 555-4321 19 3.75 William Edwards Anytown, CA 94539
a. Identify the critical missing information from the Local_phone and Cell_phone attributes. (Hint: How do you call someone who lives in a dif- ferent state or province?)
b. Would you store this additional information in the Local_phone and Cell_phone attributes or add new attributes to the schema for STUDENT?
c. Consider the Name attribute. What are the advantages and disadvantages of splitting this field from one attribute into three attributes (first name, middle name, and last name)?
d. What general guideline would you recommend for deciding when to store information in a single attribute and when to split the information?
e. Suppose the student can have between 0 and 5 phones. Suggest two differ- ent designs that allow this type of information.
3.20. Recent changes in privacy laws have disallowed organizations from using Social Security numbers to identify individuals unless certain restrictions are satisfied. As a result, most U.S. universities cannot use SSNs as primary keys (except for financial data). In practice, Student_id, a unique identifier assigned to every student, is likely to be used as the primary key rather than SSN since Student_id can be used throughout the system.
a. Some database designers are reluctant to use generated keys (also known as surrogate keys) for primary keys (such as Student_id) because they are artificial. Can you propose any natural choices of keys that can be used to identify the student record in a UNIVERSITY database?
b. Suppose that you are able to guarantee uniqueness of a natural key that includes last name. Are you guaranteed that the last name will not change during the lifetime of the database? If last name can change, what solu- tions can you propose for creating a primary key that still includes last name but remains unique?
c. What are the advantages and disadvantages of using generated (surro- gate) keys?
Selected Bibliography 85
Selected Bibliography The relational model was introduced by Codd (1970) in a classic paper. Codd also introduced relational algebra and laid the theoretical foundations for the relational model in a series of papers (Codd 1971, 1972, 1972a, 1974); he was later given the Turing Award, the highest honor of the ACM (Association for Computing Machinery) for his work on the relational model. In a later paper, Codd (1979) dis- cussed extending the relational model to incorporate more meta-data and seman- tics about the relations; he also proposed a three-valued logic to deal with uncertainty in relations and incorporating NULLs in the relational algebra. The resulting model is known as RM/T. Childs (1968) had earlier used set theory to model databases. Later, Codd (1990) published a book examining over 300 features of the relational data model and database systems. Date (2001) provides a retro- spective review and analysis of the relational data model.
Since Codd’s pioneering work, much research has been conducted on various aspects of the relational model. Todd (1976) describes an experimental DBMS called PRTV that directly implements the relational algebra operations. Schmidt and Swenson (1975) introduce additional semantics into the relational model by classifying different types of relations. Chen’s (1976) Entity-Relationship model, which is discussed in Chapter 7, is a means to communicate the real-world seman- tics of a relational database at the conceptual level. Wiederhold and Elmasri (1979) introduce various types of connections between relations to enhance its constraints. Extensions of the relational model are discussed in Chapters 11 and 26. Additional bibliographic notes for other aspects of the relational model and its languages, sys- tems, extensions, and theory are given in Chapters 4 to 6, 9, 11, 13, 15, 16, 24, and 25. Maier (1983) and Atzeni and De Antonellis (1993) provide an extensive theoretical treatment of the relational data model.
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87
Basic SQL
The SQL language may be considered one of themajor reasons for the commercial success of rela- tional databases. Because it became a standard for relational databases, users were less concerned about migrating their database applications from other types of database systems—for example, network or hierarchical systems—to relational sys- tems. This is because even if the users became dissatisfied with the particular rela- tional DBMS product they were using, converting to another relational DBMS product was not expected to be too expensive and time-consuming because both systems followed the same language standards. In practice, of course, there are many differences between various commercial relational DBMS packages. However, if the user is diligent in using only those features that are part of the standard, and if both relational systems faithfully support the standard, then conversion between the two systems should be much simplified. Another advantage of having such a standard is that users may write statements in a database application program that can access data stored in two or more relational DBMSs without having to change the database sublanguage (SQL) if both relational DBMSs support standard SQL.
This chapter presents the main features of the SQL standard for commercial rela- tional DBMSs, whereas Chapter 3 presented the most important concepts underly- ing the formal relational data model. In Chapter 6 (Sections 6.1 through 6.5) we shall discuss the relational algebra operations, which are very important for under- standing the types of requests that may be specified on a relational database. They are also important for query processing and optimization in a relational DBMS, as we shall see in Chapter 19. However, the relational algebra operations are consid- ered to be too technical for most commercial DBMS users because a query in rela- tional algebra is written as a sequence of operations that, when executed, produces the required result. Hence, the user must specify how—that is, in what order—to execute the query operations. On the other hand, the SQL language provides a
4chapter 4
88 Chapter 4 Basic SQL
higher-level declarative language interface, so the user only specifies what the result is to be, leaving the actual optimization and decisions on how to execute the query to the DBMS. Although SQL includes some features from relational algebra, it is based to a greater extent on the tuple relational calculus, which we describe in Section 6.6. However, the SQL syntax is more user-friendly than either of the two formal languages.
The name SQL is presently expanded as Structured Query Language. Originally, SQL was called SEQUEL (Structured English QUEry Language) and was designed and implemented at IBM Research as the interface for an experimental relational database system called SYSTEM R. SQL is now the standard language for commer- cial relational DBMSs. A joint effort by the American National Standards Institute (ANSI) and the International Standards Organization (ISO) has led to a standard version of SQL (ANSI 1986), called SQL-86 or SQL1. A revised and much expanded standard called SQL-92 (also referred to as SQL2) was subsequently developed. The next standard that is well-recognized is SQL:1999, which started out as SQL3. Two later updates to the standard are SQL:2003 and SQL:2006, which added XML fea- tures (see Chapter 12) among other updates to the language. Another update in 2008 incorporated more object database features in SQL (see Chapter 11). We will try to cover the latest version of SQL as much as possible.
SQL is a comprehensive database language: It has statements for data definitions, queries, and updates. Hence, it is both a DDL and a DML. In addition, it has facili- ties for defining views on the database, for specifying security and authorization, for defining integrity constraints, and for specifying transaction controls. It also has rules for embedding SQL statements into a general-purpose programming language such as Java, COBOL, or C/C++.1
The later SQL standards (starting with SQL:1999) are divided into a core specifica- tion plus specialized extensions. The core is supposed to be implemented by all RDBMS vendors that are SQL compliant. The extensions can be implemented as optional modules to be purchased independently for specific database applications such as data mining, spatial data, temporal data, data warehousing, online analytical processing (OLAP), multimedia data, and so on.
Because SQL is very important (and quite large), we devote two chapters to its fea- tures. In this chapter, Section 4.1 describes the SQL DDL commands for creating schemas and tables, and gives an overview of the basic data types in SQL. Section 4.2 presents how basic constraints such as key and referential integrity are specified. Section 4.3 describes the basic SQL constructs for specifying retrieval queries, and Section 4.4 describes the SQL commands for insertion, deletion, and data updates.
In Chapter 5, we will describe more complex SQL retrieval queries, as well as the ALTER commands for changing the schema. We will also describe the CREATE ASSERTION statement, which allows the specification of more general constraints on the database. We also introduce the concept of triggers, which is presented in
1Originally, SQL had statements for creating and dropping indexes on the files that represent relations, but these have been dropped from the SQL standard for some time.
4.1 SQL Data Definition and Data Types 89
more detail in Chapter 26 and we will describe the SQL facility for defining views on the database in Chapter 5. Views are also called virtual or derived tables because they present the user with what appear to be tables; however, the information in those tables is derived from previously defined tables.
Section 4.5 lists some SQL features that are presented in other chapters of the book; these include transaction control in Chapter 21, security/authorization in Chapter 24, active databases (triggers) in Chapter 26, object-oriented features in Chapter 11, and online analytical processing (OLAP) features in Chapter 29. Section 4.6 sum- marizes the chapter. Chapters 13 and 14 discuss the various database programming techniques for programming with SQL.
4.1 SQL Data Definition and Data Types SQL uses the terms table, row, and column for the formal relational model terms relation, tuple, and attribute, respectively. We will use the corresponding terms inter- changeably. The main SQL command for data definition is the CREATE statement, which can be used to create schemas, tables (relations), and domains (as well as other constructs such as views, assertions, and triggers). Before we describe the rel- evant CREATE statements, we discuss schema and catalog concepts in Section 4.1.1 to place our discussion in perspective. Section 4.1.2 describes how tables are created, and Section 4.1.3 describes the most important data types available for attribute specification. Because the SQL specification is very large, we give a description of the most important features. Further details can be found in the various SQL stan- dards documents (see end-of-chapter bibliographic notes).
4.1.1 Schema and Catalog Concepts in SQL Early versions of SQL did not include the concept of a relational database schema; all tables (relations) were considered part of the same schema. The concept of an SQL schema was incorporated starting with SQL2 in order to group together tables and other constructs that belong to the same database application. An SQL schema is identified by a schema name, and includes an authorization identifier to indicate the user or account who owns the schema, as well as descriptors for each element in the schema. Schema elements include tables, constraints, views, domains, and other constructs (such as authorization grants) that describe the schema. A schema is cre- ated via the CREATE SCHEMA statement, which can include all the schema elements’ definitions. Alternatively, the schema can be assigned a name and authorization identifier, and the elements can be defined later. For example, the following state- ment creates a schema called COMPANY, owned by the user with authorization iden- tifier ‘Jsmith’. Note that each statement in SQL ends with a semicolon.
CREATE SCHEMA COMPANY AUTHORIZATION ‘Jsmith’;
In general, not all users are authorized to create schemas and schema elements. The privilege to create schemas, tables, and other constructs must be explicitly granted to the relevant user accounts by the system administrator or DBA.
90 Chapter 4 Basic SQL
In addition to the concept of a schema, SQL uses the concept of a catalog—a named collection of schemas in an SQL environment. An SQL environment is basically an installation of an SQL-compliant RDBMS on a computer system.2 A catalog always contains a special schema called INFORMATION_SCHEMA, which provides informa- tion on all the schemas in the catalog and all the element descriptors in these schemas. Integrity constraints such as referential integrity can be defined between relations only if they exist in schemas within the same catalog. Schemas within the same catalog can also share certain elements, such as domain definitions.
4.1.2 The CREATE TABLE Command in SQL The CREATE TABLE command is used to specify a new relation by giving it a name and specifying its attributes and initial constraints. The attributes are specified first, and each attribute is given a name, a data type to specify its domain of values, and any attribute constraints, such as NOT NULL. The key, entity integrity, and referen- tial integrity constraints can be specified within the CREATE TABLE statement after the attributes are declared, or they can be added later using the ALTER TABLE com- mand (see Chapter 5). Figure 4.1 shows sample data definition statements in SQL for the COMPANY relational database schema shown in Figure 3.7.
Typically, the SQL schema in which the relations are declared is implicitly specified in the environment in which the CREATE TABLE statements are executed. Alternatively, we can explicitly attach the schema name to the relation name, sepa- rated by a period. For example, by writing
CREATE TABLE COMPANY.EMPLOYEE ...
rather than
CREATE TABLE EMPLOYEE ...
as in Figure 4.1, we can explicitly (rather than implicitly) make the EMPLOYEE table part of the COMPANY schema.
The relations declared through CREATE TABLE statements are called base tables (or base relations); this means that the relation and its tuples are actually created and stored as a file by the DBMS. Base relations are distinguished from virtual relations, created through the CREATE VIEW statement (see Chapter 5), which may or may not correspond to an actual physical file. In SQL, the attributes in a base table are considered to be ordered in the sequence in which they are specified in the CREATE TABLE statement. However, rows (tuples) are not considered to be ordered within a relation.
It is important to note that in Figure 4.1, there are some foreign keys that may cause errors because they are specified either via circular references or because they refer to a table that has not yet been created. For example, the foreign key Super_ssn in the EMPLOYEE table is a circular reference because it refers to the table itself. The foreign key Dno in the EMPLOYEE table refers to the DEPARTMENT table, which has
2SQL also includes the concept of a cluster of catalogs within an environment.
4.1 SQL Data Definition and Data Types 91
CREATE TABLE EMPLOYEE ( Fname VARCHAR(15) NOT NULL,
Minit CHAR, Lname VARCHAR(15) NOT NULL, Ssn CHAR(9) NOT NULL, Bdate DATE, Address VARCHAR(30), Sex CHAR, Salary DECIMAL(10,2), Super_ssn CHAR(9), Dno INT NOT NULL,
PRIMARY KEY (Ssn), FOREIGN KEY (Super_ssn) REFERENCES EMPLOYEE(Ssn), FOREIGN KEY (Dno) REFERENCES DEPARTMENT(Dnumber) );
CREATE TABLE DEPARTMENT ( Dname VARCHAR(15) NOT NULL,
Dnumber INT NOT NULL, Mgr_ssn CHAR(9) NOT NULL, Mgr_start_date DATE,
PRIMARY KEY (Dnumber), UNIQUE (Dname), FOREIGN KEY (Mgr_ssn) REFERENCES EMPLOYEE(Ssn) );
CREATE TABLE DEPT_LOCATIONS ( Dnumber INT NOT NULL,
Dlocation VARCHAR(15) NOT NULL, PRIMARY KEY (Dnumber, Dlocation), FOREIGN KEY (Dnumber) REFERENCES DEPARTMENT(Dnumber) );
CREATE TABLE PROJECT ( Pname VARCHAR(15) NOT NULL,
Pnumber INT NOT NULL, Plocation VARCHAR(15), Dnum INT NOT NULL,
PRIMARY KEY (Pnumber), UNIQUE (Pname), FOREIGN KEY (Dnum) REFERENCES DEPARTMENT(Dnumber) );
CREATE TABLE WORKS_ON ( Essn CHAR(9) NOT NULL,
Pno INT NOT NULL, Hours DECIMAL(3,1) NOT NULL,
PRIMARY KEY (Essn, Pno), FOREIGN KEY (Essn) REFERENCES EMPLOYEE(Ssn), FOREIGN KEY (Pno) REFERENCES PROJECT(Pnumber) );
CREATE TABLE DEPENDENT ( Essn CHAR(9) NOT NULL,
Dependent_name VARCHAR(15) NOT NULL, Sex CHAR, Bdate DATE, Relationship VARCHAR(8),
PRIMARY KEY (Essn, Dependent_name), FOREIGN KEY (Essn) REFERENCES EMPLOYEE(Ssn) );
Figure 4.1 SQL CREATE TABLE data definition state- ments for defining the COMPANY schema from Figure 3.7.
92 Chapter 4 Basic SQL
not been created yet. To deal with this type of problem, these constraints can be left out of the initial CREATE TABLE statement, and then added later using the ALTER TABLE statement (see Chapter 5). We displayed all the foreign keys in Figure 4.1 to show the complete COMPANY schema in one place.
4.1.3 Attribute Data Types and Domains in SQL The basic data types available for attributes include numeric, character string, bit string, Boolean, date, and time.
■ Numeric data types include integer numbers of various sizes (INTEGER or INT, and SMALLINT) and floating-point (real) numbers of various precision (FLOAT or REAL, and DOUBLE PRECISION). Formatted numbers can be declared by using DECIMAL(i,j)—or DEC(i,j) or NUMERIC(i,j)—where i, the precision, is the total number of decimal digits and j, the scale, is the number of digits after the decimal point. The default for scale is zero, and the default for precision is implementation-defined.
■ Character-string data types are either fixed length—CHAR(n) or CHARACTER(n), where n is the number of characters—or varying length— VARCHAR(n) or CHAR VARYING(n) or CHARACTER VARYING(n), where n is the maximum number of characters. When specifying a literal string value, it is placed between single quotation marks (apostrophes), and it is case sensi- tive (a distinction is made between uppercase and lowercase).3 For fixed- length strings, a shorter string is padded with blank characters to the right. For example, if the value ‘Smith’ is for an attribute of type CHAR(10), it is padded with five blank characters to become ‘Smith ’ if needed. Padded blanks are generally ignored when strings are compared. For comparison purposes, strings are considered ordered in alphabetic (or lexicographic) order; if a string str1 appears before another string str2 in alphabetic order, then str1 is considered to be less than str2.4 There is also a concatenation operator denoted by || (double vertical bar) that can concatenate two strings in SQL. For example, ‘abc’ || ‘XYZ’ results in a single string ‘abcXYZ’. Another variable-length string data type called CHARACTER LARGE OBJECT or CLOB is also available to specify columns that have large text values, such as documents. The CLOB maximum length can be specified in kilobytes (K), megabytes (M), or gigabytes (G). For example, CLOB(20M) specifies a max- imum length of 20 megabytes.
■ Bit-string data types are either of fixed length n—BIT(n)—or varying length—BIT VARYING(n), where n is the maximum number of bits. The default for n, the length of a character string or bit string, is 1. Literal bit strings are placed between single quotes but preceded by a B to distinguish
3This is not the case with SQL keywords, such as CREATE or CHAR. With keywords, SQL is case insen- sitive, meaning that SQL treats uppercase and lowercase letters as equivalent in keywords. 4For nonalphabetic characters, there is a defined order.
4.1 SQL Data Definition and Data Types 93
them from character strings; for example, B‘10101’.5 Another variable-length bitstring data type called BINARY LARGE OBJECT or BLOB is also available to specify columns that have large binary values, such as images. As for CLOB, the maximum length of a BLOB can be specified in kilobits (K), megabits (M), or gigabits (G). For example, BLOB(30G) specifies a maxi- mum length of 30 gigabits.
■ A Boolean data type has the traditional values of TRUE or FALSE. In SQL, because of the presence of NULL values, a three-valued logic is used, so a third possible value for a Boolean data type is UNKNOWN. We discuss the need for UNKNOWN and the three-valued logic in Chapter 5.
■ The DATE data type has ten positions, and its components are YEAR, MONTH, and DAY in the form YYYY-MM-DD. The TIME data type has at least eight positions, with the components HOUR, MINUTE, and SECOND in the form HH:MM:SS. Only valid dates and times should be allowed by the SQL implementation. This implies that months should be between 1 and 12 and dates must be between 1 and 31; furthermore, a date should be a valid date for the corresponding month. The < (less than) comparison can be used with dates or times—an earlier date is considered to be smaller than a later date, and similarly with time. Literal values are represented by single-quoted strings preceded by the keyword DATE or TIME; for example, DATE ‘2008-09- 27’ or TIME ‘09:12:47’. In addition, a data type TIME(i), where i is called time fractional seconds precision, specifies i + 1 additional positions for TIME—one position for an additional period (.) separator character, and i positions for specifying decimal fractions of a second. A TIME WITH TIME ZONE data type includes an additional six positions for specifying the displacement from the standard universal time zone, which is in the range +13:00 to –12:59 in units of HOURS:MINUTES. If WITH TIME ZONE is not included, the default is the local time zone for the SQL session.
Some additional data types are discussed below. The list of types discussed here is not exhaustive; different implementations have added more data types to SQL.
■ A timestamp data type (TIMESTAMP) includes the DATE and TIME fields, plus a minimum of six positions for decimal fractions of seconds and an optional WITH TIME ZONE qualifier. Literal values are represented by single- quoted strings preceded by the keyword TIMESTAMP, with a blank space between data and time; for example, TIMESTAMP ‘2008-09-27 09:12:47.648302’.
■ Another data type related to DATE, TIME, and TIMESTAMP is the INTERVAL data type. This specifies an interval—a relative value that can be used to increment or decrement an absolute value of a date, time, or timestamp. Intervals are qualified to be either YEAR/MONTH intervals or DAY/TIME intervals.
5Bit strings whose length is a multiple of 4 can be specified in hexadecimal notation, where the literal string is preceded by X and each hexadecimal character represents 4 bits.
94 Chapter 4 Basic SQL
The format of DATE, TIME, and TIMESTAMP can be considered as a special type of string. Hence, they can generally be used in string comparisons by being cast (or coerced or converted) into the equivalent strings.
It is possible to specify the data type of each attribute directly, as in Figure 4.1; alter- natively, a domain can be declared, and the domain name used with the attribute specification. This makes it easier to change the data type for a domain that is used by numerous attributes in a schema, and improves schema readability. For example, we can create a domain SSN_TYPE by the following statement:
CREATE DOMAIN SSN_TYPE AS CHAR(9);
We can use SSN_TYPE in place of CHAR(9) in Figure 4.1 for the attributes Ssn and Super_ssn of EMPLOYEE, Mgr_ssn of DEPARTMENT, Essn of WORKS_ON, and Essn of DEPENDENT. A domain can also have an optional default specification via a DEFAULT clause, as we discuss later for attributes. Notice that domains may not be available in some implementations of SQL.
4.2 Specifying Constraints in SQL This section describes the basic constraints that can be specified in SQL as part of table creation. These include key and referential integrity constraints, restrictions on attribute domains and NULLs, and constraints on individual tuples within a rela- tion. We discuss the specification of more general constraints, called assertions, in Chapter 5.
4.2.1 Specifying Attribute Constraints and Attribute Defaults Because SQL allows NULLs as attribute values, a constraint NOT NULL may be speci- fied if NULL is not permitted for a particular attribute. This is always implicitly spec- ified for the attributes that are part of the primary key of each relation, but it can be specified for any other attributes whose values are required not to be NULL, as shown in Figure 4.1.
It is also possible to define a default value for an attribute by appending the clause DEFAULT <value> to an attribute definition. The default value is included in any new tuple if an explicit value is not provided for that attribute. Figure 4.2 illustrates an example of specifying a default manager for a new department and a default department for a new employee. If no default clause is specified, the default default value is NULL for attributes that do not have the NOT NULL constraint.
Another type of constraint can restrict attribute or domain values using the CHECK clause following an attribute or domain definition.6 For example, suppose that department numbers are restricted to integer numbers between 1 and 20; then, we can change the attribute declaration of Dnumber in the DEPARTMENT table (see Figure 4.1) to the following:
Dnumber INT NOT NULL CHECK (Dnumber > 0 AND Dnumber < 21);
6The CHECK clause can also be used for other purposes, as we shall see.
4.2 Specifying Constraints in SQL 95
CREATE TABLE EMPLOYEE ( . . . ,
Dno INT NOT NULL DEFAULT 1, CONSTRAINT EMPPK
PRIMARY KEY (Ssn), CONSTRAINT EMPSUPERFK
FOREIGN KEY (Super_ssn) REFERENCES EMPLOYEE(Ssn) ON DELETE SET NULL ON UPDATE CASCADE,
CONSTRAINT EMPDEPTFK FOREIGN KEY(Dno) REFERENCES DEPARTMENT(Dnumber)
ON DELETE SET DEFAULT ON UPDATE CASCADE); CREATE TABLE DEPARTMENT
( . . . , Mgr_ssn CHAR(9) NOT NULL DEFAULT ‘888665555’, . . . ,
CONSTRAINT DEPTPK PRIMARY KEY(Dnumber),
CONSTRAINT DEPTSK UNIQUE (Dname),
CONSTRAINT DEPTMGRFK FOREIGN KEY (Mgr_ssn) REFERENCES EMPLOYEE(Ssn)
ON DELETE SET DEFAULT ON UPDATE CASCADE); CREATE TABLE DEPT_LOCATIONS
( . . . , PRIMARY KEY (Dnumber, Dlocation), FOREIGN KEY (Dnumber) REFERENCES DEPARTMENT(Dnumber)
ON DELETE CASCADE ON UPDATE CASCADE);
Figure 4.2 Example illustrating how default attribute values and referential integrity triggered actions are specified in SQL.
The CHECK clause can also be used in conjunction with the CREATE DOMAIN state- ment. For example, we can write the following statement:
CREATE DOMAIN D_NUM AS INTEGER CHECK (D_NUM > 0 AND D_NUM < 21);
We can then use the created domain D_NUM as the attribute type for all attributes that refer to department numbers in Figure 4.1, such as Dnumber of DEPARTMENT, Dnum of PROJECT, Dno of EMPLOYEE, and so on.
4.2.2 Specifying Key and Referential Integrity Constraints Because keys and referential integrity constraints are very important, there are spe- cial clauses within the CREATE TABLE statement to specify them. Some examples to illustrate the specification of keys and referential integrity are shown in Figure 4.1.7
The PRIMARY KEY clause specifies one or more attributes that make up the primary key of a relation. If a primary key has a single attribute, the clause can follow the attribute directly. For example, the primary key of DEPARTMENT can be specified as follows (instead of the way it is specified in Figure 4.1):
Dnumber INT PRIMARY KEY;
7Key and referential integrity constraints were not included in early versions of SQL. In some earlier implementations, keys were specified implicitly at the internal level via the CREATE INDEX command.
96 Chapter 4 Basic SQL
The UNIQUE clause specifies alternate (secondary) keys, as illustrated in the DEPARTMENT and PROJECT table declarations in Figure 4.1. The UNIQUE clause can also be specified directly for a secondary key if the secondary key is a single attribute, as in the following example:
Dname VARCHAR(15) UNIQUE;
Referential integrity is specified via the FOREIGN KEY clause, as shown in Figure 4.1. As we discussed in Section 3.2.4, a referential integrity constraint can be vio- lated when tuples are inserted or deleted, or when a foreign key or primary key attribute value is modified. The default action that SQL takes for an integrity viola- tion is to reject the update operation that will cause a violation, which is known as the RESTRICT option. However, the schema designer can specify an alternative action to be taken by attaching a referential triggered action clause to any foreign key constraint. The options include SET NULL, CASCADE, and SET DEFAULT. An option must be qualified with either ON DELETE or ON UPDATE. We illustrate this with the examples shown in Figure 4.2. Here, the database designer chooses ON DELETE SET NULL and ON UPDATE CASCADE for the foreign key Super_ssn of EMPLOYEE. This means that if the tuple for a supervising employee is deleted, the value of Super_ssn is automatically set to NULL for all employee tuples that were ref- erencing the deleted employee tuple. On the other hand, if the Ssn value for a super- vising employee is updated (say, because it was entered incorrectly), the new value is cascaded to Super_ssn for all employee tuples referencing the updated employee tuple.8
In general, the action taken by the DBMS for SET NULL or SET DEFAULT is the same for both ON DELETE and ON UPDATE: The value of the affected referencing attrib- utes is changed to NULL for SET NULL and to the specified default value of the refer- encing attribute for SET DEFAULT. The action for CASCADE ON DELETE is to delete all the referencing tuples, whereas the action for CASCADE ON UPDATE is to change the value of the referencing foreign key attribute(s) to the updated (new) primary key value for all the referencing tuples. It is the responsibility of the database designer to choose the appropriate action and to specify it in the database schema. As a general rule, the CASCADE option is suitable for “relationship” relations (see Section 9.1), such as WORKS_ON; for relations that represent multivalued attrib- utes, such as DEPT_LOCATIONS; and for relations that represent weak entity types, such as DEPENDENT.
4.2.3 Giving Names to Constraints Figure 4.2 also illustrates how a constraint may be given a constraint name, follow- ing the keyword CONSTRAINT. The names of all constraints within a particular schema must be unique. A constraint name is used to identify a particular con-
8Notice that the foreign key Super_ssn in the EMPLOYEE table is a circular reference and hence may have to be added later as a named constraint using the ALTER TABLE statement as we discussed at the end of Section 4.1.2.
4.3 Basic Retrieval Queries in SQL 97
straint in case the constraint must be dropped later and replaced with another con- straint, as we discuss in Chapter 5. Giving names to constraints is optional.
4.2.4 Specifying Constraints on Tuples Using CHECK In addition to key and referential integrity constraints, which are specified by spe- cial keywords, other table constraints can be specified through additional CHECK clauses at the end of a CREATE TABLE statement. These can be called tuple-based constraints because they apply to each tuple individually and are checked whenever a tuple is inserted or modified. For example, suppose that the DEPARTMENT table in Figure 4.1 had an additional attribute Dept_create_date, which stores the date when the department was created. Then we could add the following CHECK clause at the end of the CREATE TABLE statement for the DEPARTMENT table to make sure that a manager’s start date is later than the department creation date.
CHECK (Dept_create_date <= Mgr_start_date);
The CHECK clause can also be used to specify more general constraints using the CREATE ASSERTION statement of SQL. We discuss this in Chapter 5 because it requires the full power of queries, which are discussed in Sections 4.3 and 5.1.
4.3 Basic Retrieval Queries in SQL SQL has one basic statement for retrieving information from a database: the SELECT statement. The SELECT statement is not the same as the SELECT operation of relational algebra, which we discuss in Chapter 6. There are many options and flavors to the SELECT statement in SQL, so we will introduce its features gradually. We will use sample queries specified on the schema of Figure 3.5 and will refer to the sample database state shown in Figure 3.6 to show the results of some of the sample queries. In this section, we present the features of SQL for simple retrieval queries. Features of SQL for specifying more complex retrieval queries are presented in Section 5.1.
Before proceeding, we must point out an important distinction between SQL and the formal relational model discussed in Chapter 3: SQL allows a table (relation) to have two or more tuples that are identical in all their attribute values. Hence, in gen- eral, an SQL table is not a set of tuples, because a set does not allow two identical members; rather, it is a multiset (sometimes called a bag) of tuples. Some SQL rela- tions are constrained to be sets because a key constraint has been declared or because the DISTINCT option has been used with the SELECT statement (described later in this section). We should be aware of this distinction as we discuss the examples.
4.3.1 The SELECT-FROM-WHERE Structure of Basic SQL Queries
Queries in SQL can be very complex. We will start with simple queries, and then progress to more complex ones in a step-by-step manner. The basic form of the SELECT statement, sometimes called a mapping or a select-from-where block, is
98 Chapter 4 Basic SQL
formed of the three clauses SELECT, FROM, and WHERE and has the following form:9
SELECT <attribute list> FROM <table list> WHERE <condition>;
where
■ <attribute list> is a list of attribute names whose values are to be retrieved by the query.
■ <table list> is a list of the relation names required to process the query.
■ <condition> is a conditional (Boolean) expression that identifies the tuples to be retrieved by the query.
In SQL, the basic logical comparison operators for comparing attribute values with one another and with literal constants are =, <, <=, >, >=, and <>. These corre- spond to the relational algebra operators =, <, ≤, >, ≥, and ≠, respectively, and to the C/C++ programming language operators =, <, <=, >, >=, and !=. The main syntac- tic difference is the not equal operator. SQL has additional comparison operators that we will present gradually.
We illustrate the basic SELECT statement in SQL with some sample queries. The queries are labeled here with the same query numbers used in Chapter 6 for easy cross-reference.
Query 0. Retrieve the birth date and address of the employee(s) whose name is ‘John B. Smith’.
Q0: SELECT Bdate, Address FROM EMPLOYEE WHERE Fname=‘John’ AND Minit=‘B’ AND Lname=‘Smith’;
This query involves only the EMPLOYEE relation listed in the FROM clause. The query selects the individual EMPLOYEE tuples that satisfy the condition of the WHERE clause, then projects the result on the Bdate and Address attributes listed in the SELECT clause.
The SELECT clause of SQL specifies the attributes whose values are to be retrieved, which are called the projection attributes, and the WHERE clause specifies the Boolean condition that must be true for any retrieved tuple, which is known as the selection condition. Figure 4.3(a) shows the result of query Q0 on the database of Figure 3.6.
We can think of an implicit tuple variable or iterator in the SQL query ranging or looping over each individual tuple in the EMPLOYEE table and evaluating the condi- tion in the WHERE clause. Only those tuples that satisfy the condition—that is,
9The SELECT and FROM clauses are required in all SQL queries. The WHERE is optional (see Section 4.3.3).
4.3 Basic Retrieval Queries in SQL 99
(a) Bdate
1965-01-09 731Fondren, Houston, TX
Address (b) Fname
John
Franklin
Ramesh
Joyce
Smith
Wong
Narayan
English
731 Fondren, Houston, TX
638 Voss, Houston, TX
975 Fire Oak, Humble, TX
5631 Rice, Houston, TX
Lname Address
(d) E.Fname
John
Franklin
Alicia Zelaya
Joyce
Ramesh
Jennifer Wallace
Ahmad Jabbar
Smith
Wong
Narayan
English
Jennifer
James
Jennifer
Franklin
James
Franklin
Franklin
Wallace
Borg
Wallace
Wong
Borg
Wong
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E.Lname S.Fname S.Lname
Fname
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Franklin
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Ramesh
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Narayan
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Smith
Wong
975 Fire Oak, Humble, TX
5631 Rice, Houston, TX
731 Fondren, Houston, TX
638 Voss, Houston, TX
1962-09-15
1972-07-31
1965-09-01
1955-12-08
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Wallace 291Berry, Bellaire, TX
291Berry, Bellaire, TXWallace
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Research
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Administration
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Administration
Administration
Administration
Administration
Administration
Administration
Headquarters
Headquarters
Headquarters
Headquarters
Headquarters
Headquarters
Headquarters
Headquarters
Dname
Figure 4.3 Results of SQL queries when applied to the COMPANY database state shown in Figure 3.6. (a) Q0. (b) Q1. (c) Q2. (d) Q8. (e) Q9. (f) Q10. (g) Q1C.
those tuples for which the condition evaluates to TRUE after substituting their cor- responding attribute values—are selected.
Query 1. Retrieve the name and address of all employees who work for the ‘Research’ department.
Q1: SELECT Fname, Lname, Address FROM EMPLOYEE, DEPARTMENT WHERE Dname=‘Research’ AND Dnumber=Dno;
In the WHERE clause of Q1, the condition Dname = ‘Research’ is a selection condi- tion that chooses the particular tuple of interest in the DEPARTMENT table, because Dname is an attribute of DEPARTMENT. The condition Dnumber = Dno is called a join condition, because it combines two tuples: one from DEPARTMENT and one from EMPLOYEE, whenever the value of Dnumber in DEPARTMENT is equal to the value of Dno in EMPLOYEE. The result of query Q1 is shown in Figure 4.3(b). In general, any number of selection and join conditions may be specified in a single SQL query.
A query that involves only selection and join conditions plus projection attributes is known as a select-project-join query. The next example is a select-project-join query with two join conditions.
Query 2. For every project located in ‘Stafford’, list the project number, the controlling department number, and the department manager’s last name, address, and birth date.
Q2: SELECT Pnumber, Dnum, Lname, Address, Bdate FROM PROJECT, DEPARTMENT, EMPLOYEE WHERE Dnum=Dnumber AND Mgr_ssn=Ssn AND
Plocation=‘Stafford’;
The join condition Dnum = Dnumber relates a project tuple to its controlling depart- ment tuple, whereas the join condition Mgr_ssn = Ssn relates the controlling depart- ment tuple to the employee tuple who manages that department. Each tuple in the result will be a combination of one project, one department, and one employee that satisfies the join conditions. The projection attributes are used to choose the attrib- utes to be displayed from each combined tuple. The result of query Q2 is shown in Figure 4.3(c).
4.3.2 Ambiguous Attribute Names, Aliasing, Renaming, and Tuple Variables
In SQL, the same name can be used for two (or more) attributes as long as the attrib- utes are in different relations. If this is the case, and a multitable query refers to two or more attributes with the same name, we must qualify the attribute name with the relation name to prevent ambiguity. This is done by prefixing the relation name to the attribute name and separating the two by a period. To illustrate this, suppose that in Figures 3.5 and 3.6 the Dno and Lname attributes of the EMPLOYEE relation were
100 Chapter 4 Basic SQL
called Dnumber and Name, and the Dname attribute of DEPARTMENT was also called Name; then, to prevent ambiguity, query Q1 would be rephrased as shown in Q1A. We must prefix the attributes Name and Dnumber in Q1A to specify which ones we are referring to, because the same attribute names are used in both relations:
Q1A: SELECT Fname, EMPLOYEE.Name, Address FROM EMPLOYEE, DEPARTMENT WHERE DEPARTMENT.Name=‘Research’ AND
DEPARTMENT.Dnumber=EMPLOYEE.Dnumber;
Fully qualified attribute names can be used for clarity even if there is no ambiguity in attribute names. Q1 is shown in this manner as is Q1� below. We can also create an alias for each table name to avoid repeated typing of long table names (see Q8 below).
Q1�: SELECT EMPLOYEE.Fname, EMPLOYEE.LName, EMPLOYEE.Address
FROM EMPLOYEE, DEPARTMENT WHERE DEPARTMENT.DName=‘Research’ AND
DEPARTMENT.Dnumber=EMPLOYEE.Dno;
The ambiguity of attribute names also arises in the case of queries that refer to the same relation twice, as in the following example.
Query 8. For each employee, retrieve the employee’s first and last name and the first and last name of his or her immediate supervisor.
Q8: SELECT E.Fname, E.Lname, S.Fname, S.Lname FROM EMPLOYEE AS E, EMPLOYEE AS S WHERE E.Super_ssn=S.Ssn;
In this case, we are required to declare alternative relation names E and S, called aliases or tuple variables, for the EMPLOYEE relation. An alias can follow the key- word AS, as shown in Q8, or it can directly follow the relation name—for example, by writing EMPLOYEE E, EMPLOYEE S in the FROM clause of Q8. It is also possible to rename the relation attributes within the query in SQL by giving them aliases. For example, if we write
EMPLOYEE AS E(Fn, Mi, Ln, Ssn, Bd, Addr, Sex, Sal, Sssn, Dno)
in the FROM clause, Fn becomes an alias for Fname, Mi for Minit, Ln for Lname, and so on.
In Q8, we can think of E and S as two different copies of the EMPLOYEE relation; the first, E, represents employees in the role of supervisees or subordinates; the second, S, represents employees in the role of supervisors. We can now join the two copies. Of course, in reality there is only one EMPLOYEE relation, and the join condition is meant to join the relation with itself by matching the tuples that satisfy the join con- dition E.Super_ssn = S.Ssn. Notice that this is an example of a one-level recursive query, as we will discuss in Section 6.4.2. In earlier versions of SQL, it was not pos- sible to specify a general recursive query, with an unknown number of levels, in a
4.3 Basic Retrieval Queries in SQL 101
102 Chapter 4 Basic SQL
single SQL statement. A construct for specifying recursive queries has been incorpo- rated into SQL:1999 (see Chapter 5).
The result of query Q8 is shown in Figure 4.3(d). Whenever one or more aliases are given to a relation, we can use these names to represent different references to that same relation. This permits multiple references to the same relation within a query.
We can use this alias-naming mechanism in any SQL query to specify tuple vari- ables for every table in the WHERE clause, whether or not the same relation needs to be referenced more than once. In fact, this practice is recommended since it results in queries that are easier to comprehend. For example, we could specify query Q1 as in Q1B:
Q1B: SELECT E.Fname, E.LName, E.Address FROM EMPLOYEE E, DEPARTMENT D WHERE D.DName=‘Research’ AND D.Dnumber=E.Dno;
4.3.3 Unspecified WHERE Clause and Use of the Asterisk
We discuss two more features of SQL here. A missing WHERE clause indicates no condition on tuple selection; hence, all tuples of the relation specified in the FROM clause qualify and are selected for the query result. If more than one relation is spec- ified in the FROM clause and there is no WHERE clause, then the CROSS PRODUCT—all possible tuple combinations—of these relations is selected. For example, Query 9 selects all EMPLOYEE Ssns (Figure 4.3(e)), and Query 10 selects all combinations of an EMPLOYEE Ssn and a DEPARTMENT Dname, regardless of whether the employee works for the department or not (Figure 4.3(f )).
Queries 9 and 10. Select all EMPLOYEE Ssns (Q9) and all combinations of EMPLOYEE Ssn and DEPARTMENT Dname (Q10) in the database.
Q9: SELECT Ssn FROM EMPLOYEE;
Q10: SELECT Ssn, Dname FROM EMPLOYEE, DEPARTMENT;
It is extremely important to specify every selection and join condition in the WHERE clause; if any such condition is overlooked, incorrect and very large rela- tions may result. Notice that Q10 is similar to a CROSS PRODUCT operation fol- lowed by a PROJECT operation in relational algebra (see Chapter 6). If we specify all the attributes of EMPLOYEE and DEPARTMENT in Q10, we get the actual CROSS PRODUCT (except for duplicate elimination, if any).
To retrieve all the attribute values of the selected tuples, we do not have to list the attribute names explicitly in SQL; we just specify an asterisk (*), which stands for all the attributes. For example, query Q1C retrieves all the attribute values of any EMPLOYEE who works in DEPARTMENT number 5 (Figure 4.3(g)), query Q1D retrieves all the attributes of an EMPLOYEE and the attributes of the DEPARTMENT in
4.3 Basic Retrieval Queries in SQL 103
which he or she works for every employee of the ‘Research’ department, and Q10A specifies the CROSS PRODUCT of the EMPLOYEE and DEPARTMENT relations.
Q1C: SELECT * FROM EMPLOYEE WHERE Dno=5;
Q1D: SELECT * FROM EMPLOYEE, DEPARTMENT WHERE Dname=‘Research’ AND Dno=Dnumber;
Q10A: SELECT * FROM EMPLOYEE, DEPARTMENT;
4.3.4 Tables as Sets in SQL As we mentioned earlier, SQL usually treats a table not as a set but rather as a multiset; duplicate tuples can appear more than once in a table, and in the result of a query. SQL does not automatically eliminate duplicate tuples in the results of queries, for the following reasons:
■ Duplicate elimination is an expensive operation. One way to implement it is to sort the tuples first and then eliminate duplicates.
■ The user may want to see duplicate tuples in the result of a query.
■ When an aggregate function (see Section 5.1.7) is applied to tuples, in most cases we do not want to eliminate duplicates.
An SQL table with a key is restricted to being a set, since the key value must be dis- tinct in each tuple.10 If we do want to eliminate duplicate tuples from the result of an SQL query, we use the keyword DISTINCT in the SELECT clause, meaning that only distinct tuples should remain in the result. In general, a query with SELECT DISTINCT eliminates duplicates, whereas a query with SELECT ALL does not. Specifying SELECT with neither ALL nor DISTINCT—as in our previous examples— is equivalent to SELECT ALL. For example, Q11 retrieves the salary of every employee; if several employees have the same salary, that salary value will appear as many times in the result of the query, as shown in Figure 4.4(a). If we are interested only in distinct salary values, we want each value to appear only once, regardless of how many employees earn that salary. By using the keyword DISTINCT as in Q11A, we accomplish this, as shown in Figure 4.4(b).
Query 11. Retrieve the salary of every employee (Q11) and all distinct salary values (Q11A).
Q11: SELECT ALL Salary FROM EMPLOYEE;
Q11A: SELECT DISTINCT Salary FROM EMPLOYEE;
10In general, an SQL table is not required to have a key, although in most cases there will be one.
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(b)Salary
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Fname Lname
(d) Fname Lname
James Borg
Figure 4.4 Results of additional SQL queries when applied to the COM- PANY database state shown in Figure 3.6. (a) Q11. (b) Q11A. (c) Q16. (d) Q18.
SQL has directly incorporated some of the set operations from mathematical set theory, which are also part of relational algebra (see Chapter 6). There are set union (UNION), set difference (EXCEPT),11 and set intersection (INTERSECT) operations. The relations resulting from these set operations are sets of tuples; that is, duplicate tuples are eliminated from the result. These set operations apply only to union-com- patible relations, so we must make sure that the two relations on which we apply the operation have the same attributes and that the attributes appear in the same order in both relations. The next example illustrates the use of UNION.
Query 4. Make a list of all project numbers for projects that involve an employee whose last name is ‘Smith’, either as a worker or as a manager of the department that controls the project.
Q4A: ( SELECT DISTINCT Pnumber FROM PROJECT, DEPARTMENT, EMPLOYEE WHERE Dnum=Dnumber AND Mgr_ssn=Ssn
AND Lname=‘Smith’ ) UNION
( SELECT DISTINCT Pnumber FROM PROJECT, WORKS_ON, EMPLOYEE WHERE Pnumber=Pno AND Essn=Ssn
AND Lname=‘Smith’ );
The first SELECT query retrieves the projects that involve a ‘Smith’ as manager of the department that controls the project, and the second retrieves the projects that involve a ‘Smith’ as a worker on the project. Notice that if several employees have the last name ‘Smith’, the project names involving any of them will be retrieved. Applying the UNION operation to the two SELECT queries gives the desired result.
SQL also has corresponding multiset operations, which are followed by the keyword ALL (UNION ALL, EXCEPT ALL, INTERSECT ALL). Their results are multisets (dupli- cates are not eliminated). The behavior of these operations is illustrated by the examples in Figure 4.5. Basically, each tuple—whether it is a duplicate or not—is considered as a different tuple when applying these operations.
11In some systems, the keyword MINUS is used for the set difference operation instead of EXCEPT.
4.3 Basic Retrieval Queries in SQL 105
T(b)
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a5 Figure 4.5 The results of SQL multiset operations. (a) Two tables, R(A) and S(A). (b) R(A) UNION ALL S(A). (c) R(A) EXCEPT ALL S(A). (d) R(A) INTERSECT ALL S(A).
4.3.5 Substring Pattern Matching and Arithmetic Operators In this section we discuss several more features of SQL. The first feature allows com- parison conditions on only parts of a character string, using the LIKE comparison operator. This can be used for string pattern matching. Partial strings are specified using two reserved characters: % replaces an arbitrary number of zero or more characters, and the underscore (_) replaces a single character. For example, consider the following query.
Query 12. Retrieve all employees whose address is in Houston, Texas.
Q12: SELECT Fname, Lname FROM EMPLOYEE WHERE Address LIKE ‘%Houston,TX%’;
To retrieve all employees who were born during the 1950s, we can use Query Q12A. Here, ‘5’ must be the third character of the string (according to our format for date), so we use the value ‘_ _ 5 _ _ _ _ _ _ _’, with each underscore serving as a placeholder for an arbitrary character.
Query 12A. Find all employees who were born during the 1950s.
Q12: SELECT Fname, Lname FROM EMPLOYEE WHERE Bdate LIKE ‘_ _ 5 _ _ _ _ _ _ _’;
If an underscore or % is needed as a literal character in the string, the character should be preceded by an escape character, which is specified after the string using the keyword ESCAPE. For example, ‘AB\_CD\%EF’ ESCAPE ‘\’ represents the literal string ‘AB_CD%EF’ because \ is specified as the escape character. Any character not used in the string can be chosen as the escape character. Also, we need a rule to specify apostrophes or single quotation marks (‘ ’) if they are to be included in a string because they are used to begin and end strings. If an apostrophe (’) is needed, it is represented as two consecutive apostrophes (”) so that it will not be interpreted as ending the string. Notice that substring comparison implies that attribute values
106 Chapter 4 Basic SQL
are not atomic (indivisible) values, as we had assumed in the formal relational model (see Section 3.1).
Another feature allows the use of arithmetic in queries. The standard arithmetic operators for addition (+), subtraction (–), multiplication (*), and division (/) can be applied to numeric values or attributes with numeric domains. For example, suppose that we want to see the effect of giving all employees who work on the ‘ProductX’ project a 10 percent raise; we can issue Query 13 to see what their salaries would become. This example also shows how we can rename an attribute in the query result using AS in the SELECT clause.
Query 13. Show the resulting salaries if every employee working on the ‘ProductX’ project is given a 10 percent raise.
Q13: SELECT E.Fname, E.Lname, 1.1 * E.Salary AS Increased_sal FROM EMPLOYEE AS E, WORKS_ON AS W, PROJECT AS P WHERE E.Ssn=W.Essn AND W.Pno=P.Pnumber AND
P.Pname=‘ProductX’;
For string data types, the concatenate operator || can be used in a query to append two string values. For date, time, timestamp, and interval data types, operators include incrementing (+) or decrementing (–) a date, time, or timestamp by an interval. In addition, an interval value is the result of the difference between two date, time, or timestamp values. Another comparison operator, which can be used for convenience, is BETWEEN, which is illustrated in Query 14.
Query 14. Retrieve all employees in department 5 whose salary is between $30,000 and $40,000.
Q14: SELECT * FROM EMPLOYEE WHERE (Salary BETWEEN 30000 AND 40000) AND Dno = 5;
The condition (Salary BETWEEN 30000 AND 40000) in Q14 is equivalent to the con- dition ((Salary >= 30000) AND (Salary <= 40000)).
4.3.6 Ordering of Query Results SQL allows the user to order the tuples in the result of a query by the values of one or more of the attributes that appear in the query result, by using the ORDER BY clause. This is illustrated by Query 15.
Query 15. Retrieve a list of employees and the projects they are working on, ordered by department and, within each department, ordered alphabetically by last name, then first name.
Q15: SELECT D.Dname, E.Lname, E.Fname, P.Pname FROM DEPARTMENT D, EMPLOYEE E, WORKS_ON W,
PROJECT P WHERE D.Dnumber= E.Dno AND E.Ssn= W.Essn AND
W.Pno= P.Pnumber ORDER BY D.Dname, E.Lname, E.Fname;
4.4 INSERT, DELETE, and UPDATE Statements in SQL 107
The default order is in ascending order of values. We can specify the keyword DESC if we want to see the result in a descending order of values. The keyword ASC can be used to specify ascending order explicitly. For example, if we want descending alphabetical order on Dname and ascending order on Lname, Fname, the ORDER BY clause of Q15 can be written as
ORDER BY D.Dname DESC, E.Lname ASC, E.Fname ASC
4.3.7 Discussion and Summary of Basic SQL Retrieval Queries
A simple retrieval query in SQL can consist of up to four clauses, but only the first two—SELECT and FROM—are mandatory. The clauses are specified in the follow- ing order, with the clauses between square brackets [ ... ] being optional:
SELECT <attribute list> FROM <table list> [ WHERE <condition> ] [ ORDER BY <attribute list> ];
The SELECT clause lists the attributes to be retrieved, and the FROM clause specifies all relations (tables) needed in the simple query. The WHERE clause identifies the conditions for selecting the tuples from these relations, including join conditions if needed. ORDER BY specifies an order for displaying the results of a query. Two addi- tional clauses GROUP BY and HAVING will be described in Section 5.1.8.
In Chapter 5, we will present more complex features of SQL retrieval queries. These include the following: nested queries that allow one query to be included as part of another query; aggregate functions that are used to provide summaries of the infor- mation in the tables; two additional clauses (GROUP BY and HAVING) that can be used to provide additional power to aggregate functions; and various types of joins that can combine records from various tables in different ways.
4.4 INSERT, DELETE, and UPDATE Statements in SQL
In SQL, three commands can be used to modify the database: INSERT, DELETE, and UPDATE. We discuss each of these in turn.
4.4.1 The INSERT Command In its simplest form, INSERT is used to add a single tuple to a relation. We must spec- ify the relation name and a list of values for the tuple. The values should be listed in the same order in which the corresponding attributes were specified in the CREATE TABLE command. For example, to add a new tuple to the EMPLOYEE relation shown
108 Chapter 4 Basic SQL
in Figure 3.5 and specified in the CREATE TABLE EMPLOYEE ... command in Figure 4.1, we can use U1:
U1: INSERT INTO EMPLOYEE VALUES ( ‘Richard’, ‘K’, ‘Marini’, ‘653298653’, ‘1962-12-30’, ‘98
Oak Forest, Katy, TX’, ‘M’, 37000, ‘653298653’, 4 );
A second form of the INSERT statement allows the user to specify explicit attribute names that correspond to the values provided in the INSERT command. This is use- ful if a relation has many attributes but only a few of those attributes are assigned values in the new tuple. However, the values must include all attributes with NOT NULL specification and no default value. Attributes with NULL allowed or DEFAULT values are the ones that can be left out. For example, to enter a tuple for a new EMPLOYEE for whom we know only the Fname, Lname, Dno, and Ssn attributes, we can use U1A:
U1A: INSERT INTO EMPLOYEE (Fname, Lname, Dno, Ssn) VALUES (‘Richard’, ‘Marini’, 4, ‘653298653’);
Attributes not specified in U1A are set to their DEFAULT or to NULL, and the values are listed in the same order as the attributes are listed in the INSERT command itself. It is also possible to insert into a relation multiple tuples separated by commas in a single INSERT command. The attribute values forming each tuple are enclosed in parentheses.
A DBMS that fully implements SQL should support and enforce all the integrity constraints that can be specified in the DDL. For example, if we issue the command in U2 on the database shown in Figure 3.6, the DBMS should reject the operation because no DEPARTMENT tuple exists in the database with Dnumber = 2. Similarly, U2A would be rejected because no Ssn value is provided and it is the primary key, which cannot be NULL.
U3: INSERT INTO EMPLOYEE (Fname, Lname, Ssn, Dno) VALUES (‘Robert’, ‘Hatcher’, ‘980760540’, 2); (U2 is rejected if referential integrity checking is provided by DBMS.)
U2A: INSERT INTO EMPLOYEE (Fname, Lname, Dno) VALUES (‘Robert’, ‘Hatcher’, 5); (U2A is rejected if NOT NULL checking is provided by DBMS.)
A variation of the INSERT command inserts multiple tuples into a relation in con- junction with creating the relation and loading it with the result of a query. For example, to create a temporary table that has the employee last name, project name, and hours per week for each employee working on a project, we can write the state- ments in U3A and U3B:
U3A: CREATE TABLE WORKS_ON_INFO ( Emp_name VARCHAR(15),
Proj_name VARCHAR(15), Hours_per_week DECIMAL(3,1) );
4.4 INSERT, DELETE, and UPDATE Statements in SQL 109
U3B: INSERT INTO WORKS_ON_INFO ( Emp_name, Proj_name, Hours_per_week )
SELECT E.Lname, P.Pname, W.Hours FROM PROJECT P, WORKS_ON W, EMPLOYEE E WHERE P.Pnumber=W.Pno AND W.Essn=E.Ssn;
A table WORKS_ON_INFO is created by U3A and is loaded with the joined informa- tion retrieved from the database by the query in U3B. We can now query WORKS_ON_INFO as we would any other relation; when we do not need it any more, we can remove it by using the DROP TABLE command (see Chapter 5). Notice that the WORKS_ON_INFO table may not be up-to-date; that is, if we update any of the PROJECT, WORKS_ON, or EMPLOYEE relations after issuing U3B, the informa- tion in WORKS_ON_INFO may become outdated. We have to create a view (see Chapter 5) to keep such a table up-to-date.
4.4.2 The DELETE Command The DELETE command removes tuples from a relation. It includes a WHERE clause, similar to that used in an SQL query, to select the tuples to be deleted. Tuples are explicitly deleted from only one table at a time. However, the deletion may propa- gate to tuples in other relations if referential triggered actions are specified in the ref- erential integrity constraints of the DDL (see Section 4.2.2).12 Depending on the number of tuples selected by the condition in the WHERE clause, zero, one, or sev- eral tuples can be deleted by a single DELETE command. A missing WHERE clause specifies that all tuples in the relation are to be deleted; however, the table remains in the database as an empty table. We must use the DROP TABLE command to remove the table definition (see Chapter 5). The DELETE commands in U4A to U4D, if applied independently to the database in Figure 3.6, will delete zero, one, four, and all tuples, respectively, from the EMPLOYEE relation:
U4A: DELETE FROM EMPLOYEE WHERE Lname=‘Brown’;
U4B: DELETE FROM EMPLOYEE WHERE Ssn=‘123456789’;
U4C: DELETE FROM EMPLOYEE WHERE Dno=5;
U4D: DELETE FROM EMPLOYEE;
4.4.3 The UPDATE Command The UPDATE command is used to modify attribute values of one or more selected tuples. As in the DELETE command, a WHERE clause in the UPDATE command selects the tuples to be modified from a single relation. However, updating a
12Other actions can be automatically applied through triggers (see Section 26.1) and other mechanisms.
110 Chapter 4 Basic SQL
primary key value may propagate to the foreign key values of tuples in other rela- tions if such a referential triggered action is specified in the referential integrity con- straints of the DDL (see Section 4.2.2). An additional SET clause in the UPDATE command specifies the attributes to be modified and their new values. For example, to change the location and controlling department number of project number 10 to ‘Bellaire’ and 5, respectively, we use U5:
U5: UPDATE PROJECT SET Plocation = ‘Bellaire’, Dnum = 5 WHERE Pnumber=10;
Several tuples can be modified with a single UPDATE command. An example is to give all employees in the ‘Research’ department a 10 percent raise in salary, as shown in U6. In this request, the modified Salary value depends on the original Salary value in each tuple, so two references to the Salary attribute are needed. In the SET clause, the reference to the Salary attribute on the right refers to the old Salary value before modification, and the one on the left refers to the new Salary value after modification:
U6: UPDATE EMPLOYEE SET Salary = Salary * 1.1 WHERE Dno = 5;
It is also possible to specify NULL or DEFAULT as the new attribute value. Notice that each UPDATE command explicitly refers to a single relation only. To modify multiple relations, we must issue several UPDATE commands.
4.5 Additional Features of SQL SQL has a number of additional features that we have not described in this chapter but that we discuss elsewhere in the book. These are as follows:
■ In Chapter 5, which is a continuation of this chapter, we will present the fol- lowing SQL features: various techniques for specifying complex retrieval queries, including nested queries, aggregate functions, grouping, joined tables, outer joins, and recursive queries; SQL views, triggers, and assertions; and commands for schema modification.
■ SQL has various techniques for writing programs in various programming languages that include SQL statements to access one or more databases. These include embedded (and dynamic) SQL, SQL/CLI (Call Level Interface) and its predecessor ODBC (Open Data Base Connectivity), and SQL/PSM (Persistent Stored Modules). We discuss these techniques in Chapter 13. We also discuss how to access SQL databases through the Java programming language using JDBC and SQLJ.
■ Each commercial RDBMS will have, in addition to the SQL commands, a set of commands for specifying physical database design parameters, file struc- tures for relations, and access paths such as indexes. We called these com- mands a storage definition language (SDL) in Chapter 2. Earlier versions of SQL had commands for creating indexes, but these were removed from the
4.6 Summary 111
language because they were not at the conceptual schema level. Many sys- tems still have the CREATE INDEX commands.
■ SQL has transaction control commands. These are used to specify units of database processing for concurrency control and recovery purposes. We dis- cuss these commands in Chapter 21 after we discuss the concept of transac- tions in more detail.
■ SQL has language constructs for specifying the granting and revoking of priv- ileges to users. Privileges typically correspond to the right to use certain SQL commands to access certain relations. Each relation is assigned an owner, and either the owner or the DBA staff can grant to selected users the privi- lege to use an SQL statement—such as SELECT, INSERT, DELETE, or UPDATE—to access the relation. In addition, the DBA staff can grant the privileges to create schemas, tables, or views to certain users. These SQL commands—called GRANT and REVOKE—are discussed in Chapter 24, where we discuss database security and authorization.
■ SQL has language constructs for creating triggers. These are generally referred to as active database techniques, since they specify actions that are automatically triggered by events such as database updates. We discuss these features in Section 26.1, where we discuss active database concepts.
■ SQL has incorporated many features from object-oriented models to have more powerful capabilities, leading to enhanced relational systems known as object-relational. Capabilities such as creating complex-structured attrib- utes (also called nested relations), specifying abstract data types (called UDTs or user-defined types) for attributes and tables, creating object iden- tifiers for referencing tuples, and specifying operations on types are dis- cussed in Chapter 11.
■ SQL and relational databases can interact with new technologies such as XML (see Chapter 12) and OLAP (Chapter 29).
4.6 Summary In this chapter we presented the SQL database language. This language and its vari- ations have been implemented as interfaces to many commercial relational DBMSs, including Oracle’s Oracle and Rdb13; IBM’s DB2, Informix Dynamic Server, and SQL/DS; Microsoft’s SQL Server and Access; and INGRES. Some open source sys- tems also provide SQL, such as MySQL and PostgreSQL. The original version of SQL was implemented in the experimental DBMS called SYSTEM R, which was developed at IBM Research. SQL is designed to be a comprehensive language that includes statements for data definition, queries, updates, constraint specification, and view definition. We discussed the following features of SQL in this chapter: the data definition commands for creating tables, commands for constraint specifica- tion, simple retrieval queries, and database update commands. In the next chapter,
13Rdb was originally produced by Digital Equipment Corporation. It was acquired by Oracle from Digital in 1994 and is being supported and enhanced.
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we will present the following features of SQL: complex retrieval queries; views; trig- gers and assertions; and schema modification commands.
Review Questions 4.1. How do the relations (tables) in SQL differ from the relations defined for-
mally in Chapter 3? Discuss the other differences in terminology. Why does SQL allow duplicate tuples in a table or in a query result?
4.2. List the data types that are allowed for SQL attributes.
4.3. How does SQL allow implementation of the entity integrity and referential integrity constraints described in Chapter 3? What about referential trig- gered actions?
4.4. Describe the four clauses in the syntax of a simple SQL retrieval query. Show what type of constructs can be specified in each of the clauses. Which are required and which are optional?
Exercises 4.5. Consider the database shown in Figure 1.2, whose schema is shown in Figure
2.1. What are the referential integrity constraints that should hold on the schema? Write appropriate SQL DDL statements to define the database.
4.6. Repeat Exercise 4.5, but use the AIRLINE database schema of Figure 3.8.
4.7. Consider the LIBRARY relational database schema shown in Figure 4.6. Choose the appropriate action (reject, cascade, set to NULL, set to default) for each referential integrity constraint, both for the deletion of a referenced tuple and for the update of a primary key attribute value in a referenced tuple. Justify your choices.
4.8. Write appropriate SQL DDL statements for declaring the LIBRARY relational database schema of Figure 4.6. Specify the keys and referential triggered actions.
4.9. How can the key and foreign key constraints be enforced by the DBMS? Is the enforcement technique you suggest difficult to implement? Can the con- straint checks be executed efficiently when updates are applied to the data- base?
4.10. Specify the following queries in SQL on the COMPANY relational database schema shown in Figure 3.5. Show the result of each query if it is applied to the COMPANY database in Figure 3.6.
a. Retrieve the names of all employees in department 5 who work more than 10 hours per week on the ProductX project.
b. List the names of all employees who have a dependent with the same first name as themselves.
Exercises 113
Publisher_nameBook_id Title
BOOK
BOOK_COPIES Book_id Branch_id No_of_copies
BOOK_AUTHORS
Book_id Author_name
LIBRARY_BRANCH Branch_id Branch_name Address
PUBLISHER
Name Address Phone
BOOK_LOANS
Book_id Branch_id Card_no Date_out Due_date
BORROWER Card_no Name Address Phone
Figure 4.6 A relational database schema for a LIBRARY database.
c. Find the names of all employees who are directly supervised by ‘Franklin Wong’.
4.11. Specify the updates of Exercise 3.11 using the SQL update commands.
4.12. Specify the following queries in SQL on the database schema of Figure 1.2.
a. Retrieve the names of all senior students majoring in ‘CS’ (computer sci- ence).
b. Retrieve the names of all courses taught by Professor King in 2007 and 2008.
c. For each section taught by Professor King, retrieve the course number, semester, year, and number of students who took the section.
d. Retrieve the name and transcript of each senior student (Class = 4) majoring in CS. A transcript includes course name, course number, credit hours, semester, year, and grade for each course completed by the student.
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4.13. Write SQL update statements to do the following on the database schema shown in Figure 1.2.
a. Insert a new student, <‘Johnson’, 25, 1, ‘Math’>, in the database.
b. Change the class of student ‘Smith’ to 2.
c. Insert a new course, <‘Knowledge Engineering’, ‘CS4390’, 3, ‘CS’>.
d. Delete the record for the student whose name is ‘Smith’ and whose stu- dent number is 17.
4.14. Design a relational database schema for a database application of your choice.
a. Declare your relations, using the SQL DDL.
b. Specify a number of queries in SQL that are needed by your database application.
c. Based on your expected use of the database, choose some attributes that should have indexes specified on them.
d. Implement your database, if you have a DBMS that supports SQL.
4.15. Consider the EMPLOYEE table’s constraint EMPSUPERFK as specified in Figure 4.2 is changed to read as follows:
CONSTRAINT EMPSUPERFK FOREIGN KEY (Super_ssn) REFERENCES EMPLOYEE(Ssn)
ON DELETE CASCADE ON UPDATE CASCADE,
Answer the following questions:
a. What happens when the following command is run on the database state shown in Figure 3.6?
DELETE EMPLOYEE WHERE Lname = ‘Borg’
b. Is it better to CASCADE or SET NULL in case of EMPSUPERFK constraint ON DELETE?
4.16. Write SQL statements to create a table EMPLOYEE_BACKUP to back up the EMPLOYEE table shown in Figure 3.6.
Selected Bibliography The SQL language, originally named SEQUEL, was based on the language SQUARE (Specifying Queries as Relational Expressions), described by Boyce et al. (1975). The syntax of SQUARE was modified into SEQUEL (Chamberlin and Boyce, 1974) and then into SEQUEL 2 (Chamberlin et al. 1976), on which SQL is based. The original implementation of SEQUEL was done at IBM Research, San Jose, California. We will give additional references to various aspects of SQL at the end of Chapter 5.
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Schema Modification
This chapter describes more advanced features of theSQL language standard for relational databases. We start in Section 5.1 by presenting more complex features of SQL retrieval queries, such as nested queries, joined tables, outer joins, aggregate functions, and grouping. In Section 5.2, we describe the CREATE ASSERTION statement, which allows the specification of more general constraints on the database. We also introduce the concept of triggers and the CREATE TRIGGER statement, which will be presented in more detail in Section 26.1 when we present the principles of active databases. Then, in Section 5.3, we describe the SQL facility for defining views on the database. Views are also called virtual or derived tables because they present the user with what appear to be tables; however, the information in those tables is derived from previously defined tables. Section 5.4 introduces the SQL ALTER TABLE statement, which is used for modifying the database tables and constraints. Section 5.5 is the chapter summary.
This chapter is a continuation of Chapter 4. The instructor may skip parts of this chapter if a less detailed introduction to SQL is intended.
5.1 More Complex SQL Retrieval Queries In Section 4.3, we described some basic types of retrieval queries in SQL. Because of the generality and expressive power of the language, there are many additional fea- tures that allow users to specify more complex retrievals from the database. We dis- cuss several of these features in this section.
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5.1.1 Comparisons Involving NULL and Three-Valued Logic
SQL has various rules for dealing with NULL values. Recall from Section 3.1.2 that NULL is used to represent a missing value, but that it usually has one of three different interpretations—value unknown (exists but is not known), value not available (exists but is purposely withheld), or value not applicable (the attribute is undefined for this tuple). Consider the following examples to illustrate each of the meanings of NULL.
1. Unknown value. A person’s date of birth is not known, so it is represented by NULL in the database.
2. Unavailable or withheld value. A person has a home phone but does not want it to be listed, so it is withheld and represented as NULL in the database.
3. Not applicable attribute. An attribute LastCollegeDegree would be NULL for a person who has no college degrees because it does not apply to that person.
It is often not possible to determine which of the meanings is intended; for example, a NULL for the home phone of a person can have any of the three meanings. Hence, SQL does not distinguish between the different meanings of NULL.
In general, each individual NULL value is considered to be different from every other NULL value in the various database records. When a NULL is involved in a compari- son operation, the result is considered to be UNKNOWN (it may be TRUE or it may be FALSE). Hence, SQL uses a three-valued logic with values TRUE, FALSE, and UNKNOWN instead of the standard two-valued (Boolean) logic with values TRUE or FALSE. It is therefore necessary to define the results (or truth values) of three-valued logical expressions when the logical connectives AND, OR, and NOT are used. Table 5.1 shows the resulting values.
Table 5.1 Logical Connectives in Three-Valued Logic
(a) AND TRUE FALSE UNKNOWN
TRUE TRUE FALSE UNKNOWN
FALSE FALSE FALSE FALSE
UNKNOWN UNKNOWN FALSE UNKNOWN
(b) OR TRUE FALSE UNKNOWN
TRUE TRUE TRUE TRUE
FALSE TRUE FALSE UNKNOWN
UNKNOWN TRUE UNKNOWN UNKNOWN
(c) NOT
TRUE FALSE
FALSE TRUE
UNKNOWN UNKNOWN
5.1 More Complex SQL Retrieval Queries 117
In Tables 5.1(a) and 5.1(b), the rows and columns represent the values of the results of comparison conditions, which would typically appear in the WHERE clause of an SQL query. Each expression result would have a value of TRUE, FALSE, or UNKNOWN. The result of combining the two values using the AND logical connec- tive is shown by the entries in Table 5.1(a). Table 5.1(b) shows the result of using the OR logical connective. For example, the result of (FALSE AND UNKNOWN) is FALSE, whereas the result of (FALSE OR UNKNOWN) is UNKNOWN. Table 5.1(c) shows the result of the NOT logical operation. Notice that in standard Boolean logic, only TRUE or FALSE values are permitted; there is no UNKNOWN value.
In select-project-join queries, the general rule is that only those combinations of tuples that evaluate the logical expression in the WHERE clause of the query to TRUE are selected. Tuple combinations that evaluate to FALSE or UNKNOWN are not selected. However, there are exceptions to that rule for certain operations, such as outer joins, as we shall see in Section 5.1.6.
SQL allows queries that check whether an attribute value is NULL. Rather than using = or <> to compare an attribute value to NULL, SQL uses the comparison operators IS or IS NOT. This is because SQL considers each NULL value as being distinct from every other NULL value, so equality comparison is not appropriate. It follows that when a join condition is specified, tuples with NULL values for the join attributes are not included in the result (unless it is an OUTER JOIN; see Section 5.1.6). Query 18 illustrates this.
Query 18. Retrieve the names of all employees who do not have supervisors.
Q18: SELECT Fname, Lname FROM EMPLOYEE WHERE Super_ssn IS NULL;
5.1.2 Nested Queries, Tuples, and Set/Multiset Comparisons
Some queries require that existing values in the database be fetched and then used in a comparison condition. Such queries can be conveniently formulated by using nested queries, which are complete select-from-where blocks within the WHERE clause of another query. That other query is called the outer query. Query 4 is for- mulated in Q4 without a nested query, but it can be rephrased to use nested queries as shown in Q4A. Q4A introduces the comparison operator IN, which compares a value v with a set (or multiset) of values V and evaluates to TRUE if v is one of the elements in V.
The first nested query selects the project numbers of projects that have an employee with last name ‘Smith’ involved as manager, while the second nested query selects the project numbers of projects that have an employee with last name ‘Smith’ involved as worker. In the outer query, we use the OR logical connective to retrieve a PROJECT tuple if the PNUMBER value of that tuple is in the result of either nested query.
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Q4A: SELECT DISTINCT Pnumber FROM PROJECT WHERE Pnumber IN
( SELECT Pnumber FROM PROJECT, DEPARTMENT, EMPLOYEE WHERE Dnum=Dnumber AND
Mgr_ssn=Ssn AND Lname=‘Smith’ ) OR Pnumber IN ( SELECT Pno
FROM WORKS_ON, EMPLOYEE WHERE Essn=Ssn AND Lname=‘Smith’ );
If a nested query returns a single attribute and a single tuple, the query result will be a single (scalar) value. In such cases, it is permissible to use = instead of IN for the comparison operator. In general, the nested query will return a table (relation), which is a set or multiset of tuples.
SQL allows the use of tuples of values in comparisons by placing them within parentheses. To illustrate this, consider the following query:
SELECT DISTINCT Essn FROM WORKS_ON WHERE (Pno, Hours) IN ( SELECT Pno, Hours
FROM WORKS_ON WHERE Essn=‘123456789’ );
This query will select the Essns of all employees who work the same (project, hours) combination on some project that employee ‘John Smith’ (whose Ssn = ‘123456789’) works on. In this example, the IN operator compares the subtuple of values in parentheses (Pno, Hours) within each tuple in WORKS_ON with the set of type-compatible tuples produced by the nested query.
In addition to the IN operator, a number of other comparison operators can be used to compare a single value v (typically an attribute name) to a set or multiset v (typ- ically a nested query). The = ANY (or = SOME) operator returns TRUE if the value v is equal to some value in the set V and is hence equivalent to IN. The two keywords ANY and SOME have the same effect. Other operators that can be combined with ANY (or SOME) include >, >=, <, <=, and <>. The keyword ALL can also be com- bined with each of these operators. For example, the comparison condition (v > ALL V ) returns TRUE if the value v is greater than all the values in the set (or multiset) V. An example is the following query, which returns the names of employees whose salary is greater than the salary of all the employees in department 5:
SELECT Lname, Fname FROM EMPLOYEE WHERE Salary > ALL ( SELECT Salary
FROM EMPLOYEE WHERE Dno=5 );
5.1 More Complex SQL Retrieval Queries 119
Notice that this query can also be specified using the MAX aggregate function (see Section 5.1.7).
In general, we can have several levels of nested queries. We can once again be faced with possible ambiguity among attribute names if attributes of the same name exist—one in a relation in the FROM clause of the outer query, and another in a rela- tion in the FROM clause of the nested query. The rule is that a reference to an unqualified attribute refers to the relation declared in the innermost nested query. For example, in the SELECT clause and WHERE clause of the first nested query of Q4A, a reference to any unqualified attribute of the PROJECT relation refers to the PROJECT relation specified in the FROM clause of the nested query. To refer to an attribute of the PROJECT relation specified in the outer query, we specify and refer to an alias (tuple variable) for that relation. These rules are similar to scope rules for program variables in most programming languages that allow nested procedures and functions. To illustrate the potential ambiguity of attribute names in nested queries, consider Query 16.
Query 16. Retrieve the name of each employee who has a dependent with the same first name and is the same sex as the employee.
Q16: SELECT E.Fname, E.Lname FROM EMPLOYEE AS E WHERE E.Ssn IN ( SELECT Essn
FROM DEPENDENT AS D WHERE E.Fname=D.Dependent_name
AND E.Sex=D.Sex );
In the nested query of Q16, we must qualify E.Sex because it refers to the Sex attrib- ute of EMPLOYEE from the outer query, and DEPENDENT also has an attribute called Sex. If there were any unqualified references to Sex in the nested query, they would refer to the Sex attribute of DEPENDENT. However, we would not have to qualify the attributes Fname and Ssn of EMPLOYEE if they appeared in the nested query because the DEPENDENT relation does not have attributes called Fname and Ssn, so there is no ambiguity.
It is generally advisable to create tuple variables (aliases) for all the tables referenced in an SQL query to avoid potential errors and ambiguities, as illustrated in Q16.
5.1.3 Correlated Nested Queries Whenever a condition in the WHERE clause of a nested query references some attrib- ute of a relation declared in the outer query, the two queries are said to be correlated. We can understand a correlated query better by considering that the nested query is evaluated once for each tuple (or combination of tuples) in the outer query. For exam- ple, we can think of Q16 as follows: For each EMPLOYEE tuple, evaluate the nested query, which retrieves the Essn values for all DEPENDENT tuples with the same sex and name as that EMPLOYEE tuple; if the Ssn value of the EMPLOYEE tuple is in the result of the nested query, then select that EMPLOYEE tuple.
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In general, a query written with nested select-from-where blocks and using the = or IN comparison operators can always be expressed as a single block query. For exam- ple, Q16 may be written as in Q16A:
Q16A: SELECT E.Fname, E.Lname FROM EMPLOYEE AS E, DEPENDENT AS D WHERE E.Ssn=D.Essn AND E.Sex=D.Sex
AND E.Fname=D.Dependent_name;
5.1.4 The EXISTS and UNIQUE Functions in SQL The EXISTS function in SQL is used to check whether the result of a correlated nested query is empty (contains no tuples) or not. The result of EXISTS is a Boolean value TRUE if the nested query result contains at least one tuple, or FALSE if the nested query result contains no tuples. We illustrate the use of EXISTS—and NOT EXISTS—with some examples. First, we formulate Query 16 in an alternative form that uses EXISTS as in Q16B:
Q16B: SELECT E.Fname, E.Lname FROM EMPLOYEE AS E WHERE EXISTS ( SELECT *
FROM DEPENDENT AS D WHERE E.Ssn=D.Essn AND E.Sex=D.Sex
AND E.Fname=D.Dependent_name);
EXISTS and NOT EXISTS are typically used in conjunction with a correlated nested query. In Q16B, the nested query references the Ssn, Fname, and Sex attributes of the EMPLOYEE relation from the outer query. We can think of Q16B as follows: For each EMPLOYEE tuple, evaluate the nested query, which retrieves all DEPENDENT tuples with the same Essn, Sex, and Dependent_name as the EMPLOYEE tuple; if at least one tuple EXISTS in the result of the nested query, then select that EMPLOYEE tuple. In general, EXISTS(Q) returns TRUE if there is at least one tuple in the result of the nested query Q, and it returns FALSE otherwise. On the other hand, NOT EXISTS(Q) returns TRUE if there are no tuples in the result of nested query Q, and it returns FALSE otherwise. Next, we illustrate the use of NOT EXISTS.
Query 6. Retrieve the names of employees who have no dependents.
Q6: SELECT Fname, Lname FROM EMPLOYEE WHERE NOT EXISTS ( SELECT *
FROM DEPENDENT WHERE Ssn=Essn );
In Q6, the correlated nested query retrieves all DEPENDENT tuples related to a par- ticular EMPLOYEE tuple. If none exist, the EMPLOYEE tuple is selected because the WHERE-clause condition will evaluate to TRUE in this case. We can explain Q6 as follows: For each EMPLOYEE tuple, the correlated nested query selects all DEPENDENT tuples whose Essn value matches the EMPLOYEE Ssn; if the result is
5.1 More Complex SQL Retrieval Queries 121
empty, no dependents are related to the employee, so we select that EMPLOYEE tuple and retrieve its Fname and Lname.
Query 7. List the names of managers who have at least one dependent.
Q7: SELECT Fname, Lname FROM EMPLOYEE WHERE EXISTS ( SELECT *
FROM DEPENDENT WHERE Ssn=Essn )
AND EXISTS ( SELECT *
FROM DEPARTMENT WHERE Ssn=Mgr_ssn );
One way to write this query is shown in Q7, where we specify two nested correlated queries; the first selects all DEPENDENT tuples related to an EMPLOYEE, and the sec- ond selects all DEPARTMENT tuples managed by the EMPLOYEE. If at least one of the first and at least one of the second exists, we select the EMPLOYEE tuple. Can you rewrite this query using only a single nested query or no nested queries?
The query Q3: Retrieve the name of each employee who works on all the projects con- trolled by department number 5 can be written using EXISTS and NOT EXISTS in SQL systems. We show two ways of specifying this query Q3 in SQL as Q3A and Q3B. This is an example of certain types of queries that require universal quantification, as we will discuss in Section 6.6.7. One way to write this query is to use the construct (S2 EXCEPT S1) as explained next, and checking whether the result is empty.1 This option is shown as Q3A.
Q3A: SELECT Fname, Lname FROM EMPLOYEE WHERE NOT EXISTS ( ( SELECT Pnumber
FROM PROJECT WHERE Dnum=5) EXCEPT ( SELECT Pno
FROM WORKS_ON WHERE Ssn=Essn) );
In Q3A, the first subquery (which is not correlated with the outer query) selects all projects controlled by department 5, and the second subquery (which is correlated) selects all projects that the particular employee being considered works on. If the set difference of the first subquery result MINUS (EXCEPT) the second subquery result is empty, it means that the employee works on all the projects and is therefore selected.
The second option is shown as Q3B. Notice that we need two-level nesting in Q3B and that this formulation is quite a bit more complex than Q3A, which uses NOT EXISTS and EXCEPT.
1Recall that EXCEPT is the set difference operator. The keyword MINUS is also sometimes used, for example, in Oracle.
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Q3B: SELECT Lname, Fname FROM EMPLOYEE WHERE NOT EXISTS ( SELECT *
FROM WORKS_ON B WHERE ( B.Pno IN ( SELECT Pnumber
FROM PROJECT WHERE Dnum=5 )
AND NOT EXISTS ( SELECT *
FROM WORKS_ON C WHERE C.Essn=Ssn AND C.Pno=B.Pno )));
In Q3B, the outer nested query selects any WORKS_ON (B) tuples whose Pno is of a project controlled by department 5, if there is not a WORKS_ON (C) tuple with the same Pno and the same Ssn as that of the EMPLOYEE tuple under consideration in the outer query. If no such tuple exists, we select the EMPLOYEE tuple. The form of Q3B matches the following rephrasing of Query 3: Select each employee such that there does not exist a project controlled by department 5 that the employee does not work on. It corresponds to the way we will write this query in tuple relation calculus (see Section 6.6.7).
There is another SQL function, UNIQUE(Q), which returns TRUE if there are no duplicate tuples in the result of query Q; otherwise, it returns FALSE. This can be used to test whether the result of a nested query is a set or a multiset.
5.1.5 Explicit Sets and Renaming of Attributes in SQL We have seen several queries with a nested query in the WHERE clause. It is also pos- sible to use an explicit set of values in the WHERE clause, rather than a nested query. Such a set is enclosed in parentheses in SQL.
Query 17. Retrieve the Social Security numbers of all employees who work on project numbers 1, 2, or 3.
Q17: SELECT DISTINCT Essn FROM WORKS_ON WHERE Pno IN (1, 2, 3);
In SQL, it is possible to rename any attribute that appears in the result of a query by adding the qualifier AS followed by the desired new name. Hence, the AS construct can be used to alias both attribute and relation names, and it can be used in both the SELECT and FROM clauses. For example, Q8A shows how query Q8 from Section 4.3.2 can be slightly changed to retrieve the last name of each employee and his or her supervisor, while renaming the resulting attribute names as Employee_name and Supervisor_name. The new names will appear as column headers in the query result.
Q8A: SELECT E.Lname AS Employee_name, S.Lname AS Supervisor_name FROM EMPLOYEE AS E, EMPLOYEE AS S WHERE E.Super_ssn=S.Ssn;
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5.1.6 Joined Tables in SQL and Outer Joins The concept of a joined table (or joined relation) was incorporated into SQL to permit users to specify a table resulting from a join operation in the FROM clause of a query. This construct may be easier to comprehend than mixing together all the select and join conditions in the WHERE clause. For example, consider query Q1, which retrieves the name and address of every employee who works for the ‘Research’ department. It may be easier to specify the join of the EMPLOYEE and DEPARTMENT relations first, and then to select the desired tuples and attributes. This can be written in SQL as in Q1A:
Q1A: SELECT Fname, Lname, Address FROM (EMPLOYEE JOIN DEPARTMENT ON Dno=Dnumber) WHERE Dname=‘Research’;
The FROM clause in Q1A contains a single joined table. The attributes of such a table are all the attributes of the first table, EMPLOYEE, followed by all the attributes of the second table, DEPARTMENT. The concept of a joined table also allows the user to specify different types of join, such as NATURAL JOIN and various types of OUTER JOIN. In a NATURAL JOIN on two relations R and S, no join condition is specified; an implicit EQUIJOIN condition for each pair of attributes with the same name from R and S is created. Each such pair of attributes is included only once in the resulting relation (see Section 6.3.2 and 6.4.4 for more details on the various types of join operations in relational algebra).
If the names of the join attributes are not the same in the base relations, it is possi- ble to rename the attributes so that they match, and then to apply NATURAL JOIN. In this case, the AS construct can be used to rename a relation and all its attributes in the FROM clause. This is illustrated in Q1B, where the DEPARTMENT relation is renamed as DEPT and its attributes are renamed as Dname, Dno (to match the name of the desired join attribute Dno in the EMPLOYEE table), Mssn, and Msdate. The implied join condition for this NATURAL JOIN is EMPLOYEE.Dno=DEPT.Dno, because this is the only pair of attributes with the same name after renaming:
Q1B: SELECT Fname, Lname, Address FROM (EMPLOYEE NATURAL JOIN
(DEPARTMENT AS DEPT (Dname, Dno, Mssn, Msdate))) WHERE Dname=‘Research’;
The default type of join in a joined table is called an inner join, where a tuple is included in the result only if a matching tuple exists in the other relation. For exam- ple, in query Q8A, only employees who have a supervisor are included in the result; an EMPLOYEE tuple whose value for Super_ssn is NULL is excluded. If the user requires that all employees be included, an OUTER JOIN must be used explicitly (see Section 6.4.4 for the definition of OUTER JOIN). In SQL, this is handled by explicitly specifying the keyword OUTER JOIN in a joined table, as illustrated in Q8B:
Q8B: SELECT E.Lname AS Employee_name, S.Lname AS Supervisor_name
FROM (EMPLOYEE AS E LEFT OUTER JOIN EMPLOYEE AS S ON E.Super_ssn=S.Ssn);
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There are a variety of outer join operations, which we shall discuss in more detail in Section 6.4.4. In SQL, the options available for specifying joined tables include INNER JOIN (only pairs of tuples that match the join condition are retrieved, same as JOIN), LEFT OUTER JOIN (every tuple in the left table must appear in the result; if it does not have a matching tuple, it is padded with NULL values for the attributes of the right table), RIGHT OUTER JOIN (every tuple in the right table must appear in the result; if it does not have a matching tuple, it is padded with NULL values for the attributes of the left table), and FULL OUTER JOIN. In the latter three options, the keyword OUTER may be omitted. If the join attributes have the same name, one can also specify the natural join variation of outer joins by using the keyword NATURAL before the operation (for example, NATURAL LEFT OUTER JOIN). The keyword CROSS JOIN is used to specify the CARTESIAN PRODUCT operation (see Section 6.2.2), although this should be used only with the utmost care because it generates all possible tuple combinations.
It is also possible to nest join specifications; that is, one of the tables in a join may itself be a joined table. This allows the specification of the join of three or more tables as a single joined table, which is called a multiway join. For example, Q2A is a different way of specifying query Q2 from Section 4.3.1 using the concept of a joined table:
Q2A: SELECT Pnumber, Dnum, Lname, Address, Bdate FROM ((PROJECT JOIN DEPARTMENT ON Dnum=Dnumber)
JOIN EMPLOYEE ON Mgr_ssn=Ssn) WHERE Plocation=‘Stafford’;
Not all SQL implementations have implemented the new syntax of joined tables. In some systems, a different syntax was used to specify outer joins by using the com- parison operators +=, =+, and +=+ for left, right, and full outer join, respectively, when specifying the join condition. For example, this syntax is available in Oracle. To specify the left outer join in Q8B using this syntax, we could write the query Q8C as follows:
Q8C: SELECT E.Lname, S.Lname FROM EMPLOYEE E, EMPLOYEE S WHERE E.Super_ssn += S.Ssn;
5.1.7 Aggregate Functions in SQL In Section 6.4.2, we will introduce the concept of an aggregate function as a rela- tional algebra operation. Aggregate functions are used to summarize information from multiple tuples into a single-tuple summary. Grouping is used to create sub- groups of tuples before summarization. Grouping and aggregation are required in many database applications, and we will introduce their use in SQL through exam- ples. A number of built-in aggregate functions exist: COUNT, SUM, MAX, MIN, and AVG.2 The COUNT function returns the number of tuples or values as specified in a
2Additional aggregate functions for more advanced statistical calculation were added in SQL-99.
5.1 More Complex SQL Retrieval Queries 125
query. The functions SUM, MAX, MIN, and AVG can be applied to a set or multiset of numeric values and return, respectively, the sum, maximum value, minimum value, and average (mean) of those values. These functions can be used in the SELECT clause or in a HAVING clause (which we introduce later). The functions MAX and MIN can also be used with attributes that have nonnumeric domains if the domain values have a total ordering among one another.3 We illustrate the use of these func- tions with sample queries.
Query 19. Find the sum of the salaries of all employees, the maximum salary, the minimum salary, and the average salary.
Q19: SELECT SUM (Salary), MAX (Salary), MIN (Salary), AVG (Salary) FROM EMPLOYEE;
If we want to get the preceding function values for employees of a specific depart- ment—say, the ‘Research’ department—we can write Query 20, where the EMPLOYEE tuples are restricted by the WHERE clause to those employees who work for the ‘Research’ department.
Query 20. Find the sum of the salaries of all employees of the ‘Research’ department, as well as the maximum salary, the minimum salary, and the aver- age salary in this department.
Q20: SELECT SUM (Salary), MAX (Salary), MIN (Salary), AVG (Salary) FROM (EMPLOYEE JOIN DEPARTMENT ON Dno=Dnumber) WHERE Dname=‘Research’;
Queries 21 and 22. Retrieve the total number of employees in the company (Q21) and the number of employees in the ‘Research’ department (Q22).
Q21: SELECT COUNT (*) FROM EMPLOYEE;
Q22: SELECT COUNT (*) FROM EMPLOYEE, DEPARTMENT WHERE DNO=DNUMBER AND DNAME=‘Research’;
Here the asterisk (*) refers to the rows (tuples), so COUNT (*) returns the number of rows in the result of the query. We may also use the COUNT function to count values in a column rather than tuples, as in the next example.
Query 23. Count the number of distinct salary values in the database.
Q23: SELECT COUNT (DISTINCT Salary) FROM EMPLOYEE;
If we write COUNT(SALARY) instead of COUNT(DISTINCT SALARY) in Q23, then duplicate values will not be eliminated. However, any tuples with NULL for SALARY
3Total order means that for any two values in the domain, it can be determined that one appears before the other in the defined order; for example, DATE, TIME, and TIMESTAMP domains have total orderings on their values, as do alphabetic strings.
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will not be counted. In general, NULL values are discarded when aggregate func- tions are applied to a particular column (attribute).
The preceding examples summarize a whole relation (Q19, Q21, Q23) or a selected subset of tuples (Q20, Q22), and hence all produce single tuples or single values. They illustrate how functions are applied to retrieve a summary value or summary tuple from the database. These functions can also be used in selection conditions involving nested queries. We can specify a correlated nested query with an aggregate function, and then use the nested query in the WHERE clause of an outer query. For example, to retrieve the names of all employees who have two or more dependents (Query 5), we can write the following:
Q5: SELECT Lname, Fname FROM EMPLOYEE WHERE ( SELECT COUNT (*)
FROM DEPENDENT WHERE Ssn=Essn ) >= 2;
The correlated nested query counts the number of dependents that each employee has; if this is greater than or equal to two, the employee tuple is selected.
5.1.8 Grouping: The GROUP BY and HAVING Clauses In many cases we want to apply the aggregate functions to subgroups of tuples in a relation, where the subgroups are based on some attribute values. For example, we may want to find the average salary of employees in each department or the number of employees who work on each project. In these cases we need to partition the rela- tion into nonoverlapping subsets (or groups) of tuples. Each group (partition) will consist of the tuples that have the same value of some attribute(s), called the grouping attribute(s). We can then apply the function to each such group inde- pendently to produce summary information about each group. SQL has a GROUP BY clause for this purpose. The GROUP BY clause specifies the grouping attributes, which should also appear in the SELECT clause, so that the value resulting from applying each aggregate function to a group of tuples appears along with the value of the grouping attribute(s).
Query 24. For each department, retrieve the department number, the number of employees in the department, and their average salary.
Q24: SELECT Dno, COUNT (*), AVG (Salary) FROM EMPLOYEE GROUP BY Dno;
In Q24, the EMPLOYEE tuples are partitioned into groups—each group having the same value for the grouping attribute Dno. Hence, each group contains the employees who work in the same department. The COUNT and AVG functions are applied to each such group of tuples. Notice that the SELECT clause includes only the grouping attribute and the aggregate functions to be applied on each group of tuples. Figure 5.1(a) illustrates how grouping works on Q24; it also shows the result of Q24.
5.1 More Complex SQL Retrieval Queries 127
Dno
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Pname
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Computerization
Reorganization
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Result of Q26
These groups are not selected by the HAVING condition of Q26.
Grouping EMPLOYEE tuples by the value of Dno
After applying the WHERE clause but before applying HAVING
After applying the HAVING clause condition
Fname
John
Franklin
Ramesh K
Jennifer
Alicia
Joyce A
Ahmad
James
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Narayan
English
Jabbar Bong
Smith
Wong
Zelaya
Wallace
666884444
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Salary
. . .
Pname
ProductX
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ProductZ ProductY
ProductY
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Computerization
Computerization
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Newbenefits
Reorganization Reorganization
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Newbenefits
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. . .
Pname
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Computerization Computerization
Computerization
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Pnumber Essn Pno Hours
. . . (Pnumber not shown)
Ssn . . .(a)
(b) PnoEssn. . .
. . .
Figure 5.1 Results of GROUP BY and HAVING. (a) Q24. (b) Q26.
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If NULLs exist in the grouping attribute, then a separate group is created for all tuples with a NULL value in the grouping attribute. For example, if the EMPLOYEE table had some tuples that had NULL for the grouping attribute Dno, there would be a separate group for those tuples in the result of Q24.
Query 25. For each project, retrieve the project number, the project name, and the number of employees who work on that project.
Q25: SELECT Pnumber, Pname, COUNT (*) FROM PROJECT, WORKS_ON WHERE Pnumber=Pno GROUP BY Pnumber, Pname;
Q25 shows how we can use a join condition in conjunction with GROUP BY. In this case, the grouping and functions are applied after the joining of the two relations. Sometimes we want to retrieve the values of these functions only for groups that sat- isfy certain conditions. For example, suppose that we want to modify Query 25 so that only projects with more than two employees appear in the result. SQL provides a HAVING clause, which can appear in conjunction with a GROUP BY clause, for this purpose. HAVING provides a condition on the summary information regarding the group of tuples associated with each value of the grouping attributes. Only the groups that satisfy the condition are retrieved in the result of the query. This is illus- trated by Query 26.
Query 26. For each project on which more than two employees work, retrieve the project number, the project name, and the number of employees who work on the project.
Q26: SELECT Pnumber, Pname, COUNT (*) FROM PROJECT, WORKS_ON WHERE Pnumber=Pno GROUP BY Pnumber, Pname HAVING COUNT (*) > 2;
Notice that while selection conditions in the WHERE clause limit the tuples to which functions are applied, the HAVING clause serves to choose whole groups. Figure 5.1(b) illustrates the use of HAVING and displays the result of Q26.
Query 27. For each project, retrieve the project number, the project name, and the number of employees from department 5 who work on the project.
Q27: SELECT Pnumber, Pname, COUNT (*) FROM PROJECT, WORKS_ON, EMPLOYEE WHERE Pnumber=Pno AND Ssn=Essn AND Dno=5 GROUP BY Pnumber, Pname;
Here we restrict the tuples in the relation (and hence the tuples in each group) to those that satisfy the condition specified in the WHERE clause—namely, that they work in department number 5. Notice that we must be extra careful when two dif- ferent conditions apply (one to the aggregate function in the SELECT clause and another to the function in the HAVING clause). For example, suppose that we want
5.1 More Complex SQL Retrieval Queries 129
to count the total number of employees whose salaries exceed $40,000 in each department, but only for departments where more than five employees work. Here, the condition (SALARY > 40000) applies only to the COUNT function in the SELECT clause. Suppose that we write the following incorrect query:
SELECT Dname, COUNT (*) FROM DEPARTMENT, EMPLOYEE WHERE Dnumber=Dno AND Salary>40000 GROUP BY Dname HAVING COUNT (*) > 5;
This is incorrect because it will select only departments that have more than five employees who each earn more than $40,000. The rule is that the WHERE clause is executed first, to select individual tuples or joined tuples; the HAVING clause is applied later, to select individual groups of tuples. Hence, the tuples are already restricted to employees who earn more than $40,000 before the function in the HAVING clause is applied. One way to write this query correctly is to use a nested query, as shown in Query 28.
Query 28. For each department that has more than five employees, retrieve the department number and the number of its employees who are making more than $40,000.
Q28: SELECT Dnumber, COUNT (*) FROM DEPARTMENT, EMPLOYEE WHERE Dnumber=Dno AND Salary>40000 AND
( SELECT Dno FROM EMPLOYEE GROUP BY Dno HAVING COUNT (*) > 5)
5.1.9 Discussion and Summary of SQL Queries A retrieval query in SQL can consist of up to six clauses, but only the first two— SELECT and FROM—are mandatory. The query can span several lines, and is ended by a semicolon. Query terms are separated by spaces, and parentheses can be used to group relevant parts of a query in the standard way. The clauses are specified in the following order, with the clauses between square brackets [ ... ] being optional:
SELECT <attribute and function list> FROM <table list> [ WHERE <condition> ] [ GROUP BY <grouping attribute(s)> ] [ HAVING <group condition> ] [ ORDER BY <attribute list> ];
The SELECT clause lists the attributes or functions to be retrieved. The FROM clause specifies all relations (tables) needed in the query, including joined relations, but not those in nested queries. The WHERE clause specifies the conditions for selecting the tuples from these relations, including join conditions if needed. GROUP BY
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specifies grouping attributes, whereas HAVING specifies a condition on the groups being selected rather than on the individual tuples. The built-in aggregate functions COUNT, SUM, MIN, MAX, and AVG are used in conjunction with grouping, but they can also be applied to all the selected tuples in a query without a GROUP BY clause. Finally, ORDER BY specifies an order for displaying the result of a query.
In order to formulate queries correctly, it is useful to consider the steps that define the meaning or semantics of each query. A query is evaluated conceptually4 by first applying the FROM clause (to identify all tables involved in the query or to material- ize any joined tables), followed by the WHERE clause to select and join tuples, and then by GROUP BY and HAVING. Conceptually, ORDER BY is applied at the end to sort the query result. If none of the last three clauses (GROUP BY, HAVING, and ORDER BY) are specified, we can think conceptually of a query as being executed as follows: For each combination of tuples—one from each of the relations specified in the FROM clause—evaluate the WHERE clause; if it evaluates to TRUE, place the val- ues of the attributes specified in the SELECT clause from this tuple combination in the result of the query. Of course, this is not an efficient way to implement the query in a real system, and each DBMS has special query optimization routines to decide on an execution plan that is efficient to execute. We discuss query processing and optimization in Chapter 19.
In general, there are numerous ways to specify the same query in SQL. This flexibil- ity in specifying queries has advantages and disadvantages. The main advantage is that users can choose the technique with which they are most comfortable when specifying a query. For example, many queries may be specified with join conditions in the WHERE clause, or by using joined relations in the FROM clause, or with some form of nested queries and the IN comparison operator. Some users may be more comfortable with one approach, whereas others may be more comfortable with another. From the programmer’s and the system’s point of view regarding query optimization, it is generally preferable to write a query with as little nesting and implied ordering as possible.
The disadvantage of having numerous ways of specifying the same query is that this may confuse the user, who may not know which technique to use to specify particu- lar types of queries. Another problem is that it may be more efficient to execute a query specified in one way than the same query specified in an alternative way. Ideally, this should not be the case: The DBMS should process the same query in the same way regardless of how the query is specified. But this is quite difficult in prac- tice, since each DBMS has different methods for processing queries specified in dif- ferent ways. Thus, an additional burden on the user is to determine which of the alternative specifications is the most efficient to execute. Ideally, the user should worry only about specifying the query correctly, whereas the DBMS would deter- mine how to execute the query efficiently. In practice, however, it helps if the user is aware of which types of constructs in a query are more expensive to process than others (see Chapter 20).
4The actual order of query evaluation is implementation dependent; this is just a way to conceptually view a query in order to correctly formulate it.
5.2 Specifying Constraints as Assertions and Actions as Triggers 131
5.2 Specifying Constraints as Assertions and Actions as Triggers
In this section, we introduce two additional features of SQL: the CREATE ASSER- TION statement and the CREATE TRIGGER statement. Section 5.2.1 discusses CREATE ASSERTION, which can be used to specify additional types of constraints that are outside the scope of the built-in relational model constraints (primary and unique keys, entity integrity, and referential integrity) that we presented in Section 3.2. These built-in constraints can be specified within the CREATE TABLE statement of SQL (see Sections 4.1 and 4.2).
Then in Section 5.2.2 we introduce CREATE TRIGGER, which can be used to specify automatic actions that the database system will perform when certain events and conditions occur. This type of functionality is generally referred to as active data- bases. We only introduce the basics of triggers in this chapter, and present a more complete discussion of active databases in Section 26.1.
5.2.1 Specifying General Constraints as Assertions in SQL In SQL, users can specify general constraints—those that do not fall into any of the categories described in Sections 4.1 and 4.2—via declarative assertions, using the CREATE ASSERTION statement of the DDL. Each assertion is given a constraint name and is specified via a condition similar to the WHERE clause of an SQL query. For example, to specify the constraint that the salary of an employee must not be greater than the salary of the manager of the department that the employee works for in SQL, we can write the following assertion:
CREATE ASSERTION SALARY_CONSTRAINT CHECK ( NOT EXISTS ( SELECT *
FROM EMPLOYEE E, EMPLOYEE M, DEPARTMENT D
WHERE E.Salary>M.Salary AND E.Dno=D.Dnumber AND D.Mgr_ssn=M.Ssn ) );
The constraint name SALARY_CONSTRAINT is followed by the keyword CHECK, which is followed by a condition in parentheses that must hold true on every data- base state for the assertion to be satisfied. The constraint name can be used later to refer to the constraint or to modify or drop it. The DBMS is responsible for ensur- ing that the condition is not violated. Any WHERE clause condition can be used, but many constraints can be specified using the EXISTS and NOT EXISTS style of SQL conditions. Whenever some tuples in the database cause the condition of an ASSERTION statement to evaluate to FALSE, the constraint is violated. The con- straint is satisfied by a database state if no combination of tuples in that database state violates the constraint.
The basic technique for writing such assertions is to specify a query that selects any tuples that violate the desired condition. By including this query inside a NOT EXISTS
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clause, the assertion will specify that the result of this query must be empty so that the condition will always be TRUE. Thus, the assertion is violated if the result of the query is not empty. In the preceding example, the query selects all employees whose salaries are greater than the salary of the manager of their department. If the result of the query is not empty, the assertion is violated.
Note that the CHECK clause and constraint condition can also be used to specify constraints on individual attributes and domains (see Section 4.2.1) and on individual tuples (see Section 4.2.4). A major difference between CREATE ASSER- TION and the individual domain constraints and tuple constraints is that the CHECK clauses on individual attributes, domains, and tuples are checked in SQL only when tuples are inserted or updated. Hence, constraint checking can be imple- mented more efficiently by the DBMS in these cases. The schema designer should use CHECK on attributes, domains, and tuples only when he or she is sure that the constraint can only be violated by insertion or updating of tuples. On the other hand, the schema designer should use CREATE ASSERTION only in cases where it is not possible to use CHECK on attributes, domains, or tuples, so that simple checks are implemented more efficiently by the DBMS.
5.2.2 Introduction to Triggers in SQL Another important statement in SQL is CREATE TRIGGER. In many cases it is con- venient to specify the type of action to be taken when certain events occur and when certain conditions are satisfied. For example, it may be useful to specify a condition that, if violated, causes some user to be informed of the violation. A manager may want to be informed if an employee’s travel expenses exceed a certain limit by receiving a message whenever this occurs. The action that the DBMS must take in this case is to send an appropriate message to that user. The condition is thus used to monitor the database. Other actions may be specified, such as executing a specific stored procedure or triggering other updates. The CREATE TRIGGER statement is used to implement such actions in SQL. We discuss triggers in detail in Section 26.1 when we describe active databases. Here we just give a simple example of how trig- gers may be used.
Suppose we want to check whenever an employee’s salary is greater than the salary of his or her direct supervisor in the COMPANY database (see Figures 3.5 and 3.6). Several events can trigger this rule: inserting a new employee record, changing an employee’s salary, or changing an employee’s supervisor. Suppose that the action to take would be to call an external stored procedure SALARY_VIOLATION,5 which will notify the supervisor. The trigger could then be written as in R5 below. Here we are using the syntax of the Oracle database system.
R5: CREATE TRIGGER SALARY_VIOLATION BEFORE INSERT OR UPDATE OF SALARY, SUPERVISOR_SSN
ON EMPLOYEE
5Assuming that an appropriate external procedure has been declared. We discuss stored procedures in Chapter 13.
5.3 Views (Virtual Tables) in SQL 133
FOR EACH ROW WHEN ( NEW.SALARY > ( SELECT SALARY FROM EMPLOYEE
WHERE SSN = NEW.SUPERVISOR_SSN ) ) INFORM_SUPERVISOR(NEW.Supervisor_ssn, NEW.Ssn );
The trigger is given the name SALARY_VIOLATION, which can be used to remove or deactivate the trigger later. A typical trigger has three components:
1. The event(s): These are usually database update operations that are explicitly applied to the database. In this example the events are: inserting a new employee record, changing an employee’s salary, or changing an employee’s supervisor. The person who writes the trigger must make sure that all possi- ble events are accounted for. In some cases, it may be necessary to write more than one trigger to cover all possible cases. These events are specified after the keyword BEFORE in our example, which means that the trigger should be executed before the triggering operation is executed. An alternative is to use the keyword AFTER, which specifies that the trigger should be executed after the operation specified in the event is completed.
2. The condition that determines whether the rule action should be executed: Once the triggering event has occurred, an optional condition may be evalu- ated. If no condition is specified, the action will be executed once the event occurs. If a condition is specified, it is first evaluated, and only if it evaluates to true will the rule action be executed. The condition is specified in the WHEN clause of the trigger.
3. The action to be taken: The action is usually a sequence of SQL statements, but it could also be a database transaction or an external program that will be automatically executed. In this example, the action is to execute the stored procedure INFORM_SUPERVISOR.
Triggers can be used in various applications, such as maintaining database consis- tency, monitoring database updates, and updating derived data automatically. A more complete discussion is given in Section 26.1.
5.3 Views (Virtual Tables) in SQL In this section we introduce the concept of a view in SQL. We show how views are specified, and then we discuss the problem of updating views and how views can be implemented by the DBMS.
5.3.1 Concept of a View in SQL A view in SQL terminology is a single table that is derived from other tables.6 These other tables can be base tables or previously defined views. A view does not necessarily
6As used in SQL, the term view is more limited than the term user view discussed in Chapters 1 and 2, since a user view would possibly include many relations.
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DEPT_INFO
Dept_name No_of_emps Total_sal
WORKS_ON1
Fname Lname Pname Hours
Figure 5.2 Two views specified on the database schema of Figure 3.5.
exist in physical form; it is considered to be a virtual table, in contrast to base tables, whose tuples are always physically stored in the database. This limits the possible update operations that can be applied to views, but it does not provide any limitations on querying a view.
We can think of a view as a way of specifying a table that we need to reference fre- quently, even though it may not exist physically. For example, referring to the COMPANY database in Figure 3.5 we may frequently issue queries that retrieve the employee name and the project names that the employee works on. Rather than having to specify the join of the three tables EMPLOYEE, WORKS_ON, and PROJECT every time we issue this query, we can define a view that is specified as the result of these joins. Then we can issue queries on the view, which are specified as single- table retrievals rather than as retrievals involving two joins on three tables. We call the EMPLOYEE, WORKS_ON, and PROJECT tables the defining tables of the view.
5.3.2 Specification of Views in SQL In SQL, the command to specify a view is CREATE VIEW. The view is given a (vir- tual) table name (or view name), a list of attribute names, and a query to specify the contents of the view. If none of the view attributes results from applying functions or arithmetic operations, we do not have to specify new attribute names for the view, since they would be the same as the names of the attributes of the defining tables in the default case. The views in V1 and V2 create virtual tables whose schemas are illustrated in Figure 5.2 when applied to the database schema of Figure 3.5.
V1: CREATE VIEW WORKS_ON1 AS SELECT Fname, Lname, Pname, Hours
FROM EMPLOYEE, PROJECT, WORKS_ON WHERE Ssn=Essn AND Pno=Pnumber;
V2: CREATE VIEW DEPT_INFO(Dept_name, No_of_emps, Total_sal) AS SELECT Dname, COUNT (*), SUM (Salary)
FROM DEPARTMENT, EMPLOYEE WHERE Dnumber=Dno GROUP BY Dname;
In V1, we did not specify any new attribute names for the view WORKS_ON1 (although we could have); in this case, WORKS_ON1 inherits the names of the view attributes from the defining tables EMPLOYEE, PROJECT, and WORKS_ON. View V2
5.3 Views (Virtual Tables) in SQL 135
explicitly specifies new attribute names for the view DEPT_INFO, using a one-to-one correspondence between the attributes specified in the CREATE VIEW clause and those specified in the SELECT clause of the query that defines the view.
We can now specify SQL queries on a view—or virtual table—in the same way we specify queries involving base tables. For example, to retrieve the last name and first name of all employees who work on the ‘ProductX’ project, we can utilize the WORKS_ON1 view and specify the query as in QV1:
QV1: SELECT Fname, Lname FROM WORKS_ON1 WHERE Pname=‘ProductX’;
The same query would require the specification of two joins if specified on the base relations directly; one of the main advantages of a view is to simplify the specifica- tion of certain queries. Views are also used as a security and authorization mecha- nism (see Chapter 24).
A view is supposed to be always up-to-date; if we modify the tuples in the base tables on which the view is defined, the view must automatically reflect these changes. Hence, the view is not realized or materialized at the time of view definition but rather at the time when we specify a query on the view. It is the responsibility of the DBMS and not the user to make sure that the view is kept up-to-date. We will discuss various ways the DBMS can apply to keep a view up-to-date in the next subsection.
If we do not need a view any more, we can use the DROP VIEW command to dispose of it. For example, to get rid of the view V1, we can use the SQL statement in V1A:
V1A: DROP VIEW WORKS_ON1;
5.3.3 View Implementation, View Update, and Inline Views
The problem of efficiently implementing a view for querying is complex. Two main approaches have been suggested. One strategy, called query modification, involves modifying or transforming the view query (submitted by the user) into a query on the underlying base tables. For example, the query QV1 would be automatically modified to the following query by the DBMS:
SELECT Fname, Lname FROM EMPLOYEE, PROJECT, WORKS_ON WHERE Ssn=Essn AND Pno=Pnumber
AND Pname=‘ProductX’;
The disadvantage of this approach is that it is inefficient for views defined via com- plex queries that are time-consuming to execute, especially if multiple queries are going to be applied to the same view within a short period of time. The second strategy, called view materialization, involves physically creating a temporary view table when the view is first queried and keeping that table on the assumption that
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other queries on the view will follow. In this case, an efficient strategy for automati- cally updating the view table when the base tables are updated must be developed in order to keep the view up-to-date. Techniques using the concept of incremental update have been developed for this purpose, where the DBMS can determine what new tuples must be inserted, deleted, or modified in a materialized view table when a database update is applied to one of the defining base tables. The view is generally kept as a materialized (physically stored) table as long as it is being queried. If the view is not queried for a certain period of time, the system may then automatically remove the physical table and recompute it from scratch when future queries refer- ence the view.
Updating of views is complicated and can be ambiguous. In general, an update on a view defined on a single table without any aggregate functions can be mapped to an update on the underlying base table under certain conditions. For a view involving joins, an update operation may be mapped to update operations on the underlying base relations in multiple ways. Hence, it is often not possible for the DBMS to determine which of the updates is intended. To illustrate potential problems with updating a view defined on multiple tables, consider the WORKS_ON1 view, and suppose that we issue the command to update the PNAME attribute of ‘John Smith’ from ‘ProductX’ to ‘ProductY’. This view update is shown in UV1:
UV1: UPDATE WORKS_ON1 SET Pname = ‘ProductY’ WHERE Lname=‘Smith’ AND Fname=‘John’
AND Pname=‘ProductX’;
This query can be mapped into several updates on the base relations to give the desired update effect on the view. In addition, some of these updates will create additional side effects that affect the result of other queries. For example, here are two possible updates, (a) and (b), on the base relations corresponding to the view update operation in UV1:
(a): UPDATE WORKS_ON SET Pno = ( SELECT Pnumber
FROM PROJECT WHERE Pname=‘ProductY’ )
WHERE Essn IN ( SELECT Ssn FROM EMPLOYEE WHERE Lname=‘Smith’ AND Fname=‘John’ )
AND Pno = ( SELECT Pnumber
FROM PROJECT WHERE Pname=‘ProductX’ );
(b): UPDATE PROJECT SET Pname = ‘ProductY’ WHERE Pname = ‘ProductX’;
Update (a) relates ‘John Smith’ to the ‘ProductY’ PROJECT tuple instead of the ‘ProductX’ PROJECT tuple and is the most likely desired update. However, (b)
5.4 Schema Change Statements in SQL 137
would also give the desired update effect on the view, but it accomplishes this by changing the name of the ‘ProductX’ tuple in the PROJECT relation to ‘ProductY’. It is quite unlikely that the user who specified the view update UV1 wants the update to be interpreted as in (b), since it also has the side effect of changing all the view tuples with Pname = ‘ProductX’.
Some view updates may not make much sense; for example, modifying the Total_sal attribute of the DEPT_INFO view does not make sense because Total_sal is defined to be the sum of the individual employee salaries. This request is shown as UV2:
UV2: UPDATE DEPT_INFO SET Total_sal=100000 WHERE Dname=‘Research’;
A large number of updates on the underlying base relations can satisfy this view update.
Generally, a view update is feasible when only one possible update on the base rela- tions can accomplish the desired update effect on the view. Whenever an update on the view can be mapped to more than one update on the underlying base relations, we must have a certain procedure for choosing one of the possible updates as the most likely one. Some researchers have developed methods for choosing the most likely update, while other researchers prefer to have the user choose the desired update mapping during view definition.
In summary, we can make the following observations:
■ A view with a single defining table is updatable if the view attributes contain the primary key of the base relation, as well as all attributes with the NOT NULL constraint that do not have default values specified.
■ Views defined on multiple tables using joins are generally not updatable.
■ Views defined using grouping and aggregate functions are not updatable.
In SQL, the clause WITH CHECK OPTION must be added at the end of the view defi- nition if a view is to be updated. This allows the system to check for view updatabil- ity and to plan an execution strategy for view updates.
It is also possible to define a view table in the FROM clause of an SQL query. This is known as an in-line view. In this case, the view is defined within the query itself.
5.4 Schema Change Statements in SQL In this section, we give an overview of the schema evolution commands available in SQL, which can be used to alter a schema by adding or dropping tables, attributes, constraints, and other schema elements. This can be done while the database is operational and does not require recompilation of the database schema. Certain checks must be done by the DBMS to ensure that the changes do not affect the rest of the database and make it inconsistent.
138 Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification
5.4.1 The DROP Command The DROP command can be used to drop named schema elements, such as tables, domains, or constraints. One can also drop a schema. For example, if a whole schema is no longer needed, the DROP SCHEMA command can be used. There are two drop behavior options: CASCADE and RESTRICT. For example, to remove the COMPANY database schema and all its tables, domains, and other elements, the CASCADE option is used as follows:
DROP SCHEMA COMPANY CASCADE;
If the RESTRICT option is chosen in place of CASCADE, the schema is dropped only if it has no elements in it; otherwise, the DROP command will not be executed. To use the RESTRICT option, the user must first individually drop each element in the schema, then drop the schema itself.
If a base relation within a schema is no longer needed, the relation and its definition can be deleted by using the DROP TABLE command. For example, if we no longer wish to keep track of dependents of employees in the COMPANY database of Figure 4.1, we can get rid of the DEPENDENT relation by issuing the following command:
DROP TABLE DEPENDENT CASCADE;
If the RESTRICT option is chosen instead of CASCADE, a table is dropped only if it is not referenced in any constraints (for example, by foreign key definitions in another relation) or views (see Section 5.3) or by any other elements. With the CASCADE option, all such constraints, views, and other elements that reference the table being dropped are also dropped automatically from the schema, along with the table itself.
Notice that the DROP TABLE command not only deletes all the records in the table if successful, but also removes the table definition from the catalog. If it is desired to delete only the records but to leave the table definition for future use, then the DELETE command (see Section 4.4.2) should be used instead of DROP TABLE.
The DROP command can also be used to drop other types of named schema ele- ments, such as constraints or domains.
5.4.2 The ALTER Command The definition of a base table or of other named schema elements can be changed by using the ALTER command. For base tables, the possible alter table actions include adding or dropping a column (attribute), changing a column definition, and adding or dropping table constraints. For example, to add an attribute for keeping track of jobs of employees to the EMPLOYEE base relation in the COMPANY schema (see Figure 4.1), we can use the command
ALTER TABLE COMPANY.EMPLOYEE ADD COLUMN Job VARCHAR(12);
We must still enter a value for the new attribute Job for each individual EMPLOYEE tuple. This can be done either by specifying a default clause or by using the UPDATE
5.5 Summary 139
command individually on each tuple (see Section 4.4.3). If no default clause is spec- ified, the new attribute will have NULLs in all the tuples of the relation immediately after the command is executed; hence, the NOT NULL constraint is not allowed in this case.
To drop a column, we must choose either CASCADE or RESTRICT for drop behav- ior. If CASCADE is chosen, all constraints and views that reference the column are dropped automatically from the schema, along with the column. If RESTRICT is chosen, the command is successful only if no views or constraints (or other schema elements) reference the column. For example, the following command removes the attribute Address from the EMPLOYEE base table:
ALTER TABLE COMPANY.EMPLOYEE DROP COLUMN Address CASCADE;
It is also possible to alter a column definition by dropping an existing default clause or by defining a new default clause. The following examples illustrate this clause:
ALTER TABLE COMPANY.DEPARTMENT ALTER COLUMN Mgr_ssn DROP DEFAULT;
ALTER TABLE COMPANY.DEPARTMENT ALTER COLUMN Mgr_ssn SET DEFAULT ‘333445555’;
One can also change the constraints specified on a table by adding or dropping a named constraint. To be dropped, a constraint must have been given a name when it was specified. For example, to drop the constraint named EMPSUPERFK in Figure 4.2 from the EMPLOYEE relation, we write:
ALTER TABLE COMPANY.EMPLOYEE DROP CONSTRAINT EMPSUPERFK CASCADE;
Once this is done, we can redefine a replacement constraint by adding a new con- straint to the relation, if needed. This is specified by using the ADD keyword in the ALTER TABLE statement followed by the new constraint, which can be named or unnamed and can be of any of the table constraint types discussed.
The preceding subsections gave an overview of the schema evolution commands of SQL. It is also possible to create new tables and views within a database schema using the appropriate commands. There are many other details and options; we refer the interested reader to the SQL documents listed in the Selected Bibliography at the end of this chapter.
5.5 Summary In this chapter we presented additional features of the SQL database language. We started in Section 5.1 by presenting more complex features of SQL retrieval queries, including nested queries, joined tables, outer joins, aggregate functions, and group- ing. In Section 5.2, we described the CREATE ASSERTION statement, which allows the specification of more general constraints on the database, and introduced the concept of triggers and the CREATE TRIGGER statement. Then, in Section 5.3, we described the SQL facility for defining views on the database. Views are also called
140 Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification
Table 5.2 Summary of SQL Syntax
CREATE TABLE <table name> ( <column name> <column type> [ <attribute constraint> ] { , <column name> <column type> [ <attribute constraint> ] } [ <table constraint> { , <table constraint> } ] )
DROP TABLE <table name> ALTER TABLE <table name> ADD <column name> <column type>
SELECT [ DISTINCT ] <attribute list> FROM ( <table name> { <alias> } | <joined table> ) { , ( <table name> { <alias> } | <joined table> ) } [ WHERE <condition> ] [ GROUP BY <grouping attributes> [ HAVING <group selection condition> ] ] [ ORDER BY <column name> [ <order> ] { , <column name> [ <order> ] } ]
<attribute list> ::= ( * | ( <column name> | <function> ( ( [ DISTINCT ] <column name> | * ) ) ) { , ( <column name> | <function> ( ( [ DISTINCT] <column name> | * ) ) } ) )
<grouping attributes> ::= <column name> { , <column name> }
<order> ::= ( ASC | DESC )
INSERT INTO <table name> [ ( <column name> { , <column name> } ) ] ( VALUES ( <constant value> , { <constant value> } ) { , ( <constant value> { , <constant value> } ) } | <select statement> )
DELETE FROM <table name> [ WHERE <selection condition> ]
UPDATE <table name> SET <column name> = <value expression> { , <column name> = <value expression> } [ WHERE <selection condition> ]
CREATE [ UNIQUE] INDEX <index name> ON <table name> ( <column name> [ <order> ] { , <column name> [ <order> ] } ) [ CLUSTER ]
DROP INDEX <index name>
CREATE VIEW <view name> [ ( <column name> { , <column name> } ) ] AS <select statement>
DROP VIEW <view name>
NOTE: The commands for creating and dropping indexes are not part of standard SQL.
virtual or derived tables because they present the user with what appear to be tables; however, the information in those tables is derived from previously defined tables. Section 5.4 introduced the SQL ALTER TABLE statement, which is used for modify- ing the database tables and constraints.
Table 5.2 summarizes the syntax (or structure) of various SQL statements. This sum- mary is not meant to be comprehensive or to describe every possible SQL construct; rather, it is meant to serve as a quick reference to the major types of constructs avail- able in SQL. We use BNF notation, where nonterminal symbols are shown in angled brackets <...>, optional parts are shown in square brackets [...], repetitions are shown in braces {...}, and alternatives are shown in parentheses (... | ... | ...).7
7The full syntax of SQL is described in many voluminous documents of hundreds of pages.
Exercises 141
Review Questions 5.1. Describe the six clauses in the syntax of an SQL retrieval query. Show what
type of constructs can be specified in each of the six clauses. Which of the six clauses are required and which are optional?
5.2. Describe conceptually how an SQL retrieval query will be executed by speci- fying the conceptual order of executing each of the six clauses.
5.3. Discuss how NULLs are treated in comparison operators in SQL. How are NULLs treated when aggregate functions are applied in an SQL query? How are NULLs treated if they exist in grouping attributes?
5.4. Discuss how each of the following constructs is used in SQL, and discuss the various options for each construct. Specify what each construct is useful for.
a. Nested queries.
b. Joined tables and outer joins.
c. Aggregate functions and grouping.
d. Triggers.
e. Assertions and how they differ from triggers.
f. Views and their updatability.
g. Schema change commands.
Exercises 5.5. Specify the following queries on the database in Figure 3.5 in SQL. Show the
query results if each query is applied to the database in Figure 3.6.
a. For each department whose average employee salary is more than $30,000, retrieve the department name and the number of employees working for that department.
b. Suppose that we want the number of male employees in each department making more than $30,000, rather than all employees (as in Exercise 5.4a). Can we specify this query in SQL? Why or why not?
5.6. Specify the following queries in SQL on the database schema in Figure 1.2.
a. Retrieve the names and major departments of all straight-A students (students who have a grade of A in all their courses).
b. Retrieve the names and major departments of all students who do not have a grade of A in any of their courses.
5.7. In SQL, specify the following queries on the database in Figure 3.5 using the concept of nested queries and concepts described in this chapter.
a. Retrieve the names of all employees who work in the department that has the employee with the highest salary among all employees.
b. Retrieve the names of all employees whose supervisor’s supervisor has ‘888665555’ for Ssn.
142 Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification
c. Retrieve the names of employees who make at least $10,000 more than the employee who is paid the least in the company.
5.8. Specify the following views in SQL on the COMPANY database schema shown in Figure 3.5.
a. A view that has the department name, manager name, and manager salary for every department.
b. A view that has the employee name, supervisor name, and employee salary for each employee who works in the ‘Research’ department.
c. A view that has the project name, controlling department name, number of employees, and total hours worked per week on the project for each project.
d. A view that has the project name, controlling department name, number of employees, and total hours worked per week on the project for each project with more than one employee working on it.
5.9. Consider the following view, DEPT_SUMMARY, defined on the COMPANY database in Figure 3.6:
CREATE VIEW DEPT_SUMMARY (D, C, Total_s, Average_s) AS SELECT Dno, COUNT (*), SUM (Salary), AVG (Salary) FROM EMPLOYEE GROUP BY Dno;
State which of the following queries and updates would be allowed on the view. If a query or update would be allowed, show what the corresponding query or update on the base relations would look like, and give its result when applied to the database in Figure 3.6.
a. SELECT * FROM DEPT_SUMMARY;
b. SELECT D, C FROM DEPT_SUMMARY WHERE TOTAL_S > 100000;
c. SELECT D, AVERAGE_S FROM DEPT_SUMMARY WHERE C > ( SELECT C FROM DEPT_SUMMARY WHERE D=4);
d. UPDATE DEPT_SUMMARY SET D=3 WHERE D=4;
e. DELETE FROM DEPT_SUMMARY WHERE C > 4;
Selected Bibliography 143
Selected Bibliography Reisner (1977) describes a human factors evaluation of SEQUEL, a precursor of SQL, in which she found that users have some difficulty with specifying join condi- tions and grouping correctly. Date (1984) contains a critique of the SQL language that points out its strengths and shortcomings. Date and Darwen (1993) describes SQL2. ANSI (1986) outlines the original SQL standard. Various vendor manuals describe the characteristics of SQL as implemented on DB2, SQL/DS, Oracle, INGRES, Informix, and other commercial DBMS products. Melton and Simon (1993) give a comprehensive treatment of the ANSI 1992 standard called SQL2. Horowitz (1992) discusses some of the problems related to referential integrity and propagation of updates in SQL2.
The question of view updates is addressed by Dayal and Bernstein (1978), Keller (1982), and Langerak (1990), among others. View implementation is discussed in Blakeley et al. (1989). Negri et al. (1991) describes formal semantics of SQL queries.
There are many books that describe various aspects of SQL. For example, two refer- ences that describe SQL-99 are Melton and Simon (2002) and Melton (2003). Further SQL standards—SQL 2006 and SQL 2008—are described in a variety of technical reports; but no standard references exist.
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145
The Relational Algebra and Relational Calculus
In this chapter we discuss the two formal languages forthe relational model: the relational algebra and the relational calculus. In contrast, Chapters 4 and 5 described the practical language for the relational model, namely the SQL standard. Historically, the relational algebra and calculus were developed before the SQL language. In fact, in some ways, SQL is based on concepts from both the algebra and the calculus, as we shall see. Because most relational DBMSs use SQL as their language, we presented the SQL language first.
Recall from Chapter 2 that a data model must include a set of operations to manip- ulate the database, in addition to the data model’s concepts for defining the data- base’s structure and constraints. We presented the structures and constraints of the formal relational model in Chapter 3. The basic set of operations for the relational model is the relational algebra. These operations enable a user to specify basic retrieval requests as relational algebra expressions. The result of a retrieval is a new relation, which may have been formed from one or more relations. The algebra operations thus produce new relations, which can be further manipulated using operations of the same algebra. A sequence of relational algebra operations forms a relational algebra expression, whose result will also be a relation that represents the result of a database query (or retrieval request).
The relational algebra is very important for several reasons. First, it provides a for- mal foundation for relational model operations. Second, and perhaps more impor- tant, it is used as a basis for implementing and optimizing queries in the query processing and optimization modules that are integral parts of relational database management systems (RDBMSs), as we shall discuss in Chapter 19. Third, some of its concepts are incorporated into the SQL standard query language for RDBMSs.
6chapter 6
146 Chapter 6 The Relational Algebra and Relational Calculus
Although most commercial RDBMSs in use today do not provide user interfaces for relational algebra queries, the core operations and functions in the internal modules of most relational systems are based on relational algebra operations. We will define these operations in detail in Sections 6.1 through 6.4 of this chapter.
Whereas the algebra defines a set of operations for the relational model, the relational calculus provides a higher-level declarative language for specifying rela- tional queries. A relational calculus expression creates a new relation. In a relational calculus expression, there is no order of operations to specify how to retrieve the query result—only what information the result should contain. This is the main distinguishing feature between relational algebra and relational calculus. The rela- tional calculus is important because it has a firm basis in mathematical logic and because the standard query language (SQL) for RDBMSs has some of its founda- tions in a variation of relational calculus known as the tuple relational calculus.1
The relational algebra is often considered to be an integral part of the relational data model. Its operations can be divided into two groups. One group includes set oper- ations from mathematical set theory; these are applicable because each relation is defined to be a set of tuples in the formal relational model (see Section 3.1). Set operations include UNION, INTERSECTION, SET DIFFERENCE, and CARTESIAN PRODUCT (also known as CROSS PRODUCT). The other group consists of opera- tions developed specifically for relational databases—these include SELECT, PROJECT, and JOIN, among others. First, we describe the SELECT and PROJECT operations in Section 6.1 because they are unary operations that operate on single relations. Then we discuss set operations in Section 6.2. In Section 6.3, we discuss JOIN and other complex binary operations, which operate on two tables by com- bining related tuples (records) based on join conditions. The COMPANY relational database shown in Figure 3.6 is used for our examples.
Some common database requests cannot be performed with the original relational algebra operations, so additional operations were created to express these requests. These include aggregate functions, which are operations that can summarize data from the tables, as well as additional types of JOIN and UNION operations, known as OUTER JOINs and OUTER UNIONs. These operations, which were added to the orig- inal relational algebra because of their importance to many database applications, are described in Section 6.4. We give examples of specifying queries that use rela- tional operations in Section 6.5. Some of these same queries were used in Chapters 4 and 5. By using the same query numbers in this chapter, the reader can contrast how the same queries are written in the various query languages.
In Sections 6.6 and 6.7 we describe the other main formal language for relational databases, the relational calculus. There are two variations of relational calculus. The tuple relational calculus is described in Section 6.6 and the domain relational calculus is described in Section 6.7. Some of the SQL constructs discussed in Chapters 4 and 5 are based on the tuple relational calculus. The relational calculus is a formal language, based on the branch of mathematical logic called predicate cal-
1SQL is based on tuple relational calculus, but also incorporates some of the operations from the rela- tional algebra and its extensions, as illustrated in Chapters 4, 5, and 9.
6.1 Unary Relational Operations: SELECT and PROJECT 147
culus.2 In tuple relational calculus, variables range over tuples, whereas in domain relational calculus, variables range over the domains (values) of attributes. In Appendix C we give an overview of the Query-By-Example (QBE) language, which is a graphical user-friendly relational language based on domain relational calculus. Section 6.8 summarizes the chapter.
For the reader who is interested in a less detailed introduction to formal relational languages, Sections 6.4, 6.6, and 6.7 may be skipped.
6.1 Unary Relational Operations: SELECT and PROJECT
6.1.1 The SELECT Operation The SELECT operation is used to choose a subset of the tuples from a relation that satisfies a selection condition.3 One can consider the SELECT operation to be a filter that keeps only those tuples that satisfy a qualifying condition. Alternatively, we can consider the SELECT operation to restrict the tuples in a relation to only those tuples that satisfy the condition. The SELECT operation can also be visualized as a horizontal partition of the relation into two sets of tuples—those tuples that sat- isfy the condition and are selected, and those tuples that do not satisfy the condition and are discarded. For example, to select the EMPLOYEE tuples whose department is 4, or those whose salary is greater than $30,000, we can individually specify each of these two conditions with a SELECT operation as follows:
σDno=4(EMPLOYEE) σSalary>30000(EMPLOYEE)
In general, the SELECT operation is denoted by σ<selection condition>(R)
where the symbol σ (sigma) is used to denote the SELECT operator and the selec- tion condition is a Boolean expression (condition) specified on the attributes of relation R. Notice that R is generally a relational algebra expression whose result is a relation—the simplest such expression is just the name of a database relation. The relation resulting from the SELECT operation has the same attributes as R.
The Boolean expression specified in <selection condition> is made up of a number of clauses of the form
<attribute name> <comparison op> <constant value>
or
<attribute name> <comparison op> <attribute name>
2In this chapter no familiarity with first-order predicate calculus—which deals with quantified variables and values—is assumed. 3The SELECT operation is different from the SELECT clause of SQL. The SELECT operation chooses tuples from a table, and is sometimes called a RESTRICT or FILTER operation.
148 Chapter 6 The Relational Algebra and Relational Calculus
where <attribute name> is the name of an attribute of R, <comparison op> is nor- mally one of the operators {=, <, ≤, >, ≥, ≠}, and <constant value> is a constant value from the attribute domain. Clauses can be connected by the standard Boolean oper- ators and, or, and not to form a general selection condition. For example, to select the tuples for all employees who either work in department 4 and make over $25,000 per year, or work in department 5 and make over $30,000, we can specify the following SELECT operation:
σ(Dno=4 AND Salary>25000) OR (Dno=5 AND Salary>30000)(EMPLOYEE)
The result is shown in Figure 6.1(a).
Notice that all the comparison operators in the set {=, <, ≤, >, ≥, ≠} can apply to attributes whose domains are ordered values, such as numeric or date domains. Domains of strings of characters are also considered to be ordered based on the col- lating sequence of the characters. If the domain of an attribute is a set of unordered values, then only the comparison operators in the set {=, ≠} can be used. An exam- ple of an unordered domain is the domain Color = { ‘red’, ‘blue’, ‘green’, ‘white’, ‘yel- low’, ...}, where no order is specified among the various colors. Some domains allow additional types of comparison operators; for example, a domain of character strings may allow the comparison operator SUBSTRING_OF.
In general, the result of a SELECT operation can be determined as follows. The <selection condition> is applied independently to each individual tuple t in R. This is done by substituting each occurrence of an attribute Ai in the selection condition with its value in the tuple t[Ai]. If the condition evaluates to TRUE, then tuple t is
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
Franklin
Jennifer
Ramesh
T Wong
Wallace
Narayan
333445555
987654321
666884444
1955-12-08
1941-06-20
1962-09-15
638 Voss, Houston, TX
291 Berry, Bellaire, TX
975 Fire Oak, Humble, TX
M
F
M
40000
43000
38000
888665555
888665555
333445555
5
4
5
Lname Fname Salary
Smith
Wong
Zelaya
Wallace
Narayan
English
Jabbar
Borg
John
Franklin
Alicia
Jennifer
Ramesh
Joyce
Ahmad
James
30000
40000
25000
43000
38000
25000
25000
30000
40000
25000
43000
38000
25000
55000
55000
Sex Salary
M
M
F
F
M
M
M
(c)(b)
(a)
S
K
Figure 6.1 Results of SELECT and PROJECT operations. (a) σ(Dno=4 AND Salary>25000) OR (Dno=5 AND Salary>30000) (EMPLOYEE). (b) πLname, Fname, Salary(EMPLOYEE). (c) πSex, Salary(EMPLOYEE).
6.1 Unary Relational Operations: SELECT and PROJECT 149
selected. All the selected tuples appear in the result of the SELECT operation. The Boolean conditions AND, OR, and NOT have their normal interpretation, as follows:
■ (cond1 AND cond2) is TRUE if both (cond1) and (cond2) are TRUE; other- wise, it is FALSE.
■ (cond1 OR cond2) is TRUE if either (cond1) or (cond2) or both are TRUE; otherwise, it is FALSE.
■ (NOT cond) is TRUE if cond is FALSE; otherwise, it is FALSE.
The SELECT operator is unary; that is, it is applied to a single relation. Moreover, the selection operation is applied to each tuple individually; hence, selection condi- tions cannot involve more than one tuple. The degree of the relation resulting from a SELECT operation—its number of attributes—is the same as the degree of R. The number of tuples in the resulting relation is always less than or equal to the number of tuples in R. That is, |σc (R)| ≤ |R| for any condition C. The fraction of tuples selected by a selection condition is referred to as the selectivity of the condition.
Notice that the SELECT operation is commutative; that is,
σ<cond1>(σ<cond2>(R)) = σ<cond2>(σ<cond1>(R))
Hence, a sequence of SELECTs can be applied in any order. In addition, we can always combine a cascade (or sequence) of SELECT operations into a single SELECT operation with a conjunctive (AND) condition; that is,
σ<cond1>(σ<cond2>(...(σ<condn>(R)) ...)) = σ<cond1> AND<cond2> AND...AND <condn>(R)
In SQL, the SELECT condition is typically specified in the WHERE clause of a query. For example, the following operation:
σDno=4 AND Salary>25000 (EMPLOYEE)
would correspond to the following SQL query:
SELECT * FROM EMPLOYEE WHERE Dno=4 AND Salary>25000;
6.1.2 The PROJECT Operation If we think of a relation as a table, the SELECT operation chooses some of the rows from the table while discarding other rows. The PROJECT operation, on the other hand, selects certain columns from the table and discards the other columns. If we are interested in only certain attributes of a relation, we use the PROJECT operation to project the relation over these attributes only. Therefore, the result of the PROJECT operation can be visualized as a vertical partition of the relation into two relations: one has the needed columns (attributes) and contains the result of the operation, and the other contains the discarded columns. For example, to list each employee’s first and last name and salary, we can use the PROJECT operation as follows:
πLname, Fname, Salary(EMPLOYEE)
150 Chapter 6 The Relational Algebra and Relational Calculus
The resulting relation is shown in Figure 6.1(b). The general form of the PROJECT operation is
π<attribute list>(R)
where π (pi) is the symbol used to represent the PROJECT operation, and <attribute list> is the desired sublist of attributes from the attributes of relation R. Again, notice that R is, in general, a relational algebra expression whose result is a relation, which in the simplest case is just the name of a database relation. The result of the PROJECT operation has only the attributes specified in <attribute list> in the same order as they appear in the list. Hence, its degree is equal to the number of attributes in <attribute list>.
If the attribute list includes only nonkey attributes of R, duplicate tuples are likely to occur. The PROJECT operation removes any duplicate tuples, so the result of the PROJECT operation is a set of distinct tuples, and hence a valid relation. This is known as duplicate elimination. For example, consider the following PROJECT operation:
πSex, Salary(EMPLOYEE)
The result is shown in Figure 6.1(c). Notice that the tuple <‘F’, 25000> appears only once in Figure 6.1(c), even though this combination of values appears twice in the EMPLOYEE relation. Duplicate elimination involves sorting or some other tech- nique to detect duplicates and thus adds more processing. If duplicates are not elim- inated, the result would be a multiset or bag of tuples rather than a set. This was not permitted in the formal relational model, but is allowed in SQL (see Section 4.3).
The number of tuples in a relation resulting from a PROJECT operation is always less than or equal to the number of tuples in R. If the projection list is a superkey of R—that is, it includes some key of R—the resulting relation has the same number of tuples as R. Moreover,
π<list1> (π<list2>(R)) = π<list1>(R)
as long as <list2> contains the attributes in <list1>; otherwise, the left-hand side is an incorrect expression. It is also noteworthy that commutativity does not hold on PROJECT.
In SQL, the PROJECT attribute list is specified in the SELECT clause of a query. For example, the following operation:
πSex, Salary(EMPLOYEE)
would correspond to the following SQL query:
SELECT DISTINCT Sex, Salary FROM EMPLOYEE
Notice that if we remove the keyword DISTINCT from this SQL query, then dupli- cates will not be eliminated. This option is not available in the formal relational algebra.
6.1 Unary Relational Operations: SELECT and PROJECT 151
6.1.3 Sequences of Operations and the RENAME Operation The relations shown in Figure 6.1 that depict operation results do not have any names. In general, for most queries, we need to apply several relational algebra operations one after the other. Either we can write the operations as a single relational algebra expression by nesting the operations, or we can apply one oper- ation at a time and create intermediate result relations. In the latter case, we must give names to the relations that hold the intermediate results. For example, to retrieve the first name, last name, and salary of all employees who work in depart- ment number 5, we must apply a SELECT and a PROJECT operation. We can write a single relational algebra expression, also known as an in-line expression, as follows:
πFname, Lname, Salary(σDno=5(EMPLOYEE))
Figure 6.2(a) shows the result of this in-line relational algebra expression. Alternatively, we can explicitly show the sequence of operations, giving a name to each intermediate relation, as follows:
DEP5_EMPS ← σDno=5(EMPLOYEE) RESULT ← πFname, Lname, Salary(DEP5_EMPS)
It is sometimes simpler to break down a complex sequence of operations by specify- ing intermediate result relations than to write a single relational algebra expression. We can also use this technique to rename the attributes in the intermediate and
(b)
(a)
TEMP
Fname John Franklin
Ramesh Joyce
Minit B T
K A
Lname Smith Wong
Narayan English
Ssn 123456789 333445555
666884444 453453453
Bdate 1965-01-09 1955-12-08
1962-09-15 1972-07-31
Address 731 Fondren, Houston,TX 638 Voss, Houston,TX
975 Fire Oak, Humble,TX 5631 Rice, Houston, TX
Sex M M
M F
Salary 30000 40000
38000 25000
Dno 5 5 5 5
Super_ssn 333445555 888665555
333445555 333445555
Smith Wong
Narayan English
30000 40000
38000 25000
Fname Lname Salary John Franklin
Ramesh Joyce
Smith Wong
Narayan English
30000 40000
38000 25000
First_name Last_name Salary John Franklin
Ramesh Joyce
R
Figure 6.2 Results of a sequence of operations. (a) πFname, Lname, Salary (σDno=5(EMPLOYEE)). (b) Using intermediate relations and renaming of attributes.
152 Chapter 6 The Relational Algebra and Relational Calculus
result relations. This can be useful in connection with more complex operations such as UNION and JOIN, as we shall see. To rename the attributes in a relation, we simply list the new attribute names in parentheses, as in the following example:
TEMP ← σDno=5(EMPLOYEE) R(First_name, Last_name, Salary) ← πFname, Lname, Salary(TEMP)
These two operations are illustrated in Figure 6.2(b).
If no renaming is applied, the names of the attributes in the resulting relation of a SELECT operation are the same as those in the original relation and in the same order. For a PROJECT operation with no renaming, the resulting relation has the same attribute names as those in the projection list and in the same order in which they appear in the list.
We can also define a formal RENAME operation—which can rename either the rela- tion name or the attribute names, or both—as a unary operator. The general RENAME operation when applied to a relation R of degree n is denoted by any of the following three forms:
ρS(B1, B2, ..., Bn)(R) or ρS(R) or ρ(B1, B2, ..., Bn)(R)
where the symbol ρ (rho) is used to denote the RENAME operator, S is the new rela- tion name, and B1, B2, ..., Bn are the new attribute names. The first expression renames both the relation and its attributes, the second renames the relation only, and the third renames the attributes only. If the attributes of R are (A1, A2, ..., An) in that order, then each Ai is renamed as Bi.
In SQL, a single query typically represents a complex relational algebra expression. Renaming in SQL is accomplished by aliasing using AS, as in the following example:
SELECT E.Fname AS First_name, E.Lname AS Last_name, E.Salary AS Salary FROM EMPLOYEE AS E WHERE E.Dno=5,
6.2 Relational Algebra Operations from Set Theory
6.2.1 The UNION, INTERSECTION, and MINUS Operations The next group of relational algebra operations are the standard mathematical operations on sets. For example, to retrieve the Social Security numbers of all employees who either work in department 5 or directly supervise an employee who works in department 5, we can use the UNION operation as follows:4
4As a single relational algebra expression, this becomes Result ← πSsn (σDno=5 (EMPLOYEE) ) ∪ πSuper_ssn (σDno=5 (EMPLOYEE))
6.2 Relational Algebra Operations from Set Theory 153
DEP5_EMPS ← σDno=5(EMPLOYEE) RESULT1 ← πSsn(DEP5_EMPS) RESULT2(Ssn) ← πSuper_ssn(DEP5_EMPS) RESULT ← RESULT1 ∪ RESULT2
The relation RESULT1 has the Ssn of all employees who work in department 5, whereas RESULT2 has the Ssn of all employees who directly supervise an employee who works in department 5. The UNION operation produces the tuples that are in either RESULT1 or RESULT2 or both (see Figure 6.3), while eliminating any dupli- cates. Thus, the Ssn value ‘333445555’ appears only once in the result.
Several set theoretic operations are used to merge the elements of two sets in vari- ous ways, including UNION, INTERSECTION, and SET DIFFERENCE (also called MINUS or EXCEPT). These are binary operations; that is, each is applied to two sets (of tuples). When these operations are adapted to relational databases, the two rela- tions on which any of these three operations are applied must have the same type of tuples; this condition has been called union compatibility or type compatibility. Two relations R(A1, A2, ..., An) and S(B1, B2, ..., Bn) are said to be union compatible (or type compatible) if they have the same degree n and if dom(Ai) = dom(Bi) for 1 i
n. This means that the two relations have the same number of attributes and each corresponding pair of attributes has the same domain.
We can define the three operations UNION, INTERSECTION, and SET DIFFERENCE on two union-compatible relations R and S as follows:
■ UNION: The result of this operation, denoted by R ∪ S, is a relation that includes all tuples that are either in R or in S or in both R and S. Duplicate tuples are eliminated.
■ INTERSECTION: The result of this operation, denoted by R ∩ S, is a relation that includes all tuples that are in both R and S.
■ SET DIFFERENCE (or MINUS): The result of this operation, denoted by R – S, is a relation that includes all tuples that are in R but not in S.
We will adopt the convention that the resulting relation has the same attribute names as the first relation R. It is always possible to rename the attributes in the result using the rename operator.
RESULT1
Ssn
123456789
333445555
666884444
453453453
RESULT
Ssn
123456789
333445555
666884444
453453453
888665555
RESULT2
Ssn
333445555
888665555
Figure 6.3 Result of the UNION operation RESULT ← RESULT1 ∪ RESULT2.
STUDENT(a)
Fn
Susan
Ramesh
Johnny
Barbara
Amy
Jimmy
Ernest
Ln
Yao
Shah
Kohler
Jones
Ford
Wang
Gilbert
(b) Fn
Susan
Ramesh
Johnny
Barbara
Amy
Jimmy
Ernest
Ln
Yao
Shah
Kohler
Jones
Ford
Wang
Gilbert
John Smith
Rica rdo Browne
F rancis Johnson
(d) Fn
Johnny
Barbara
Amy
Jimmy
Ernest
Ln
Kohler
Jones
Ford
Wang
Gilbert
(c) Fn
Susan
Ramesh
Ln
Yao
Shah
INSTRUCTOR
Fname
John
Ricardo
Susan
Francis
Ramesh
Lname
Smith
Browne
Yao
Johnson
Shah
(e) Fname
John
Ricardo
Francis
Lname
Smith
Browne
Johnson
154 Chapter 6 The Relational Algebra and Relational Calculus
Figure 6.4 illustrates the three operations. The relations STUDENT and INSTRUCTOR in Figure 6.4(a) are union compatible and their tuples represent the names of students and the names of instructors, respectively. The result of the UNION operation in Figure 6.4(b) shows the names of all students and instructors. Note that duplicate tuples appear only once in the result. The result of the INTERSECTION operation (Figure 6.4(c)) includes only those who are both students and instructors.
Notice that both UNION and INTERSECTION are commutative operations; that is,
R ∪ S = S ∪ R and R ∩ S = S ∩ R
Both UNION and INTERSECTION can be treated as n-ary operations applicable to any number of relations because both are also associative operations; that is,
R ∪ (S ∪ T ) = (R ∪ S ) ∪ T and (R ∩ S ) ∩ T = R ∩ (S ∩ T )
The MINUS operation is not commutative; that is, in general,
R − S ≠ S − R
Figure 6.4 The set operations UNION, INTERSECTION, and MINUS. (a) Two union-compatible relations. (b) STUDENT ∪ INSTRUCTOR. (c) STUDENT ∩ INSTRUCTOR. (d) STUDENT − INSTRUCTOR. (e) INSTRUCTOR − STUDENT.
6.2 Relational Algebra Operations from Set Theory 155
Figure 6.4(d) shows the names of students who are not instructors, and Figure 6.4(e) shows the names of instructors who are not students.
Note that INTERSECTION can be expressed in terms of union and set difference as follows:
R ∩ S = ((R ∪ S ) − (R − S )) − (S − R )
In SQL, there are three operations—UNION, INTERSECT, and EXCEPT—that corre- spond to the set operations described here. In addition, there are multiset opera- tions (UNION ALL, INTERSECT ALL, and EXCEPT ALL) that do not eliminate duplicates (see Section 4.3.4).
6.2.2 The CARTESIAN PRODUCT (CROSS PRODUCT) Operation
Next, we discuss the CARTESIAN PRODUCT operation—also known as CROSS PRODUCT or CROSS JOIN—which is denoted by ×. This is also a binary set opera- tion, but the relations on which it is applied do not have to be union compatible. In its binary form, this set operation produces a new element by combining every member (tuple) from one relation (set) with every member (tuple) from the other relation (set). In general, the result of R(A1, A2, ..., An) × S(B1, B2, ..., Bm) is a rela- tion Q with degree n + m attributes Q(A1, A2, ..., An, B1, B2, ..., Bm), in that order. The resulting relation Q has one tuple for each combination of tuples—one from R and one from S. Hence, if R has nR tuples (denoted as |R| = nR), and S has nS tuples, then R × S will have nR * nS tuples.
The n-ary CARTESIAN PRODUCT operation is an extension of the above concept, which produces new tuples by concatenating all possible combinations of tuples from n underlying relations.
In general, the CARTESIAN PRODUCT operation applied by itself is generally mean- ingless. It is mostly useful when followed by a selection that matches values of attributes coming from the component relations. For example, suppose that we want to retrieve a list of names of each female employee’s dependents. We can do this as follows:
FEMALE_EMPS ← σSex=‘F’(EMPLOYEE) EMPNAMES ← πFname, Lname, Ssn(FEMALE_EMPS) EMP_DEPENDENTS ← EMPNAMES × DEPENDENT ACTUAL_DEPENDENTS ← σSsn=Essn(EMP_DEPENDENTS) RESULT ← πFname, Lname, Dependent_name(ACTUAL_DEPENDENTS)
The resulting relations from this sequence of operations are shown in Figure 6.5. The EMP_DEPENDENTS relation is the result of applying the CARTESIAN PROD- UCT operation to EMPNAMES from Figure 6.5 with DEPENDENT from Figure 3.6. In EMP_DEPENDENTS, every tuple from EMPNAMES is combined with every tuple from DEPENDENT, giving a result that is not very meaningful (every dependent is combined with every female employee). We want to combine a female employee tuple only with her particular dependents—namely, the DEPENDENT tuples whose
156 Chapter 6 The Relational Algebra and Relational Calculus
Fname
FEMALE_EMPS
Alicia
Jennifer
Joyce A
J
S
Minit
English
Zelaya
Wallace
Lname
453453453
999887777 3321Castle, Spring, TX
987654321
Ssn
1972-07-31
1968-07-19
1941-06-20
Bdate
5631 Rice, Houston, TX
291Berry, Bellaire, TX
F
F
F
Address Sex Dno 25000
25000
43000
4
5
4
Salary 987654321
333445555
888665555
Super_ssn
Fname
EMPNAMES
Alicia
Jennifer
Joyce English
Zelaya
Wallace
Lname
453453453
999887777
987654321
Ssn
Fname
EMP_DEPENDENTS
Alicia
Alicia
Alicia
Alicia
Alicia
Alicia
Alicia
Jennifer
Jennifer
Jennifer
Jennifer
Jennifer
Joyce
Jennifer
Jennifer
Joyce
Joyce
Zelaya
Zelaya
Zelaya
Zelaya
Zelaya
Zelaya
Wallace
Wallace
Wallace
Wallace
Wallace
Wallace
English
Zelaya
English
Wallace
English
Lname
999887777
999887777 Alice
999887777
Ssn
333445555
333445555
333445555
Essn
Abner
Theodore
Joy
F
F
M
Dependent_name Sex . . . . . .
. . .
. . .
1986-04-05
1958-05-03
1983-10-25
999887777
999887777
Michael999887777
123456789
987654321
123456789
Elizabeth
Alice
M
F
M
. . .
. . .
. . .
1942-02-28
1988-12-30
1988-01-04
987654321
999887777
Alice987654321
333445555
123456789
333445555
Joy
Theodore
F
M
F
. . .
. . .
. . .
1967-05-05
1983-10-25
1986-04-05
987654321
987654321
Abner987654321
123456789
333445555
987654321
Alice
Michael
F
M
M
. . .
. . .
. . .
1958-05-03
1988-01-04
1942-02-28
453453453
987654321
Elizabeth987654321
333445555
123456789
123456789
Theodore
Alice
F
F
F
. . .
. . .
. . .
1988-12-30
1986-04-05
1967-05-05
453453453
Joy453453453
333445555
333445555
M
F
. . .
. . .
1983-10-25
1958-05-03
Bdate
Joyce
Joyce
Joyce
Joyce
English
English
English
English
453453453
Abner453453453
123456789
987654321
Alice
Michael M
M
. . .
. . .
1988-01-04
1942-02-28
453453453
Elizabeth453453453
123456789
123456789
F
F
. . .
. . .
1988-12-30
1967-05-05
Fname
ACTUAL_DEPENDENTS
Lname Ssn Essn Dependent_name Sex . . .Bdate Jennifer Wallace Abner987654321 987654321 M . . .1942-02-28
Fname
RESULT
Lname Dependent_name Jennifer Wallace Abner
Figure 6.5 The Cartesian Product (Cross Product) operation.
6.3 Binary Relational Operations: JOIN and DIVISION 157
Essn value match the Ssn value of the EMPLOYEE tuple. The ACTUAL_DEPENDENTS relation accomplishes this. The EMP_DEPENDENTS relation is a good example of the case where relational algebra can be correctly applied to yield results that make no sense at all. It is the responsibility of the user to make sure to apply only mean- ingful operations to relations.
The CARTESIAN PRODUCT creates tuples with the combined attributes of two rela- tions. We can SELECT related tuples only from the two relations by specifying an appropriate selection condition after the Cartesian product, as we did in the preced- ing example. Because this sequence of CARTESIAN PRODUCT followed by SELECT is quite commonly used to combine related tuples from two relations, a special oper- ation, called JOIN, was created to specify this sequence as a single operation. We dis- cuss the JOIN operation next.
In SQL, CARTESIAN PRODUCT can be realized by using the CROSS JOIN option in joined tables (see Section 5.1.6). Alternatively, if there are two tables in the WHERE clause and there is no corresponding join condition in the query, the result will also be the CARTESIAN PRODUCT of the two tables (see Q10 in Section 4.3.3).
6.3 Binary Relational Operations: JOIN and DIVISION
6.3.1 The JOIN Operation The JOIN operation, denoted by , is used to combine related tuples from two rela- tions into single “longer” tuples. This operation is very important for any relational database with more than a single relation because it allows us to process relation- ships among relations. To illustrate JOIN, suppose that we want to retrieve the name of the manager of each department. To get the manager’s name, we need to combine each department tuple with the employee tuple whose Ssn value matches the Mgr_ssn value in the department tuple. We do this by using the JOIN operation and then projecting the result over the necessary attributes, as follows:
DEPT_MGR ← DEPARTMENT Mgr_ssn=Ssn EMPLOYEE RESULT ← πDname, Lname, Fname(DEPT_MGR)
The first operation is illustrated in Figure 6.6. Note that Mgr_ssn is a foreign key of the DEPARTMENT relation that references Ssn, the primary key of the EMPLOYEE relation. This referential integrity constraint plays a role in having matching tuples in the referenced relation EMPLOYEE.
The JOIN operation can be specified as a CARTESIAN PRODUCT operation followed by a SELECT operation. However, JOIN is very important because it is used very fre- quently when specifying database queries. Consider the earlier example illustrating CARTESIAN PRODUCT, which included the following sequence of operations:
EMP_DEPENDENTS ← EMPNAMES × DEPENDENT ACTUAL_DEPENDENTS ← σSsn=Essn(EMP_DEPENDENTS)
158 Chapter 6 The Relational Algebra and Relational Calculus
DEPT_MGR
Dname Dnumber Mgr_ssn Fname Minit Lname Ssn
Research 5 333445555 Franklin T Wong 333445555
Administration 4 987654321 Jennifer S Wallace 987654321
Headquarters 1 888665555 James E Borg 888665555
. . . . . .
. . .
. . .
. . .
. . .
. . .
. . .
Figure 6.6 Result of the JOIN operation DEPT_MGR ← DEPARTMENT Mgr_ssn=SsnEMPLOYEE.
These two operations can be replaced with a single JOIN operation as follows:
ACTUAL_DEPENDENTS ← EMPNAMES Ssn=EssnDEPENDENT
The general form of a JOIN operation on two relations5 R(A1, A2, ..., An) and S(B1, B2, ..., Bm) is
R <join condition>S
The result of the JOIN is a relation Q with n + m attributes Q(A1, A2, ..., An, B1, B2, ... , Bm) in that order; Q has one tuple for each combination of tuples—one from R and one from S—whenever the combination satisfies the join condition. This is the main difference between CARTESIAN PRODUCT and JOIN. In JOIN, only combina- tions of tuples satisfying the join condition appear in the result, whereas in the CARTESIAN PRODUCT all combinations of tuples are included in the result. The join condition is specified on attributes from the two relations R and S and is evalu- ated for each combination of tuples. Each tuple combination for which the join condition evaluates to TRUE is included in the resulting relation Q as a single com- bined tuple.
A general join condition is of the form
<condition> AND <condition> AND...AND <condition>
where each <condition> is of the form Ai θ Bj, Ai is an attribute of R, Bj is an attrib- ute of S, Ai and Bj have the same domain, and θ (theta) is one of the comparison operators {=, <, ≤, >, ≥, ≠}. A JOIN operation with such a general join condition is called a THETA JOIN. Tuples whose join attributes are NULL or for which the join condition is FALSE do not appear in the result. In that sense, the JOIN operation does not necessarily preserve all of the information in the participating relations, because tuples that do not get combined with matching ones in the other relation do not appear in the result.
5Again, notice that R and S can be any relations that result from general relational algebra expressions.
6.3 Binary Relational Operations: JOIN and DIVISION 159
6.3.2 Variations of JOIN: The EQUIJOIN and NATURAL JOIN
The most common use of JOIN involves join conditions with equality comparisons only. Such a JOIN, where the only comparison operator used is =, is called an EQUIJOIN. Both previous examples were EQUIJOINs. Notice that in the result of an EQUIJOIN we always have one or more pairs of attributes that have identical values in every tuple. For example, in Figure 6.6, the values of the attributes Mgr_ssn and Ssn are identical in every tuple of DEPT_MGR (the EQUIJOIN result) because the equality join condition specified on these two attributes requires the values to be identical in every tuple in the result. Because one of each pair of attributes with identical values is superfluous, a new operation called NATURAL JOIN—denoted by
* —was created to get rid of the second (superfluous) attribute in an EQUIJOIN con- dition.6 The standard definition of NATURAL JOIN requires that the two join attrib- utes (or each pair of join attributes) have the same name in both relations. If this is not the case, a renaming operation is applied first.
Suppose we want to combine each PROJECT tuple with the DEPARTMENT tuple that controls the project. In the following example, first we rename the Dnumber attribute of DEPARTMENT to Dnum—so that it has the same name as the Dnum attribute in PROJECT—and then we apply NATURAL JOIN:
PROJ_DEPT ← PROJECT * ρ(Dname, Dnum, Mgr_ssn, Mgr_start_date)(DEPARTMENT)
The same query can be done in two steps by creating an intermediate table DEPT as follows:
DEPT ← ρ(Dname, Dnum, Mgr_ssn, Mgr_start_date)(DEPARTMENT) PROJ_DEPT ← PROJECT * DEPT
The attribute Dnum is called the join attribute for the NATURAL JOIN operation, because it is the only attribute with the same name in both relations. The resulting relation is illustrated in Figure 6.7(a). In the PROJ_DEPT relation, each tuple com- bines a PROJECT tuple with the DEPARTMENT tuple for the department that con- trols the project, but only one join attribute value is kept.
If the attributes on which the natural join is specified already have the same names in both relations, renaming is unnecessary. For example, to apply a natural join on the Dnumber attributes of DEPARTMENT and DEPT_LOCATIONS, it is sufficient to write
DEPT_LOCS ← DEPARTMENT * DEPT_LOCATIONS
The resulting relation is shown in Figure 6.7(b), which combines each department with its locations and has one tuple for each location. In general, the join condition for NATURAL JOIN is constructed by equating each pair of join attributes that have the same name in the two relations and combining these conditions with AND. There can be a list of join attributes from each relation, and each corresponding pair must have the same name.
6NATURAL JOIN is basically an EQUIJOIN followed by the removal of the superfluous attributes.
160 Chapter 6 The Relational Algebra and Relational Calculus
Pname
PROJ_DEPT
(a)
ProductX
ProductY
ProductZ
Computerization
Reorganization
Newbenefits
3
1
2
30
10
20
Pnumber
Houston
Bellaire
Sugarland
Stafford
Stafford
Houston
Plocation
5
5 333445555
5
4
4
1
Dnum
Research
Research
Research
Administration
Administration
Headquarters
Dname
333445555
333445555
987654321
987654321
888665555
1988-05-22
1988-05-22
1988-05-22
1995-01-01
1995-01-01
1981-06-19
Mgr_ssn Mgr_start_date
Dname
DEPT_LOCS
(b)
5
1
4
5 5
Dnumber
333445555
888665555
987654321
333445555 333445555
Mgr_ssn
1988-05-22
1981-06-19
1995-01-01
Research
Research
Research
Administration
1988-05-22 1988-05-22
Headquarters Houston
Bellaire
Stafford
Sugarland Houston
LocationMgr_start_date
Figure 6.7 Results of two NATURAL JOIN operations. (a) PROJ_DEPT ← PROJECT * DEPT. (b) DEPT_LOCS ← DEPARTMENT * DEPT_LOCATIONS.
A more general, but nonstandard definition for NATURAL JOIN is
Q ← R *(<list1>),(<list2>)S
In this case, <list1> specifies a list of i attributes from R, and <list2> specifies a list of i attributes from S. The lists are used to form equality comparison conditions between pairs of corresponding attributes, and the conditions are then ANDed together. Only the list corresponding to attributes of the first relation R—<list1>— is kept in the result Q.
Notice that if no combination of tuples satisfies the join condition, the result of a JOIN is an empty relation with zero tuples. In general, if R has nR tuples and S has nS tuples, the result of a JOIN operation R <join condition> S will have between zero and nR * nS tuples. The expected size of the join result divided by the maximum size nR * nS leads to a ratio called join selectivity, which is a property of each join condition. If there is no join condition, all combinations of tuples qualify and the JOIN degen- erates into a CARTESIAN PRODUCT, also called CROSS PRODUCT or CROSS JOIN.
As we can see, a single JOIN operation is used to combine data from two relations so that related information can be presented in a single table. These operations are also known as inner joins, to distinguish them from a different join variation called
6.3 Binary Relational Operations: JOIN and DIVISION 161
outer joins (see Section 6.4.4). Informally, an inner join is a type of match and com- bine operation defined formally as a combination of CARTESIAN PRODUCT and SELECTION. Note that sometimes a join may be specified between a relation and itself, as we will illustrate in Section 6.4.3. The NATURAL JOIN or EQUIJOIN opera- tion can also be specified among multiple tables, leading to an n-way join. For example, consider the following three-way join:
((PROJECT Dnum=DnumberDEPARTMENT) Mgr_ssn=SsnEMPLOYEE)
This combines each project tuple with its controlling department tuple into a single tuple, and then combines that tuple with an employee tuple that is the department manager. The net result is a consolidated relation in which each tuple contains this project-department-manager combined information.
In SQL, JOIN can be realized in several different ways. The first method is to specify the <join conditions> in the WHERE clause, along with any other selection condi- tions. This is very common, and is illustrated by queries Q1, Q1A, Q1B, Q2, and Q8 in Sections 4.3.1 and 4.3.2, as well as by many other query examples in Chapters 4 and 5. The second way is to use a nested relation, as illustrated by queries Q4A and Q16 in Section 5.1.2. Another way is to use the concept of joined tables, as illus- trated by the queries Q1A, Q1B, Q8B, and Q2A in Section 5.1.6. The construct of joined tables was added to SQL2 to allow the user to specify explicitly all the various types of joins, because the other methods were more limited. It also allows the user to clearly distinguish join conditions from the selection conditions in the WHERE clause.
6.3.3 A Complete Set of Relational Algebra Operations It has been shown that the set of relational algebra operations {σ, π, ∪, ρ, –, ×} is a complete set; that is, any of the other original relational algebra operations can be expressed as a sequence of operations from this set. For example, the INTERSECTION operation can be expressed by using UNION and MINUS as follows:
R ∩ S ≡ (R ∪ S) – ((R – S) ∪ (S – R))
Although, strictly speaking, INTERSECTION is not required, it is inconvenient to specify this complex expression every time we wish to specify an intersection. As another example, a JOIN operation can be specified as a CARTESIAN PRODUCT fol- lowed by a SELECT operation, as we discussed:
R <condition>S ≡ σ<condition>(R × S)
Similarly, a NATURAL JOIN can be specified as a CARTESIAN PRODUCT preceded by RENAME and followed by SELECT and PROJECT operations. Hence, the various JOIN operations are also not strictly necessary for the expressive power of the rela- tional algebra. However, they are important to include as separate operations because they are convenient to use and are very commonly applied in database applications. Other operations have been included in the basic relational algebra for convenience rather than necessity. We discuss one of these—the DIVISION opera- tion—in the next section.
162 Chapter 6 The Relational Algebra and Relational Calculus
Essn
SSN_PNOS (a)
123456789
123456789
666884444
453453453
453453453
333445555
333445555
333445555
333445555
999887777
999887777
987987987
987987987
987654321
987654321
888665555
3
1
2
2
1
2
30
30
30
10
10
3
10
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Pno A
R (b)
a1
a2
a3
a4
a1
a3
a2
a3
a4
a1
a2
a3
b1
b1
b1
b2
b1
b2
b4
b4
b4
b3
b3
b3
B
SMITH_PNOS
1
2
Pno
S
a1
a2
a3
A
T
b1
b4
B
SSNS
123456789
453453453
Ssn
Figure 6.8 The DIVISION operation. (a) Dividing SSN_PNOS by SMITH_PNOS. (b) T ← R ÷ S.
6.3.4 The DIVISION Operation The DIVISION operation, denoted by ÷, is useful for a special kind of query that sometimes occurs in database applications. An example is Retrieve the names of employees who work on all the projects that ‘John Smith’ works on. To express this query using the DIVISION operation, proceed as follows. First, retrieve the list of project numbers that ‘John Smith’ works on in the intermediate relation SMITH_PNOS:
SMITH ← σFname=‘John’ AND Lname=‘Smith’(EMPLOYEE) SMITH_PNOS ← πPno(WORKS_ON Essn=SsnSMITH)
Next, create a relation that includes a tuple <Pno, Essn> whenever the employee whose Ssn is Essn works on the project whose number is Pno in the intermediate relation SSN_PNOS:
SSN_PNOS ← πEssn, Pno(WORKS_ON)
Finally, apply the DIVISION operation to the two relations, which gives the desired employees’ Social Security numbers:
SSNS(Ssn) ← SSN_PNOS ÷ SMITH_PNOS RESULT ← πFname, Lname(SSNS * EMPLOYEE)
The preceding operations are shown in Figure 6.8(a).
6.3 Binary Relational Operations: JOIN and DIVISION 163
In general, the DIVISION operation is applied to two relations R(Z) ÷ S(X), where the attributes of R are a subset of the attributes of S; that is, X ⊆ Z. Let Y be the set of attributes of R that are not attributes of S; that is, Y = Z – X (and hence Z = X ∪ Y ). The result of DIVISION is a relation T(Y) that includes a tuple t if tuples tR appear in R with tR [Y] = t, and with tR [X] = tS for every tuple tS in S. This means that, for a tuple t to appear in the result T of the DIVISION, the values in t must appear in R in combination with every tuple in S. Note that in the formulation of the DIVISION operation, the tuples in the denominator relation S restrict the numerator relation R by selecting those tuples in the result that match all values present in the denomina- tor. It is not necessary to know what those values are as they can be computed by another operation, as illustrated in the SMITH_PNOS relation in the above example.
Figure 6.8(b) illustrates a DIVISION operation where X = {A}, Y = {B}, and Z = {A, B}. Notice that the tuples (values) b1 and b4 appear in R in combination with all three tuples in S; that is why they appear in the resulting relation T. All other values of B in R do not appear with all the tuples in S and are not selected: b2 does not appear with a2, and b3 does not appear with a1.
The DIVISION operation can be expressed as a sequence of π, ×, and – operations as follows:
T1 ← πY(R) T2 ← πY((S × T1) – R) T ← T1 – T2
The DIVISION operation is defined for convenience for dealing with queries that involve universal quantification (see Section 6.6.7) or the all condition. Most RDBMS implementations with SQL as the primary query language do not directly implement division. SQL has a roundabout way of dealing with the type of query illustrated above (see Section 5.1.4, queries Q3A and Q3B). Table 6.1 lists the various basic relational algebra operations we have discussed.
6.3.5 Notation for Query Trees In this section we describe a notation typically used in relational systems to repre- sent queries internally. The notation is called a query tree or sometimes it is known as a query evaluation tree or query execution tree. It includes the relational algebra operations being executed and is used as a possible data structure for the internal representation of the query in an RDBMS.
A query tree is a tree data structure that corresponds to a relational algebra expres- sion. It represents the input relations of the query as leaf nodes of the tree, and rep- resents the relational algebra operations as internal nodes. An execution of the query tree consists of executing an internal node operation whenever its operands (represented by its child nodes) are available, and then replacing that internal node by the relation that results from executing the operation. The execution terminates when the root node is executed and produces the result relation for the query.
164 Chapter 6 The Relational Algebra and Relational Calculus
Table 6.1 Operations of Relational Algebra
OPERATION PURPOSE NOTATION
SELECT Selects all tuples that satisfy the selection condition from a relation R.
σ<selection condition>(R)
PROJECT Produces a new relation with only some of the attrib- utes of R, and removes duplicate tuples.
π<attribute list>(R)
THETA JOIN Produces all combinations of tuples from R1 and R2 that satisfy the join condition.
R1 <join condition> R2
EQUIJOIN Produces all the combinations of tuples from R1 and R2 that satisfy a join condition with only equality comparisons.
R1 <join condition> R2, OR R1 (<join attributes 1>),
(<join attributes 2>) R2
NATURAL JOIN Same as EQUIJOIN except that the join attributes of R2 are not included in the resulting relation; if the join attributes have the same names, they do not have to be specified at all.
R1*<join condition> R2, OR R1* (<join attributes 1>),
(<join attributes 2>) R2 OR R1 * R2
UNION Produces a relation that includes all the tuples in R1 or R2 or both R1 and R2; R1 and R2 must be union compatible.
R1 ∪ R2
INTERSECTION Produces a relation that includes all the tuples in both R1 and R2; R1 and R2 must be union compatible.
R1 ∩ R2
DIFFERENCE Produces a relation that includes all the tuples in R1 that are not in R2; R1 and R2 must be union compatible.
R1 – R2
CARTESIAN
PRODUCT
Produces a relation that has the attributes of R1 and R2 and includes as tuples all possible combinations of tuples from R1 and R2.
R1 × R2
DIVISION Produces a relation R(X) that includes all tuples t[X] in R1(Z) that appear in R1 in combination with every tuple from R2(Y), where Z = X ∪ Y.
R1(Z) ÷ R2(Y)
Figure 6.9 shows a query tree for Query 2 (see Section 4.3.1): For every project located in ‘Stafford’, list the project number, the controlling department number, and the department manager’s last name, address, and birth date. This query is specified on the relational schema of Figure 3.5 and corresponds to the following relational algebra expression:
πPnumber, Dnum, Lname, Address, Bdate(((σPlocation=‘Stafford’(PROJECT))
Dnum=Dnumber(DEPARTMENT)) Mgr_ssn=Ssn(EMPLOYEE))
In Figure 6.9, the three leaf nodes P, D, and E represent the three relations PROJECT, DEPARTMENT, and EMPLOYEE. The relational algebra operations in the expression
6.4 Additional Relational Operations 165
(1)
(2)
(3)
P.Pnumber,P.Dnum,E.Lname,E.Address,E.Bdateπ
D.Mgr_ssn=E.Ssn
P.Dnum=D.Dnumber
σ P.Plocation= ‘Stafford’
E
D
P
EMPLOYEE
DEPARTMENT
PROJECT
Figure 6.9 Query tree corresponding to the relational algebra expression for Q2.
are represented by internal tree nodes. The query tree signifies an explicit order of execution in the following sense. In order to execute Q2, the node marked (1) in Figure 6.9 must begin execution before node (2) because some resulting tuples of operation (1) must be available before we can begin to execute operation (2). Similarly, node (2) must begin to execute and produce results before node (3) can start execution, and so on. In general, a query tree gives a good visual representation and understanding of the query in terms of the relational operations it uses and is recommended as an additional means for expressing queries in relational algebra. We will revisit query trees when we discuss query processing and optimization in Chapter 19.
6.4 Additional Relational Operations Some common database requests—which are needed in commercial applications for RDBMSs—cannot be performed with the original relational algebra operations described in Sections 6.1 through 6.3. In this section we define additional opera- tions to express these requests. These operations enhance the expressive power of the original relational algebra.
6.4.1 Generalized Projection The generalized projection operation extends the projection operation by allowing functions of attributes to be included in the projection list. The generalized form can be expressed as:
πF1, F2, ..., Fn (R)
166 Chapter 6 The Relational Algebra and Relational Calculus
where F1, F2, ..., Fn are functions over the attributes in relation R and may involve arithmetic operations and constant values. This operation is helpful when develop- ing reports where computed values have to be produced in the columns of a query result.
As an example, consider the relation
EMPLOYEE (Ssn, Salary, Deduction, Years_service)
A report may be required to show
Net Salary = Salary – Deduction, Bonus = 2000 * Years_service, and Tax = 0.25 * Salary.
Then a generalized projection combined with renaming may be used as follows:
REPORT ← ρ(Ssn, Net_salary, Bonus, Tax)(πSsn, Salary – Deduction, 2000 * Years_service, 0.25 * Salary
(EMPLOYEE)).
6.4.2 Aggregate Functions and Grouping Another type of request that cannot be expressed in the basic relational algebra is to specify mathematical aggregate functions on collections of values from the data- base. Examples of such functions include retrieving the average or total salary of all employees or the total number of employee tuples. These functions are used in sim- ple statistical queries that summarize information from the database tuples. Common functions applied to collections of numeric values include SUM, AVERAGE, MAXIMUM, and MINIMUM. The COUNT function is used for counting tuples or values.
Another common type of request involves grouping the tuples in a relation by the value of some of their attributes and then applying an aggregate function independently to each group. An example would be to group EMPLOYEE tuples by Dno, so that each group includes the tuples for employees working in the same department. We can then list each Dno value along with, say, the average salary of employees within the department, or the number of employees who work in the department.
We can define an AGGREGATE FUNCTION operation, using the symbol ℑ (pro- nounced script F)7, to specify these types of requests as follows:
<grouping attributes> ℑ <function list> (R)
where <grouping attributes> is a list of attributes of the relation specified in R, and <function list> is a list of (<function> <attribute>) pairs. In each such pair, <function> is one of the allowed functions—such as SUM, AVERAGE, MAXIMUM, MINIMUM, COUNT—and <attribute> is an attribute of the relation specified by R. The
7There is no single agreed-upon notation for specifying aggregate functions. In some cases a “script A” is used.
6.4 Additional Relational Operations 167
Count_ssn
8 35125
Dno Count_ssn
5
4
1
4
3
1
33250
31000
55000
Average_salary
Average_salary
(b)
(c)
4
3
1
33250
31000
55000
(a) Dno
5
4
1
No_of_employees Average_sal
R
Figure 6.10 The aggregate function operation. a. ρR(Dno, No_of_employees, Average_sal)(Dno ℑ COUNT Ssn, AVERAGE Salary(EMPLOYEE)). b. Dno ℑ COUNT Ssn, AVERAGE Salary(EMPLOYEE). c. ℑ COUNT Ssn, AVERAGE Salary(EMPLOYEE).
resulting relation has the grouping attributes plus one attribute for each element in the function list. For example, to retrieve each department number, the number of employees in the department, and their average salary, while renaming the resulting attributes as indicated below, we write:
ρR(Dno, No_of_employees, Average_sal)(Dno ℑ COUNT Ssn, AVERAGE Salary (EMPLOYEE))
The result of this operation on the EMPLOYEE relation of Figure 3.6 is shown in Figure 6.10(a).
In the above example, we specified a list of attribute names—between parentheses in the RENAME operation—for the resulting relation R. If no renaming is applied, then the attributes of the resulting relation that correspond to the function list will each be the concatenation of the function name with the attribute name in the form <function>_<attribute>.8 For example, Figure 6.10(b) shows the result of the fol- lowing operation:
Dno ℑ COUNT Ssn, AVERAGE Salary(EMPLOYEE)
If no grouping attributes are specified, the functions are applied to all the tuples in the relation, so the resulting relation has a single tuple only. For example, Figure 6.10(c) shows the result of the following operation:
ℑ COUNT Ssn, AVERAGE Salary(EMPLOYEE)
It is important to note that, in general, duplicates are not eliminated when an aggre- gate function is applied; this way, the normal interpretation of functions such as
8Note that this is an arbitrary notation we are suggesting. There is no standard notation.
168 Chapter 6 The Relational Algebra and Relational Calculus
SUM and AVERAGE is computed.9 It is worth emphasizing that the result of apply- ing an aggregate function is a relation, not a scalar number—even if it has a single value. This makes the relational algebra a closed mathematical system.
6.4.3 Recursive Closure Operations Another type of operation that, in general, cannot be specified in the basic original relational algebra is recursive closure. This operation is applied to a recursive rela- tionship between tuples of the same type, such as the relationship between an employee and a supervisor. This relationship is described by the foreign key Super_ssn of the EMPLOYEE relation in Figures 3.5 and 3.6, and it relates each employee tuple (in the role of supervisee) to another employee tuple (in the role of supervisor). An example of a recursive operation is to retrieve all supervisees of an employee e at all levels—that is, all employees e� directly supervised by e, all employees e�ℑ directly supervised by each employee e�, all employees e��� directly supervised by each employee e��, and so on.
It is relatively straightforward in the relational algebra to specify all employees supervised by e at a specific level by joining the table with itself one or more times. However, it is difficult to specify all supervisees at all levels. For example, to specify the Ssns of all employees e� directly supervised—at level one—by the employee e whose name is ‘James Borg’ (see Figure 3.6), we can apply the following operation:
BORG_SSN ← πSsn(σFname=‘James’ AND Lname=‘Borg’(EMPLOYEE)) SUPERVISION(Ssn1, Ssn2) ← πSsn,Super_ssn(EMPLOYEE) RESULT1(Ssn) ← πSsn1(SUPERVISION Ssn2=SsnBORG_SSN)
To retrieve all employees supervised by Borg at level 2—that is, all employees e�� supervised by some employee e� who is directly supervised by Borg—we can apply another JOIN to the result of the first query, as follows:
RESULT2(Ssn) ← πSsn1(SUPERVISION Ssn2=SsnRESULT1)
To get both sets of employees supervised at levels 1 and 2 by ‘James Borg’, we can apply the UNION operation to the two results, as follows:
RESULT ← RESULT2 ∪ RESULT1
The results of these queries are illustrated in Figure 6.11. Although it is possible to retrieve employees at each level and then take their UNION, we cannot, in general, specify a query such as “retrieve the supervisees of ‘James Borg’ at all levels” without utilizing a looping mechanism unless we know the maximum number of levels.10
An operation called the transitive closure of relations has been proposed to compute the recursive relationship as far as the recursion proceeds.
9In SQL, the option of eliminating duplicates before applying the aggregate function is available by including the keyword DISTINCT (see Section 4.4.4). 10The SQL3 standard includes syntax for recursive closure.
6.4 Additional Relational Operations 169
SUPERVISION
Ssn1 Ssn2
123456789
333445555
999887777
987654321
666884444
453453453
987987987
888665555
333445555
888665555
987654321
888665555
333445555
333445555
987654321
null
(Borg’s Ssn is 888665555) (Ssn) (Super_ssn)
RESULT1
Ssn
333445555
987654321
(Supervised by Borg)
RESULT
Ssn
123456789
999887777
666884444
453453453
987987987
333445555
987654321
(RESULT1 ∪ RESULT2)
RESULT2
Ssn
123456789
999887777
666884444
453453453
987987987
(Supervised by Borg’s subordinates)
Figure 6.11 A two-level recursive query.
6.4.4 OUTER JOIN Operations Next, we discuss some additional extensions to the JOIN operation that are neces- sary to specify certain types of queries. The JOIN operations described earlier match tuples that satisfy the join condition. For example, for a NATURAL JOIN operation R * S, only tuples from R that have matching tuples in S—and vice versa—appear in the result. Hence, tuples without a matching (or related) tuple are eliminated from the JOIN result. Tuples with NULL values in the join attributes are also eliminated. This type of join, where tuples with no match are eliminated, is known as an inner join. The join operations we described earlier in Section 6.3 are all inner joins. This amounts to the loss of information if the user wants the result of the JOIN to include all the tuples in one or more of the component relations.
A set of operations, called outer joins, were developed for the case where the user wants to keep all the tuples in R, or all those in S, or all those in both relations in the result of the JOIN, regardless of whether or not they have matching tuples in the other relation. This satisfies the need of queries in which tuples from two tables are
170 Chapter 6 The Relational Algebra and Relational Calculus
RESULT
Fname Minit Lname Dname
John
Franklin
Alicia
Jennifer
Ramesh
Joyce
Ahmad
James
B
T
J
S
K
A
V
E
Smith
Wong
Zelaya
Wallace
Narayan
English
Jabbar
Borg
NULL
Research
NULL
Administration
NULL
NULL
NULL
Headquarters
Figure 6.12 The result of a LEFT OUTER JOIN opera- tion.
to be combined by matching corresponding rows, but without losing any tuples for lack of matching values. For example, suppose that we want a list of all employee names as well as the name of the departments they manage if they happen to manage a department; if they do not manage one, we can indicate it with a NULL value. We can apply an operation LEFT OUTER JOIN, denoted by , to retrieve the result as follows:
TEMP ← (EMPLOYEE Ssn=Mgr_ssnDEPARTMENT)
RESULT ← πFname, Minit, Lname, Dname(TEMP)
The LEFT OUTER JOIN operation keeps every tuple in the first, or left, relation R in R S; if no matching tuple is found in S, then the attributes of S in the join result are
filled or padded with NULL values. The result of these operations is shown in Figure 6.12.
A similar operation, RIGHT OUTER JOIN, denoted by , keeps every tuple in the second, or right, relation S in the result of R S. A third operation, FULL OUTER JOIN, denoted by , keeps all tuples in both the left and the right relations when no matching tuples are found, padding them with NULL values as needed. The three outer join operations are part of the SQL2 standard (see Section 5.1.6). These oper- ations were provided later as an extension of relational algebra in response to the typical need in business applications to show related information from multiple tables exhaustively. Sometimes a complete reporting of data from multiple tables is required whether or not there are matching values.
6.4.5 The OUTER UNION Operation The OUTER UNION operation was developed to take the union of tuples from two relations that have some common attributes, but are not union (type) compatible. This operation will take the UNION of tuples in two relations R(X, Y ) and S(X, Z) that are partially compatible, meaning that only some of their attributes, say X, are union compatible. The attributes that are union compatible are represented only once in the result, and those attributes that are not union compatible from either
6.5 Examples of Queries in Relational Algebra 171
relation are also kept in the result relation T(X, Y, Z). It is therefore the same as a FULL OUTER JOIN on the common attributes.
Two tuples t1 in R and t2 in S are said to match if t1[X]=t2[X]. These will be com- bined (unioned) into a single tuple in t. Tuples in either relation that have no matching tuple in the other relation are padded with NULL values. For example, an OUTER UNION can be applied to two relations whose schemas are STUDENT(Name, Ssn, Department, Advisor) and INSTRUCTOR(Name, Ssn, Department, Rank). Tuples from the two relations are matched based on having the same combination of values of the shared attributes—Name, Ssn, Department. The resulting relation, STUDENT_OR_INSTRUCTOR, will have the following attributes:
STUDENT_OR_INSTRUCTOR(Name, Ssn, Department, Advisor, Rank)
All the tuples from both relations are included in the result, but tuples with the same (Name, Ssn, Department) combination will appear only once in the result. Tuples appearing only in STUDENT will have a NULL for the Rank attribute, whereas tuples appearing only in INSTRUCTOR will have a NULL for the Advisor attribute. A tuple that exists in both relations, which represent a student who is also an instructor, will have values for all its attributes.11
Notice that the same person may still appear twice in the result. For example, we could have a graduate student in the Mathematics department who is an instructor in the Computer Science department. Although the two tuples representing that person in STUDENT and INSTRUCTOR will have the same (Name, Ssn) values, they will not agree on the Department value, and so will not be matched. This is because Department has two different meanings in STUDENT (the department where the per- son studies) and INSTRUCTOR (the department where the person is employed as an instructor). If we wanted to apply the OUTER UNION based on the same (Name, Ssn) combination only, we should rename the Department attribute in each table to reflect that they have different meanings and designate them as not being part of the union-compatible attributes. For example, we could rename the attributes as MajorDept in STUDENT and WorkDept in INSTRUCTOR.
6.5 Examples of Queries in Relational Algebra
The following are additional examples to illustrate the use of the relational algebra operations. All examples refer to the database in Figure 3.6. In general, the same query can be stated in numerous ways using the various operations. We will state each query in one way and leave it to the reader to come up with equivalent formu- lations.
Query 1. Retrieve the name and address of all employees who work for the ‘Research’ department.
11Note that OUTER UNION is equivalent to a FULL OUTER JOIN if the join attributes are all the com- mon attributes of the two relations.
172 Chapter 6 The Relational Algebra and Relational Calculus
RESEARCH_DEPT ← σDname=‘Research’(DEPARTMENT) RESEARCH_EMPS ← (RESEARCH_DEPT Dnumber=DnoEMPLOYEE) RESULT ← πFname, Lname, Address(RESEARCH_EMPS)
As a single in-line expression, this query becomes:
πFname, Lname, Address (σDname=‘Research’(DEPARTMENT Dnumber=Dno(EMPLOYEE))
This query could be specified in other ways; for example, the order of the JOIN and SELECT operations could be reversed, or the JOIN could be replaced by a NATURAL JOIN after renaming one of the join attributes to match the other join attribute name.
Query 2. For every project located in ‘Stafford’, list the project number, the controlling department number, and the department manager’s last name, address, and birth date.
STAFFORD_PROJS ← σPlocation=‘Stafford’(PROJECT) CONTR_DEPTS ← (STAFFORD_PROJS Dnum=DnumberDEPARTMENT) PROJ_DEPT_MGRS ← (CONTR_DEPTS Mgr_ssn=SsnEMPLOYEE) RESULT ← πPnumber, Dnum, Lname, Address, Bdate(PROJ_DEPT_MGRS)
In this example, we first select the projects located in Stafford, then join them with their controlling departments, and then join the result with the department man- agers. Finally, we apply a project operation on the desired attributes.
Query 3. Find the names of employees who work on all the projects controlled by department number 5.
DEPT5_PROJS ← ρ(Pno)(πPnumber(σDnum=5(PROJECT))) EMP_PROJ ← ρ(Ssn, Pno)(πEssn, Pno(WORKS_ON)) RESULT_EMP_SSNS ← EMP_PROJ ÷ DEPT5_PROJS RESULT ← πLname, Fname(RESULT_EMP_SSNS * EMPLOYEE)
In this query, we first create a table DEPT5_PROJS that contains the project numbers of all projects controlled by department 5. Then we create a table EMP_PROJ that holds (Ssn, Pno) tuples, and apply the division operation. Notice that we renamed the attributes so that they will be correctly used in the division operation. Finally, we join the result of the division, which holds only Ssn values, with the EMPLOYEE table to retrieve the desired attributes from EMPLOYEE.
Query 4. Make a list of project numbers for projects that involve an employee whose last name is ‘Smith’, either as a worker or as a manager of the department that controls the project.
SMITHS(Essn) ← πSsn (σLname=‘Smith’(EMPLOYEE)) SMITH_WORKER_PROJS ← πPno(WORKS_ON * SMITHS) MGRS ← πLname, Dnumber(EMPLOYEE Ssn=Mgr_ssnDEPARTMENT) SMITH_MANAGED_DEPTS(Dnum) ← πDnumber (σLname=‘Smith’(MGRS)) SMITH_MGR_PROJS(Pno) ← πPnumber(SMITH_MANAGED_DEPTS * PROJECT) RESULT ← (SMITH_WORKER_PROJS ∪ SMITH_MGR_PROJS)
6.5 Examples of Queries in Relational Algebra 173
In this query, we retrieved the project numbers for projects that involve an employee named Smith as a worker in SMITH_WORKER_PROJS. Then we retrieved the project numbers for projects that involve an employee named Smith as manager of the department that controls the project in SMITH_MGR_PROJS. Finally, we applied the UNION operation on SMITH_WORKER_PROJS and SMITH_MGR_PROJS. As a single in-line expression, this query becomes:
πPno (WORKS_ON Essn=Ssn(πSsn (σLname=‘Smith’(EMPLOYEE))) ∪ πPno ((πDnumber (σLname=‘Smith’(πLname, Dnumber(EMPLOYEE)))
Ssn=Mgr_ssnDEPARTMENT)) Dnumber=DnumPROJECT)
Query 5. List the names of all employees with two or more dependents.
Strictly speaking, this query cannot be done in the basic (original) relational algebra. We have to use the AGGREGATE FUNCTION operation with the COUNT aggregate function. We assume that dependents of the same employee have distinct Dependent_name values.
T1(Ssn, No_of_dependents)← Essn ℑ COUNT Dependent_name(DEPENDENT) T2 ← σNo_of_dependents>2(T1) RESULT ← πLname, Fname(T2 * EMPLOYEE)
Query 6. Retrieve the names of employees who have no dependents.
This is an example of the type of query that uses the MINUS (SET DIFFERENCE) operation.
ALL_EMPS ← πSsn(EMPLOYEE) EMPS_WITH_DEPS(Ssn) ← πEssn(DEPENDENT) EMPS_WITHOUT_DEPS ← (ALL_EMPS – EMPS_WITH_DEPS) RESULT ← πLname, Fname(EMPS_WITHOUT_DEPS * EMPLOYEE)
We first retrieve a relation with all employee Ssns in ALL_EMPS. Then we create a table with the Ssns of employees who have at least one dependent in EMPS_WITH_DEPS. Then we apply the SET DIFFERENCE operation to retrieve employees Ssns with no dependents in EMPS_WITHOUT_DEPS, and finally join this with EMPLOYEE to retrieve the desired attributes. As a single in-line expression, this query becomes:
πLname, Fname((πSsn(EMPLOYEE) – ρSsn(πEssn(DEPENDENT))) * EMPLOYEE)
Query 7. List the names of managers who have at least one dependent.
MGRS(Ssn) ← πMgr_ssn(DEPARTMENT) EMPS_WITH_DEPS(Ssn) ← πEssn(DEPENDENT) MGRS_WITH_DEPS ← (MGRS ∩ EMPS_WITH_DEPS) RESULT ← πLname, Fname(MGRS_WITH_DEPS * EMPLOYEE)
In this query, we retrieve the Ssns of managers in MGRS, and the Ssns of employees with at least one dependent in EMPS_WITH_DEPS, then we apply the SET INTERSECTION operation to get the Ssns of managers who have at least one dependent.
174 Chapter 6 The Relational Algebra and Relational Calculus
As we mentioned earlier, the same query can be specified in many different ways in relational algebra. In particular, the operations can often be applied in various orders. In addition, some operations can be used to replace others; for example, the INTERSECTION operation in Q7 can be replaced by a NATURAL JOIN. As an exercise, try to do each of these sample queries using different operations.12 We showed how to write queries as single relational algebra expressions for queries Q1, Q4, and Q6. Try to write the remaining queries as single expressions. In Chapters 4 and 5 and in Sections 6.6 and 6.7, we show how these queries are written in other relational languages.
6.6 The Tuple Relational Calculus In this and the next section, we introduce another formal query language for the relational model called relational calculus. This section introduces the language known as tuple relational calculus, and Section 6.7 introduces a variation called domain relational calculus. In both variations of relational calculus, we write one declarative expression to specify a retrieval request; hence, there is no description of how, or in what order, to evaluate a query. A calculus expression specifies what is to be retrieved rather than how to retrieve it. Therefore, the relational calculus is con- sidered to be a nonprocedural language. This differs from relational algebra, where we must write a sequence of operations to specify a retrieval request in a particular order of applying the operations; thus, it can be considered as a procedural way of stating a query. It is possible to nest algebra operations to form a single expression; however, a certain order among the operations is always explicitly specified in a rela- tional algebra expression. This order also influences the strategy for evaluating the query. A calculus expression may be written in different ways, but the way it is writ- ten has no bearing on how a query should be evaluated.
It has been shown that any retrieval that can be specified in the basic relational alge- bra can also be specified in relational calculus, and vice versa; in other words, the expressive power of the languages is identical. This led to the definition of the con- cept of a relationally complete language. A relational query language L is considered relationally complete if we can express in L any query that can be expressed in rela- tional calculus. Relational completeness has become an important basis for compar- ing the expressive power of high-level query languages. However, as we saw in Section 6.4, certain frequently required queries in database applications cannot be expressed in basic relational algebra or calculus. Most relational query languages are relationally complete but have more expressive power than relational algebra or rela- tional calculus because of additional operations such as aggregate functions, group- ing, and ordering. As we mentioned in the introduction to this chapter, the relational calculus is important for two reasons. First, it has a firm basis in mathe- matical logic. Second, the standard query language (SQL) for RDBMSs has some of its foundations in the tuple relational calculus.
12When queries are optimized (see Chapter 19), the system will choose a particular sequence of opera- tions that corresponds to an execution strategy that can be executed efficiently.
6.6 The Tuple Relational Calculus 175
Our examples refer to the database shown in Figures 3.6 and 3.7. We will use the same queries that were used in Section 6.5. Sections 6.6.6, 6.6.7, and 6.6.8 discuss dealing with universal quantifiers and safety of expression issues. (Students inter- ested in a basic introduction to tuple relational calculus may skip these sections.)
6.6.1 Tuple Variables and Range Relations The tuple relational calculus is based on specifying a number of tuple variables. Each tuple variable usually ranges over a particular database relation, meaning that the variable may take as its value any individual tuple from that relation. A simple tuple relational calculus query is of the form:
{t | COND(t)}
where t is a tuple variable and COND(t) is a conditional (Boolean) expression involving t that evaluates to either TRUE or FALSE for different assignments of tuples to the variable t. The result of such a query is the set of all tuples t that evalu- ate COND(t) to TRUE. These tuples are said to satisfy COND(t). For example, to find all employees whose salary is above $50,000, we can write the following tuple calcu- lus expression:
{t | EMPLOYEE(t) AND t.Salary>50000}
The condition EMPLOYEE(t) specifies that the range relation of tuple variable t is EMPLOYEE. Each EMPLOYEE tuple t that satisfies the condition t.Salary>50000 will be retrieved. Notice that t.Salary references attribute Salary of tuple variable t; this notation resembles how attribute names are qualified with relation names or aliases in SQL, as we saw in Chapter 4. In the notation of Chapter 3, t.Salary is the same as writing t[Salary].
The above query retrieves all attribute values for each selected EMPLOYEE tuple t. To retrieve only some of the attributes—say, the first and last names—we write
{t.Fname, t.Lname | EMPLOYEE(t) AND t.Salary>50000}
Informally, we need to specify the following information in a tuple relational calcu- lus expression:
■ For each tuple variable t, the range relation R of t. This value is specified by a condition of the form R(t). If we do not specify a range relation, then the variable t will range over all possible tuples “in the universe” as it is not restricted to any one relation.
■ A condition to select particular combinations of tuples. As tuple variables range over their respective range relations, the condition is evaluated for every possible combination of tuples to identify the selected combinations for which the condition evaluates to TRUE.
■ A set of attributes to be retrieved, the requested attributes. The values of these attributes are retrieved for each selected combination of tuples.
Before we discuss the formal syntax of tuple relational calculus, consider another query.
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Query 0. Retrieve the birth date and address of the employee (or employees) whose name is John B. Smith.
Q0: {t.Bdate, t.Address | EMPLOYEE(t) AND t.Fname=‘John’ AND t.Minit=‘B’ AND t.Lname=‘Smith’}
In tuple relational calculus, we first specify the requested attributes t.Bdate and t.Address for each selected tuple t. Then we specify the condition for selecting a tuple following the bar (|)—namely, that t be a tuple of the EMPLOYEE relation whose Fname, Minit, and Lname attribute values are ‘John’, ‘B’, and ‘Smith’, respectively.
6.6.2 Expressions and Formulas in Tuple Relational Calculus
A general expression of the tuple relational calculus is of the form
{t1.Aj, t2.Ak, ..., tn.Am | COND(t1, t2, ..., tn, tn+1, tn+2, ..., tn+m)}
where t1, t2, ..., tn, tn+1, ..., tn+m are tuple variables, each Ai is an attribute of the rela- tion on which ti ranges, and COND is a condition or formula.
13 of the tuple rela- tional calculus. A formula is made up of predicate calculus atoms, which can be one of the following:
1. An atom of the form R(ti), where R is a relation name and ti is a tuple vari- able. This atom identifies the range of the tuple variable ti as the relation whose name is R. It evaluates to TRUE if ti is a tuple in the relation R, and evaluates to FALSE otherwise.
2. An atom of the form ti.A op tj.B, where op is one of the comparison opera- tors in the set {=, <, ≤, >, ≥, ≠}, ti and tj are tuple variables, A is an attribute of the relation on which ti ranges, and B is an attribute of the relation on which tj ranges.
3. An atom of the form ti.A op c or c op tj.B, where op is one of the compari- son operators in the set {=, <, ≤, >, ≥, ≠}, ti and tj are tuple variables, A is an attribute of the relation on which ti ranges, B is an attribute of the relation on which tj ranges, and c is a constant value.
Each of the preceding atoms evaluates to either TRUE or FALSE for a specific combi- nation of tuples; this is called the truth value of an atom. In general, a tuple variable t ranges over all possible tuples in the universe. For atoms of the form R(t), if t is assigned to a tuple that is a member of the specified relation R, the atom is TRUE; oth- erwise, it is FALSE. In atoms of types 2 and 3, if the tuple variables are assigned to tuples such that the values of the specified attributes of the tuples satisfy the condi- tion, then the atom is TRUE.
A formula (Boolean condition) is made up of one or more atoms connected via the logical operators AND, OR, and NOT and is defined recursively by Rules 1 and 2 as follows:
■ Rule 1: Every atom is a formula.
13Also called a well-formed formula, or WFF, in mathematical logic.
6.6 The Tuple Relational Calculus 177
■ Rule 2: If F1 and F2 are formulas, then so are (F1 AND F2), (F1 OR F2), NOT (F1), and NOT (F2). The truth values of these formulas are derived from their component formulas F1 and F2 as follows:
a. (F1 AND F2) is TRUE if both F1 and F2 are TRUE; otherwise, it is FALSE.
b. (F1 OR F2) is FALSE if both F1 and F2 are FALSE; otherwise, it is TRUE.
c. NOT (F1) is TRUE if F1 is FALSE; it is FALSE if F1 is TRUE.
d. NOT (F2) is TRUE if F2 is FALSE; it is FALSE if F2 is TRUE.
6.6.3 The Existential and Universal Quantifiers In addition, two special symbols called quantifiers can appear in formulas; these are the universal quantifier (∀) and the existential quantifier (∃). Truth values for formulas with quantifiers are described in Rules 3 and 4 below; first, however, we need to define the concepts of free and bound tuple variables in a formula. Informally, a tuple variable t is bound if it is quantified, meaning that it appears in an (∃ t) or (∀t) clause; otherwise, it is free. Formally, we define a tuple variable in a formula as free or bound according to the following rules:
■ An occurrence of a tuple variable in a formula F that is an atom is free in F.
■ An occurrence of a tuple variable t is free or bound in a formula made up of logical connectives—(F1 AND F2), (F1 OR F2), NOT(F1), and NOT(F2)— depending on whether it is free or bound in F1 or F2 (if it occurs in either). Notice that in a formula of the form F = (F1 AND F2) or F = (F1 OR F2), a tuple variable may be free in F1 and bound in F2, or vice versa; in this case, one occurrence of the tuple variable is bound and the other is free in F.
■ All free occurrences of a tuple variable t in F are bound in a formula F � of the form F �= (∃ t)(F) or F � = (∀t)(F). The tuple variable is bound to the quanti- fier specified in F �. For example, consider the following formulas:
F1 : d.Dname=‘Research’ F2 : (∃ t)(d.Dnumber=t.Dno) F3 : (∀d)(d.Mgr_ssn=‘333445555’)
The tuple variable d is free in both F1 and F2, whereas it is bound to the (∀) quan- tifier in F3. Variable t is bound to the (∃) quantifier in F2.
We can now give Rules 3 and 4 for the definition of a formula we started earlier:
■ Rule 3: If F is a formula, then so is (∃ t)(F), where t is a tuple variable. The formula (∃ t)(F) is TRUE if the formula F evaluates to TRUE for some (at least one) tuple assigned to free occurrences of t in F; otherwise, (∃ t)(F) is FALSE.
■ Rule 4: If F is a formula, then so is (∀t)(F), where t is a tuple variable. The formula (∀t)(F) is TRUE if the formula F evaluates to TRUE for every tuple (in the universe) assigned to free occurrences of t in F; otherwise, (∀t)(F) is FALSE.
The (∃) quantifier is called an existential quantifier because a formula (∃ t)(F) is TRUE if there exists some tuple that makes F TRUE. For the universal quantifier,
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(∀t)(F) is TRUE if every possible tuple that can be assigned to free occurrences of t in F is substituted for t, and F is TRUE for every such substitution. It is called the uni- versal or for all quantifier because every tuple in the universe of tuples must make F TRUE to make the quantified formula TRUE.
6.6.4 Sample Queries in Tuple Relational Calculus We will use some of the same queries from Section 6.5 to give a flavor of how the same queries are specified in relational algebra and in relational calculus. Notice that some queries are easier to specify in the relational algebra than in the relational calculus, and vice versa.
Query 1. List the name and address of all employees who work for the ‘Research’ department.
Q1: {t.Fname, t.Lname, t.Address | EMPLOYEE(t) AND (∃ d)(DEPARTMENT(d) AND d.Dname=‘Research’ AND d.Dnumber=t.Dno)}
The only free tuple variables in a tuple relational calculus expression should be those that appear to the left of the bar (|). In Q1, t is the only free variable; it is then bound successively to each tuple. If a tuple satisfies the conditions specified after the bar in Q1, the attributes Fname, Lname, and Address are retrieved for each such tuple. The conditions EMPLOYEE(t) and DEPARTMENT(d) specify the range relations for t and d. The condition d.Dname = ‘Research’ is a selection condition and corresponds to a SELECT operation in the relational algebra, whereas the condition d.Dnumber = t.Dno is a join condition and is similar in purpose to the (INNER) JOIN operation (see Section 6.3).
Query 2. For every project located in ‘Stafford’, list the project number, the controlling department number, and the department manager’s last name, birth date, and address.
Q2: {p.Pnumber, p.Dnum, m.Lname, m.Bdate, m.Address | PROJECT(p) AND EMPLOYEE(m) AND p.Plocation=‘Stafford’ AND ((∃ d)(DEPARTMENT(d) AND p.Dnum=d.Dnumber AND d.Mgr_ssn=m.Ssn))}
In Q2 there are two free tuple variables, p and m. Tuple variable d is bound to the existential quantifier. The query condition is evaluated for every combination of tuples assigned to p and m, and out of all possible combinations of tuples to which p and m are bound, only the combinations that satisfy the condition are selected.
Several tuple variables in a query can range over the same relation. For example, to specify Q8—for each employee, retrieve the employee’s first and last name and the first and last name of his or her immediate supervisor—we specify two tuple vari- ables e and s that both range over the EMPLOYEE relation:
Q8: {e.Fname, e.Lname, s.Fname, s.Lname | EMPLOYEE(e) AND EMPLOYEE(s) AND e.Super_ssn=s.Ssn}
Query 3�. List the name of each employee who works on some project con- trolled by department number 5. This is a variation of Q3 in which all is
6.6 The Tuple Relational Calculus 179
[P.Pnumber,P.Dnum] [E.Lname,E.address,E.Bdate]
P.Dnum=D.Dnumber
P.Plocation=‘Stafford’
P D E
‘Stafford’
D.Mgr_ssn=E.Ssn
Figure 6.13 Query graph for Q2.
changed to some. In this case we need two join conditions and two existential quantifiers.
Q0�: {e.Lname, e.Fname | EMPLOYEE(e) AND ((∃ x)(∃ w)(PROJECT(x) AND WORKS_ON(w) AND x.Dnum=5 AND w.Essn=e.Ssn AND x.Pnumber=w.Pno))}
Query 4. Make a list of project numbers for projects that involve an employee whose last name is ‘Smith’, either as a worker or as manager of the controlling department for the project.
Q4: { p.Pnumber | PROJECT(p) AND (((∃ e)(∃ w)(EMPLOYEE(e) AND WORKS_ON(w) AND w.Pno=p.Pnumber AND e.Lname=‘Smith’ AND e.Ssn=w.Essn) ) OR ((∃ m)(∃ d)(EMPLOYEE(m) AND DEPARTMENT(d) AND p.Dnum=d.Dnumber AND d.Mgr_ssn=m.Ssn AND m.Lname=‘Smith’)))}
Compare this with the relational algebra version of this query in Section 6.5. The UNION operation in relational algebra can usually be substituted with an OR con- nective in relational calculus.
6.6.5 Notation for Query Graphs In this section we describe a notation that has been proposed to represent relational calculus queries that do not involve complex quantification in a graphical form. These types of queries are known as select-project-join queries, because they only involve these three relational algebra operations. The notation may be expanded to more general queries, but we do not discuss these extensions here. This graphical representation of a query is called a query graph. Figure 6.13 shows the query graph for Q2. Relations in the query are represented by relation nodes, which are dis- played as single circles. Constant values, typically from the query selection condi- tions, are represented by constant nodes, which are displayed as double circles or ovals. Selection and join conditions are represented by the graph edges (the lines that connect the nodes), as shown in Figure 6.13. Finally, the attributes to be retrieved from each relation are displayed in square brackets above each relation.
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The query graph representation does not indicate a particular order to specify which operations to perform first, and is hence a more neutral representation of a select-project-join query than the query tree representation (see Section 6.3.5), where the order of execution is implicitly specified. There is only a single query graph corresponding to each query. Although some query optimization techniques were based on query graphs, it is now generally accepted that query trees are prefer- able because, in practice, the query optimizer needs to show the order of operations for query execution, which is not possible in query graphs.
In the next section we discuss the relationship between the universal and existential quantifiers and show how one can be transformed into the other.
6.6.6 Transforming the Universal and Existential Quantifiers We now introduce some well-known transformations from mathematical logic that relate the universal and existential quantifiers. It is possible to transform a universal quantifier into an existential quantifier, and vice versa, to get an equivalent expres- sion. One general transformation can be described informally as follows: Transform one type of quantifier into the other with negation (preceded by NOT); AND and OR replace one another; a negated formula becomes unnegated; and an unnegated for- mula becomes negated. Some special cases of this transformation can be stated as follows, where the ≡ symbol stands for equivalent to:
(∀x) (P(x)) ≡ NOT (∃ x) (NOT (P(x))) (∃ x) (P(x)) ≡ NOT (∀x) (NOT (P(x))) (∀x) (P(x) AND Q(x)) ≡ NOT (∃ x) (NOT (P(x)) OR NOT (Q(x))) (∀x) (P(x) OR Q(x)) ≡ NOT (∃ x) (NOT (P(x)) AND NOT (Q(x))) (∃ x) (P(x)) OR Q(x)) ≡ NOT (∀x) (NOT (P(x)) AND NOT (Q(x))) (∃ x) (P(x) AND Q(x)) ≡ NOT (∀x) (NOT (P(x)) OR NOT (Q(x)))
Notice also that the following is TRUE, where the ⇒ symbol stands for implies:
(∀x)(P(x)) ⇒ (∃ x)(P(x)) NOT (∃ x)(P(x)) ⇒ NOT (∀x)(P(x))
6.6.7 Using the Universal Quantifier in Queries Whenever we use a universal quantifier, it is quite judicious to follow a few rules to ensure that our expression makes sense. We discuss these rules with respect to the query Q3.
Query 3. List the names of employees who work on all the projects controlled by department number 5. One way to specify this query is to use the universal quantifier as shown:
Q3: {e.Lname, e.Fname | EMPLOYEE(e) AND ((∀x)(NOT(PROJECT(x)) OR NOT (x.Dnum=5) OR ((∃ w)(WORKS_ON(w) AND w.Essn=e.Ssn AND x.Pnumber=w.Pno))))}
6.6 The Tuple Relational Calculus 181
We can break up Q3 into its basic components as follows:
Q3: {e.Lname, e.Fname | EMPLOYEE(e) AND F �} F � = ((∀x)(NOT(PROJECT(x)) OR F1)) F1 = NOT(x.Dnum=5) OR F2 F2 = ((∃ w)(WORKS_ON(w) AND w.Essn=e.Ssn AND x.Pnumber=w.Pno))
We want to make sure that a selected employee e works on all the projects controlled by department 5, but the definition of universal quantifier says that to make the quantified formula TRUE, the inner formula must be TRUE for all tuples in the uni- verse. The trick is to exclude from the universal quantification all tuples that we are not interested in by making the condition TRUE for all such tuples. This is necessary because a universally quantified tuple variable, such as x in Q3, must evaluate to TRUE for every possible tuple assigned to it to make the quantified formula TRUE.
The first tuples to exclude (by making them evaluate automatically to TRUE) are those that are not in the relation R of interest. In Q3, using the expression NOT(PROJECT(x)) inside the universally quantified formula evaluates to TRUE all tuples x that are not in the PROJECT relation. Then we exclude the tuples we are not interested in from R itself. In Q3, using the expression NOT(x.Dnum=5) evaluates to TRUE all tuples x that are in the PROJECT relation but are not controlled by depart- ment 5. Finally, we specify a condition F2 that must hold on all the remaining tuples in R. Hence, we can explain Q3 as follows:
1. For the formula F� = (∀x)(F) to be TRUE, we must have the formula F be TRUE for all tuples in the universe that can be assigned to x. However, in Q3 we are only interested in F being TRUE for all tuples of the PROJECT relation that are controlled by department 5. Hence, the formula F is of the form (NOT(PROJECT(x)) OR F1). The ‘NOT (PROJECT(x)) OR ...’ condition is TRUE for all tuples not in the PROJECT relation and has the effect of elimi- nating these tuples from consideration in the truth value of F1. For every tuple in the PROJECT relation, F1 must be TRUE if F� is to be TRUE.
2. Using the same line of reasoning, we do not want to consider tuples in the PROJECT relation that are not controlled by department number 5, since we are only interested in PROJECT tuples whose Dnum=5. Therefore, we can write:
IF (x.Dnum=5) THEN F2 which is equivalent to
(NOT (x.Dnum=5) OR F2)
3. Formula F1, hence, is of the form NOT(x.Dnum=5) OR F2. In the context of Q3, this means that, for a tuple x in the PROJECT relation, either its Dnum≠5 or it must satisfy F2.
4. Finally, F2 gives the condition that we want to hold for a selected EMPLOYEE tuple: that the employee works on every PROJECT tuple that has not been excluded yet. Such employee tuples are selected by the query.
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In English, Q3 gives the following condition for selecting an EMPLOYEE tuple e: For every tuple x in the PROJECT relation with x.Dnum=5, there must exist a tuple w in WORKS_ON such that w.Essn=e.Ssn and w.Pno=x.Pnumber. This is equivalent to saying that EMPLOYEE e works on every PROJECT x in DEPARTMENT number 5. (Whew!)
Using the general transformation from universal to existential quantifiers given in Section 6.6.6, we can rephrase the query in Q3 as shown in Q3A, which uses a negated existential quantifier instead of the universal quantifier:
Q3A: {e.Lname, e.Fname | EMPLOYEE(e) AND (NOT (∃ x) (PROJECT(x) AND (x.Dnum=5) AND (NOT (∃ w)(WORKS_ON(w) AND w.Essn=e.Ssn AND x.Pnumber=w.Pno))))}
We now give some additional examples of queries that use quantifiers.
Query 6. List the names of employees who have no dependents.
Q6: {e.Fname, e.Lname | EMPLOYEE(e) AND (NOT (∃ d)(DEPENDENT(d) AND e.Ssn=d.Essn))}
Using the general transformation rule, we can rephrase Q6 as follows:
Q6A: {e.Fname, e.Lname | EMPLOYEE(e) AND ((∀d)(NOT(DEPENDENT(d)) OR NOT(e.Ssn=d.Essn)))}
Query 7. List the names of managers who have at least one dependent.
Q7: {e.Fname, e.Lname | EMPLOYEE(e) AND ((∃ d)(∃ ρ)(DEPARTMENT(d) AND DEPENDENT(ρ) AND e.Ssn=d.Mgr_ssn AND ρ.Essn=e.Ssn))}
This query is handled by interpreting managers who have at least one dependent as managers for whom there exists some dependent.
6.6.8 Safe Expressions Whenever we use universal quantifiers, existential quantifiers, or negation of predi- cates in a calculus expression, we must make sure that the resulting expression makes sense. A safe expression in relational calculus is one that is guaranteed to yield a finite number of tuples as its result; otherwise, the expression is called unsafe. For example, the expression
{t | NOT (EMPLOYEE(t))}
is unsafe because it yields all tuples in the universe that are not EMPLOYEE tuples, which are infinitely numerous. If we follow the rules for Q3 discussed earlier, we will get a safe expression when using universal quantifiers. We can define safe expres- sions more precisely by introducing the concept of the domain of a tuple relational calculus expression: This is the set of all values that either appear as constant values in the expression or exist in any tuple in the relations referenced in the expression. For example, the domain of {t | NOT(EMPLOYEE(t))} is the set of all attribute values appearing in some tuple of the EMPLOYEE relation (for any attribute). The domain
6.7 The Domain Relational Calculus 183
of the expression Q3A would include all values appearing in EMPLOYEE, PROJECT, and WORKS_ON (unioned with the value 5 appearing in the query itself ).
An expression is said to be safe if all values in its result are from the domain of the expression. Notice that the result of {t | NOT(EMPLOYEE(t))} is unsafe, since it will, in general, include tuples (and hence values) from outside the EMPLOYEE relation; such values are not in the domain of the expression. All of our other examples are safe expressions.
6.7 The Domain Relational Calculus There is another type of relational calculus called the domain relational calculus, or simply, domain calculus. Historically, while SQL (see Chapters 4 and 5), which was based on tuple relational calculus, was being developed by IBM Research at San Jose, California, another language called QBE (Query-By-Example), which is related to domain calculus, was being developed almost concurrently at the IBM T.J. Watson Research Center in Yorktown Heights, New York. The formal specification of the domain calculus was proposed after the development of the QBE language and system.
Domain calculus differs from tuple calculus in the type of variables used in formu- las: Rather than having variables range over tuples, the variables range over single values from domains of attributes. To form a relation of degree n for a query result, we must have n of these domain variables—one for each attribute. An expression of the domain calculus is of the form
{x1, x2, ..., xn | COND(x1, x2, ..., xn, xn+1, xn+2, ..., xn+m)}
where x1, x2, ..., xn, xn+1, xn+2, ..., xn+m are domain variables that range over domains (of attributes), and COND is a condition or formula of the domain rela- tional calculus.
A formula is made up of atoms. The atoms of a formula are slightly different from those for the tuple calculus and can be one of the following:
1. An atom of the form R(x1, x2, ..., xj), where R is the name of a relation of degree j and each xi, 1 ≤ i ≤ j, is a domain variable. This atom states that a list of values of <x1, x2, ..., xj> must be a tuple in the relation whose name is R, where xi is the value of the ith attribute value of the tuple. To make a domain calculus expression more concise, we can drop the commas in a list of vari- ables; thus, we can write:
{x1, x2, ..., xn | R(x1 x2 x3) AND ...}
instead of: {x1, x2, ... , xn | R(x1, x2, x3) AND ...}
2. An atom of the form xi op xj, where op is one of the comparison operators in the set {=, <, ≤, >, ≥, ≠}, and xi and xj are domain variables.
3. An atom of the form xi op c or c op xj, where op is one of the comparison operators in the set {=, <, ≤, >, ≥, ≠}, xi and xj are domain variables, and c is a constant value.
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As in tuple calculus, atoms evaluate to either TRUE or FALSE for a specific set of val- ues, called the truth values of the atoms. In case 1, if the domain variables are assigned values corresponding to a tuple of the specified relation R, then the atom is TRUE. In cases 2 and 3, if the domain variables are assigned values that satisfy the condition, then the atom is TRUE.
In a similar way to the tuple relational calculus, formulas are made up of atoms, variables, and quantifiers, so we will not repeat the specifications for formulas here. Some examples of queries specified in the domain calculus follow. We will use low- ercase letters l, m, n, ..., x, y, z for domain variables.
Query 0. List the birth date and address of the employee whose name is ‘John B. Smith’.
Q0: {u, v | (∃ q) (∃ r) (∃ s) (∃ t) (∃ w) (∃ x) (∃ y) (∃ z) (EMPLOYEE(qrstuvwxyz) AND q=‘John’ AND r=‘B’ AND s=‘Smith’)}
We need ten variables for the EMPLOYEE relation, one to range over each of the domains of attributes of EMPLOYEE in order. Of the ten variables q, r, s, ..., z, only u and v are free, because they appear to the left of the bar and hence should not be bound to a quantifier. We first specify the requested attributes, Bdate and Address, by the free domain variables u for BDATE and v for ADDRESS. Then we specify the con- dition for selecting a tuple following the bar (|)—namely, that the sequence of val- ues assigned to the variables qrstuvwxyz be a tuple of the EMPLOYEE relation and that the values for q (Fname), r (Minit), and s (Lname) be equal to ‘John’, ‘B’, and ‘Smith’, respectively. For convenience, we will quantify only those variables actually appearing in a condition (these would be q, r, and s in Q0) in the rest of our examples.14
An alternative shorthand notation, used in QBE, for writing this query is to assign the constants ‘John’, ‘B’, and ‘Smith’ directly as shown in Q0A. Here, all variables not appearing to the left of the bar are implicitly existentially quantified:15
Q0A: {u, v | EMPLOYEE(‘John’,‘B’,‘Smith’,t,u,v,w,x,y,z) }
Query 1. Retrieve the name and address of all employees who work for the ‘Research’ department.
Q1: {q, s, v | (∃ z) (∃ l) (∃ m) (EMPLOYEE(qrstuvwxyz) AND DEPARTMENT(lmno) AND l=‘Research’ AND m=z)}
A condition relating two domain variables that range over attributes from two rela- tions, such as m = z in Q1, is a join condition, whereas a condition that relates a domain variable to a constant, such as l = ‘Research’, is a selection condition.
14Note that the notation of quantifying only the domain variables actually used in conditions and of showing a predicate such as EMPLOYEE(qrstuvwxyz) without separating domain variables with commas is an abbreviated notation used for convenience; it is not the correct formal notation. 15Again, this is not a formally accurate notation.
6.8 Summary 185
Query 2. For every project located in ‘Stafford’, list the project number, the controlling department number, and the department manager’s last name, birth date, and address.
Q2: {i, k, s, u, v | (∃ j)(∃ m)(∃ n)(∃ t)(PROJECT(hijk) AND EMPLOYEE(qrstuvwxyz) AND DEPARTMENT(lmno) AND k=m AND n=t AND j=‘Stafford’)}
Query 6. List the names of employees who have no dependents.
Q6: {q, s | (∃ t)(EMPLOYEE(qrstuvwxyz) AND (NOT(∃ l)(DEPENDENT(lmnop) AND t=l)))}
Q6 can be restated using universal quantifiers instead of the existential quantifiers, as shown in Q6A:
Q6A: {q, s | (∃ t)(EMPLOYEE(qrstuvwxyz) AND ((∀l)(NOT(DEPENDENT(lmnop)) OR NOT(t=l))))}
Query 7. List the names of managers who have at least one dependent.
Q7: {s, q | (∃ t)(∃ j)(∃ l)(EMPLOYEE(qrstuvwxyz) AND DEPARTMENT(hijk) AND DEPENDENT(lmnop) AND t=j AND l=t)}
As we mentioned earlier, it can be shown that any query that can be expressed in the basic relational algebra can also be expressed in the domain or tuple relational cal- culus. Also, any safe expression in the domain or tuple relational calculus can be expressed in the basic relational algebra.
The QBE language was based on the domain relational calculus, although this was realized later, after the domain calculus was formalized. QBE was one of the first graphical query languages with minimum syntax developed for database systems. It was developed at IBM Research and is available as an IBM commercial product as part of the Query Management Facility (QMF) interface option to DB2. The basic ideas used in QBE have been applied in several other commercial products. Because of its important place in the history of relational languages, we have included an overview of QBE in Appendix C.
6.8 Summary In this chapter we presented two formal languages for the relational model of data. They are used to manipulate relations and produce new relations as answers to queries. We discussed the relational algebra and its operations, which are used to specify a sequence of operations to specify a query. Then we introduced two types of relational calculi called tuple calculus and domain calculus.
In Sections 6.1 through 6.3, we introduced the basic relational algebra operations and illustrated the types of queries for which each is used. First, we discussed the unary relational operators SELECT and PROJECT, as well as the RENAME operation. Then, we discussed binary set theoretic operations requiring that relations on which they
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are applied be union (or type) compatible; these include UNION, INTERSECTION, and SET DIFFERENCE. The CARTESIAN PRODUCT operation is a set operation that can be used to combine tuples from two relations, producing all possible combinations. It is rarely used in practice; however, we showed how CARTESIAN PRODUCT followed by SELECT can be used to define matching tuples from two relations and leads to the JOIN operation. Different JOIN operations called THETA JOIN, EQUIJOIN, and NATURAL JOIN were introduced. Query trees were introduced as a graphical represen- tation of relational algebra queries, which can also be used as the basis for internal data structures that the DBMS can use to represent a query.
We discussed some important types of queries that cannot be stated with the basic relational algebra operations but are important for practical situations. We intro- duced GENERALIZED PROJECTION to use functions of attributes in the projection list and the AGGREGATE FUNCTION operation to deal with aggregate types of sta- tistical requests that summarize the information in the tables. We discussed recur- sive queries, for which there is no direct support in the algebra but which can be handled in a step-by-step approach, as we demonstrated. Then we presented the OUTER JOIN and OUTER UNION operations, which extend JOIN and UNION and allow all information in source relations to be preserved in the result.
The last two sections described the basic concepts behind relational calculus, which is based on the branch of mathematical logic called predicate calculus. There are two types of relational calculi: (1) the tuple relational calculus, which uses tuple variables that range over tuples (rows) of relations, and (2) the domain relational calculus, which uses domain variables that range over domains (columns of rela- tions). In relational calculus, a query is specified in a single declarative statement, without specifying any order or method for retrieving the query result. Hence, rela- tional calculus is often considered to be a higher-level declarative language than the relational algebra, because a relational calculus expression states what we want to retrieve regardless of how the query may be executed.
We discussed the syntax of relational calculus queries using both tuple and domain variables. We introduced query graphs as an internal representation for queries in relational calculus. We also discussed the existential quantifier (∃) and the universal quantifier (∀). We saw that relational calculus variables are bound by these quanti- fiers. We described in detail how queries with universal quantification are written, and we discussed the problem of specifying safe queries whose results are finite. We also discussed rules for transforming universal into existential quantifiers, and vice versa. It is the quantifiers that give expressive power to the relational calculus, mak- ing it equivalent to the basic relational algebra. There is no analog to grouping and aggregation functions in basic relational calculus, although some extensions have been suggested.
Review Questions 6.1. List the operations of relational algebra and the purpose of each.
Exercises 187
6.2. What is union compatibility? Why do the UNION, INTERSECTION, and DIFFERENCE operations require that the relations on which they are applied be union compatible?
6.3. Discuss some types of queries for which renaming of attributes is necessary in order to specify the query unambiguously.
6.4. Discuss the various types of inner join operations. Why is theta join required?
6.5. What role does the concept of foreign key play when specifying the most common types of meaningful join operations?
6.6. What is the FUNCTION operation? What is it used for?
6.7. How are the OUTER JOIN operations different from the INNER JOIN opera- tions? How is the OUTER UNION operation different from UNION?
6.8. In what sense does relational calculus differ from relational algebra, and in what sense are they similar?
6.9. How does tuple relational calculus differ from domain relational calculus?
6.10. Discuss the meanings of the existential quantifier (∃) and the universal quantifier (∀).
6.11. Define the following terms with respect to the tuple calculus: tuple variable, range relation, atom, formula, and expression.
6.12. Define the following terms with respect to the domain calculus: domain vari- able, range relation, atom, formula, and expression.
6.13. What is meant by a safe expression in relational calculus?
6.14. When is a query language called relationally complete?
Exercises 6.15. Show the result of each of the sample queries in Section 6.5 as it would apply
to the database state in Figure 3.6.
6.16. Specify the following queries on the COMPANYrelational database schema shown in Figure 5.5, using the relational operators discussed in this chapter. Also show the result of each query as it would apply to the database state in Figure 3.6.
a. Retrieve the names of all employees in department 5 who work more than 10 hours per week on the ProductX project.
b. List the names of all employees who have a dependent with the same first name as themselves.
c. Find the names of all employees who are directly supervised by ‘Franklin Wong’.
d. For each project, list the project name and the total hours per week (by all employees) spent on that project.
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e. Retrieve the names of all employees who work on every project.
f. Retrieve the names of all employees who do not work on any project.
g. For each department, retrieve the department name and the average salary of all employees working in that department.
h. Retrieve the average salary of all female employees.
i. Find the names and addresses of all employees who work on at least one project located in Houston but whose department has no location in Houston.
j. List the last names of all department managers who have no dependents.
6.17. Consider the AIRLINE relational database schema shown in Figure 3.8, which was described in Exercise 3.12. Specify the following queries in relational algebra:
a. For each flight, list the flight number, the departure airport for the first leg of the flight, and the arrival airport for the last leg of the flight.
b. List the flight numbers and weekdays of all flights or flight legs that depart from Houston Intercontinental Airport (airport code ‘IAH’) and arrive in Los Angeles International Airport (airport code ‘LAX’).
c. List the flight number, departure airport code, scheduled departure time, arrival airport code, scheduled arrival time, and weekdays of all flights or flight legs that depart from some airport in the city of Houston and arrive at some airport in the city of Los Angeles.
d. List all fare information for flight number ‘CO197’.
e. Retrieve the number of available seats for flight number ‘CO197’ on ‘2009-10-09’.
6.18. Consider the LIBRARY relational database schema shown in Figure 6.14, which is used to keep track of books, borrowers, and book loans. Referential integrity constraints are shown as directed arcs in Figure 6.14, as in the nota- tion of Figure 3.7. Write down relational expressions for the following queries:
a. How many copies of the book titled The Lost Tribe are owned by the library branch whose name is ‘Sharpstown’?
b. How many copies of the book titled The Lost Tribe are owned by each library branch?
c. Retrieve the names of all borrowers who do not have any books checked out.
d. For each book that is loaned out from the Sharpstown branch and whose Due_date is today, retrieve the book title, the borrower’s name, and the borrower’s address.
e. For each library branch, retrieve the branch name and the total number of books loaned out from that branch.
Exercises 189
Publisher_nameBook_id Title
BOOK
BOOK_COPIES Book_id Branch_id No_of_copies
BOOK_AUTHORS
Book_id Author_name
LIBRARY_BRANCH Branch_id Branch_name Address
PUBLISHER
Name Address Phone
BOOK_LOANS
Book_id Branch_id Card_no Date_out Due_date
BORROWER Card_no Name Address Phone
Figure 6.14 A relational database schema for a LIBRARY database.
f. Retrieve the names, addresses, and number of books checked out for all borrowers who have more than five books checked out.
g. For each book authored (or coauthored) by Stephen King, retrieve the title and the number of copies owned by the library branch whose name is Central.
6.19. Specify the following queries in relational algebra on the database schema given in Exercise 3.14:
a. List the Order# and Ship_date for all orders shipped from Warehouse# W2.
b. List the WAREHOUSE information from which the CUSTOMER named Jose Lopez was supplied his orders. Produce a listing: Order#, Warehouse#.
190 Chapter 6 The Relational Algebra and Relational Calculus
P Q R A B C
10
15
25
a
b
a
5
8
6
10
25
10
b
c
b
6
3
5
TABLE T1 TABLE T2 Figure 6.15 A database state for the relations T1 and T 2.
c. Produce a listing Cname, No_of_orders, Avg_order_amt, where the middle column is the total number of orders by the customer and the last column is the average order amount for that customer.
d. List the orders that were not shipped within 30 days of ordering.
e. List the Order# for orders that were shipped from all warehouses that the company has in New York.
6.20. Specify the following queries in relational algebra on the database schema given in Exercise 3.15:
a. Give the details (all attributes of trip relation) for trips that exceeded $2,000 in expenses.
b. Print the Ssns of salespeople who took trips to Honolulu.
c. Print the total trip expenses incurred by the salesperson with SSN = ‘234- 56-7890’.
6.21. Specify the following queries in relational algebra on the database schema given in Exercise 3.16:
a. List the number of courses taken by all students named John Smith in Winter 2009 (i.e., Quarter=W09).
b. Produce a list of textbooks (include Course#, Book_isbn, Book_title) for courses offered by the ‘CS’ department that have used more than two books.
c. List any department that has all its adopted books published by ‘Pearson Publishing’.
6.22. Consider the two tables T1 and T2 shown in Figure 6.15. Show the results of the following operations:
a. T 1 T1.P = T2.A T 2
b. T 1 T1.Q = T2.B T 2
c. T 1 T1.P = T2.A T 2
d. T 1 T1.Q = T2.B T 2
e. T 1 ∪ T2 f. T 1 (T1.P = T2.A AND T1.R = T2.C) T 2
Exercises 191
6.23. Specify the following queries in relational algebra on the database schema in Exercise 3.17:
a. For the salesperson named ‘Jane Doe’, list the following information for all the cars she sold: Serial#, Manufacturer, Sale_price.
b. List the Serial# and Model of cars that have no options.
c. Consider the NATURAL JOIN operation between SALESPERSON and SALE. What is the meaning of a left outer join for these tables (do not change the order of relations)? Explain with an example.
d. Write a query in relational algebra involving selection and one set opera- tion and say in words what the query does.
6.24. Specify queries a, b, c, e, f, i, and j of Exercise 6.16 in both tuple and domain relational calculus.
6.25. Specify queries a, b, c, and d of Exercise 6.17 in both tuple and domain rela- tional calculus.
6.26. Specify queries c, d, and f of Exercise 6.18 in both tuple and domain rela- tional calculus.
6.27. In a tuple relational calculus query with n tuple variables, what would be the typical minimum number of join conditions? Why? What is the effect of having a smaller number of join conditions?
6.28. Rewrite the domain relational calculus queries that followed Q0 in Section 6.7 in the style of the abbreviated notation of Q0A, where the objective is to minimize the number of domain variables by writing constants in place of variables wherever possible.
6.29. Consider this query: Retrieve the Ssns of employees who work on at least those projects on which the employee with Ssn=123456789 works. This may be stated as (FORALL x) (IF P THEN Q), where
■ x is a tuple variable that ranges over the PROJECT relation.
■ P ≡ EMPLOYEE with Ssn=123456789 works on PROJECT x. ■ Q ≡ EMPLOYEE e works on PROJECT x.
Express the query in tuple relational calculus, using the rules
■ (∀ x)(P(x)) ≡ NOT(∃x)(NOT(P(x))). ■ (IF P THEN Q) ≡ (NOT(P) OR Q).
6.30. Show how you can specify the following relational algebra operations in both tuple and domain relational calculus.
a. σA=C(R(A, B, C)) b. π<A, B>(R(A, B, C)) c. R(A, B, C) * S(C, D, E)
d. R(A, B, C) ∪ S(A, B, C) e. R(A, B, C) ∩ S(A, B, C)
192 Chapter 6 The Relational Algebra and Relational Calculus
f. R(A, B, C) = S(A, B, C)
g. R(A, B, C) × S(D, E, F) h. R(A, B) ÷ S(A)
6.31. Suggest extensions to the relational calculus so that it may express the fol- lowing types of operations that were discussed in Section 6.4: (a) aggregate functions and grouping; (b) OUTER JOIN operations; (c) recursive closure queries.
6.32. A nested query is a query within a query. More specifically, a nested query is a parenthesized query whose result can be used as a value in a number of places, such as instead of a relation. Specify the following queries on the database specified in Figure 3.5 using the concept of nested queries and the relational operators discussed in this chapter. Also show the result of each query as it would apply to the database state in Figure 3.6.
a. List the names of all employees who work in the department that has the employee with the highest salary among all employees.
b. List the names of all employees whose supervisor’s supervisor has ‘888665555’ for Ssn.
c. List the names of employees who make at least $10,000 more than the employee who is paid the least in the company.
6.33. State whether the following conclusions are true or false:
a. NOT (P(x) OR Q(x)) → (NOT (P(x)) AND (NOT (Q(x))) b. NOT (∃x) (P(x)) → ∀ x (NOT (P(x)) c. (∃x) (P(x)) → ∀ x ((P(x))
Laboratory Exercises 6.34. Specify and execute the following queries in relational algebra (RA) using
the RA interpreter on the COMPANY database schema in Figure 3.5.
a. List the names of all employees in department 5 who work more than 10 hours per week on the ProductX project.
b. List the names of all employees who have a dependent with the same first name as themselves.
c. List the names of employees who are directly supervised by Franklin Wong.
d. List the names of employees who work on every project.
e. List the names of employees who do not work on any project.
f. List the names and addresses of employees who work on at least one proj- ect located in Houston but whose department has no location in Houston.
g. List the names of department managers who have no dependents.
Laboratory Exercises 193
6.35. Consider the following MAILORDER relational schema describing the data for a mail order company.
PARTS(Pno, Pname, Qoh, Price, Olevel) CUSTOMERS(Cno, Cname, Street, Zip, Phone) EMPLOYEES(Eno, Ename, Zip, Hdate) ZIP_CODES(Zip, City) ORDERS(Ono, Cno, Eno, Received, Shipped) ODETAILS(Ono, Pno, Qty)
Qoh stands for quantity on hand: the other attribute names are self- explanatory. Specify and execute the following queries using the RA inter- preter on the MAILORDER database schema.
a. Retrieve the names of parts that cost less than $20.00.
b. Retrieve the names and cities of employees who have taken orders for parts costing more than $50.00.
c. Retrieve the pairs of customer number values of customers who live in the same ZIP Code.
d. Retrieve the names of customers who have ordered parts from employees living in Wichita.
e. Retrieve the names of customers who have ordered parts costing less than $20.00.
f. Retrieve the names of customers who have not placed an order.
g. Retrieve the names of customers who have placed exactly two orders.
6.36. Consider the following GRADEBOOK relational schema describing the data for a grade book of a particular instructor. (Note: The attributes A, B, C, and D of COURSES store grade cutoffs.)
CATALOG(Cno, Ctitle) STUDENTS(Sid, Fname, Lname, Minit) COURSES(Term, Sec_no, Cno, A, B, C, D) ENROLLS(Sid, Term, Sec_no)
Specify and execute the following queries using the RA interpreter on the GRADEBOOK database schema.
a. Retrieve the names of students enrolled in the Automata class during the fall 2009 term.
b. Retrieve the Sid values of students who have enrolled in CSc226 and CSc227.
c. Retrieve the Sid values of students who have enrolled in CSc226 or CSc227.
d. Retrieve the names of students who have not enrolled in any class.
e. Retrieve the names of students who have enrolled in all courses in the CATALOG table.
194 Chapter 6 The Relational Algebra and Relational Calculus
6.37. Consider a database that consists of the following relations.
SUPPLIER(Sno, Sname) PART(Pno, Pname) PROJECT(Jno, Jname) SUPPLY(Sno, Pno, Jno)
The database records information about suppliers, parts, and projects and includes a ternary relationship between suppliers, parts, and projects. This relationship is a many-many-many relationship. Specify and execute the fol- lowing queries using the RA interpreter.
a. Retrieve the part numbers that are supplied to exactly two projects.
b. Retrieve the names of suppliers who supply more than two parts to proj- ect ‘J1’.
c. Retrieve the part numbers that are supplied by every supplier.
d. Retrieve the project names that are supplied by supplier ‘S1’ only.
e. Retrieve the names of suppliers who supply at least two different parts each to at least two different projects.
6.38. Specify and execute the following queries for the database in Exercise 3.16 using the RA interpreter.
a. Retrieve the names of students who have enrolled in a course that uses a textbook published by Addison-Wesley.
b. Retrieve the names of courses in which the textbook has been changed at least once.
c. Retrieve the names of departments that adopt textbooks published by Addison-Wesley only.
d. Retrieve the names of departments that adopt textbooks written by Navathe and published by Addison-Wesley.
e. Retrieve the names of students who have never used a book (in a course) written by Navathe and published by Addison-Wesley.
6.39. Repeat Laboratory Exercises 6.34 through 6.38 in domain relational calculus (DRC) by using the DRC interpreter.
Selected Bibliography Codd (1970) defined the basic relational algebra. Date (1983a) discusses outer joins. Work on extending relational operations is discussed by Carlis (1986) and Ozsoyoglu et al. (1985). Cammarata et al. (1989) extends the relational model integrity constraints and joins.
Codd (1971) introduced the language Alpha, which is based on concepts of tuple relational calculus. Alpha also includes the notion of aggregate functions, which goes beyond relational calculus. The original formal definition of relational calculus
Selected Bibliography 195
was given by Codd (1972), which also provided an algorithm that transforms any tuple relational calculus expression to relational algebra. The QUEL (Stonebraker et al. 1976) is based on tuple relational calculus, with implicit existential quantifiers, but no universal quantifiers, and was implemented in the INGRES system as a com- mercially available language. Codd defined relational completeness of a query lan- guage to mean at least as powerful as relational calculus. Ullman (1988) describes a formal proof of the equivalence of relational algebra with the safe expressions of tuple and domain relational calculus. Abiteboul et al. (1995) and Atzeni and deAntonellis (1993) give a detailed treatment of formal relational languages.
Although ideas of domain relational calculus were initially proposed in the QBE language (Zloof 1975), the concept was formally defined by Lacroix and Pirotte (1977a). The experimental version of the Query-By-Example system is described in Zloof (1975). The ILL (Lacroix and Pirotte 1977b) is based on domain relational calculus. Whang et al. (1990) extends QBE with universal quantifiers. Visual query languages, of which QBE is an example, are being proposed as a means of querying databases; conferences such as the Visual Database Systems Working Conference (e.g., Arisawa and Catarci (2000) or Zhou and Pu (2002)) have a number of propos- als for such languages.
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part 3 Conceptual Modeling and Database Design
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199
Data Modeling Using the Entity-Relationship (ER) Model
Conceptual modeling is a very important phase indesigning a successful database application. Generally, the term database application refers to a particular database and the associated programs that implement the database queries and updates. For exam- ple, a BANK database application that keeps track of customer accounts would include programs that implement database updates corresponding to customer deposits and withdrawals. These programs provide user-friendly graphical user interfaces (GUIs) utilizing forms and menus for the end users of the application— the bank tellers, in this example. Hence, a major part of the database application will require the design, implementation, and testing of these application programs. Traditionally, the design and testing of application programs has been considered to be part of software engineering rather than database design. In many software design tools, the database design methodologies and software engineering method- ologies are intertwined since these activities are strongly related.
In this chapter, we follow the traditional approach of concentrating on the database structures and constraints during conceptual database design. The design of appli- cation programs is typically covered in software engineering courses. We present the modeling concepts of the Entity-Relationship (ER) model, which is a popular high-level conceptual data model. This model and its variations are frequently used for the conceptual design of database applications, and many database design tools employ its concepts. We describe the basic data-structuring concepts and con- straints of the ER model and discuss their use in the design of conceptual schemas for database applications. We also present the diagrammatic notation associated with the ER model, known as ER diagrams.
7chapter 7
200 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
Object modeling methodologies such as the Unified Modeling Language (UML) are becoming increasingly popular in both database and software design. These methodologies go beyond database design to specify detailed design of software modules and their interactions using various types of diagrams. An important part of these methodologies—namely, class diagrams1—are similar in many ways to the ER diagrams. In class diagrams, operations on objects are specified, in addition to specifying the database schema structure. Operations can be used to specify the functional requirements during database design, as we will discuss in Section 7.1. We present some of the UML notation and concepts for class diagrams that are partic- ularly relevant to database design in Section 7.8, and briefly compare these to ER notation and concepts. Additional UML notation and concepts are presented in Section 8.6 and in Chapter 10.
This chapter is organized as follows: Section 7.1 discusses the role of high-level con- ceptual data models in database design. We introduce the requirements for a sample database application in Section 7.2 to illustrate the use of concepts from the ER model. This sample database is also used throughout the book. In Section 7.3 we present the concepts of entities and attributes, and we gradually introduce the dia- grammatic technique for displaying an ER schema. In Section 7.4 we introduce the concepts of binary relationships and their roles and structural constraints. Section 7.5 introduces weak entity types. Section 7.6 shows how a schema design is refined to include relationships. Section 7.7 reviews the notation for ER diagrams, summa- rizes the issues and common pitfalls that occur in schema design, and discusses how to choose the names for database schema constructs. Section 7.8 introduces some UML class diagram concepts, compares them to ER model concepts, and applies them to the same database example. Section 7.9 discusses more complex types of relationships. Section 7.10 summarizes the chapter.
The material in Sections 7.8 and 7.9 may be excluded from an introductory course. If a more thorough coverage of data modeling concepts and conceptual database design is desired, the reader should continue to Chapter 8, where we describe extensions to the ER model that lead to the Enhanced-ER (EER) model, which includes concepts such as specialization, generalization, inheritance, and union types (categories). We also introduce some additional UML concepts and notation in Chapter 8.
7.1 Using High-Level Conceptual Data Models for Database Design
Figure 7.1 shows a simplified overview of the database design process. The first step shown is requirements collection and analysis. During this step, the database designers interview prospective database users to understand and document their data requirements. The result of this step is a concisely written set of users’ require- ments. These requirements should be specified in as detailed and complete a form as possible. In parallel with specifying the data requirements, it is useful to specify
1A class is similar to an entity type in many ways.
7.1 Using High-Level Conceptual Data Models for Database Design 201
Functional Requirements
REQUIREMENTS COLLECTION AND
ANALYSIS
Miniworld
Data Requirements
CONCEPTUAL DESIGN
Conceptual Schema (In a high-level data model)
LOGICAL DESIGN (DATA MODEL MAPPING)
Logical (Conceptual) Schema (In the data model of a specific DBMS)
PHYSICAL DESIGN
Internal Schema
Application Programs
TRANSACTION IMPLEMENTATION
APPLICATION PROGRAM DESIGN
DBMS-specific
DBMS-independent
High-Level Transaction Specification
FUNCTIONAL ANALYSIS
Figure 7.1 A simplified diagram to illustrate the main phases of database design.
the known functional requirements of the application. These consist of the user- defined operations (or transactions) that will be applied to the database, including both retrievals and updates. In software design, it is common to use data flow dia- grams, sequence diagrams, scenarios, and other techniques to specify functional requirements. We will not discuss any of these techniques here; they are usually described in detail in software engineering texts. We give an overview of some of these techniques in Chapter 10.
Once the requirements have been collected and analyzed, the next step is to create a conceptual schema for the database, using a high-level conceptual data model. This step is called conceptual design. The conceptual schema is a concise description of
202 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
the data requirements of the users and includes detailed descriptions of the entity types, relationships, and constraints; these are expressed using the concepts pro- vided by the high-level data model. Because these concepts do not include imple- mentation details, they are usually easier to understand and can be used to communicate with nontechnical users. The high-level conceptual schema can also be used as a reference to ensure that all users’ data requirements are met and that the requirements do not conflict. This approach enables database designers to concen- trate on specifying the properties of the data, without being concerned with storage and implementation details. This makes it is easier to create a good conceptual data- base design.
During or after the conceptual schema design, the basic data model operations can be used to specify the high-level user queries and operations identified during func- tional analysis. This also serves to confirm that the conceptual schema meets all the identified functional requirements. Modifications to the conceptual schema can be introduced if some functional requirements cannot be specified using the initial schema.
The next step in database design is the actual implementation of the database, using a commercial DBMS. Most current commercial DBMSs use an implementation data model—such as the relational or the object-relational database model—so the conceptual schema is transformed from the high-level data model into the imple- mentation data model. This step is called logical design or data model mapping; its result is a database schema in the implementation data model of the DBMS. Data model mapping is often automated or semiautomated within the database design tools.
The last step is the physical design phase, during which the internal storage struc- tures, file organizations, indexes, access paths, and physical design parameters for the database files are specified. In parallel with these activities, application programs are designed and implemented as database transactions corresponding to the high- level transaction specifications. We discuss the database design process in more detail in Chapter 10.
We present only the basic ER model concepts for conceptual schema design in this chapter. Additional modeling concepts are discussed in Chapter 8, when we intro- duce the EER model.
7.2 A Sample Database Application In this section we describe a sample database application, called COMPANY, which serves to illustrate the basic ER model concepts and their use in schema design. We list the data requirements for the database here, and then create its conceptual schema step-by-step as we introduce the modeling concepts of the ER model. The COMPANY database keeps track of a company’s employees, departments, and proj- ects. Suppose that after the requirements collection and analysis phase, the database designers provide the following description of the miniworld—the part of the com- pany that will be represented in the database.
7.3 Entity Types, Entity Sets, Attributes, and Keys 203
■ The company is organized into departments. Each department has a unique name, a unique number, and a particular employee who manages the department. We keep track of the start date when that employee began man- aging the department. A department may have several locations.
■ A department controls a number of projects, each of which has a unique name, a unique number, and a single location.
■ We store each employee’s name, Social Security number,2 address, salary, sex (gender), and birth date. An employee is assigned to one department, but may work on several projects, which are not necessarily controlled by the same department. We keep track of the current number of hours per week that an employee works on each project. We also keep track of the direct supervisor of each employee (who is another employee).
■ We want to keep track of the dependents of each employee for insurance purposes. We keep each dependent’s first name, sex, birth date, and relation- ship to the employee.
Figure 7.2 shows how the schema for this database application can be displayed by means of the graphical notation known as ER diagrams. This figure will be explained gradually as the ER model concepts are presented. We describe the step- by-step process of deriving this schema from the stated requirements—and explain the ER diagrammatic notation—as we introduce the ER model concepts.
7.3 Entity Types, Entity Sets, Attributes, and Keys
The ER model describes data as entities, relationships, and attributes. In Section 7.3.1 we introduce the concepts of entities and their attributes. We discuss entity types and key attributes in Section 7.3.2. Then, in Section 7.3.3, we specify the initial con- ceptual design of the entity types for the COMPANY database. Relationships are described in Section 7.4.
7.3.1 Entities and Attributes Entities and Their Attributes. The basic object that the ER model represents is an entity, which is a thing in the real world with an independent existence. An entity may be an object with a physical existence (for example, a particular person, car, house, or employee) or it may be an object with a conceptual existence (for instance, a company, a job, or a university course). Each entity has attributes—the particular properties that describe it. For example, an EMPLOYEE entity may be described by the employee’s name, age, address, salary, and job. A particular entity will have a
2The Social Security number, or SSN, is a unique nine-digit identifier assigned to each individual in the United States to keep track of his or her employment, benefits, and taxes. Other countries may have similar identification schemes, such as personal identification card numbers.
204 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
EMPLOYEE
Fname Minit Lname
Name Address
Sex
Salary
Ssn
Bdate
Supervisor Supervisee
SUPERVISION1 N
Hours
WORKS_ON
CONTROLS
M N
1
DEPENDENTS_OF
Name
Location
N
1 1 1
PROJECT
DEPARTMENT
Locations
Name Number
Number
Number_of_employees
MANAGES
Start_date
WORKS_FOR 1N
N
DEPENDENT
Sex Birth_date RelationshipName
Figure 7.2 An ER schema diagram for the COMPANY database. The diagrammatic notation is introduced gradually throughout this chapter and is summarized in Figure 7.14.
value for each of its attributes. The attribute values that describe each entity become a major part of the data stored in the database.
Figure 7.3 shows two entities and the values of their attributes. The EMPLOYEE entity e1 has four attributes: Name, Address, Age, and Home_phone; their values are ‘John Smith,’ ‘2311 Kirby, Houston, Texas 77001’, ‘55’, and ‘713-749-2630’, respec- tively. The COMPANY entity c1 has three attributes: Name, Headquarters, and President; their values are ‘Sunco Oil’, ‘Houston’, and ‘John Smith’, respectively.
Several types of attributes occur in the ER model: simple versus composite, single- valued versus multivalued, and stored versus derived. First we define these attribute
7.3 Entity Types, Entity Sets, Attributes, and Keys 205
Name = John Smith Name = Sunco Oil
Headquarters = Houston
President = John Smith
Address = 2311 Kirby Houston, Texas 77001
Age = 55
e1 c1
Home_phone = 713-749-2630
Figure 7.3 Two entities, EMPLOYEE e1, and COMPANY c1, and their attributes.
Address
CityStreet_address
Number Street Apartment_number
State Zip
Figure 7.4 A hierarchy of composite attributes.
types and illustrate their use via examples. Then we discuss the concept of a NULL value for an attribute.
Composite versus Simple (Atomic) Attributes. Composite attributes can be divided into smaller subparts, which represent more basic attributes with indepen- dent meanings. For example, the Address attribute of the EMPLOYEE entity shown in Figure 7.3 can be subdivided into Street_address, City, State, and Zip,3 with the values ‘2311 Kirby’, ‘Houston’, ‘Texas’, and ‘77001.’ Attributes that are not divisible are called simple or atomic attributes. Composite attributes can form a hierarchy; for example, Street_address can be further subdivided into three simple component attributes: Number, Street, and Apartment_number, as shown in Figure 7.4. The value of a composite attribute is the concatenation of the values of its component simple attributes.
Composite attributes are useful to model situations in which a user sometimes refers to the composite attribute as a unit but at other times refers specifically to its components. If the composite attribute is referenced only as a whole, there is no
3Zip Code is the name used in the United States for a five-digit postal code, such as 76019, which can be extended to nine digits, such as 76019-0015. We use the five-digit Zip in our examples.
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need to subdivide it into component attributes. For example, if there is no need to refer to the individual components of an address (Zip Code, street, and so on), then the whole address can be designated as a simple attribute.
Single-Valued versus Multivalued Attributes. Most attributes have a single value for a particular entity; such attributes are called single-valued. For example, Age is a single-valued attribute of a person. In some cases an attribute can have a set of values for the same entity—for instance, a Colors attribute for a car, or a College_degrees attribute for a person. Cars with one color have a single value, whereas two-tone cars have two color values. Similarly, one person may not have a college degree, another person may have one, and a third person may have two or more degrees; therefore, different people can have different numbers of values for the College_degrees attribute. Such attributes are called multivalued. A multivalued attribute may have lower and upper bounds to constrain the number of values allowed for each individual entity. For example, the Colors attribute of a car may be restricted to have between one and three values, if we assume that a car can have three colors at most.
Stored versus Derived Attributes. In some cases, two (or more) attribute val- ues are related—for example, the Age and Birth_date attributes of a person. For a particular person entity, the value of Age can be determined from the current (today’s) date and the value of that person’s Birth_date. The Age attribute is hence called a derived attribute and is said to be derivable from the Birth_date attribute, which is called a stored attribute. Some attribute values can be derived from related entities; for example, an attribute Number_of_employees of a DEPARTMENT entity can be derived by counting the number of employees related to (working for) that department.
NULL Values. In some cases, a particular entity may not have an applicable value for an attribute. For example, the Apartment_number attribute of an address applies only to addresses that are in apartment buildings and not to other types of resi- dences, such as single-family homes. Similarly, a College_degrees attribute applies only to people with college degrees. For such situations, a special value called NULL is created. An address of a single-family home would have NULL for its Apartment_number attribute, and a person with no college degree would have NULL for College_degrees. NULL can also be used if we do not know the value of an attrib- ute for a particular entity—for example, if we do not know the home phone num- ber of ‘John Smith’ in Figure 7.3. The meaning of the former type of NULL is not applicable, whereas the meaning of the latter is unknown. The unknown category of NULL can be further classified into two cases. The first case arises when it is known that the attribute value exists but is missing—for instance, if the Height attribute of a person is listed as NULL. The second case arises when it is not known whether the attribute value exists—for example, if the Home_phone attribute of a person is NULL.
Complex Attributes. Notice that, in general, composite and multivalued attrib- utes can be nested arbitrarily. We can represent arbitrary nesting by grouping com-
7.3 Entity Types, Entity Sets, Attributes, and Keys 207
{Address_phone( {Phone(Area_code,Phone_number)},Address(Street_address (Number,Street,Apartment_number),City,State,Zip) )}
Figure 7.5 A complex attribute: Address_phone.
Entity Type Name:
Entity Set: (Extension)
COMPANY
Name, Headquarters, President
EMPLOYEE
Name, Age, Salary
(John Smith, 55, 80k)
(Fred Brown, 40, 30K)
(Judy Clark, 25, 20K)
e1 c1
c2e2
e3
(Sunco Oil, Houston, John Smith)
(Fast Computer, Dallas, Bob King)
Figure 7.6 Two entity types, EMPLOYEE and COMPANY, and some member entities of each.
ponents of a composite attribute between parentheses () and separating the compo- nents with commas, and by displaying multivalued attributes between braces { }. Such attributes are called complex attributes. For example, if a person can have more than one residence and each residence can have a single address and multiple phones, an attribute Address_phone for a person can be specified as shown in Figure 7.5.4 Both Phone and Address are themselves composite attributes.
7.3.2 Entity Types, Entity Sets, Keys, and Value Sets
Entity Types and Entity Sets. A database usually contains groups of entities that are similar. For example, a company employing hundreds of employees may want to store similar information concerning each of the employees. These employee entities share the same attributes, but each entity has its own value(s) for each attribute. An entity type defines a collection (or set) of entities that have the same attributes. Each entity type in the database is described by its name and attributes. Figure 7.6 shows two entity types: EMPLOYEE and COMPANY, and a list of some of the attributes for
4For those familiar with XML, we should note that complex attributes are similar to complex elements in XML (see Chapter 12).
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Model
Make
Vehicle_id
Year
Color
Registration
State(a)
(b)
Number
CAR
CAR1 ((ABC 123, TEXAS), TK629, Ford Mustang, convertible, 2004 {red, black})
CAR2 ((ABC 123, NEW YORK), WP9872, Nissan Maxima, 4-door, 2005, {blue})
CAR3 ((VSY 720, TEXAS), TD729, Chrysler LeBaron, 4-door, 2002, {white, blue})
CAR Registration (Number, State), Vehicle_id, Make, Model, Year, {Color}
Figure 7.7 The CAR entity type with two key attributes, Registration and Vehicle_id. (a) ER diagram notation. (b) Entity set with three entities.
each. A few individual entities of each type are also illustrated, along with the values of their attributes. The collection of all entities of a particular entity type in the data- base at any point in time is called an entity set; the entity set is usually referred to using the same name as the entity type. For example, EMPLOYEE refers to both a type of entity as well as the current set of all employee entities in the database.
An entity type is represented in ER diagrams5 (see Figure 7.2) as a rectangular box enclosing the entity type name. Attribute names are enclosed in ovals and are attached to their entity type by straight lines. Composite attributes are attached to their component attributes by straight lines. Multivalued attributes are displayed in double ovals. Figure 7.7(a) shows a CAR entity type in this notation.
An entity type describes the schema or intension for a set of entities that share the same structure. The collection of entities of a particular entity type is grouped into an entity set, which is also called the extension of the entity type.
Key Attributes of an Entity Type. An important constraint on the entities of an entity type is the key or uniqueness constraint on attributes. An entity type usually
5We use a notation for ER diagrams that is close to the original proposed notation (Chen 1976). Many other notations are in use; we illustrate some of them later in this chapter when we present UML class diagrams and in Appendix A.
7.3 Entity Types, Entity Sets, Attributes, and Keys 209
has one or more attributes whose values are distinct for each individual entity in the entity set. Such an attribute is called a key attribute, and its values can be used to identify each entity uniquely. For example, the Name attribute is a key of the COMPANY entity type in Figure 7.6 because no two companies are allowed to have the same name. For the PERSON entity type, a typical key attribute is Ssn (Social Security number). Sometimes several attributes together form a key, meaning that the combination of the attribute values must be distinct for each entity. If a set of attributes possesses this property, the proper way to represent this in the ER model that we describe here is to define a composite attribute and designate it as a key attribute of the entity type. Notice that such a composite key must be minimal; that is, all component attributes must be included in the composite attribute to have the uniqueness property. Superfluous attributes must not be included in a key. In ER diagrammatic notation, each key attribute has its name underlined inside the oval, as illustrated in Figure 7.7(a).
Specifying that an attribute is a key of an entity type means that the preceding uniqueness property must hold for every entity set of the entity type. Hence, it is a constraint that prohibits any two entities from having the same value for the key attribute at the same time. It is not the property of a particular entity set; rather, it is a constraint on any entity set of the entity type at any point in time. This key con- straint (and other constraints we discuss later) is derived from the constraints of the miniworld that the database represents.
Some entity types have more than one key attribute. For example, each of the Vehicle_id and Registration attributes of the entity type CAR (Figure 7.7) is a key in its own right. The Registration attribute is an example of a composite key formed from two simple component attributes, State and Number, neither of which is a key on its own. An entity type may also have no key, in which case it is called a weak entity type (see Section 7.5).
In our diagrammatic notation, if two attributes are underlined separately, then each is a key on its own. Unlike the relational model (see Section 3.2.2), there is no con- cept of primary key in the ER model that we present here; the primary key will be chosen during mapping to a relational schema (see Chapter 9).
Value Sets (Domains) of Attributes. Each simple attribute of an entity type is associated with a value set (or domain of values), which specifies the set of values that may be assigned to that attribute for each individual entity. In Figure 7.6, if the range of ages allowed for employees is between 16 and 70, we can specify the value set of the Age attribute of EMPLOYEE to be the set of integer numbers between 16 and 70. Similarly, we can specify the value set for the Name attribute to be the set of strings of alphabetic characters separated by blank characters, and so on. Value sets are not displayed in ER diagrams, and are typically specified using the basic data types available in most programming languages, such as integer, string, Boolean, float, enumerated type, subrange, and so on. Additional data types to represent common database types such as date, time, and other concepts are also employed.
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Mathematically, an attribute A of entity set E whose value set is V can be defined as a function from E to the power set6 P(V ) of V:
A : E → P(V )
We refer to the value of attribute A for entity e as A(e). The previous definition cov- ers both single-valued and multivalued attributes, as well as NULLs. A NULL value is represented by the empty set. For single-valued attributes, A(e) is restricted to being a singleton set for each entity e in E, whereas there is no restriction on multivalued attributes.7 For a composite attribute A, the value set V is the power set of the Cartesian product of P(V1), P(V2), ..., P(Vn), where V1, V2, ..., Vn are the value sets of the simple component attributes that form A:
V = P (P(V1) × P(V2) × ... × P(Vn))
The value set provides all possible values. Usually only a small number of these val- ues exist in the database at a particular time. Those values represent the data from the current state of the miniworld. They correspond to the data as it actually exists in the miniworld.
7.3.3 Initial Conceptual Design of the COMPANY Database We can now define the entity types for the COMPANY database, based on the requirements described in Section 7.2. After defining several entity types and their attributes here, we refine our design in Section 7.4 after we introduce the concept of a relationship. According to the requirements listed in Section 7.2, we can identify four entity types—one corresponding to each of the four items in the specification (see Figure 7.8):
1. An entity type DEPARTMENT with attributes Name, Number, Locations, Manager, and Manager_start_date. Locations is the only multivalued attribute. We can specify that both Name and Number are (separate) key attributes because each was specified to be unique.
2. An entity type PROJECT with attributes Name, Number, Location, and Controlling_department. Both Name and Number are (separate) key attributes.
3. An entity type EMPLOYEE with attributes Name, Ssn, Sex, Address, Salary, Birth_date, Department, and Supervisor. Both Name and Address may be com- posite attributes; however, this was not specified in the requirements. We must go back to the users to see if any of them will refer to the individual components of Name—First_name, Middle_initial, Last_name—or of Address.
4. An entity type DEPENDENT with attributes Employee, Dependent_name, Sex, Birth_date, and Relationship (to the employee).
6The power set P (V ) of a set V is the set of all subsets of V. 7A singleton set is a set with only one element (value).
7.3 Entity Types, Entity Sets, Attributes, and Keys 211
Address
Sex
Birth_date
Project Hours
Works_on
Fname Minit Lname
Department
Salary
Supervisor
Name
EMPLOYEE
Ssn
Sex
Relationship
Employee
Dependent_name DEPENDENT
Birth_date
Location
Number
Controlling_department
Name
PROJECT
Manager_start_date
Number
ManagerDEPARTMENT
Name
Locations
Figure 7.8 Preliminary design of entity types for the COMPANY database. Some of the shown attributes will be refined into relationships.
So far, we have not represented the fact that an employee can work on several proj- ects, nor have we represented the number of hours per week an employee works on each project. This characteristic is listed as part of the third requirement in Section 7.2, and it can be represented by a multivalued composite attribute of EMPLOYEE called Works_on with the simple components (Project, Hours). Alternatively, it can be represented as a multivalued composite attribute of PROJECT called Workers with the simple components (Employee, Hours). We choose the first alternative in Figure 7.8, which shows each of the entity types just described. The Name attribute of EMPLOYEE is shown as a composite attribute, presumably after consultation with the users.
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7.4 Relationship Types, Relationship Sets, Roles, and Structural Constraints
In Figure 7.8 there are several implicit relationships among the various entity types. In fact, whenever an attribute of one entity type refers to another entity type, some relationship exists. For example, the attribute Manager of DEPARTMENT refers to an employee who manages the department; the attribute Controlling_department of PROJECT refers to the department that controls the project; the attribute Supervisor of EMPLOYEE refers to another employee (the one who supervises this employee); the attribute Department of EMPLOYEE refers to the department for which the employee works; and so on. In the ER model, these references should not be repre- sented as attributes but as relationships, which are discussed in this section. The COMPANY database schema will be refined in Section 7.6 to represent relationships explicitly. In the initial design of entity types, relationships are typically captured in the form of attributes. As the design is refined, these attributes get converted into relationships between entity types.
This section is organized as follows: Section 7.4.1 introduces the concepts of rela- tionship types, relationship sets, and relationship instances. We define the concepts of relationship degree, role names, and recursive relationships in Section 7.4.2, and then we discuss structural constraints on relationships—such as cardinality ratios and existence dependencies—in Section 7.4.3. Section 7.4.4 shows how relationship types can also have attributes.
7.4.1 Relationship Types, Sets, and Instances A relationship type R among n entity types E1, E2, ..., En defines a set of associa- tions—or a relationship set—among entities from these entity types. As for the case of entity types and entity sets, a relationship type and its corresponding rela- tionship set are customarily referred to by the same name, R. Mathematically, the relationship set R is a set of relationship instances ri, where each ri associates n individual entities (e1, e2, ..., en), and each entity ej in ri is a member of entity set Ej, 1 j n. Hence, a relationship set is a mathematical relation on E1, E2, ..., En; alter- natively, it can be defined as a subset of the Cartesian product of the entity sets E1 × E2 × ... × En. Each of the entity types E1, E 2, ..., En is said to participate in the rela- tionship type R; similarly, each of the individual entities e1, e2, ..., en is said to participate in the relationship instance ri = (e1, e2, ..., en).
Informally, each relationship instance ri in R is an association of entities, where the association includes exactly one entity from each participating entity type. Each such relationship instance ri represents the fact that the entities participating in ri are related in some way in the corresponding miniworld situation. For example, consider a relationship type WORKS_FOR between the two entity types EMPLOYEE and DEPARTMENT, which associates each employee with the department for which the employee works in the corresponding entity set. Each relationship instance in the relationship set WORKS_FOR associates one EMPLOYEE entity and one DEPARTMENT entity. Figure 7.9 illustrates this example, where each relationship
7.4 Relationship Types, Relationship Sets, Roles, and Structural Constraints 213
EMPLOYEE WORKS_FOR DEPARTMENT
e1
e2
e3
e4
e5
e6
e7
r1
r2
r3
r4
r5
r6
r7
d1
d2
d3
Figure 7.9 Some instances in the WORKS_FOR relationship set, which represents a relationship type WORKS_FOR between EMPLOYEE and DEPARTMENT.
instance ri is shown connected to the EMPLOYEE and DEPARTMENT entities that participate in ri. In the miniworld represented by Figure 7.9, employees e1, e3, and e6 work for department d1; employees e2 and e4 work for department d2; and employ- ees e5 and e7 work for department d3.
In ER diagrams, relationship types are displayed as diamond-shaped boxes, which are connected by straight lines to the rectangular boxes representing the participat- ing entity types. The relationship name is displayed in the diamond-shaped box (see Figure 7.2).
7.4.2 Relationship Degree, Role Names, and Recursive Relationships
Degree of a Relationship Type. The degree of a relationship type is the number of participating entity types. Hence, the WORKS_FOR relationship is of degree two. A relationship type of degree two is called binary, and one of degree three is called ternary. An example of a ternary relationship is SUPPLY, shown in Figure 7.10, where each relationship instance ri associates three entities—a supplier s, a part p, and a project j—whenever s supplies part p to project j. Relationships can generally be of any degree, but the ones most common are binary relationships. Higher- degree relationships are generally more complex than binary relationships; we char- acterize them further in Section 7.9.
214 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
SUPPLIER
PART
SUPPLY PROJECT
p1
p2
p3
r1
r2
r3
r4
r5
r6
r7
j1
j2
j3
s1
s2
Figure 7.10 Some relationship instances in the SUPPLY ternary relationship set.
Relationships as Attributes. It is sometimes convenient to think of a binary relationship type in terms of attributes, as we discussed in Section 7.3.3. Consider the WORKS_FOR relationship type in Figure 7.9. One can think of an attribute called Department of the EMPLOYEE entity type, where the value of Department for each EMPLOYEE entity is (a reference to) the DEPARTMENT entity for which that employee works. Hence, the value set for this Department attribute is the set of all DEPARTMENT entities, which is the DEPARTMENT entity set. This is what we did in Figure 7.8 when we specified the initial design of the entity type EMPLOYEE for the COMPANY database. However, when we think of a binary relationship as an attrib- ute, we always have two options. In this example, the alternative is to think of a mul- tivalued attribute Employee of the entity type DEPARTMENT whose values for each DEPARTMENT entity is the set of EMPLOYEE entities who work for that department. The value set of this Employee attribute is the power set of the EMPLOYEE entity set. Either of these two attributes—Department of EMPLOYEE or Employee of DEPARTMENT—can represent the WORKS_FOR relationship type. If both are repre- sented, they are constrained to be inverses of each other.8
8This concept of representing relationship types as attributes is used in a class of data models called functional data models. In object databases (see Chapter 11), relationships can be represented by ref- erence attributes, either in one direction or in both directions as inverses. In relational databases (see Chapter 3), foreign keys are a type of reference attribute used to represent relationships.
7.4 Relationship Types, Relationship Sets, Roles, and Structural Constraints 215
EMPLOYEE
2
2
2
SUPERVISION
e1
e2
e3
e4
e5
e6
e7
r1
r2
r3
r4
r5
r6
2
2
2
1
1
1
1
1
1
Figure 7.11 A recursive relationship SUPERVISION between EMPLOYEE in the supervisor role (1) and EMPLOYEE in the subordinate role (2).
Role Names and Recursive Relationships. Each entity type that participates in a relationship type plays a particular role in the relationship. The role name sig- nifies the role that a participating entity from the entity type plays in each relation- ship instance, and helps to explain what the relationship means. For example, in the WORKS_FOR relationship type, EMPLOYEE plays the role of employee or worker and DEPARTMENT plays the role of department or employer.
Role names are not technically necessary in relationship types where all the partici- pating entity types are distinct, since each participating entity type name can be used as the role name. However, in some cases the same entity type participates more than once in a relationship type in different roles. In such cases the role name becomes essential for distinguishing the meaning of the role that each participating entity plays. Such relationship types are called recursive relationships. Figure 7.11 shows an example. The SUPERVISION relationship type relates an employee to a supervisor, where both employee and supervisor entities are members of the same EMPLOYEE entity set. Hence, the EMPLOYEE entity type participates twice in SUPERVISION: once in the role of supervisor (or boss), and once in the role of supervisee (or subordinate). Each relationship instance ri in SUPERVISION associates two employee entities ej and ek, one of which plays the role of supervisor and the other the role of supervisee. In Figure 7.11, the lines marked ‘1’ represent the super- visor role, and those marked ‘2’ represent the supervisee role; hence, e1 supervises e2 and e3, e4 supervises e6 and e7, and e5 supervises e1 and e4. In this example, each rela- tionship instance must be connected with two lines, one marked with ‘1’ (supervi- sor) and the other with ‘2’ (supervisee).
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EMPLOYEE MANAGES DEPARTMENT
e1
e2
e3
e4
e5
e6
e7
d1
d2
d3
r1
r2
r3
Figure 7.12 A 1:1 relationship, MANAGES.
7.4.3 Constraints on Binary Relationship Types Relationship types usually have certain constraints that limit the possible combina- tions of entities that may participate in the corresponding relationship set. These constraints are determined from the miniworld situation that the relationships rep- resent. For example, in Figure 7.9, if the company has a rule that each employee must work for exactly one department, then we would like to describe this con- straint in the schema. We can distinguish two main types of binary relationship constraints: cardinality ratio and participation.
Cardinality Ratios for Binary Relationships. The cardinality ratio for a binary relationship specifies the maximum number of relationship instances that an entity can participate in. For example, in the WORKS_FOR binary relationship type, DEPARTMENT:EMPLOYEE is of cardinality ratio 1:N, meaning that each department can be related to (that is, employs) any number of employees,9 but an employee can be related to (work for) only one department. This means that for this particular relationship WORKS_FOR, a particular department entity can be related to any number of employees (N indicates there is no maximum number). On the other hand, an employee can be related to a maximum of one department. The possible cardinality ratios for binary relationship types are 1:1, 1:N, N:1, and M:N.
An example of a 1:1 binary relationship is MANAGES (Figure 7.12), which relates a department entity to the employee who manages that department. This represents the miniworld constraints that—at any point in time—an employee can manage one department only and a department can have one manager only. The relation- ship type WORKS_ON (Figure 7.13) is of cardinality ratio M:N, because the mini-
9N stands for any number of related entities (zero or more).
7.4 Relationship Types, Relationship Sets, Roles, and Structural Constraints 217
EMPLOYEE WORKS_ON PROJECT
e1
e2
e3
e4
r1
r2
r3
r4
r5
r6
r7
p1
p2
p3
p4
Figure 7.13 An M:N relationship, WORKS_ON.
world rule is that an employee can work on several projects and a project can have several employees.
Cardinality ratios for binary relationships are represented on ER diagrams by dis- playing 1, M, and N on the diamonds as shown in Figure 7.2. Notice that in this notation, we can either specify no maximum (N) or a maximum of one (1) on par- ticipation. An alternative notation (see Section 7.7.4) allows the designer to specify a specific maximum number on participation, such as 4 or 5.
Participation Constraints and Existence Dependencies. The participation constraint specifies whether the existence of an entity depends on its being related to another entity via the relationship type. This constraint specifies the minimum number of relationship instances that each entity can participate in, and is some- times called the minimum cardinality constraint. There are two types of participa- tion constraints—total and partial—that we illustrate by example. If a company policy states that every employee must work for a department, then an employee entity can exist only if it participates in at least one WORKS_FOR relationship instance (Figure 7.9). Thus, the participation of EMPLOYEE in WORKS_FOR is called total participation, meaning that every entity in the total set of employee entities must be related to a department entity via WORKS_FOR. Total participation is also called existence dependency. In Figure 7.12 we do not expect every employee to manage a department, so the participation of EMPLOYEE in the MANAGES rela- tionship type is partial, meaning that some or part of the set of employee entities are related to some department entity via MANAGES, but not necessarily all. We will
218 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
refer to the cardinality ratio and participation constraints, taken together, as the structural constraints of a relationship type.
In ER diagrams, total participation (or existence dependency) is displayed as a double line connecting the participating entity type to the relationship, whereas par- tial participation is represented by a single line (see Figure 7.2). Notice that in this notation, we can either specify no minimum (partial participation) or a minimum of one (total participation). The alternative notation (see Section 7.7.4) allows the designer to specify a specific minimum number on participation in the relationship, such as 4 or 5.
We will discuss constraints on higher-degree relationships in Section 7.9.
7.4.4 Attributes of Relationship Types Relationship types can also have attributes, similar to those of entity types. For example, to record the number of hours per week that an employee works on a par- ticular project, we can include an attribute Hours for the WORKS_ON relationship type in Figure 7.13. Another example is to include the date on which a manager started managing a department via an attribute Start_date for the MANAGES rela- tionship type in Figure 7.12.
Notice that attributes of 1:1 or 1:N relationship types can be migrated to one of the participating entity types. For example, the Start_date attribute for the MANAGES relationship can be an attribute of either EMPLOYEE or DEPARTMENT, although conceptually it belongs to MANAGES. This is because MANAGES is a 1:1 relation- ship, so every department or employee entity participates in at most one relationship instance. Hence, the value of the Start_date attribute can be determined separately, either by the participating department entity or by the participating employee (manager) entity.
For a 1:N relationship type, a relationship attribute can be migrated only to the entity type on the N-side of the relationship. For example, in Figure 7.9, if the WORKS_FOR relationship also has an attribute Start_date that indicates when an employee started working for a department, this attribute can be included as an attribute of EMPLOYEE. This is because each employee works for only one depart- ment, and hence participates in at most one relationship instance in WORKS_FOR. In both 1:1 and 1:N relationship types, the decision where to place a relationship attribute—as a relationship type attribute or as an attribute of a participating entity type—is determined subjectively by the schema designer.
For M:N relationship types, some attributes may be determined by the combination of participating entities in a relationship instance, not by any single entity. Such attributes must be specified as relationship attributes. An example is the Hours attrib- ute of the M:N relationship WORKS_ON (Figure 7.13); the number of hours per week an employee currently works on a project is determined by an employee- project combination and not separately by either entity.
7.5 Weak Entity Types 219
7.5 Weak Entity Types Entity types that do not have key attributes of their own are called weak entity types. In contrast, regular entity types that do have a key attribute—which include all the examples discussed so far—are called strong entity types. Entities belonging to a weak entity type are identified by being related to specific entities from another entity type in combination with one of their attribute values. We call this other entity type the identifying or owner entity type,10 and we call the relationship type that relates a weak entity type to its owner the identifying relationship of the weak entity type.11 A weak entity type always has a total participation constraint (existence dependency) with respect to its identifying relationship because a weak entity can- not be identified without an owner entity. However, not every existence dependency results in a weak entity type. For example, a DRIVER_LICENSE entity cannot exist unless it is related to a PERSON entity, even though it has its own key (License_number) and hence is not a weak entity.
Consider the entity type DEPENDENT, related to EMPLOYEE, which is used to keep track of the dependents of each employee via a 1:N relationship (Figure 7.2). In our example, the attributes of DEPENDENT are Name (the first name of the dependent), Birth_date, Sex, and Relationship (to the employee). Two dependents of two distinct employees may, by chance, have the same values for Name, Birth_date, Sex, and Relationship, but they are still distinct entities. They are identified as distinct entities only after determining the particular employee entity to which each dependent is related. Each employee entity is said to own the dependent entities that are related to it.
A weak entity type normally has a partial key, which is the attribute that can uniquely identify weak entities that are related to the same owner entity.12 In our example, if we assume that no two dependents of the same employee ever have the same first name, the attribute Name of DEPENDENT is the partial key. In the worst case, a composite attribute of all the weak entity’s attributes will be the partial key.
In ER diagrams, both a weak entity type and its identifying relationship are distin- guished by surrounding their boxes and diamonds with double lines (see Figure 7.2). The partial key attribute is underlined with a dashed or dotted line.
Weak entity types can sometimes be represented as complex (composite, multival- ued) attributes. In the preceding example, we could specify a multivalued attribute Dependents for EMPLOYEE, which is a composite attribute with component attrib- utes Name, Birth_date, Sex, and Relationship. The choice of which representation to use is made by the database designer. One criterion that may be used is to choose the
10The identifying entity type is also sometimes called the parent entity type or the dominant entity type. 11The weak entity type is also sometimes called the child entity type or the subordinate entity type. 12The partial key is sometimes called the discriminator.
220 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
weak entity type representation if there are many attributes. If the weak entity par- ticipates independently in relationship types other than its identifying relationship type, then it should not be modeled as a complex attribute.
In general, any number of levels of weak entity types can be defined; an owner entity type may itself be a weak entity type. In addition, a weak entity type may have more than one identifying entity type and an identifying relationship type of degree higher than two, as we illustrate in Section 7.9.
7.6 Refining the ER Design for the COMPANY Database
We can now refine the database design in Figure 7.8 by changing the attributes that represent relationships into relationship types. The cardinality ratio and participa- tion constraint of each relationship type are determined from the requirements listed in Section 7.2. If some cardinality ratio or dependency cannot be determined from the requirements, the users must be questioned further to determine these structural constraints.
In our example, we specify the following relationship types:
■ MANAGES, a 1:1 relationship type between EMPLOYEE and DEPARTMENT. EMPLOYEE participation is partial. DEPARTMENT participation is not clear from the requirements. We question the users, who say that a department must have a manager at all times, which implies total participation.13 The attribute Start_date is assigned to this relationship type.
■ WORKS_FOR, a 1:N relationship type between DEPARTMENT and EMPLOYEE. Both participations are total.
■ CONTROLS, a 1:N relationship type between DEPARTMENT and PROJECT. The participation of PROJECT is total, whereas that of DEPARTMENT is determined to be partial, after consultation with the users indicates that some departments may control no projects.
■ SUPERVISION, a 1:N relationship type between EMPLOYEE (in the supervi- sor role) and EMPLOYEE (in the supervisee role). Both participations are determined to be partial, after the users indicate that not every employee is a supervisor and not every employee has a supervisor.
■ WORKS_ON, determined to be an M:N relationship type with attribute Hours, after the users indicate that a project can have several employees working on it. Both participations are determined to be total.
■ DEPENDENTS_OF, a 1:N relationship type between EMPLOYEE and DEPENDENT, which is also the identifying relationship for the weak entity
13The rules in the miniworld that determine the constraints are sometimes called the business rules, since they are determined by the business or organization that will utilize the database.
7.7 ER Diagrams, Naming Conventions, and Design Issues 221
type DEPENDENT. The participation of EMPLOYEE is partial, whereas that of DEPENDENT is total.
After specifying the above six relationship types, we remove from the entity types in Figure 7.8 all attributes that have been refined into relationships. These include Manager and Manager_start_date from DEPARTMENT; Controlling_department from PROJECT; Department, Supervisor, and Works_on from EMPLOYEE; and Employee from DEPENDENT. It is important to have the least possible redundancy when we design the conceptual schema of a database. If some redundancy is desired at the storage level or at the user view level, it can be introduced later, as discussed in Section 1.6.1.
7.7 ER Diagrams, Naming Conventions, and Design Issues
7.7.1 Summary of Notation for ER Diagrams Figures 7.9 through 7.13 illustrate examples of the participation of entity types in relationship types by displaying their sets or extensions—the individual entity instances in an entity set and the individual relationship instances in a relationship set. In ER diagrams the emphasis is on representing the schemas rather than the instances. This is more useful in database design because a database schema changes rarely, whereas the contents of the entity sets change frequently. In addition, the schema is obviously easier to display, because it is much smaller.
Figure 7.2 displays the COMPANY ER database schema as an ER diagram. We now review the full ER diagram notation. Entity types such as EMPLOYEE, DEPARTMENT, and PROJECT are shown in rectangular boxes. Relationship types such as WORKS_FOR, MANAGES, CONTROLS, and WORKS_ON are shown in diamond-shaped boxes attached to the participating entity types with straight lines. Attributes are shown in ovals, and each attribute is attached by a straight line to its entity type or relationship type. Component attributes of a composite attribute are attached to the oval representing the composite attribute, as illustrated by the Name attribute of EMPLOYEE. Multivalued attributes are shown in double ovals, as illus- trated by the Locations attribute of DEPARTMENT. Key attributes have their names underlined. Derived attributes are shown in dotted ovals, as illustrated by the Number_of_employees attribute of DEPARTMENT.
Weak entity types are distinguished by being placed in double rectangles and by having their identifying relationship placed in double diamonds, as illustrated by the DEPENDENT entity type and the DEPENDENTS_OF identifying relationship type. The partial key of the weak entity type is underlined with a dotted line.
In Figure 7.2 the cardinality ratio of each binary relationship type is specified by attaching a 1, M, or N on each participating edge. The cardinality ratio of DEPARTMENT:EMPLOYEE in MANAGES is 1:1, whereas it is 1:N for DEPARTMENT: EMPLOYEE in WORKS_FOR, and M:N for WORKS_ON. The participation
222 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
constraint is specified by a single line for partial participation and by double lines for total participation (existence dependency).
In Figure 7.2 we show the role names for the SUPERVISION relationship type because the same EMPLOYEE entity type plays two distinct roles in that relationship. Notice that the cardinality ratio is 1:N from supervisor to supervisee because each employee in the role of supervisee has at most one direct supervisor, whereas an employee in the role of supervisor can supervise zero or more employees.
Figure 7.14 summarizes the conventions for ER diagrams. It is important to note that there are many other alternative diagrammatic notations (see Section 7.7.4 and Appendix A).
7.7.2 Proper Naming of Schema Constructs When designing a database schema, the choice of names for entity types, attributes, relationship types, and (particularly) roles is not always straightforward. One should choose names that convey, as much as possible, the meanings attached to the different constructs in the schema. We choose to use singular names for entity types, rather than plural ones, because the entity type name applies to each individual entity belonging to that entity type. In our ER diagrams, we will use the convention that entity type and relationship type names are uppercase letters, attribute names have their initial letter capitalized, and role names are lowercase letters. We have used this convention in Figure 7.2.
As a general practice, given a narrative description of the database requirements, the nouns appearing in the narrative tend to give rise to entity type names, and the verbs tend to indicate names of relationship types. Attribute names generally arise from additional nouns that describe the nouns corresponding to entity types.
Another naming consideration involves choosing binary relationship names to make the ER diagram of the schema readable from left to right and from top to bot- tom. We have generally followed this guideline in Figure 7.2. To explain this naming convention further, we have one exception to the convention in Figure 7.2—the DEPENDENTS_OF relationship type, which reads from bottom to top. When we describe this relationship, we can say that the DEPENDENT entities (bottom entity type) are DEPENDENTS_OF (relationship name) an EMPLOYEE (top entity type). To change this to read from top to bottom, we could rename the relationship type to HAS_DEPENDENTS, which would then read as follows: An EMPLOYEE entity (top entity type) HAS_DEPENDENTS (relationship name) of type DEPENDENT (bottom entity type). Notice that this issue arises because each binary relationship can be described starting from either of the two participating entity types, as discussed in the beginning of Section 7.4.
7.7.3 Design Choices for ER Conceptual Design It is occasionally difficult to decide whether a particular concept in the miniworld should be modeled as an entity type, an attribute, or a relationship type. In this
7.7 ER Diagrams, Naming Conventions, and Design Issues 223
MeaningSymbol
Entity
Weak Entity
Indentifying Relationship
Relationship
Composite Attribute
. . .
Key Attribute
Attribute
Derived Attribute
Multivalued Attribute
Total Participation of E2 in RRE1 E2
Cardinality Ratio 1: N for E1:E2 in RRE1 E2 N1
Structural Constraint (min, max) on Participation of E in R
R E
(min, max)
Figure 7.14 Summary of the notation for ER diagrams.
224 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
section, we give some brief guidelines as to which construct should be chosen in particular situations.
In general, the schema design process should be considered an iterative refinement process, where an initial design is created and then iteratively refined until the most suitable design is reached. Some of the refinements that are often used include the following:
■ A concept may be first modeled as an attribute and then refined into a rela- tionship because it is determined that the attribute is a reference to another entity type. It is often the case that a pair of such attributes that are inverses of one another are refined into a binary relationship. We discussed this type of refinement in detail in Section 7.6. It is important to note that in our notation, once an attribute is replaced by a relationship, the attribute itself should be removed from the entity type to avoid duplication and redundancy.
■ Similarly, an attribute that exists in several entity types may be elevated or promoted to an independent entity type. For example, suppose that several entity types in a UNIVERSITY database, such as STUDENT, INSTRUCTOR, and COURSE, each has an attribute Department in the initial design; the designer may then choose to create an entity type DEPARTMENT with a single attrib- ute Dept_name and relate it to the three entity types (STUDENT, INSTRUCTOR, and COURSE) via appropriate relationships. Other attrib- utes/relationships of DEPARTMENT may be discovered later.
■ An inverse refinement to the previous case may be applied—for example, if an entity type DEPARTMENT exists in the initial design with a single attribute Dept_name and is related to only one other entity type, STUDENT. In this case, DEPARTMENT may be reduced or demoted to an attribute of STUDENT.
■ Section 7.9 discusses choices concerning the degree of a relationship. In Chapter 8, we discuss other refinements concerning specialization/general- ization. Chapter 10 discusses additional top-down and bottom-up refine- ments that are common in large-scale conceptual schema design.
7.7.4 Alternative Notations for ER Diagrams There are many alternative diagrammatic notations for displaying ER diagrams. Appendix A gives some of the more popular notations. In Section 7.8, we introduce the Unified Modeling Language (UML) notation for class diagrams, which has been proposed as a standard for conceptual object modeling.
In this section, we describe one alternative ER notation for specifying structural constraints on relationships, which replaces the cardinality ratio (1:1, 1:N, M:N) and single/double line notation for participation constraints. This notation involves associating a pair of integer numbers (min, max) with each participation of an entity type E in a relationship type R, where 0 ≤ min ≤ max and max ≥ 1. The num- bers mean that for each entity e in E, e must participate in at least min and at most
7.7 ER Diagrams, Naming Conventions, and Design Issues 225
EMPLOYEE
Minit Lname
Name Address
Sex
Salary
Ssn
Bdate
Supervisor (0,N) (0,1)
(1,1) Employee
(1,1)
(1,N)
(1,1)
(0,N)Department Managed
(4,N)
Department
(0,1) Manager
Supervisee
SUPERVISION
Hours
WORKS_ON
CONTROLS
DEPENDENTS_OF
Name Location
PROJECT
DEPARTMENT
Locations
Name Number
Number
Number_of_employees
MANAGES
Start_date
WORKS_FOR
DEPENDENT
Sex Birth_date RelationshipName
Controlling Department
Controlled Project
Project
(1,N) Worker
(0,N) Employee
(1,1) Dependent
Fname Figure 7.15 ER diagrams for the company schema, with structural con- straints specified using (min, max) notation and role names.
14In some notations, particularly those used in object modeling methodologies such as UML, the (min, max) is placed on the opposite sides to the ones we have shown. For example, for the WORKS_FOR relationship in Figure 7.15, the (1,1) would be on the DEPARTMENT side, and the (4,N) would be on the EMPLOYEE side. Here we used the original notation from Abrial (1974).
max relationship instances in R at any point in time. In this method, min = 0 implies partial participation, whereas min > 0 implies total participation.
Figure 7.15 displays the COMPANY database schema using the (min, max) notation.14 Usually, one uses either the cardinality ratio/single-line/double-line notation or the (min, max) notation. The (min, max)
226 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
supervisee
Name: Name_dom Fname Minit Lname
Ssn Bdate: Date Sex: {M,F} Address Salary
4..*
1..*
1..* *
*
1..1
1..1
1..1
1..1
1..*
0..1
0..*
0..*
age change_department change_projects . . .
Sex: {M,F} Birth_date: Date Relationship
DEPENDENT
. . .
0..1 supervisor
Dependent_name
EMPLOYEE
Name Number
add_employee number_of_employees change_manager . . .
DEPARTMENT
Name Number
add_employee add_project change_manager . . .
PROJECT
Start_date
MANAGES
CONTROLS
Hours
WORKS_ON Name
LOCATION
1..1 0..* 0..1
Multiplicity Notation in OMT:
Aggregation Notation in UML:
Whole Part
WORKS_FOR
Figure 7.16 The COMPANY conceptual schema in UML class diagram notation.
notation is more precise, and we can use it to specify some structural constraints for relationship types of higher degree. However, it is not sufficient for specifying some key constraints on higher-degree relationships, as discussed in Section 7.9.
Figure 7.15 also displays all the role names for the COMPANY database schema.
7.8 Example of Other Notation: UML Class Diagrams
The UML methodology is being used extensively in software design and has many types of diagrams for various software design purposes. We only briefly present the basics of UML class diagrams here, and compare them with ER diagrams. In some ways, class diagrams can be considered as an alternative notation to ER diagrams. Additional UML notation and concepts are presented in Section 8.6, and in Chapter 10. Figure 7.16 shows how the COMPANY ER database schema in Figure 7.15 can be displayed using UML class diagram notation. The entity types in Figure 7.15 are modeled as classes in Figure 7.16. An entity in ER corresponds to an object in UML.
7.8 Example of Other Notation: UML Class Diagrams 227
In UML class diagrams, a class (similar to an entity type in ER) is displayed as a box (see Figure 7.16) that includes three sections: The top section gives the class name (similar to entity type name); the middle section includes the attributes; and the last section includes operations that can be applied to individual objects (similar to individual entities in an entity set) of the class. Operations are not specified in ER diagrams. Consider the EMPLOYEE class in Figure 7.16. Its attributes are Name, Ssn, Bdate, Sex, Address, and Salary. The designer can optionally specify the domain of an attribute if desired, by placing a colon (:) followed by the domain name or description, as illustrated by the Name, Sex, and Bdate attributes of EMPLOYEE in Figure 7.16. A composite attribute is modeled as a structured domain, as illustrated by the Name attribute of EMPLOYEE. A multivalued attribute will generally be mod- eled as a separate class, as illustrated by the LOCATION class in Figure 7.16.
Relationship types are called associations in UML terminology, and relationship instances are called links. A binary association (binary relationship type) is repre- sented as a line connecting the participating classes (entity types), and may option- ally have a name. A relationship attribute, called a link attribute, is placed in a box that is connected to the association’s line by a dashed line. The (min, max) notation described in Section 7.7.4 is used to specify relationship constraints, which are called multiplicities in UML terminology. Multiplicities are specified in the form min..max, and an asterisk (*) indicates no maximum limit on participation. However, the multiplicities are placed on the opposite ends of the relationship when compared with the notation discussed in Section 7.7.4 (compare Figures 7.15 and 7.16). In UML, a single asterisk indicates a multiplicity of 0..*, and a single 1 indi- cates a multiplicity of 1..1. A recursive relationship (see Section 7.4.2) is called a reflexive association in UML, and the role names—like the multiplicities—are placed at the opposite ends of an association when compared with the placing of role names in Figure 7.15.
In UML, there are two types of relationships: association and aggregation. Aggregation is meant to represent a relationship between a whole object and its component parts, and it has a distinct diagrammatic notation. In Figure 7.16, we modeled the locations of a department and the single location of a project as aggre- gations. However, aggregation and association do not have different structural properties, and the choice as to which type of relationship to use is somewhat sub- jective. In the ER model, both are represented as relationships.
UML also distinguishes between unidirectional and bidirectional associations (or aggregations). In the unidirectional case, the line connecting the classes is displayed with an arrow to indicate that only one direction for accessing related objects is needed. If no arrow is displayed, the bidirectional case is assumed, which is the default. For example, if we always expect to access the manager of a department starting from a DEPARTMENT object, we would draw the association line represent- ing the MANAGES association with an arrow from DEPARTMENT to EMPLOYEE. In addition, relationship instances may be specified to be ordered. For example, we could specify that the employee objects related to each department through the WORKS_FOR association (relationship) should be ordered by their Salary attribute
228 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
value. Association (relationship) names are optional in UML, and relationship attributes are displayed in a box attached with a dashed line to the line representing the association/aggregation (see Start_date and Hours in Figure 7.16).
The operations given in each class are derived from the functional requirements of the application, as we discussed in Section 7.1. It is generally sufficient to specify the operation names initially for the logical operations that are expected to be applied to individual objects of a class, as shown in Figure 7.16. As the design is refined, more details are added, such as the exact argument types (parameters) for each operation, plus a functional description of each operation. UML has function descriptions and sequence diagrams to specify some of the operation details, but these are beyond the scope of our discussion. Chapter 10 will introduce some of these diagrams.
Weak entities can be modeled using the construct called qualified association (or qualified aggregation) in UML; this can represent both the identifying relationship and the partial key, which is placed in a box attached to the owner class. This is illus- trated by the DEPENDENT class and its qualified aggregation to EMPLOYEE in Figure 7.16. The partial key Dependent_name is called the discriminator in UML ter- minology, since its value distinguishes the objects associated with (related to) the same EMPLOYEE. Qualified associations are not restricted to modeling weak enti- ties, and they can be used to model other situations in UML.
This section is not meant to be a complete description of UML class diagrams, but rather to illustrate one popular type of alternative diagrammatic notation that can be used for representing ER modeling concepts.
7.9 Relationship Types of Degree Higher than Two
In Section 7.4.2 we defined the degree of a relationship type as the number of par- ticipating entity types and called a relationship type of degree two binary and a rela- tionship type of degree three ternary. In this section, we elaborate on the differences between binary and higher-degree relationships, when to choose higher-degree ver- sus binary relationships, and how to specify constraints on higher-degree relation- ships.
7.9.1 Choosing between Binary and Ternary (or Higher-Degree) Relationships
The ER diagram notation for a ternary relationship type is shown in Figure 7.17(a), which displays the schema for the SUPPLY relationship type that was displayed at the entity set/relationship set or instance level in Figure 7.10. Recall that the rela- tionship set of SUPPLY is a set of relationship instances (s, j, p), where s is a SUPPLIER who is currently supplying a PART p to a PROJECT j. In general, a rela- tionship type R of degree n will have n edges in an ER diagram, one connecting R to each participating entity type.
7.9 Relationship Types of Degree Higher than Two 229
(a) SUPPLY
Sname
Part_no
SUPPLIER
Quantity
PROJECT
PART
Proj_name
(b)
(c)
Part_no
PART
N
Sname
SUPPLIER
Proj_name
PROJECT
N
Quantity
SUPPLY N1
Part_no
M N
CAN_SUPPLY
N
M
Sname
SUPPLIER
Proj_name
PROJECT
USES
PART
M
N
SUPPLIES
SP
SPJSS 1
1
Figure 7.17 Ternary relationship types. (a) The SUPPLY relationship. (b) Three binary relationships not equivalent to SUPPLY. (c) SUPPLY represented as a weak entity type.
Figure 7.17(b) shows an ER diagram for three binary relationship types CAN_SUPPLY, USES, and SUPPLIES. In general, a ternary relationship type repre- sents different information than do three binary relationship types. Consider the three binary relationship types CAN_SUPPLY, USES, and SUPPLIES. Suppose that CAN_SUPPLY, between SUPPLIER and PART, includes an instance (s, p) whenever supplier s can supply part p (to any project); USES, between PROJECT and PART, includes an instance ( j, p) whenever project j uses part p; and SUPPLIES, between SUPPLIER and PROJECT, includes an instance (s, j) whenever supplier s supplies
230 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
some part to project j. The existence of three relationship instances (s, p), ( j, p), and (s, j) in CAN_SUPPLY, USES, and SUPPLIES, respectively, does not necessarily imply that an instance (s, j, p) exists in the ternary relationship SUPPLY, because the meaning is different. It is often tricky to decide whether a particular relationship should be represented as a relationship type of degree n or should be broken down into several relationship types of smaller degrees. The designer must base this decision on the semantics or meaning of the particular situation being represented. The typical solution is to include the ternary relationship plus one or more of the binary relationships, if they represent different meanings and if all are needed by the application.
Some database design tools are based on variations of the ER model that permit only binary relationships. In this case, a ternary relationship such as SUPPLY must be represented as a weak entity type, with no partial key and with three identifying relationships. The three participating entity types SUPPLIER, PART, and PROJECT are together the owner entity types (see Figure 7.17(c)). Hence, an entity in the weak entity type SUPPLY in Figure 7.17(c) is identified by the combination of its three owner entities from SUPPLIER, PART, and PROJECT.
It is also possible to represent the ternary relationship as a regular entity type by introducing an artificial or surrogate key. In this example, a key attribute Supply_id could be used for the supply entity type, converting it into a regular entity type. Three binary N:1 relationships relate SUPPLY to the three participating entity types.
Another example is shown in Figure 7.18. The ternary relationship type OFFERS represents information on instructors offering courses during particular semesters; hence it includes a relationship instance (i, s, c) whenever INSTRUCTOR i offers COURSE c during SEMESTER s. The three binary relationship types shown in Figure 7.18 have the following meanings: CAN_TEACH relates a course to the instructors who can teach that course, TAUGHT_DURING relates a semester to the instructors who taught some course during that semester, and OFFERED_DURING
Cnumber CAN_TEACH
Lname
INSTRUCTOR
Sem_year
YearSemester
SEMESTER
OFFERED_DURING
COURSE
OFFERS
TAUGHT_DURING
Figure 7.18 Another example of ternary versus binary relationship types.
7.9 Relationship Types of Degree Higher than Two 231
Dept_date
DateDepartment
RESULTS_IN
Name
CANDIDATE
Cname
COMPANY
INTERVIEW JOB_OFFER
CCI
Figure 7.19 A weak entity type INTERVIEW with a ternary identifying rela- tionship type.
relates a semester to the courses offered during that semester by any instructor. These ternary and binary relationships represent different information, but certain constraints should hold among the relationships. For example, a relationship instance (i, s, c) should not exist in OFFERS unless an instance (i, s) exists in TAUGHT_DURING, an instance (s, c) exists in OFFERED_DURING, and an instance (i, c) exists in CAN_TEACH. However, the reverse is not always true; we may have instances (i, s), (s, c), and (i, c) in the three binary relationship types with no corre- sponding instance (i, s, c) in OFFERS. Note that in this example, based on the mean- ings of the relationships, we can infer the instances of TAUGHT_DURING and OFFERED_DURING from the instances in OFFERS, but we cannot infer the instances of CAN_TEACH; therefore, TAUGHT_DURING and OFFERED_DURING are redundant and can be left out.
Although in general three binary relationships cannot replace a ternary relationship, they may do so under certain additional constraints. In our example, if the CAN_TEACH relationship is 1:1 (an instructor can teach one course, and a course can be taught by only one instructor), then the ternary relationship OFFERS can be left out because it can be inferred from the three binary relationships CAN_TEACH, TAUGHT_DURING, and OFFERED_DURING. The schema designer must analyze the meaning of each specific situation to decide which of the binary and ternary rela- tionship types are needed.
Notice that it is possible to have a weak entity type with a ternary (or n-ary) identi- fying relationship type. In this case, the weak entity type can have several owner entity types. An example is shown in Figure 7.19. This example shows part of a data- base that keeps track of candidates interviewing for jobs at various companies, and may be part of an employment agency database, for example. In the requirements, a candidate can have multiple interviews with the same company (for example, with different company departments or on separate dates), but a job offer is made based on one of the interviews. Here, INTERVIEW is represented as a weak entity with two owners CANDIDATE and COMPANY, and with the partial key Dept_date. An INTERVIEW entity is uniquely identified by a candidate, a company, and the combi- nation of the date and department of the interview.
232 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
7.9.2 Constraints on Ternary (or Higher-Degree) Relationships
There are two notations for specifying structural constraints on n-ary relationships, and they specify different constraints. They should thus both be used if it is impor- tant to fully specify the structural constraints on a ternary or higher-degree rela- tionship. The first notation is based on the cardinality ratio notation of binary relationships displayed in Figure 7.2. Here, a 1, M, or N is specified on each partici- pation arc (both M and N symbols stand for many or any number).15 Let us illus- trate this constraint using the SUPPLY relationship in Figure 7.17.
Recall that the relationship set of SUPPLY is a set of relationship instances (s, j, p), where s is a SUPPLIER, j is a PROJECT, and p is a PART. Suppose that the constraint exists that for a particular project-part combination, only one supplier will be used (only one supplier supplies a particular part to a particular project). In this case, we place 1 on the SUPPLIER participation, and M, N on the PROJECT, PART participa- tions in Figure 7.17. This specifies the constraint that a particular ( j, p) combination can appear at most once in the relationship set because each such (PROJECT, PART) combination uniquely determines a single supplier. Hence, any relationship instance (s, j, p) is uniquely identified in the relationship set by its ( j, p) combina- tion, which makes ( j, p) a key for the relationship set. In this notation, the participa- tions that have a 1 specified on them are not required to be part of the identifying key for the relationship set.16 If all three cardinalities are M or N, then the key will be the combination of all three participants.
The second notation is based on the (min, max) notation displayed in Figure 7.15 for binary relationships. A (min, max) on a participation here specifies that each entity is related to at least min and at most max relationship instances in the relation- ship set. These constraints have no bearing on determining the key of an n-ary rela- tionship, where n > 2,17 but specify a different type of constraint that places restrictions on how many relationship instances each entity can participate in.
7.10 Summary In this chapter we presented the modeling concepts of a high-level conceptual data model, the Entity-Relationship (ER) model. We started by discussing the role that a high-level data model plays in the database design process, and then we presented a sample set of database requirements for the COMPANY database, which is one of the examples that is used throughout this book. We defined the basic ER model con- cepts of entities and their attributes. Then we discussed NULL values and presented
15This notation allows us to determine the key of the relationship relation, as we discuss in Chapter 9. 16This is also true for cardinality ratios of binary relationships. 17The (min, max) constraints can determine the keys for binary relationships, though.
7.10 Summary 233
the various types of attributes, which can be nested arbitrarily to produce complex attributes:
■ Simple or atomic
■ Composite
■ Multivalued
We also briefly discussed stored versus derived attributes. Then we discussed the ER model concepts at the schema or “intension” level:
■ Entity types and their corresponding entity sets
■ Key attributes of entity types
■ Value sets (domains) of attributes
■ Relationship types and their corresponding relationship sets
■ Participation roles of entity types in relationship types
We presented two methods for specifying the structural constraints on relationship types. The first method distinguished two types of structural constraints:
■ Cardinality ratios (1:1, 1:N, M:N for binary relationships)
■ Participation constraints (total, partial)
We noted that, alternatively, another method of specifying structural constraints is to specify minimum and maximum numbers (min, max) on the participation of each entity type in a relationship type. We discussed weak entity types and the related concepts of owner entity types, identifying relationship types, and partial key attributes.
Entity-Relationship schemas can be represented diagrammatically as ER diagrams. We showed how to design an ER schema for the COMPANY database by first defin- ing the entity types and their attributes and then refining the design to include rela- tionship types. We displayed the ER diagram for the COMPANY database schema. We discussed some of the basic concepts of UML class diagrams and how they relate to ER modeling concepts. We also described ternary and higher-degree relationship types in more detail, and discussed the circumstances under which they are distin- guished from binary relationships.
The ER modeling concepts we have presented thus far—entity types, relationship types, attributes, keys, and structural constraints—can model many database appli- cations. However, more complex applications—such as engineering design, medical information systems, and telecommunications—require additional concepts if we want to model them with greater accuracy. We discuss some advanced modeling concepts in Chapter 8 and revisit further advanced data modeling techniques in Chapter 26.
234 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
Review Questions 7.1. Discuss the role of a high-level data model in the database design process.
7.2. List the various cases where use of a NULL value would be appropriate.
7.3. Define the following terms: entity, attribute, attribute value, relationship instance, composite attribute, multivalued attribute, derived attribute, complex attribute, key attribute, and value set (domain).
7.4. What is an entity type? What is an entity set? Explain the differences among an entity, an entity type, and an entity set.
7.5. Explain the difference between an attribute and a value set.
7.6. What is a relationship type? Explain the differences among a relationship instance, a relationship type, and a relationship set.
7.7. What is a participation role? When is it necessary to use role names in the description of relationship types?
7.8. Describe the two alternatives for specifying structural constraints on rela- tionship types. What are the advantages and disadvantages of each?
7.9. Under what conditions can an attribute of a binary relationship type be migrated to become an attribute of one of the participating entity types?
7.10. When we think of relationships as attributes, what are the value sets of these attributes? What class of data models is based on this concept?
7.11. What is meant by a recursive relationship type? Give some examples of recursive relationship types.
7.12. When is the concept of a weak entity used in data modeling? Define the terms owner entity type, weak entity type, identifying relationship type, and partial key.
7.13. Can an identifying relationship of a weak entity type be of a degree greater than two? Give examples to illustrate your answer.
7.14. Discuss the conventions for displaying an ER schema as an ER diagram.
7.15. Discuss the naming conventions used for ER schema diagrams.
Exercises 7.16. Consider the following set of requirements for a UNIVERSITY database that is
used to keep track of students’ transcripts. This is similar but not identical to the database shown in Figure 1.2:
a. The university keeps track of each student’s name, student number, Social Security number, current address and phone number, permanent address and phone number, birth date, sex, class (freshman, sophomore, ..., grad- uate), major department, minor department (if any), and degree program
Exercises 235
(B.A., B.S., ..., Ph.D.). Some user applications need to refer to the city, state, and ZIP Code of the student’s permanent address and to the stu- dent’s last name. Both Social Security number and student number have unique values for each student.
b. Each department is described by a name, department code, office num- ber, office phone number, and college. Both name and code have unique values for each department.
c. Each course has a course name, description, course number, number of semester hours, level, and offering department. The value of the course number is unique for each course.
d. Each section has an instructor, semester, year, course, and section num- ber. The section number distinguishes sections of the same course that are taught during the same semester/year; its values are 1, 2, 3, ..., up to the number of sections taught during each semester.
e. A grade report has a student, section, letter grade, and numeric grade (0, 1, 2, 3, or 4).
Design an ER schema for this application, and draw an ER diagram for the schema. Specify key attributes of each entity type, and structural constraints on each relationship type. Note any unspecified requirements, and make appropriate assumptions to make the specification complete.
7.17. Composite and multivalued attributes can be nested to any number of levels. Suppose we want to design an attribute for a STUDENT entity type to keep track of previous college education. Such an attribute will have one entry for each college previously attended, and each such entry will be composed of college name, start and end dates, degree entries (degrees awarded at that college, if any), and transcript entries (courses completed at that college, if any). Each degree entry contains the degree name and the month and year the degree was awarded, and each transcript entry contains a course name, semester, year, and grade. Design an attribute to hold this information. Use the conventions in Figure 7.5.
7.18. Show an alternative design for the attribute described in Exercise 7.17 that uses only entity types (including weak entity types, if needed) and relation- ship types.
7.19. Consider the ER diagram in Figure 7.20, which shows a simplified schema for an airline reservations system. Extract from the ER diagram the require- ments and constraints that produced this schema. Try to be as precise as pos- sible in your requirements and constraints specification.
7.20. In Chapters 1 and 2, we discussed the database environment and database users. We can consider many entity types to describe such an environment, such as DBMS, stored database, DBA, and catalog/data dictionary. Try to specify all the entity types that can fully describe a database system and its environment; then specify the relationship types among them, and draw an ER diagram to describe such a general database environment.
236 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
Restrictions
M
N
N
1
N
N
1
1N
AIRPORT
City State
AIRPLANE_ TYPE
Dep_time
Arr_time
Name
Scheduled_dep_time
INSTANCE_OF
Weekdays
Airline
Instances
N
1
1 N
Airport_code
Number
Scheduled_arr_time
CAN_ LAND
TYPE
N
1
DEPARTS
N
1
ARRIVES
N1 ASSIGNED
ARRIVAL_ AIRPORT
DEPARTURE_ AIRPORT N1
SEAT
Max_seatsType_name
Code
AIRPLANE
Airplane_id Total_no_of_seats
LEGS
FLIGHT
FLIGHT_LEG
Le g_no
FARES
FARE
Amount
CphoneCustomer_name
Date
No_of_avail_seats
RESERVATION Seat_no
Company
LEG_INSTANCE
Notes: A LEG (segment) is a nonstop portion of a flight. A LEG_INSTANCE is a particular occurrence of a LEG on a particular date.
1
Figure 7.20 An ER diagram for an AIRLINE database schema.
7.21. Design an ER schema for keeping track of information about votes taken in the U.S. House of Representatives during the current two-year congressional session. The database needs to keep track of each U.S. STATE’s Name (e.g., ‘Texas’, ‘New York’, ‘California’) and include the Region of the state (whose domain is {‘Northeast’, ‘Midwest’, ‘Southeast’, ‘Southwest’, ‘West’}). Each
Exercises 237
CONGRESS_PERSON in the House of Representatives is described by his or her Name, plus the District represented, the Start_date when the congressper- son was first elected, and the political Party to which he or she belongs (whose domain is {‘Republican’, ‘Democrat’, ‘Independent’, ‘Other’}). The database keeps track of each BILL (i.e., proposed law), including the Bill_name, the Date_of_vote on the bill, whether the bill Passed_or_failed (whose domain is {‘Yes’, ‘No’}), and the Sponsor (the congressperson(s) who sponsored—that is, proposed—the bill). The database also keeps track of how each congressperson voted on each bill (domain of Vote attribute is {‘Yes’, ‘No’, ‘Abstain’, ‘Absent’}). Draw an ER schema diagram for this applica- tion. State clearly any assumptions you make.
7.22. A database is being constructed to keep track of the teams and games of a sports league. A team has a number of players, not all of whom participate in each game. It is desired to keep track of the players participating in each game for each team, the positions they played in that game, and the result of the game. Design an ER schema diagram for this application, stating any assumptions you make. Choose your favorite sport (e.g., soccer, baseball, football).
7.23. Consider the ER diagram shown in Figure 7.21 for part of a BANK database. Each bank can have multiple branches, and each branch can have multiple accounts and loans.
a. List the strong (nonweak) entity types in the ER diagram.
b. Is there a weak entity type? If so, give its name, partial key, and identifying relationship.
c. What constraints do the partial key and the identifying relationship of the weak entity type specify in this diagram?
d. List the names of all relationship types, and specify the (min, max) con- straint on each participation of an entity type in a relationship type. Justify your choices.
e. List concisely the user requirements that led to this ER schema design.
f. Suppose that every customer must have at least one account but is restricted to at most two loans at a time, and that a bank branch cannot have more than 1,000 loans. How does this show up on the (min, max) constraints?
7.24. Consider the ER diagram in Figure 7.22. Assume that an employee may work in up to two departments or may not be assigned to any department. Assume that each department must have one and may have up to three phone num- bers. Supply (min, max) constraints on this diagram. State clearly any addi- tional assumptions you make. Under what conditions would the relationship HAS_PHONE be redundant in this example?
7.25. Consider the ER diagram in Figure 7.23. Assume that a course may or may not use a textbook, but that a text by definition is a book that is used in some course. A course may not use more than five books. Instructors teach from
238 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
BANK
LOAN
Balance
Type
AmountLoan_no
1
N
1
N
N N
M M
NameCode
1 N BANK_BRANCH
L_CA_C
ACCTS LOANS
BRANCHES
ACCOUNT
CUSTOMER
Acct_no
Name
AddrPhone
Type
Addr Branch_noAddr
Ssn Figure 7.21 An ER diagram for a BANK database schema.
EMPLOYEE DEPARTMENT
CONTAINSHAS_PHONE
WORKS_IN
PHONE
Figure 7.22 Part of an ER diagram for a COMPANY data- base.
Exercises 239
two to four courses. Supply (min, max) constraints on this diagram. State clearly any additional assumptions you make. If we add the relationship ADOPTS, to indicate the textbook(s) that an instructor uses for a course, should it be a binary relationship between INSTRUCTOR and TEXT, or a ter- nary relationship between all three entity types? What (min, max) con- straints would you put on it? Why?
7.26. Consider an entity type SECTION in a UNIVERSITY database, which describes the section offerings of courses. The attributes of SECTION are Section_number, Semester, Year, Course_number, Instructor, Room_no (where section is taught), Building (where section is taught), Weekdays (domain is the possible combinations of weekdays in which a section can be offered {‘MWF’, ‘MW’, ‘TT’, and so on}), and Hours (domain is all possible time peri- ods during which sections are offered {‘9–9:50 A.M.’, ‘10–10:50 A.M.’, ..., ‘3:30–4:50 P.M.’, ‘5:30–6:20 P.M.’, and so on}). Assume that Section_number is unique for each course within a particular semester/year combination (that is, if a course is offered multiple times during a particular semester, its sec- tion offerings are numbered 1, 2, 3, and so on). There are several composite keys for section, and some attributes are components of more than one key. Identify three composite keys, and show how they can be represented in an ER schema diagram.
7.27. Cardinality ratios often dictate the detailed design of a database. The cardi- nality ratio depends on the real-world meaning of the entity types involved and is defined by the specific application. For the following binary relation- ships, suggest cardinality ratios based on the common-sense meaning of the entity types. Clearly state any assumptions you make.
Entity 1 Cardinality Ratio Entity 2
1. STUDENT ______________ SOCIAL_SECURITY_CARD
2. STUDENT ______________ TEACHER
3. CLASSROOM ______________ WALL
INSTRUCTOR COURSE
USES
TEACHES
TEXT
Figure 7.23 Part of an ER diagram for a COURSES data- base.
240 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
4. COUNTRY ______________ CURRENT_PRESIDENT
5. COURSE ______________ TEXTBOOK
6. ITEM (that can be found in an order) ______________ ORDER
7. STUDENT ______________ CLASS
8. CLASS ______________ INSTRUCTOR
9. INSTRUCTOR ______________ OFFICE
10. EBAY_AUCTION _ITEM ______________ EBAY_BID
7.28. Consider the ER schema for the MOVIES database in Figure 7.24.
Assume that MOVIES is a populated database. ACTOR is used as a generic term and includes actresses. Given the constraints shown in the ER schema, respond to the following statements with True, False, or Maybe. Assign a response of Maybe to statements that, while not explicitly shown to be True, cannot be proven False based on the schema as shown. Justify each answer.
ACTOR MOVIE
LEAD_ROLE
PERFORMS_IN
DIRECTSDIRECTOR
ALSO_A_ DIRECTOR
PRODUCESPRODUCER
ACTOR_ PRODUCER
1
1
1
1 1
M
M
2 N
N
N
N
Figure 7.24 An ER diagram for a MOVIES database schema.
Laboratory Exercises 241
a. There are no actors in this database that have been in no movies.
b. There are some actors who have acted in more than ten movies.
c. Some actors have done a lead role in multiple movies.
d. A movie can have only a maximum of two lead actors.
e. Every director has been an actor in some movie.
f. No producer has ever been an actor.
g. A producer cannot be an actor in some other movie.
h. There are movies with more than a dozen actors.
i. Some producers have been a director as well.
j. Most movies have one director and one producer.
k. Some movies have one director but several producers.
l. There are some actors who have done a lead role, directed a movie, and produced some movie.
m. No movie has a director who also acted in that movie.
7.29. Given the ER schema for the MOVIES database in Figure 7.24, draw an instance diagram using three movies that have been released recently. Draw instances of each entity type: MOVIES, ACTORS, PRODUCERS, DIRECTORS involved; make up instances of the relationships as they exist in reality for those movies.
7.30. Illustrate the UML Diagram for Exercise 7.16. Your UML design should observe the following requirements:
a. A student should have the ability to compute his/her GPA and add or drop majors and minors.
b. Each department should be to able add or delete courses and hire or ter- minate faculty.
c. Each instructor should be able to assign or change a student’s grade for a course.
Note: Some of these functions may be spread over multiple classes.
Laboratory Exercises 7.31. Consider the UNIVERSITY database described in Exercise 7.16. Build the ER
schema for this database using a data modeling tool such as ERwin or Rational Rose.
7.32. Consider a MAIL_ORDER database in which employees take orders for parts from customers. The data requirements are summarized as follows:
■ The mail order company has employees, each identified by a unique employee number, first and last name, and Zip Code.
■ Each customer of the company is identified by a unique customer num- ber, first and last name, and Zip Code.
242 Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
■ Each part sold by the company is identified by a unique part number, a part name, price, and quantity in stock.
■ Each order placed by a customer is taken by an employee and is given a unique order number. Each order contains specified quantities of one or more parts. Each order has a date of receipt as well as an expected ship date. The actual ship date is also recorded.
Design an Entity-Relationship diagram for the mail order database and build the design using a data modeling tool such as ERwin or Rational Rose.
7.33. Consider a MOVIE database in which data is recorded about the movie indus- try. The data requirements are summarized as follows:
■ Each movie is identified by title and year of release. Each movie has a length in minutes. Each has a production company, and each is classified under one or more genres (such as horror, action, drama, and so forth). Each movie has one or more directors and one or more actors appear in it. Each movie also has a plot outline. Finally, each movie has zero or more quotable quotes, each of which is spoken by a particular actor appearing in the movie.
■ Actors are identified by name and date of birth and appear in one or more movies. Each actor has a role in the movie.
■ Directors are also identified by name and date of birth and direct one or more movies. It is possible for a director to act in a movie (including one that he or she may also direct).
■ Production companies are identified by name and each has an address. A production company produces one or more movies.
Design an Entity-Relationship diagram for the movie database and enter the design using a data modeling tool such as ERwin or Rational Rose.
7.34. Consider a CONFERENCE_REVIEW database in which researchers submit their research papers for consideration. Reviews by reviewers are recorded for use in the paper selection process. The database system caters primarily to reviewers who record answers to evaluation questions for each paper they review and make recommendations regarding whether to accept or reject the paper. The data requirements are summarized as follows:
■ Authors of papers are uniquely identified by e-mail id. First and last names are also recorded.
■ Each paper is assigned a unique identifier by the system and is described by a title, abstract, and the name of the electronic file containing the paper.
■ A paper may have multiple authors, but one of the authors is designated as the contact author.
■ Reviewers of papers are uniquely identified by e-mail address. Each reviewer’s first name, last name, phone number, affiliation, and topics of interest are also recorded.
Selected Bibliography 243
■ Each paper is assigned between two and four reviewers. A reviewer rates each paper assigned to him or her on a scale of 1 to 10 in four categories: technical merit, readability, originality, and relevance to the conference. Finally, each reviewer provides an overall recommendation regarding each paper.
■ Each review contains two types of written comments: one to be seen by the review committee only and the other as feedback to the author(s).
Design an Entity-Relationship diagram for the CONFERENCE_REVIEW database and build the design using a data modeling tool such as ERwin or Rational Rose.
7.35. Consider the ER diagram for the AIRLINE database shown in Figure 7.20. Build this design using a data modeling tool such as ERwin or Rational Rose.
Selected Bibliography The Entity-Relationship model was introduced by Chen (1976), and related work appears in Schmidt and Swenson (1975), Wiederhold and Elmasri (1979), and Senko (1975). Since then, numerous modifications to the ER model have been sug- gested. We have incorporated some of these in our presentation. Structural con- straints on relationships are discussed in Abrial (1974), Elmasri and Wiederhold (1980), and Lenzerini and Santucci (1983). Multivalued and composite attributes are incorporated in the ER model in Elmasri et al. (1985). Although we did not dis- cuss languages for the ER model and its extensions, there have been several propos- als for such languages. Elmasri and Wiederhold (1981) proposed the GORDAS query language for the ER model. Another ER query language was proposed by Markowitz and Raz (1983). Senko (1980) presented a query language for Senko’s DIAM model. A formal set of operations called the ER algebra was presented by Parent and Spaccapietra (1985). Gogolla and Hohenstein (1991) presented another formal language for the ER model. Campbell et al. (1985) presented a set of ER operations and showed that they are relationally complete. A conference for the dis- semination of research results related to the ER model has been held regularly since 1979. The conference, now known as the International Conference on Conceptual Modeling, has been held in Los Angeles (ER 1979, ER 1983, ER 1997), Washington, D.C. (ER 1981), Chicago (ER 1985), Dijon, France (ER 1986), New York City (ER 1987), Rome (ER 1988), Toronto (ER 1989), Lausanne, Switzerland (ER 1990), San Mateo, California (ER 1991), Karlsruhe, Germany (ER 1992), Arlington, Texas (ER 1993), Manchester, England (ER 1994), Brisbane, Australia (ER 1995), Cottbus, Germany (ER 1996), Singapore (ER 1998), Paris, France (ER 1999), Salt Lake City, Utah (ER 2000), Yokohama, Japan (ER 2001), Tampere, Finland (ER 2002), Chicago, Illinois (ER 2003), Shanghai, China (ER 2004), Klagenfurt, Austria (ER 2005), Tucson, Arizona (ER 2006), Auckland, New Zealand (ER 2007), Barcelona, Catalonia, Spain (ER 2008), and Gramado, RS, Brazil (ER 2009). The 2010 confer- ence is to be held in Vancouver, BC, Canada.
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245
The Enhanced Entity-Relationship (EER) Model
The ER modeling concepts discussed in Chapter 7are sufficient for representing many database schemas for traditional database applications, which include many data-processing applications in business and industry. Since the late 1970s, however, designers of database applications have tried to design more accurate database schemas that reflect the data properties and constraints more precisely. This was particularly important for newer applications of database technology, such as databases for engineering design and manufacturing (CAD/CAM),1 telecommunications, com- plex software systems, and Geographic Information Systems (GIS), among many other applications. These types of databases have more complex requirements than do the more traditional applications. This led to the development of additional semantic data modeling concepts that were incorporated into conceptual data mod- els such as the ER model. Various semantic data models have been proposed in the literature. Many of these concepts were also developed independently in related areas of computer science, such as the knowledge representation area of artificial intelligence and the object modeling area in software engineering.
In this chapter, we describe features that have been proposed for semantic data mod- els, and show how the ER model can be enhanced to include these concepts, leading to the Enhanced ER (EER) model.2 We start in Section 8.1 by incorporating the con- cepts of class/subclass relationships and type inheritance into the ER model. Then, in Section 8.2, we add the concepts of specialization and generalization. Section 8.3
8chapter 8
2EER has also been used to stand for Extended ER model.
1CAD/CAM stands for computer-aided design/computer-aided manufacturing.
246 Chapter 8 The Enhanced Entity-Relationship (EER) Model
discusses the various types of constraints on specialization/generalization, and Section 8.4 shows how the UNION construct can be modeled by including the con- cept of category in the EER model. Section 8.5 gives a sample UNIVERSITY database schema in the EER model and summarizes the EER model concepts by giving formal definitions. We will use the terms object and entity interchangeably in this chapter, because many of these concepts are commonly used in object-oriented models.
We present the UML class diagram notation for representing specialization and gen- eralization in Section 8.6, and briefly compare these with EER notation and con- cepts. This serves as an example of alternative notation, and is a continuation of Section 7.8, which presented basic UML class diagram notation that corresponds to the basic ER model. In Section 8.7, we discuss the fundamental abstractions that are used as the basis of many semantic data models. Section 8.8 summarizes the chapter.
For a detailed introduction to conceptual modeling, Chapter 8 should be consid- ered a continuation of Chapter 7. However, if only a basic introduction to ER mod- eling is desired, this chapter may be omitted. Alternatively, the reader may choose to skip some or all of the later sections of this chapter (Sections 8.4 through 8.8).
8.1 Subclasses, Superclasses, and Inheritance The EER model includes all the modeling concepts of the ER model that were pre- sented in Chapter 7. In addition, it includes the concepts of subclass and superclass and the related concepts of specialization and generalization (see Sections 8.2 and 8.3). Another concept included in the EER model is that of a category or union type (see Section 8.4), which is used to represent a collection of objects (entities) that is the union of objects of different entity types. Associated with these concepts is the important mechanism of attribute and relationship inheritance. Unfortunately, no standard terminology exists for these concepts, so we use the most common terminology. Alternative terminology is given in foot- notes. We also describe a diagrammatic technique for displaying these concepts when they arise in an EER schema. We call the resulting schema diagrams enhanced ER or EER diagrams.
The first Enhanced ER (EER) model concept we take up is that of a subtype or subclass of an entity type. As we discussed in Chapter 7, an entity type is used to represent both a type of entity and the entity set or collection of entities of that type that exist in the database. For example, the entity type EMPLOYEE describes the type (that is, the attributes and relationships) of each employee entity, and also refers to the current set of EMPLOYEE entities in the COMPANY database. In many cases an entity type has numerous subgroupings or subtypes of its entities that are meaning- ful and need to be represented explicitly because of their significance to the database application. For example, the entities that are members of the EMPLOYEE entity type may be distinguished further into SECRETARY, ENGINEER, MANAGER, TECHNICIAN, SALARIED_EMPLOYEE, HOURLY_EMPLOYEE, and so on. The set of entities in each of the latter groupings is a subset of the entities that belong to the EMPLOYEE entity set, meaning that every entity that is a member of one of these
8.1 Subclasses, Superclasses, and Inheritance 247
subgroupings is also an employee. We call each of these subgroupings a subclass or subtype of the EMPLOYEE entity type, and the EMPLOYEE entity type is called the superclass or supertype for each of these subclasses. Figure 8.1 shows how to repre- sent these concepts diagramatically in EER diagrams. (The circle notation in Figure 8.1 will be explained in Section 8.2.)
We call the relationship between a superclass and any one of its subclasses a superclass/subclass or supertype/subtype or simply class/subclass relationship.3
In our previous example, EMPLOYEE/SECRETARY and EMPLOYEE/TECHNICIAN are two class/subclass relationships. Notice that a member entity of the subclass repre- sents the same real-world entity as some member of the superclass; for example, a SECRETARY entity ‘Joan Logano’ is also the EMPLOYEE ‘Joan Logano.’ Hence, the subclass member is the same as the entity in the superclass, but in a distinct specific role. When we implement a superclass/subclass relationship in the database system, however, we may represent a member of the subclass as a distinct database object— say, a distinct record that is related via the key attribute to its superclass entity. In Section 9.2, we discuss various options for representing superclass/subclass rela- tionships in relational databases.
MANAGES
d
Minit Lname
Name Birth_date Add ressSsn
Fname
Eng_typeTgradeTyping_speed Pay_scale
HOURLY_EMPLOYEE
SALARIED_EMPLOYEE
Salary
PROJECT
SECRETARY TECHNICIAN ENGINEER MANAGER
EMPLOYEE
TRADE_UNION
BELONGS_TO
d
Three specializations of EMPLOYEE: {SECRETARY, TECHNICIAN, ENGINEER} {MANAGER} {HOURLY_EMPLOYEE, SALARIED_EMPLOYEE}
Figure 8.1 EER diagram notation to represent subclasses and specialization.
3A class/subclass relationship is often called an IS-A (or IS-AN) relationship because of the way we refer to the concept. We say a SECRETARY is an EMPLOYEE, a TECHNICIAN is an EMPLOYEE, and so on.
248 Chapter 8 The Enhanced Entity-Relationship (EER) Model
An entity cannot exist in the database merely by being a member of a subclass; it must also be a member of the superclass. Such an entity can be included optionally as a member of any number of subclasses. For example, a salaried employee who is also an engineer belongs to the two subclasses ENGINEER and SALARIED_EMPLOYEE of the EMPLOYEE entity type. However, it is not necessary that every entity in a superclass is a member of some subclass.
An important concept associated with subclasses (subtypes) is that of type inheri- tance. Recall that the type of an entity is defined by the attributes it possesses and the relationship types in which it participates. Because an entity in the subclass rep- resents the same real-world entity from the superclass, it should possess values for its specific attributes as well as values of its attributes as a member of the superclass. We say that an entity that is a member of a subclass inherits all the attributes of the entity as a member of the superclass. The entity also inherits all the relationships in which the superclass participates. Notice that a subclass, with its own specific (or local) attributes and relationships together with all the attributes and relationships it inherits from the superclass, can be considered an entity type in its own right.4
8.2 Specialization and Generalization
8.2.1 Specialization Specialization is the process of defining a set of subclasses of an entity type; this entity type is called the superclass of the specialization. The set of subclasses that forms a specialization is defined on the basis of some distinguishing characteristic of the entities in the superclass. For example, the set of subclasses {SECRETARY, ENGINEER, TECHNICIAN} is a specialization of the superclass EMPLOYEE that dis- tinguishes among employee entities based on the job type of each employee entity. We may have several specializations of the same entity type based on different dis- tinguishing characteristics. For example, another specialization of the EMPLOYEE entity type may yield the set of subclasses {SALARIED_EMPLOYEE, HOURLY_EMPLOYEE}; this specialization distinguishes among employees based on the method of pay.
Figure 8.1 shows how we represent a specialization diagrammatically in an EER dia- gram. The subclasses that define a specialization are attached by lines to a circle that represents the specialization, which is connected in turn to the superclass. The subset symbol on each line connecting a subclass to the circle indicates the direction of the superclass/subclass relationship.5 Attributes that apply only to entities of a particular subclass—such as TypingSpeed of SECRETARY—are attached to the rec- tangle representing that subclass. These are called specific attributes (or local
4In some object-oriented programming languages, a common restriction is that an entity (or object) has only one type. This is generally too restrictive for conceptual database modeling. 5There are many alternative notations for specialization; we present the UML notation in Section 8.6 and other proposed notations in Appendix A.
8.2 Specialization and Generalization 249
attributes) of the subclass. Similarly, a subclass can participate in specific relation- ship types, such as the HOURLY_EMPLOYEE subclass participating in the BELONGS_TO relationship in Figure 8.1. We will explain the d symbol in the circles in Figure 8.1 and additional EER diagram notation shortly.
Figure 8.2 shows a few entity instances that belong to subclasses of the {SECRETARY, ENGINEER, TECHNICIAN} specialization. Again, notice that an entity that belongs to a subclass represents the same real-world entity as the entity con- nected to it in the EMPLOYEE superclass, even though the same entity is shown twice; for example, e1 is shown in both EMPLOYEE and SECRETARY in Figure 8.2. As the figure suggests, a superclass/subclass relationship such as EMPLOYEE/ SECRETARY somewhat resembles a 1:1 relationship at the instance level (see Figure 7.12). The main difference is that in a 1:1 relationship two distinct entities are related, whereas in a superclass/subclass relationship the entity in the subclass is the same real-world entity as the entity in the superclass but is playing a specialized role—for example, an EMPLOYEE specialized in the role of SECRETARY, or an EMPLOYEE specialized in the role of TECHNICIAN.
EMPLOYEE
SECRETARY
ENGINEER
TECHNICIAN
e1
e2
e3
e4
e5
e6
e7
e8
e1
e2
e3
e4 e5
e7
e8
Figure 8.2 Instances of a specialization.
250 Chapter 8 The Enhanced Entity-Relationship (EER) Model
There are two main reasons for including class/subclass relationships and specializa- tions in a data model. The first is that certain attributes may apply to some but not all entities of the superclass. A subclass is defined in order to group the entities to which these attributes apply. The members of the subclass may still share the majority of their attributes with the other members of the superclass. For example, in Figure 8.1 the SECRETARY subclass has the specific attribute Typing_speed, whereas the ENGINEER subclass has the specific attribute Eng_type, but SECRETARY and ENGINEER share their other inherited attributes from the EMPLOYEE entity type.
The second reason for using subclasses is that some relationship types may be par- ticipated in only by entities that are members of the subclass. For example, if only HOURLY_EMPLOYEES can belong to a trade union, we can represent that fact by creating the subclass HOURLY_EMPLOYEE of EMPLOYEE and relating the subclass to an entity type TRADE_UNION via the BELONGS_TO relationship type, as illus- trated in Figure 8.1.
In summary, the specialization process allows us to do the following:
■ Define a set of subclasses of an entity type
■ Establish additional specific attributes with each subclass
■ Establish additional specific relationship types between each subclass and other entity types or other subclasses
8.2.2 Generalization We can think of a reverse process of abstraction in which we suppress the differences among several entity types, identify their common features, and generalize them into a single superclass of which the original entity types are special subclasses. For example, consider the entity types CAR and TRUCK shown in Figure 8.3(a). Because they have several common attributes, they can be generalized into the entity type VEHICLE, as shown in Figure 8.3(b). Both CAR and TRUCK are now subclasses of the generalized superclass VEHICLE. We use the term generalization to refer to the process of defining a generalized entity type from the given entity types.
Notice that the generalization process can be viewed as being functionally the inverse of the specialization process. Hence, in Figure 8.3 we can view {CAR, TRUCK} as a specialization of VEHICLE, rather than viewing VEHICLE as a generalization of CAR and TRUCK. Similarly, in Figure 8.1 we can view EMPLOYEE as a generalization of SECRETARY, TECHNICIAN, and ENGINEER. A diagrammatic notation to distinguish between generalization and specialization is used in some design methodologies. An arrow pointing to the generalized superclass represents a generalization, whereas arrows pointing to the specialized subclasses represent a specialization. We will not use this notation because the decision as to which process is followed in a particular situation is often subjective. Appendix A gives some of the suggested alternative dia- grammatic notations for schema diagrams and class diagrams.
So far we have introduced the concepts of subclasses and superclass/subclass rela- tionships, as well as the specialization and generalization processes. In general, a
8.3 Constraints and Characteristics of Specialization and Generalization Hierarchies 251
(a)
(b)
Max_speed
Vehicle_id
No_of_passengers
License_plate_no
CAR Price Price
License_plate_no
No_of_axles
Vehicle_id
Tonnage
TRUCK
Vehicle_id Price License_plate_no
VEHICLE
No_of_passengers
Max_speed
CAR TRUCK
No_of_axles
Tonnage
d
Figure 8.3 Generalization. (a) Two entity types, CAR and TRUCK. (b) Generalizing CAR and TRUCK into the superclass VEHICLE.
superclass or subclass represents a collection of entities of the same type and hence also describes an entity type; that is why superclasses and subclasses are all shown in rectangles in EER diagrams, like entity types. Next, we discuss the properties of spe- cializations and generalizations in more detail.
8.3 Constraints and Characteristics of Specialization and Generalization Hierarchies
First, we discuss constraints that apply to a single specialization or a single general- ization. For brevity, our discussion refers only to specialization even though it applies to both specialization and generalization. Then, we discuss differences between specialization/generalization lattices (multiple inheritance) and hierarchies (single inheritance), and elaborate on the differences between the specialization and generalization processes during conceptual database schema design.
8.3.1 Constraints on Specialization and Generalization In general, we may have several specializations defined on the same entity type (or superclass), as shown in Figure 8.1. In such a case, entities may belong to subclasses
252 Chapter 8 The Enhanced Entity-Relationship (EER) Model
d
Minit Lname
Name Birth_date Address Job_typeSsn
Fname
Eng_typeTgrade ‘Technician’
Job_type
‘Secretary’ ‘Engineer’
Typing_speed
SECRETARY TECHNICIAN ENGINEER
EMPLOYEE
Figure 8.4 EER diagram notation for an attribute-defined specialization on Job_type.
in each of the specializations. However, a specialization may also consist of a single subclass only, such as the {MANAGER} specialization in Figure 8.1; in such a case, we do not use the circle notation.
In some specializations we can determine exactly the entities that will become members of each subclass by placing a condition on the value of some attribute of the superclass. Such subclasses are called predicate-defined (or condition-defined) subclasses. For example, if the EMPLOYEE entity type has an attribute Job_type, as shown in Figure 8.4, we can specify the condition of membership in the SECRETARY subclass by the condition (Job_type = ‘Secretary’), which we call the defining predicate of the subclass. This condition is a constraint specifying that exactly those entities of the EMPLOYEE entity type whose attribute value for Job_type is ‘Secretary’ belong to the subclass. We display a predicate-defined subclass by writing the predicate condition next to the line that connects the subclass to the specialization circle.
If all subclasses in a specialization have their membership condition on the same attribute of the superclass, the specialization itself is called an attribute-defined spe- cialization, and the attribute is called the defining attribute of the specialization.6 In this case, all the entities with the same value for the attribute belong to the same sub- class. We display an attribute-defined specialization by placing the defining attribute name next to the arc from the circle to the superclass, as shown in Figure 8.4.
When we do not have a condition for determining membership in a subclass, the subclass is called user-defined. Membership in such a subclass is determined by the database users when they apply the operation to add an entity to the subclass; hence, membership is specified individually for each entity by the user, not by any condition that may be evaluated automatically.
6Such an attribute is called a discriminator in UML terminology.
8.3 Constraints and Characteristics of Specialization and Generalization Hierarchies 253
Part_no Description
PARTManufacture_date
Drawing_no
PURCHASED_PART
Supplier_name Batch_no
List_price
o
MANUFACTURED_PART
Figure 8.5 EER diagram notation for an overlapping (nondisjoint) specialization.
Two other constraints may apply to a specialization. The first is the disjointness (or disjointedness) constraint, which specifies that the subclasses of the specialization must be disjoint. This means that an entity can be a member of at most one of the subclasses of the specialization. A specialization that is attribute-defined implies the disjointness constraint (if the attribute used to define the membership predicate is single-valued). Figure 8.4 illustrates this case, where the d in the circle stands for disjoint. The d notation also applies to user-defined subclasses of a specialization that must be disjoint, as illustrated by the specialization {HOURLY_EMPLOYEE, SALARIED_EMPLOYEE} in Figure 8.1. If the subclasses are not constrained to be dis- joint, their sets of entities may be overlapping; that is, the same (real-world) entity may be a member of more than one subclass of the specialization. This case, which is the default, is displayed by placing an o in the circle, as shown in Figure 8.5.
The second constraint on specialization is called the completeness (or totalness) constraint, which may be total or partial. A total specialization constraint specifies that every entity in the superclass must be a member of at least one subclass in the specialization. For example, if every EMPLOYEE must be either an HOURLY_EMPLOYEE or a SALARIED_EMPLOYEE, then the specialization {HOURLY_EMPLOYEE, SALARIED_EMPLOYEE} in Figure 8.1 is a total specialization of EMPLOYEE. This is shown in EER diagrams by using a double line to connect the superclass to the circle. A single line is used to display a partial specialization, which allows an entity not to belong to any of the subclasses. For example, if some EMPLOYEE entities do not belong to any of the subclasses {SECRETARY, ENGINEER, TECHNICIAN} in Figures 8.1 and 8.4, then that specialization is partial.7
Notice that the disjointness and completeness constraints are independent. Hence, we have the following four possible constraints on specialization:
■ Disjoint, total
■ Disjoint, partial
■ Overlapping, total
■ Overlapping, partial
7The notation of using single or double lines is similar to that for partial or total participation of an entity type in a relationship type, as described in Chapter 7.
254 Chapter 8 The Enhanced Entity-Relationship (EER) Model
Of course, the correct constraint is determined from the real-world meaning that applies to each specialization. In general, a superclass that was identified through the generalization process usually is total, because the superclass is derived from the subclasses and hence contains only the entities that are in the subclasses.
Certain insertion and deletion rules apply to specialization (and generalization) as a consequence of the constraints specified earlier. Some of these rules are as follows:
■ Deleting an entity from a superclass implies that it is automatically deleted from all the subclasses to which it belongs.
■ Inserting an entity in a superclass implies that the entity is mandatorily inserted in all predicate-defined (or attribute-defined) subclasses for which the entity satisfies the defining predicate.
■ Inserting an entity in a superclass of a total specialization implies that the entity is mandatorily inserted in at least one of the subclasses of the special- ization.
The reader is encouraged to make a complete list of rules for insertions and dele- tions for the various types of specializations.
8.3.2 Specialization and Generalization Hierarchies and Lattices
A subclass itself may have further subclasses specified on it, forming a hierarchy or a lattice of specializations. For example, in Figure 8.6 ENGINEER is a subclass of EMPLOYEE and is also a superclass of ENGINEERING_MANAGER; this represents the real-world constraint that every engineering manager is required to be an engi- neer. A specialization hierarchy has the constraint that every subclass participates as a subclass in only one class/subclass relationship; that is, each subclass has only
d
HOURLY_EMPLOYEE
SALARIED_EMPLOYEE
ENGINEERING_MANAGER
SECRETARY TECHNICIAN ENGINEER MANAGER
EMPLOYEE
d
Figure 8.6 A specialization lattice with shared subclass ENGINEERING_MANAGER.
8.3 Constraints and Characteristics of Specialization and Generalization Hierarchies 255
one parent, which results in a tree structure or strict hierarchy. In contrast, for a specialization lattice, a subclass can be a subclass in more than one class/subclass relationship. Hence, Figure 8.6 is a lattice.
Figure 8.7 shows another specialization lattice of more than one level. This may be part of a conceptual schema for a UNIVERSITY database. Notice that this arrange- ment would have been a hierarchy except for the STUDENT_ASSISTANT subclass, which is a subclass in two distinct class/subclass relationships.
The requirements for the part of the UNIVERSITY database shown in Figure 8.7 are the following:
1. The database keeps track of three types of persons: employees, alumni, and students. A person can belong to one, two, or all three of these types. Each person has a name, SSN, sex, address, and birth date.
2. Every employee has a salary, and there are three types of employees: faculty, staff, and student assistants. Each employee belongs to exactly one of these types. For each alumnus, a record of the degree or degrees that he or she
STAFF
Percent_time
FACULTY
Name Sex Address
PERSON
Salary
EMPLOYEE
Major_dept
Birth_date
ALUMNUS
d
o
STUDENT_ ASSISTANT
STUDENT
Degrees
DegreeYear Major
GRADUATE_ STUDENT
d
UNDERGRADUATE_ STUDENT
RESEARCH_ASSISTANT
d
TEACHING_ASSISTANT
Position Rank Degree_program Class
CourseProject
Ssn
Figure 8.7 A specialization lattice with multiple inheritance for a UNIVERSITY database.
256 Chapter 8 The Enhanced Entity-Relationship (EER) Model
earned at the university is kept, including the name of the degree, the year granted, and the major department. Each student has a major department.
3. Each faculty has a rank, whereas each staff member has a staff position. Student assistants are classified further as either research assistants or teach- ing assistants, and the percent of time that they work is recorded in the data- base. Research assistants have their research project stored, whereas teaching assistants have the current course they work on.
4. Students are further classified as either graduate or undergraduate, with the specific attributes degree program (M.S., Ph.D., M.B.A., and so on) for graduate students and class (freshman, sophomore, and so on) for under- graduates.
In Figure 8.7, all person entities represented in the database are members of the PERSON entity type, which is specialized into the subclasses {EMPLOYEE, ALUMNUS, STUDENT}. This specialization is overlapping; for example, an alumnus may also be an employee and may also be a student pursuing an advanced degree. The subclass STUDENT is the superclass for the specialization {GRADUATE_STUDENT, UNDERGRADUATE_STUDENT}, while EMPLOYEE is the superclass for the specialization {STUDENT_ASSISTANT, FACULTY, STAFF}. Notice that STUDENT_ASSISTANT is also a subclass of STUDENT. Finally, STUDENT_ASSISTANT is the superclass for the specialization into {RESEARCH_ASSISTANT, TEACHING_ASSISTANT}.
In such a specialization lattice or hierarchy, a subclass inherits the attributes not only of its direct superclass, but also of all its predecessor superclasses all the way to the root of the hierarchy or lattice if necessary. For example, an entity in GRADUATE_STUDENT inherits all the attributes of that entity as a STUDENT and as a PERSON. Notice that an entity may exist in several leaf nodes of the hierarchy, where a leaf node is a class that has no subclasses of its own. For example, a member of GRADUATE_STUDENT may also be a member of RESEARCH_ASSISTANT.
A subclass with more than one superclass is called a shared subclass, such as ENGINEERING_MANAGER in Figure 8.6. This leads to the concept known as multiple inheritance, where the shared subclass ENGINEERING_MANAGER directly inherits attributes and relationships from multiple classes. Notice that the existence of at least one shared subclass leads to a lattice (and hence to multiple inheritance); if no shared subclasses existed, we would have a hierarchy rather than a lattice and only single inheritance would exist. An important rule related to multiple inheri- tance can be illustrated by the example of the shared subclass STUDENT_ASSISTANT in Figure 8.7, which inherits attributes from both EMPLOYEE and STUDENT. Here, both EMPLOYEE and STUDENT inherit the same attributes from PERSON. The rule states that if an attribute (or relationship) originating in the same superclass (PERSON) is inherited more than once via different paths (EMPLOYEE and STUDENT) in the lattice, then it should be included only once in the shared subclass (STUDENT_ASSISTANT). Hence, the attributes of PERSON are inherited only once in the STUDENT_ASSISTANT subclass in Figure 8.7.
8.3 Constraints and Characteristics of Specialization and Generalization Hierarchies 257
8In some models, the class is further restricted to be a leaf node in the hierarchy or lattice.
It is important to note here that some models and languages are limited to single inheritance and do not allow multiple inheritance (shared subclasses). It is also important to note that some models do not allow an entity to have multiple types, and hence an entity can be a member of only one leaf class.8 In such a model, it is necessary to create additional subclasses as leaf nodes to cover all possible combina- tions of classes that may have some entity that belongs to all these classes simultane- ously. For example, in the overlapping specialization of PERSON into {EMPLOYEE, ALUMNUS, STUDENT} (or {E, A, S} for short), it would be necessary to create seven subclasses of PERSON in order to cover all possible types of entities: E, A, S, E_A, E_S, A_S, and E_A_S. Obviously, this can lead to extra complexity.
Although we have used specialization to illustrate our discussion, similar concepts apply equally to generalization, as we mentioned at the beginning of this section. Hence, we can also speak of generalization hierarchies and generalization lattices.
8.3.3 Utilizing Specialization and Generalization in Refining Conceptual Schemas
Now we elaborate on the differences between the specialization and generalization processes, and how they are used to refine conceptual schemas during conceptual database design. In the specialization process, we typically start with an entity type and then define subclasses of the entity type by successive specialization; that is, we repeatedly define more specific groupings of the entity type. For example, when designing the specialization lattice in Figure 8.7, we may first specify an entity type PERSON for a university database. Then we discover that three types of persons will be represented in the database: university employees, alumni, and students. We cre- ate the specialization {EMPLOYEE, ALUMNUS, STUDENT} for this purpose and choose the overlapping constraint, because a person may belong to more than one of the subclasses. We specialize EMPLOYEE further into {STAFF, FACULTY, STUDENT_ASSISTANT}, and specialize STUDENT into {GRADUATE_STUDENT, UNDERGRADUATE_STUDENT}. Finally, we specialize STUDENT_ASSISTANT into {RESEARCH_ASSISTANT, TEACHING_ASSISTANT}. This successive specialization corresponds to a top-down conceptual refinement process during conceptual schema design. So far, we have a hierarchy; then we realize that STUDENT_ASSISTANT is a shared subclass, since it is also a subclass of STUDENT, leading to the lattice.
It is possible to arrive at the same hierarchy or lattice from the other direction. In such a case, the process involves generalization rather than specialization and corre- sponds to a bottom-up conceptual synthesis. For example, the database designers may first discover entity types such as STAFF, FACULTY, ALUMNUS, GRADUATE_STUDENT, UNDERGRADUATE_STUDENT, RESEARCH_ASSISTANT, TEACHING_ASSISTANT, and so on; then they generalize {GRADUATE_STUDENT,
258 Chapter 8 The Enhanced Entity-Relationship (EER) Model
UNDERGRADUATE_STUDENT} into STUDENT; then they generalize {RESEARCH_ASSISTANT, TEACHING_ASSISTANT} into STUDENT_ASSISTANT; then they generalize {STAFF, FACULTY, STUDENT_ASSISTANT} into EMPLOYEE; and finally they generalize {EMPLOYEE, ALUMNUS, STUDENT} into PERSON.
In structural terms, hierarchies or lattices resulting from either process may be iden- tical; the only difference relates to the manner or order in which the schema super- classes and subclasses were created during the design process. In practice, it is likely that neither the generalization process nor the specialization process is followed strictly, but that a combination of the two processes is employed. New classes are continually incorporated into a hierarchy or lattice as they become apparent to users and designers. Notice that the notion of representing data and knowledge by using superclass/subclass hierarchies and lattices is quite common in knowledge-based sys- tems and expert systems, which combine database technology with artificial intelli- gence techniques. For example, frame-based knowledge representation schemes closely resemble class hierarchies. Specialization is also common in software engi- neering design methodologies that are based on the object-oriented paradigm.
8.4 Modeling of UNION Types Using Categories All of the superclass/subclass relationships we have seen thus far have a single super- class. A shared subclass such as ENGINEERING_MANAGER in the lattice in Figure 8.6 is the subclass in three distinct superclass/subclass relationships, where each of the three relationships has a single superclass. However, it is sometimes necessary to represent a single superclass/subclass relationship with more than one superclass, where the superclasses represent different entity types. In this case, the subclass will represent a collection of objects that is a subset of the UNION of distinct entity types; we call such a subclass a union type or a category.9
For example, suppose that we have three entity types: PERSON, BANK, and COMPANY. In a database for motor vehicle registration, an owner of a vehicle can be a person, a bank (holding a lien on a vehicle), or a company. We need to create a class (collection of entities) that includes entities of all three types to play the role of vehicle owner. A category (union type) OWNER that is a subclass of the UNION of the three entity sets of COMPANY, BANK, and PERSON can be created for this purpose. We display categories in an EER diagram as shown in Figure 8.8. The superclasses COMPANY, BANK, and PERSON are connected to the circle with the ∪ symbol, which stands for the set union operation. An arc with the subset symbol connects the circle to the (subclass) OWNER category. If a defining predicate is needed, it is dis- played next to the line from the superclass to which the predicate applies. In Figure 8.8 we have two categories: OWNER, which is a subclass of the union of PERSON, BANK, and COMPANY; and REGISTERED_VEHICLE, which is a subclass of the union of CAR and TRUCK.
9Our use of the term category is based on the ECR (Entity-Category-Relationship) model (Elmasri et al. 1985).
8.4 Modeling of UNION Types Using Categories 259
Name Address
Driver_license_no
Ssn
License_plate_no
Lien_or_regular
Purchase_date
Bname Baddress
Cname Caddress
BANK
PERSON
OWNER
OWNS
M
N
U
REGISTERED_VEHICLE
COMPANY
U
Cstyle
Cyear
Vehicle_id
Cmake
Cmodel
CAR
Tonnage
Tyear
Vehicle_id
Tmake
Tmodel
TRUCK
Figure 8.8 Two categories (union types): OWNER and REGISTERED_VEHICLE.
A category has two or more superclasses that may represent distinct entity types, whereas other superclass/subclass relationships always have a single superclass. To better understand the difference, we can compare a category, such as OWNER in Figure 8.8, with the ENGINEERING_MANAGER shared subclass in Figure 8.6. The latter is a subclass of each of the three superclasses ENGINEER, MANAGER, and SALARIED_EMPLOYEE, so an entity that is a member of ENGINEERING_MANAGER must exist in all three. This represents the constraint that an engineering manager must be an ENGINEER, a MANAGER, and a SALARIED_EMPLOYEE; that is, ENGINEERING_MANAGER is a subset of the intersection of the three classes (sets of entities). On the other hand, a category is a subset of the union of its superclasses. Hence, an entity that is a member of OWNER must exist in only one of the super-
260 Chapter 8 The Enhanced Entity-Relationship (EER) Model
classes. This represents the constraint that an OWNER may be a COMPANY, a BANK, or a PERSON in Figure 8.8.
Attribute inheritance works more selectively in the case of categories. For example, in Figure 8.8 each OWNER entity inherits the attributes of a COMPANY, a PERSON, or a BANK, depending on the superclass to which the entity belongs. On the other hand, a shared subclass such as ENGINEERING_MANAGER (Figure 8.6) inherits all the attributes of its superclasses SALARIED_EMPLOYEE, ENGINEER, and MANAGER.
It is interesting to note the difference between the category REGISTERED_VEHICLE (Figure 8.8) and the generalized superclass VEHICLE (Figure 8.3(b)). In Figure 8.3(b), every car and every truck is a VEHICLE; but in Figure 8.8, the REGISTERED_VEHICLE category includes some cars and some trucks but not neces- sarily all of them (for example, some cars or trucks may not be registered). In gen- eral, a specialization or generalization such as that in Figure 8.3(b), if it were partial, would not preclude VEHICLE from containing other types of entities, such as motorcycles. However, a category such as REGISTERED_VEHICLE in Figure 8.8 implies that only cars and trucks, but not other types of entities, can be members of REGISTERED_VEHICLE.
A category can be total or partial. A total category holds the union of all entities in its superclasses, whereas a partial category can hold a subset of the union. A total cat- egory is represented diagrammatically by a double line connecting the category and the circle, whereas a partial category is indicated by a single line.
The superclasses of a category may have different key attributes, as demonstrated by the OWNER category in Figure 8.8, or they may have the same key attribute, as demonstrated by the REGISTERED_VEHICLE category. Notice that if a category is total (not partial), it may be represented alternatively as a total specialization (or a total generalization). In this case, the choice of which representation to use is sub- jective. If the two classes represent the same type of entities and share numerous attributes, including the same key attributes, specialization/generalization is pre- ferred; otherwise, categorization (union type) is more appropriate.
It is important to note that some modeling methodologies do not have union types. In these models, a union type must be represented in a roundabout way (see Section 9.2).
8.5 A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions
In this section, we first give an example of a database schema in the EER model to illustrate the use of the various concepts discussed here and in Chapter 7. Then, we discuss design choices for conceptual schemas, and finally we summarize the EER model concepts and define them formally in the same manner in which we formally defined the concepts of the basic ER model in Chapter 7.
8.5 A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions 261
8.5.1 The UNIVERSITY Database Example For our sample database application, consider a UNIVERSITY database that keeps track of students and their majors, transcripts, and registration as well as of the uni- versity’s course offerings. The database also keeps track of the sponsored research projects of faculty and graduate students. This schema is shown in Figure 8.9. A dis- cussion of the requirements that led to this schema follows.
For each person, the database maintains information on the person’s Name [Name], Social Security number [Ssn], address [Address], sex [Sex], and birth date [Bdate]. Two subclasses of the PERSON entity type are identified: FACULTY and STUDENT. Specific attributes of FACULTY are rank [Rank] (assistant, associate, adjunct, research, visiting, and so on), office [Foffice], office phone [Fphone], and salary [Salary]. All faculty members are related to the academic department(s) with which they are affiliated [BELONGS] (a faculty member can be associated with several departments, so the relationship is M:N). A specific attribute of STUDENT is [Class] (freshman=1, sophomore=2, ..., graduate student=5). Each STUDENT is also related to his or her major and minor departments (if known) [MAJOR] and [MINOR], to the course sections he or she is currently attending [REGISTERED], and to the courses completed [TRANSCRIPT]. Each TRANSCRIPT instance includes the grade the student received [Grade] in a section of a course.
GRAD_STUDENT is a subclass of STUDENT, with the defining predicate Class = 5. For each graduate student, we keep a list of previous degrees in a composite, multi- valued attribute [Degrees]. We also relate the graduate student to a faculty advisor [ADVISOR] and to a thesis committee [COMMITTEE], if one exists.
An academic department has the attributes name [Dname], telephone [Dphone], and office number [Office] and is related to the faculty member who is its chairperson [CHAIRS] and to the college to which it belongs [CD]. Each college has attributes college name [Cname], office number [Coffice], and the name of its dean [Dean].
A course has attributes course number [C#], course name [Cname], and course description [Cdesc]. Several sections of each course are offered, with each section having the attributes section number [Sec#] and the year and quarter in which the section was offered ([Year] and [Qtr]).10 Section numbers uniquely identify each sec- tion. The sections being offered during the current quarter are in a subclass CURRENT_SECTION of SECTION, with the defining predicate Qtr = Current_qtr and Year = Current_year. Each section is related to the instructor who taught or is teach- ing it ([TEACH]), if that instructor is in the database.
The category INSTRUCTOR_RESEARCHER is a subset of the union of FACULTY and GRAD_STUDENT and includes all faculty, as well as graduate students who are sup- ported by teaching or research. Finally, the entity type GRANT keeps track of research grants and contracts awarded to the university. Each grant has attributes grant title [Title], grant number [No], the awarding agency [Agency], and the starting
10We assume that the quarter system rather than the semester system is used in this university.
262 Chapter 8 The Enhanced Entity-Relationship (EER) Model
Foffice Salary
Rank
Fphone
FACULTY
d
College Degree Year 1 N
M N
M
Degrees
Class
1
M
1
N
N
M
1
N
N
Qtr = Current_qtr and Year = Current_year
N
N
1
M
N N
1
Cname
CdescC#
1 N
1
Office
Dphone
Dname
N
1
1
N
Class=5
Fname LnameMinit
Name
BdateSsn Sex No Street Apt_no City State Zip
Address
U
ADVISOR
COMMITTEE
CHAIRS
BELONGS
MINOR
MAJOR
DCCD
Agency
St_date
NoTitle
Start
Time
End
CURRENT_SECTION
Grade
Sec# Year Qtr
CofficeCname
Dean
PERSON
GRAD_STUDENT
STUDENT
GRANT
SUPPORT
REGISTERED
TRANSCRIPT
SECTION
TEACH
DEPARTMENT
COURSECOLLEGE
CS
INSTRUCTOR_RESEARCHER
PI
Figure 8.9 An EER conceptual schema for a UNIVERSITY database.
8.5 A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions 263
date [St_date]. A grant is related to one principal investigator [PI] and to all researchers it supports [SUPPORT]. Each instance of support has as attributes the starting date of support [Start], the ending date of the support (if known) [End], and the percentage of time being spent on the project [Time] by the researcher being supported.
8.5.2 Design Choices for Specialization/Generalization It is not always easy to choose the most appropriate conceptual design for a database application. In Section 7.7.3, we presented some of the typical issues that confront a database designer when choosing among the concepts of entity types, relationship types, and attributes to represent a particular miniworld situation as an ER schema. In this section, we discuss design guidelines and choices for the EER concepts of specialization/generalization and categories (union types).
As we mentioned in Section 7.7.3, conceptual database design should be considered as an iterative refinement process until the most suitable design is reached. The fol- lowing guidelines can help to guide the design process for EER concepts:
■ In general, many specializations and subclasses can be defined to make the conceptual model accurate. However, the drawback is that the design becomes quite cluttered. It is important to represent only those subclasses that are deemed necessary to avoid extreme cluttering of the conceptual schema.
■ If a subclass has few specific (local) attributes and no specific relationships, it can be merged into the superclass. The specific attributes would hold NULL values for entities that are not members of the subclass. A type attribute could specify whether an entity is a member of the subclass.
■ Similarly, if all the subclasses of a specialization/generalization have few spe- cific attributes and no specific relationships, they can be merged into the superclass and replaced with one or more type attributes that specify the sub- class or subclasses that each entity belongs to (see Section 9.2 for how this criterion applies to relational databases).
■ Union types and categories should generally be avoided unless the situation definitely warrants this type of construct, which does occur in some practi- cal situations. If possible, we try to model using specialization/generalization as discussed at the end of Section 8.4.
■ The choice of disjoint/overlapping and total/partial constraints on special- ization/generalization is driven by the rules in the miniworld being modeled. If the requirements do not indicate any particular constraints, the default would generally be overlapping and partial, since this does not specify any restrictions on subclass membership.
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As an example of applying these guidelines, consider Figure 8.6, where no specific (local) attributes are shown. We could merge all the subclasses into the EMPLOYEE entity type, and add the following attributes to EMPLOYEE:
■ An attribute Job_type whose value set {‘Secretary’, ‘Engineer’, ‘Technician’} would indicate which subclass in the first specialization each employee belongs to.
■ An attribute Pay_method whose value set {‘Salaried’, ‘Hourly’} would indicate which subclass in the second specialization each employee belongs to.
■ An attribute Is_a_manager whose value set {‘Yes’, ‘No’} would indicate whether an individual employee entity is a manager or not.
8.5.3 Formal Definitions for the EER Model Concepts We now summarize the EER model concepts and give formal definitions. A class11
is a set or collection of entities; this includes any of the EER schema constructs of group entities, such as entity types, subclasses, superclasses, and categories. A subclass S is a class whose entities must always be a subset of the entities in another class, called the superclass C of the superclass/subclass (or IS-A) relationship. We denote such a relationship by C/S. For such a superclass/subclass relationship, we must always have
S ⊆ C
A specialization Z = {S1, S2, ..., Sn} is a set of subclasses that have the same super- class G; that is, G/Si is a superclass/subclass relationship for i = 1, 2, ..., n. G is called a generalized entity type (or the superclass of the specialization, or a generalization of the subclasses {S1, S2, ..., Sn} ). Z is said to be total if we always (at any point in time) have
Otherwise, Z is said to be partial. Z is said to be disjoint if we always have
Si ∩ Sj = ∅ (empty set) for i ≠ j
Otherwise, Z is said to be overlapping.
A subclass S of C is said to be predicate-defined if a predicate p on the attributes of C is used to specify which entities in C are members of S; that is, S = C[p], where C[p] is the set of entities in C that satisfy p. A subclass that is not defined by a pred- icate is called user-defined.
A specialization Z (or generalization G) is said to be attribute-defined if a predicate (A = ci), where A is an attribute of G and ci is a constant value from the domain of A,
n
∪ S i = G
i=1
11The use of the word class here differs from its more common use in object-oriented programming lan- guages such as C++. In C++, a class is a structured type definition along with its applicable functions (operations).
8.6 Example of Other Notation: Representing Specialization and Generalization in UML Class Diagrams 265
is used to specify membership in each subclass Si in Z. Notice that if ci ≠ cj for i ≠ j, and A is a single-valued attribute, then the specialization will be disjoint.
A category T is a class that is a subset of the union of n defining superclasses D1, D2, ..., Dn, n > 1, and is formally specified as follows:
T ⊆ (D1 ∪ D2 ... ∪ Dn) A predicate pi on the attributes of Di can be used to specify the members of each Di that are members of T. If a predicate is specified on every Di, we get
T = (D1[p1] ∪ D2[p2] ... ∪ Dn[pn]) We should now extend the definition of relationship type given in Chapter 7 by allowing any class—not only any entity type—to participate in a relationship. Hence, we should replace the words entity type with class in that definition. The graphical notation of EER is consistent with ER because all classes are represented by rectangles.
8.6 Example of Other Notation: Representing Specialization and Generalization in UML Class Diagrams
We now discuss the UML notation for generalization/specialization and inheri- tance. We already presented basic UML class diagram notation and terminology in Section 7.8. Figure 8.10 illustrates a possible UML class diagram corresponding to the EER diagram in Figure 8.7. The basic notation for specialization/generalization (see Figure 8.10) is to connect the subclasses by vertical lines to a horizontal line, which has a triangle connecting the horizontal line through another vertical line to the superclass. A blank triangle indicates a specialization/generalization with the disjoint constraint, and a filled triangle indicates an overlapping constraint. The root superclass is called the base class, and the subclasses (leaf nodes) are called leaf classes.
The above discussion and example in Figure 8.10, and the presentation in Section 7.8 gave a brief overview of UML class diagrams and terminology. We focused on the concepts that are relevant to ER and EER database modeling, rather than those concepts that are more relevant to software engineering. In UML, there are many details that we have not discussed because they are outside the scope of this book and are mainly relevant to software engineering. For example, classes can be of var- ious types:
■ Abstract classes define attributes and operations but do not have objects cor- responding to those classes. These are mainly used to specify a set of attrib- utes and operations that can be inherited.
■ Concrete classes can have objects (entities) instantiated to belong to the class.
■ Template classes specify a template that can be further used to define other classes.
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In database design, we are mainly concerned with specifying concrete classes whose collections of objects are permanently (or persistently) stored in the database. The bibliographic notes at the end of this chapter give some references to books that describe complete details of UML. Additional material related to UML is covered in Chapter 10.
Project
change_project . . .
RESEARCH_ ASSISTANT
Course
assign_to_course . . .
TEACHING_ ASSISTANT
Degree_program
change_degree_program . . .
GRADUATE_ STUDENT
Class
change_classification . . .
UNDERGRADUATE_ STUDENT
Position
hire_staff . . .
STAFF
Rank
promote . . .
FACULTY
Percent_time
hire_student . . .
STUDENT_ASSISTANT
Year Degree Major
DEGREE
. . .
Salary
hire_emp . . .
EMPLOYEE
new_alumnus 1 *
. . .
ALUMNUS
Major_dept
change_major . . .
STUDENT
Name Ssn Birth_date Sex Address
age . . .
PERSON
Figure 8.10 A UML class diagram corresponding to the EER diagram in Figure 8.7, illustrating UML notation for specialization/generalization.
8.7 Data Abstraction, Knowledge Representation, and Ontology Concepts 267
8.7 Data Abstraction, Knowledge Representation, and Ontology Concepts
In this section we discuss in general terms some of the modeling concepts that we described quite specifically in our presentation of the ER and EER models in Chapter 7 and earlier in this chapter. This terminology is not only used in concep- tual data modeling but also in artificial intelligence literature when discussing knowledge representation (KR). This section discusses the similarities and differ- ences between conceptual modeling and knowledge representation, and introduces some of the alternative terminology and a few additional concepts.
The goal of KR techniques is to develop concepts for accurately modeling some domain of knowledge by creating an ontology12 that describes the concepts of the domain and how these concepts are interrelated. Such an ontology is used to store and manipulate knowledge for drawing inferences, making decisions, or answering questions. The goals of KR are similar to those of semantic data models, but there are some important similarities and differences between the two disciplines:
■ Both disciplines use an abstraction process to identify common properties and important aspects of objects in the miniworld (also known as domain of discourse in KR) while suppressing insignificant differences and unimpor- tant details.
■ Both disciplines provide concepts, relationships, constraints, operations, and languages for defining data and representing knowledge.
■ KR is generally broader in scope than semantic data models. Different forms of knowledge, such as rules (used in inference, deduction, and search), incomplete and default knowledge, and temporal and spatial knowledge, are represented in KR schemes. Database models are being expanded to include some of these concepts (see Chapter 26).
■ KR schemes include reasoning mechanisms that deduce additional facts from the facts stored in a database. Hence, whereas most current database systems are limited to answering direct queries, knowledge-based systems using KR schemes can answer queries that involve inferences over the stored data. Database technology is being extended with inference mechanisms (see Section 26.5).
■ Whereas most data models concentrate on the representation of database schemas, or meta-knowledge, KR schemes often mix up the schemas with the instances themselves in order to provide flexibility in representing excep- tions. This often results in inefficiencies when these KR schemes are imple- mented, especially when compared with databases and when a large amount of data (facts) needs to be stored.
12An ontology is somewhat similar to a conceptual schema, but with more knowledge, rules, and excep- tions.
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We now discuss four abstraction concepts that are used in semantic data models, such as the EER model as well as in KR schemes: (1) classification and instantiation, (2) identification, (3) specialization and generalization, and (4) aggregation and association. The paired concepts of classification and instantiation are inverses of one another, as are generalization and specialization. The concepts of aggregation and association are also related. We discuss these abstract concepts and their rela- tion to the concrete representations used in the EER model to clarify the data abstraction process and to improve our understanding of the related process of con- ceptual schema design. We close the section with a brief discussion of ontology, which is being used widely in recent knowledge representation research.
8.7.1 Classification and Instantiation The process of classification involves systematically assigning similar objects/enti- ties to object classes/entity types. We can now describe (in DB) or reason about (in KR) the classes rather than the individual objects. Collections of objects that share the same types of attributes, relationships, and constraints are classified into classes in order to simplify the process of discovering their properties. Instantiation is the inverse of classification and refers to the generation and specific examination of dis- tinct objects of a class. An object instance is related to its object class by the IS-AN- INSTANCE-OF or IS-A-MEMBER-OF relationship. Although EER diagrams do not display instances, the UML diagrams allow a form of instantiation by permit- ting the display of individual objects. We did not describe this feature in our intro- duction to UML class diagrams.
In general, the objects of a class should have a similar type structure. However, some objects may display properties that differ in some respects from the other objects of the class; these exception objects also need to be modeled, and KR schemes allow more varied exceptions than do database models. In addition, certain properties apply to the class as a whole and not to the individual objects; KR schemes allow such class properties. UML diagrams also allow specification of class properties.
In the EER model, entities are classified into entity types according to their basic attributes and relationships. Entities are further classified into subclasses and cate- gories based on additional similarities and differences (exceptions) among them. Relationship instances are classified into relationship types. Hence, entity types, subclasses, categories, and relationship types are the different concepts that are used for classification in the EER model. The EER model does not provide explicitly for class properties, but it may be extended to do so. In UML, objects are classified into classes, and it is possible to display both class properties and individual objects.
Knowledge representation models allow multiple classification schemes in which one class is an instance of another class (called a meta-class). Notice that this cannot be represented directly in the EER model, because we have only two levels—classes and instances. The only relationship among classes in the EER model is a super- class/subclass relationship, whereas in some KR schemes an additional class/instance relationship can be represented directly in a class hierarchy. An instance may itself be another class, allowing multiple-level classification schemes.
8.7 Data Abstraction, Knowledge Representation, and Ontology Concepts 269
8.7.2 Identification Identification is the abstraction process whereby classes and objects are made uniquely identifiable by means of some identifier. For example, a class name uniquely identifies a whole class within a schema. An additional mechanism is nec- essary for telling distinct object instances apart by means of object identifiers. Moreover, it is necessary to identify multiple manifestations in the database of the same real-world object. For example, we may have a tuple <‘Matthew Clarke’, ‘610618’, ‘376-9821’> in a PERSON relation and another tuple <‘301-54-0836’, ‘CS’, 3.8> in a STUDENT relation that happen to represent the same real-world entity. There is no way to identify the fact that these two database objects (tuples) represent the same real-world entity unless we make a provision at design time for appropriate cross-referencing to supply this identification. Hence, identification is needed at two levels:
■ To distinguish among database objects and classes
■ To identify database objects and to relate them to their real-world counter- parts
In the EER model, identification of schema constructs is based on a system of unique names for the constructs in a schema. For example, every class in an EER schema—whether it is an entity type, a subclass, a category, or a relationship type— must have a distinct name. The names of attributes of a particular class must also be distinct. Rules for unambiguously identifying attribute name references in a special- ization or generalization lattice or hierarchy are needed as well.
At the object level, the values of key attributes are used to distinguish among entities of a particular entity type. For weak entity types, entities are identified by a combi- nation of their own partial key values and the entities they are related to in the owner entity type(s). Relationship instances are identified by some combination of the entities that they relate to, depending on the cardinality ratio specified.
8.7.3 Specialization and Generalization Specialization is the process of classifying a class of objects into more specialized subclasses. Generalization is the inverse process of generalizing several classes into a higher-level abstract class that includes the objects in all these classes. Specialization is conceptual refinement, whereas generalization is conceptual syn- thesis. Subclasses are used in the EER model to represent specialization and general- ization. We call the relationship between a subclass and its superclass an IS-A-SUBCLASS-OF relationship, or simply an IS-A relationship. This is the same as the IS-A relationship discussed earlier in Section 8.5.3.
8.7.4 Aggregation and Association Aggregation is an abstraction concept for building composite objects from their component objects. There are three cases where this concept can be related to the EER model. The first case is the situation in which we aggregate attribute values of
270 Chapter 8 The Enhanced Entity-Relationship (EER) Model
an object to form the whole object. The second case is when we represent an aggre- gation relationship as an ordinary relationship. The third case, which the EER model does not provide for explicitly, involves the possibility of combining objects that are related by a particular relationship instance into a higher-level aggregate object. This is sometimes useful when the higher-level aggregate object is itself to be related to another object. We call the relationship between the primitive objects and their aggregate object IS-A-PART-OF; the inverse is called IS-A-COMPONENT- OF. UML provides for all three types of aggregation.
The abstraction of association is used to associate objects from several independent classes. Hence, it is somewhat similar to the second use of aggregation. It is repre- sented in the EER model by relationship types, and in UML by associations. This abstract relationship is called IS-ASSOCIATED-WITH.
In order to understand the different uses of aggregation better, consider the ER schema shown in Figure 8.11(a), which stores information about interviews by job applicants to various companies. The class COMPANY is an aggregation of the attributes (or component objects) Cname (company name) and Caddress (company address), whereas JOB_APPLICANT is an aggregate of Ssn, Name, Address, and Phone. The relationship attributes Contact_name and Contact_phone represent the name and phone number of the person in the company who is responsible for the inter- view. Suppose that some interviews result in job offers, whereas others do not. We would like to treat INTERVIEW as a class to associate it with JOB_OFFER. The schema shown in Figure 8.11(b) is incorrect because it requires each interview rela- tionship instance to have a job offer. The schema shown in Figure 8.11(c) is not allowed because the ER model does not allow relationships among relationships.
One way to represent this situation is to create a higher-level aggregate class com- posed of COMPANY, JOB_APPLICANT, and INTERVIEW and to relate this class to JOB_OFFER, as shown in Figure 8.11(d). Although the EER model as described in this book does not have this facility, some semantic data models do allow it and call the resulting object a composite or molecular object. Other models treat entity types and relationship types uniformly and hence permit relationships among rela- tionships, as illustrated in Figure 8.11(c).
To represent this situation correctly in the ER model as described here, we need to create a new weak entity type INTERVIEW, as shown in Figure 8.11(e), and relate it to JOB_OFFER. Hence, we can always represent these situations correctly in the ER model by creating additional entity types, although it may be conceptually more desirable to allow direct representation of aggregation, as in Figure 8.11(d), or to allow relationships among relationships, as in Figure 8.11(c).
The main structural distinction between aggregation and association is that when an association instance is deleted, the participating objects may continue to exist. However, if we support the notion of an aggregate object—for example, a CAR that is made up of objects ENGINE, CHASSIS, and TIRES—then deleting the aggregate CAR object amounts to deleting all its component objects.
(a)
COMPANY JOB_APPLICANT
AddressName Ssn PhoneCaddressCname
Contact_phoneContact_name
Date
INTERVIEW
(c)
JOB_OFFER
COMPANY JOB_APPLICANTINTERVIEW
RESULTS_IN
(b)
JOB_OFFER
COMPANY JOB_APPLICANTINTERVIEW
(d)
JOB_OFFER
COMPANY JOB_APPLICANTINTERVIEW
RESULTS_IN
(e)
JOB_OFFER
COMPANY JOB_APPLICANT
AddressName Ssn PhoneCaddressCname
Contact_phone
Contact_name
RESULTS_IN
CJI
INTERVIEWDate
8.7 Data Abstraction, Knowledge Representation, and Ontology Concepts 271
Figure 8.11 Aggregation. (a) The relation- ship type INTERVIEW. (b) Including JOB_OFFER in a ternary relationship type (incorrect). (c) Having the RESULTS_IN relationship par- ticipate in other relationships (not allowed in ER). (d) Using aggregation and a composite (molecular) object (generally not allowed in ER but allowed by some modeling tools). (e) Correct representation in ER.
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8.7.5 Ontologies and the Semantic Web In recent years, the amount of computerized data and information available on the Web has spiraled out of control. Many different models and formats are used. In addition to the database models that we present in this book, much information is stored in the form of documents, which have considerably less structure than data- base information does. One ongoing project that is attempting to allow information exchange among computers on the Web is called the Semantic Web, which attempts to create knowledge representation models that are quite general in order to allow meaningful information exchange and search among machines. The concept of ontology is considered to be the most promising basis for achieving the goals of the Semantic Web and is closely related to knowledge representation. In this section, we give a brief introduction to what ontology is and how it can be used as a basis to automate information understanding, search, and exchange.
The study of ontologies attempts to describe the structures and relationships that are possible in reality through some common vocabulary; therefore, it can be con- sidered as a way to describe the knowledge of a certain community about reality. Ontology originated in the fields of philosophy and metaphysics. One commonly used definition of ontology is a specification of a conceptualization.13
In this definition, a conceptualization is the set of concepts that are used to repre- sent the part of reality or knowledge that is of interest to a community of users. Specification refers to the language and vocabulary terms that are used to specify the conceptualization. The ontology includes both specification and conceptualization. For example, the same conceptualization may be specified in two different languages, giving two separate ontologies. Based on this quite general def- inition, there is no consensus on what an ontology is exactly. Some possible ways to describe ontologies are as follows:
■ A thesaurus (or even a dictionary or a glossary of terms) describes the rela- tionships between words (vocabulary) that represent various concepts.
■ A taxonomy describes how concepts of a particular area of knowledge are related using structures similar to those used in a specialization or general- ization.
■ A detailed database schema is considered by some to be an ontology that describes the concepts (entities and attributes) and relationships of a mini- world from reality.
■ A logical theory uses concepts from mathematical logic to try to define con- cepts and their interrelationships.
Usually the concepts used to describe ontologies are quite similar to the concepts we discussed in conceptual modeling, such as entities, attributes, relationships, special- izations, and so on. The main difference between an ontology and, say, a database schema, is that the schema is usually limited to describing a small subset of a mini-
13This definition is given in Gruber (1995).
Review Questions 273
world from reality in order to store and manage data. An ontology is usually consid- ered to be more general in that it attempts to describe a part of reality or a domain of interest (for example, medical terms, electronic-commerce applications, sports, and so on) as completely as possible.
8.8 Summary In this chapter we discussed extensions to the ER model that improve its representa- tional capabilities. We called the resulting model the enhanced ER or EER model. We presented the concept of a subclass and its superclass and the related mechanism of attribute/relationship inheritance. We saw how it is sometimes necessary to create additional classes of entities, either because of additional specific attributes or because of specific relationship types. We discussed two main processes for defining superclass/subclass hierarchies and lattices: specialization and generalization.
Next, we showed how to display these new constructs in an EER diagram. We also discussed the various types of constraints that may apply to specialization or gener- alization. The two main constraints are total/partial and disjoint/overlapping. In addition, a defining predicate for a subclass or a defining attribute for a specializa- tion may be specified. We discussed the differences between user-defined and predicate-defined subclasses and between user-defined and attribute-defined spe- cializations. Finally, we discussed the concept of a category or union type, which is a subset of the union of two or more classes, and we gave formal definitions of all the concepts presented.
We introduced some of the notation and terminology of UML for representing spe- cialization and generalization. In Section 8.7 we briefly discussed the discipline of knowledge representation and how it is related to semantic data modeling. We also gave an overview and summary of the types of abstract data representation con- cepts: classification and instantiation, identification, specialization and generaliza- tion, and aggregation and association. We saw how EER and UML concepts are related to each of these.
Review Questions 8.1. What is a subclass? When is a subclass needed in data modeling?
8.2. Define the following terms: superclass of a subclass, superclass/subclass rela- tionship, IS-A relationship, specialization, generalization, category, specific (local) attributes, and specific relationships.
8.3. Discuss the mechanism of attribute/relationship inheritance. Why is it use- ful?
8.4. Discuss user-defined and predicate-defined subclasses, and identify the dif- ferences between the two.
8.5. Discuss user-defined and attribute-defined specializations, and identify the differences between the two.
274 Chapter 8 The Enhanced Entity-Relationship (EER) Model
8.6. Discuss the two main types of constraints on specializations and generaliza- tions.
8.7. What is the difference between a specialization hierarchy and a specialization lattice?
8.8. What is the difference between specialization and generalization? Why do we not display this difference in schema diagrams?
8.9. How does a category differ from a regular shared subclass? What is a cate- gory used for? Illustrate your answer with examples.
8.10. For each of the following UML terms (see Sections 7.8 and 8.6), discuss the corresponding term in the EER model, if any: object, class, association, aggregation, generalization, multiplicity, attributes, discriminator, link, link attribute, reflexive association, and qualified association.
8.11. Discuss the main differences between the notation for EER schema diagrams and UML class diagrams by comparing how common concepts are repre- sented in each.
8.12. List the various data abstraction concepts and the corresponding modeling concepts in the EER model.
8.13. What aggregation feature is missing from the EER model? How can the EER model be further enhanced to support it?
8.14. What are the main similarities and differences between conceptual database modeling techniques and knowledge representation techniques?
8.15. Discuss the similarities and differences between an ontology and a database schema.
Exercises 8.16. Design an EER schema for a database application that you are interested in.
Specify all constraints that should hold on the database. Make sure that the schema has at least five entity types, four relationship types, a weak entity type, a superclass/subclass relationship, a category, and an n-ary (n > 2) rela- tionship type.
8.17. Consider the BANK ER schema in Figure 7.21, and suppose that it is neces- sary to keep track of different types of ACCOUNTS (SAVINGS_ACCTS, CHECKING_ACCTS, ...) and LOANS (CAR_LOANS, HOME_LOANS, ...). Suppose that it is also desirable to keep track of each ACCOUNT’s TRANSACTIONS (deposits, withdrawals, checks, ...) and each LOAN’s PAYMENTS; both of these include the amount, date, and time. Modify the BANK schema, using ER and EER concepts of specialization and generaliza- tion. State any assumptions you make about the additional requirements.
Exercises 275
8.18. The following narrative describes a simplified version of the organization of Olympic facilities planned for the summer Olympics. Draw an EER diagram that shows the entity types, attributes, relationships, and specializations for this application. State any assumptions you make. The Olympic facilities are divided into sports complexes. Sports complexes are divided into one-sport and multisport types. Multisport complexes have areas of the complex desig- nated for each sport with a location indicator (e.g., center, NE corner, and so on). A complex has a location, chief organizing individual, total occupied area, and so on. Each complex holds a series of events (e.g., the track stadium may hold many different races). For each event there is a planned date, dura- tion, number of participants, number of officials, and so on. A roster of all officials will be maintained together with the list of events each official will be involved in. Different equipment is needed for the events (e.g., goal posts, poles, parallel bars) as well as for maintenance. The two types of facilities (one-sport and multisport) will have different types of information. For each type, the number of facilities needed is kept, together with an approxi- mate budget.
8.19. Identify all the important concepts represented in the library database case study described below. In particular, identify the abstractions of classifica- tion (entity types and relationship types), aggregation, identification, and specialization/generalization. Specify (min, max) cardinality constraints whenever possible. List details that will affect the eventual design but that have no bearing on the conceptual design. List the semantic constraints sep- arately. Draw an EER diagram of the library database.
Case Study: The Georgia Tech Library (GTL) has approximately 16,000 members, 100,000 titles, and 250,000 volumes (an average of 2.5 copies per book). About 10 percent of the volumes are out on loan at any one time. The librarians ensure that the books that members want to borrow are available when the members want to borrow them. Also, the librarians must know how many copies of each book are in the library or out on loan at any given time. A catalog of books is available online that lists books by author, title, and sub- ject area. For each title in the library, a book description is kept in the catalog that ranges from one sentence to several pages. The reference librarians want to be able to access this description when members request information about a book. Library staff includes chief librarian, departmental associate librarians, reference librarians, check-out staff, and library assistants.
Books can be checked out for 21 days. Members are allowed to have only five books out at a time. Members usually return books within three to four weeks. Most members know that they have one week of grace before a notice is sent to them, so they try to return books before the grace period ends. About 5 percent of the members have to be sent reminders to return books. Most overdue books are returned within a month of the due date. Approximately 5 percent of the overdue books are either kept or never returned. The most active members of the library are defined as those who
276 Chapter 8 The Enhanced Entity-Relationship (EER) Model
borrow books at least ten times during the year. The top 1 percent of mem- bership does 15 percent of the borrowing, and the top 10 percent of the membership does 40 percent of the borrowing. About 20 percent of the members are totally inactive in that they are members who never borrow.
To become a member of the library, applicants fill out a form including their SSN, campus and home mailing addresses, and phone numbers. The librari- ans issue a numbered, machine-readable card with the member’s photo on it. This card is good for four years. A month before a card expires, a notice is sent to a member for renewal. Professors at the institute are considered auto- matic members. When a new faculty member joins the institute, his or her information is pulled from the employee records and a library card is mailed to his or her campus address. Professors are allowed to check out books for three-month intervals and have a two-week grace period. Renewal notices to professors are sent to their campus address.
The library does not lend some books, such as reference books, rare books, and maps. The librarians must differentiate between books that can be lent and those that cannot be lent. In addition, the librarians have a list of some books they are interested in acquiring but cannot obtain, such as rare or out- of-print books and books that were lost or destroyed but have not been replaced. The librarians must have a system that keeps track of books that cannot be lent as well as books that they are interested in acquiring. Some books may have the same title; therefore, the title cannot be used as a means of identification. Every book is identified by its International Standard Book Number (ISBN), a unique international code assigned to all books. Two books with the same title can have different ISBNs if they are in different lan- guages or have different bindings (hardcover or softcover). Editions of the same book have different ISBNs.
The proposed database system must be designed to keep track of the mem- bers, the books, the catalog, and the borrowing activity.
8.20. Design a database to keep track of information for an art museum. Assume that the following requirements were collected:
■ The museum has a collection of ART_OBJECTS. Each ART_OBJECT has a unique Id_no, an Artist (if known), a Year (when it was created, if known), a Title, and a Description. The art objects are categorized in several ways, as discussed below.
■ ART_OBJECTS are categorized based on their type. There are three main types: PAINTING, SCULPTURE, and STATUE, plus another type called OTHER to accommodate objects that do not fall into one of the three main types.
■ A PAINTING has a Paint_type (oil, watercolor, etc.), material on which it is Drawn_on (paper, canvas, wood, etc.), and Style (modern, abstract, etc.).
■ A SCULPTURE or a statue has a Material from which it was created (wood, stone, etc.), Height, Weight, and Style.
Exercises 277
■ An art object in the OTHER category has a Type (print, photo, etc.) and Style.
■ ART_OBJECTs are categorized as either PERMANENT_COLLECTION (objects that are owned by the museum) and BORROWED. Information captured about objects in the PERMANENT_COLLECTION includes Date_acquired, Status (on display, on loan, or stored), and Cost. Information captured about BORROWED objects includes the Collection from which it was borrowed, Date_borrowed, and Date_returned.
■ Information describing the country or culture of Origin (Italian, Egyptian, American, Indian, and so forth) and Epoch (Renaissance, Modern, Ancient, and so forth) is captured for each ART_OBJECT.
■ The museum keeps track of ARTIST information, if known: Name, DateBorn (if known), Date_died (if not living), Country_of_origin, Epoch, Main_style, and Description. The Name is assumed to be unique.
■ Different EXHIBITIONS occur, each having a Name, Start_date, and End_date. EXHIBITIONS are related to all the art objects that were on dis- play during the exhibition.
■ Information is kept on other COLLECTIONS with which the museum interacts, including Name (unique), Type (museum, personal, etc.), Description, Address, Phone, and current Contact_person.
Draw an EER schema diagram for this application. Discuss any assumptions you make, and that justify your EER design choices.
8.21. Figure 8.12 shows an example of an EER diagram for a small private airport database that is used to keep track of airplanes, their owners, airport employees, and pilots. From the requirements for this database, the follow- ing information was collected: Each AIRPLANE has a registration number [Reg#], is of a particular plane type [OF_TYPE], and is stored in a particular hangar [STORED_IN]. Each PLANE_TYPE has a model number [Model], a capacity [Capacity], and a weight [Weight]. Each HANGAR has a number [Number], a capacity [Capacity], and a location [Location]. The database also keeps track of the OWNERs of each plane [OWNS] and the EMPLOYEEs who have maintained the plane [MAINTAIN]. Each relationship instance in OWNS relates an AIRPLANE to an OWNER and includes the purchase date [Pdate]. Each relationship instance in MAINTAIN relates an EMPLOYEE to a service record [SERVICE]. Each plane undergoes service many times; hence, it is related by [PLANE_SERVICE] to a number of SERVICE records. A SERVICE record includes as attributes the date of maintenance [Date], the number of hours spent on the work [Hours], and the type of work done [Work_code]. We use a weak entity type [SERVICE] to represent airplane service, because the airplane registration number is used to identify a service record. An OWNER is either a person or a corporation. Hence, we use a union type (cat- egory) [OWNER] that is a subset of the union of corporation [CORPORATION] and person [PERSON] entity types. Both pilots [PILOT] and employees [EMPLOYEE] are subclasses of PERSON. Each PILOT has
278 Chapter 8 The Enhanced Entity-Relationship (EER) Model
specific attributes license number [Lic_num] and restrictions [Restr]; each EMPLOYEE has specific attributes salary [Salary] and shift worked [Shift]. All PERSON entities in the database have data kept on their Social Security number [Ssn], name [Name], address [Address], and telephone number [Phone]. For CORPORATION entities, the data kept includes name [Name], address [Address], and telephone number [Phone]. The database also keeps track of the types of planes each pilot is authorized to fly [FLIES] and the types of planes each employee can do maintenance work on [WORKS_ON].
Number Location
Capacity
Name Phone
Add ress
Name
Ssn
Phone
Add ress
Lic_numRestr
Date/workcode
1
N
N
1
N
1
PLANE_TYPE
Model Capacity
Pdate
Weight
MAINTAIN
M M
N
OF_TYPE
STORED_IN NM
OWNS
FLIES
WORKS_ON N
N
M
Reg#
Date
Hours
HANGAR
PILOT
EMPLOYEE
Sala ry
PLANE_SERVICE
SERVICE
Workcode
AIRPLANE
Shift
U
CORPORATION PERSON
OWNER
Figure 8.12 EER schema for a SMALL_AIRPORT database.
Exercises 279
Entity Set
(a) Has a Relationship
with
(b) Has an Attribute
that is
(c) Is a Specialization
of
(d) Is a Generalization
of Entity Set
or Attribute 1. MOTHER PERSON 2. DAUGHTER MOTHER 3. STUDENT PERSON 4. STUDENT Student_id 5. SCHOOL STUDENT 6. SCHOOL CLASS_ROOM 7. ANIMAL HORSE 8. HORSE Breed 9. HORSE Age
10. EMPLOYEE SSN 11. FURNITURE CHAIR 12. CHAIR Weight 13. HUMAN WOMAN 14. SOLDIER PERSON 15. ENEMY_COMBATANT PERSON
Show how the SMALL_AIRPORT EER schema in Figure 8.12 may be repre- sented in UML notation. (Note: We have not discussed how to represent cat- egories (union types) in UML, so you do not have to map the categories in this and the following question.)
8.22. Show how the UNIVERSITY EER schema in Figure 8.9 may be represented in UML notation.
8.23. Consider the entity sets and attributes shown in the table below. Place a checkmark in one column in each row to indicate the relationship between the far left and right columns.
a. The left side has a relationship with the right side.
b. The right side is an attribute of the left side.
c. The left side is a specialization of the right side.
d. The left side is a generalization of the right side.
8.24. Draw a UML diagram for storing a played game of chess in a database. You may look at http://www.chessgames.com for an application similar to what you are designing. State clearly any assumptions you make in your UML dia- gram. A sample of assumptions you can make about the scope is as follows:
1. The game of chess is played between two players.
2. The game is played on an 8 × 8 board like the one shown below:
280 Chapter 8 The Enhanced Entity-Relationship (EER) Model
3. The players are assigned a color of black or white at the start of the game.
4. Each player starts with the following pieces (traditionally called chess- men):
a. king b. queen c. 2 rooks
d. 2 bishops e. 2 knights f. 8 pawns
5. Every piece has its own initial position.
6. Every piece has its own set of legal moves based on the state of the game. You do not need to worry about which moves are or are not legal except for the following issues:
a. A piece may move to an empty square or capture an opposing piece. b. If a piece is captured, it is removed from the board. c. If a pawn moves to the last row, it is “promoted” by converting it to
another piece (queen, rook, bishop, or knight).
Note: Some of these functions may be spread over multiple classes.
8.25. Draw an EER diagram for a game of chess as described in Exercise 8.24. Focus on persistent storage aspects of the system. For example, the system would need to retrieve all the moves of every game played in sequential order.
8.26. Which of the following EER diagrams is/are incorrect and why? State clearly any assumptions you make.
a.
b.
E d
E1
E2
R
1
1
E
E1
E2
R
1
E3 No
Laboratory Exercises 281
c.
8.27. Consider the following EER diagram that describes the computer systems at a company. Provide your own attributes and key for each entity type. Supply max cardinality constraints justifying your choice. Write a complete narra- tive description of what this EER diagram represents.
E1
R
E3
N
o
M
MEMORY VIDEO_CARD
d
LAPTOP DESKTOP
INSTALLED
d
COMPUTER
SOFTWARE
OPERATING_ SYSTEM
INSTALLED_OS
SUPPORTS
COMPONENT OPTIONS
SOUND_CARD
MEM_OPTIONS
KEYBOARD MOUSE
d
ACCESSORY
MONITOR
SOLD_WITH
Laboratory Exercises 8.28. Consider a GRADE_BOOK database in which instructors within an academic department record
points earned by individual students in their classes. The data requirements are summarized as follows:
■ Each student is identified by a unique identifier, first and last name, and an e-mail address.
■ Each instructor teaches certain courses each term. Each course is identified by a course num- ber, a section number, and the term in which it is taught. For each course he or she teaches, the
282 Chapter 8 The Enhanced Entity-Relationship (EER) Model
instructor specifies the minimum number of points required in order to earn letter grades A, B, C, D, and F. For example, 90 points for an A, 80 points for a B, 70 points for a C, and so forth.
■ Students are enrolled in each course taught by the instructor.
■ Each course has a number of grading components (such as midterm exam, final exam, project, and so forth). Each grading component has a maximum number of points (such as 100 or 50) and a weight (such as 20% or 10%). The weights of all the grading components of a course usu- ally total 100.
■ Finally, the instructor records the points earned by each student in each of the grading components in each of the courses. For example, student 1234 earns 84 points for the midterm exam grading component of the section 2 course CSc2310 in the fall term of 2009. The midterm exam grading component may have been defined to have a maximum of 100 points and a weight of 20% of the course grade.
Design an Enhanced Entity-Relationship diagram for the grade book data- base and build the design using a data modeling tool such as ERwin or Rational Rose.
8.29. Consider an ONLINE_AUCTION database system in which members (buyers and sellers) participate in the sale of items. The data requirements for this system are summarized as follows:
■ The online site has members, each of whom is identified by a unique member number and is described by an e-mail address, name, password, home address, and phone number.
■ A member may be a buyer or a seller. A buyer has a shipping address recorded in the database. A seller has a bank account number and routing number recorded in the database.
■ Items are placed by a seller for sale and are identified by a unique item number assigned by the system. Items are also described by an item title, a description, starting bid price, bidding increment, the start date of the auction, and the end date of the auction.
■ Items are also categorized based on a fixed classification hierarchy (for example, a modem may be classified as COMPUTER→HARDWARE →MODEM).
■ Buyers make bids for items they are interested in. Bid price and time of bid is recorded. The bidder at the end of the auction with the highest bid price is declared the winner and a transaction between buyer and seller may then proceed.
■ The buyer and seller may record feedback regarding their completed transactions. Feedback contains a rating of the other party participating in the transaction (1–10) and a comment.
Design an Enhanced Entity-Relationship diagram for the ONLINE_AUCTION database and build the design using a data modeling tool such as ERwin or Rational Rose.
8.30. Consider a database system for a baseball organization such as the major leagues. The data requirements are summarized as follows:
■ The personnel involved in the league include players, coaches, managers, and umpires. Each is identified by a unique personnel id. They are also described by their first and last names along with the date and place of birth.
■ Players are further described by other attributes such as their batting ori- entation (left, right, or switch) and have a lifetime batting average (BA).
■ Within the players group is a subset of players called pitchers. Pitchers have a lifetime ERA (earned run average) associated with them.
■ Teams are uniquely identified by their names. Teams are also described by the city in which they are located and the division and league in which they play (such as Central division of the American League).
■ Teams have one manager, a number of coaches, and a number of players.
■ Games are played between two teams with one designated as the home team and the other the visiting team on a particular date. The score (runs, hits, and errors) are recorded for each team. The team with the most runs is declared the winner of the game.
■ With each finished game, a winning pitcher and a losing pitcher are recorded. In case there is a save awarded, the save pitcher is also recorded.
■ With each finished game, the number of hits (singles, doubles, triples, and home runs) obtained by each player is also recorded.
Design an Enhanced Entity-Relationship diagram for the BASEBALL data- base and enter the design using a data modeling tool such as ERwin or Rational Rose.
8.31. Consider the EER diagram for the UNIVERSITY database shown in Figure 8.9. Enter this design using a data modeling tool such as ERwin or Rational Rose. Make a list of the differences in notation between the diagram in the text and the corresponding equivalent diagrammatic notation you end up using with the tool.
8.32. Consider the EER diagram for the small AIRPORT database shown in Figure 8.12. Build this design using a data modeling tool such as ERwin or Rational Rose. Be careful as to how you model the category OWNER in this diagram. (Hint: Consider using CORPORATION_IS_OWNER and PERSON_IS_ OWNER as two distinct relationship types.)
8.33. Consider the UNIVERSITY database described in Exercise 7.16. You already developed an ER schema for this database using a data modeling tool such as
Laboratory Exercises 283
ERwin or Rational Rose in Lab Exercise 7.31. Modify this diagram by classi- fying COURSES as either UNDERGRAD_COURSES or GRAD_COURSES and INSTRUCTORS as either JUNIOR_PROFESSORS or SENIOR_PROFESSORS. Include appropriate attributes for these new entity types. Then establish relationships indicating that junior instructors teach undergraduate courses while senior instructors teach graduate courses.
Selected Bibliography Many papers have proposed conceptual or semantic data models. We give a repre- sentative list here. One group of papers, including Abrial (1974), Senko’s DIAM model (1975), the NIAM method (Verheijen and VanBekkum 1982), and Bracchi et al. (1976), presents semantic models that are based on the concept of binary rela- tionships. Another group of early papers discusses methods for extending the rela- tional model to enhance its modeling capabilities. This includes the papers by Schmid and Swenson (1975), Navathe and Schkolnick (1978), Codd’s RM/T model (1979), Furtado (1978), and the structural model of Wiederhold and Elmasri (1979).
The ER model was proposed originally by Chen (1976) and is formalized in Ng (1981). Since then, numerous extensions of its modeling capabilities have been pro- posed, as in Scheuermann et al. (1979), Dos Santos et al. (1979), Teorey et al. (1986), Gogolla and Hohenstein (1991), and the Entity-Category-Relationship (ECR) model of Elmasri et al. (1985). Smith and Smith (1977) present the concepts of gen- eralization and aggregation. The semantic data model of Hammer and McLeod (1981) introduced the concepts of class/subclass lattices, as well as other advanced modeling concepts.
A survey of semantic data modeling appears in Hull and King (1987). Eick (1991) discusses design and transformations of conceptual schemas. Analysis of con- straints for n-ary relationships is given in Soutou (1998). UML is described in detail in Booch, Rumbaugh, and Jacobson (1999). Fowler and Scott (2000) and Stevens and Pooley (2000) give concise introductions to UML concepts.
Fensel (2000, 2003) discuss the Semantic Web and application of ontologies. Uschold and Gruninger (1996) and Gruber (1995) discuss ontologies. The June 2002 issue of Communications of the ACM is devoted to ontology concepts and applications. Fensel (2003) is a book that discusses ontologies and e-commerce.
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285
Relational Database Design by ER- and
EER-to-Relational Mapping
This chapter discusses how to design a relationaldatabase schema based on a conceptual schema design. Figure 7.1 presented a high-level view of the database design process, and in this chapter we focus on the logical database design or data model mapping step of database design. We present the procedures to create a relational schema from an Entity-Relationship (ER) or an Enhanced ER (EER) schema. Our discussion relates the constructs of the ER and EER models, presented in Chapters 7 and 8, to the con- structs of the relational model, presented in Chapters 3 through 6. Many computer- aided software engineering (CASE) tools are based on the ER or EER models, or other similar models, as we have discussed in Chapters 7 and 8. Many tools use ER or EER diagrams or variations to develop the schema graphically, and then convert it automatically into a relational database schema in the DDL of a specific relational DBMS by employing algorithms similar to the ones presented in this chapter.
We outline a seven-step algorithm in Section 9.1 to convert the basic ER model con- structs—entity types (strong and weak), binary relationships (with various struc- tural constraints), n-ary relationships, and attributes (simple, composite, and multivalued)—into relations. Then, in Section 9.2, we continue the mapping algo- rithm by describing how to map EER model constructs—specialization/generaliza- tion and union types (categories)—into relations. Section 9.3 summarizes the chapter.
9chapter 9
286 Chapter 9 Relational Database Design by ER- and EER-to-Relational Mapping
9.1 Relational Database Design Using ER-to-Relational Mapping
9.1.1 ER-to-Relational Mapping Algorithm In this section we describe the steps of an algorithm for ER-to-relational mapping. We use the COMPANY database example to illustrate the mapping procedure. The COMPANY ER schema is shown again in Figure 9.1, and the corresponding COMPANY relational database schema is shown in Figure 9.2 to illustrate the map-
EMPLOYEE
Fname Minit Lname
Name Address
Sex
Salary
Ssn
Bdate
Supervisor Supervisee
SUPERVISION 1
N
Hours
WORKS_ON
CONTROLS
M N
1
DEPENDENTS_OF
Name
Location
N
1 1 1
PROJECT
DEPARTMENT
Locations
Name Number
Number
Number_of_employees
MANAGES
Start_date
WORKS_FOR 1N
N
DEPENDENT
Sex Birth_date RelationshipName
Figure 9.1 The ER conceptual schema diagram for the COMPANY database.
9.1 Relational Database Design Using ER-to-Relational Mapping 287
DEPARTMENT
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
EMPLOYEE
DEPT_LOCATIONS
Dnumber Dlocation
PROJECT
Pname Pnumber Plocation Dnum
WORKS_ON
Essn Pno Hours
DEPENDENT
Essn Dependent_name Sex Bdate Relationship
Dname Dnumber Mgr_ssn Mgr_start_date
Figure 9.2 Result of mapping the COMPANY ER schema into a relational database schema.
ping steps. We assume that the mapping will create tables with simple single-valued attributes. The relational model constraints defined in Chapter 3, which include primary keys, unique keys (if any), and referential integrity constraints on the rela- tions, will also be specified in the mapping results.
Step 1: Mapping of Regular Entity Types. For each regular (strong) entity type E in the ER schema, create a relation R that includes all the simple attributes of E. Include only the simple component attributes of a composite attribute. Choose one of the key attributes of E as the primary key for R. If the chosen key of E is a com- posite, then the set of simple attributes that form it will together form the primary key of R.
If multiple keys were identified for E during the conceptual design, the information describing the attributes that form each additional key is kept in order to specify secondary (unique) keys of relation R. Knowledge about keys is also kept for index- ing purposes and other types of analyses.
In our example, we create the relations EMPLOYEE, DEPARTMENT, and PROJECT in Figure 9.2 to correspond to the regular entity types EMPLOYEE, DEPARTMENT, and PROJECT in Figure 9.1. The foreign key and relationship attributes, if any, are not included yet; they will be added during subsequent steps. These include the
DEPARTMENT
Fname Minit Lname Ssn Bdate Address Sex Salary
EMPLOYEE
WORKS_ON
Essn Pno Hours
Dname Dnumber
DEPT_LOCATIONS
Dnumber Dlocation
PROJECT
Pname Pnumber Plocation
DEPENDENT
(a)
(c)
(d)
(b)
Essn Dependent_name Sex Bdate Relationship
288 Chapter 9 Relational Database Design by ER- and EER-to-Relational Mapping
Figure 9.3 Illustration of some mapping steps. (a) Entity relations after step 1. (b) Additional weak entity relation after step 2. (c) Relationship relation after step 5. (d) Relation representing multivalued attribute after step 6.
attributes Super_ssn and Dno of EMPLOYEE, Mgr_ssn and Mgr_start_date of DEPARTMENT, and Dnum of PROJECT. In our example, we choose Ssn, Dnumber, and Pnumber as primary keys for the relations EMPLOYEE, DEPARTMENT, and PROJECT, respectively. Knowledge that Dname of DEPARTMENT and Pname of PROJECT are secondary keys is kept for possible use later in the design.
The relations that are created from the mapping of entity types are sometimes called entity relations because each tuple represents an entity instance. The result after this mapping step is shown in Figure 9.3(a).
Step 2: Mapping of Weak Entity Types. For each weak entity type W in the ER schema with owner entity type E, create a relation R and include all simple attrib- utes (or simple components of composite attributes) of W as attributes of R. In addition, include as foreign key attributes of R, the primary key attribute(s) of the relation(s) that correspond to the owner entity type(s); this takes care of mapping the identifying relationship type of W. The primary key of R is the combination of the primary key(s) of the owner(s) and the partial key of the weak entity type W, if any.
If there is a weak entity type E2 whose owner is also a weak entity type E1, then E1 should be mapped before E2 to determine its primary key first.
In our example, we create the relation DEPENDENT in this step to correspond to the weak entity type DEPENDENT (see Figure 9.3(b)). We include the primary key Ssn of the EMPLOYEE relation—which corresponds to the owner entity type—as a for- eign key attribute of DEPENDENT; we rename it Essn, although this is not necessary.
9.1 Relational Database Design Using ER-to-Relational Mapping 289
The primary key of the DEPENDENT relation is the combination {Essn, Dependent_name}, because Dependent_name (also renamed from Name in Figure 9.1) is the partial key of DEPENDENT.
It is common to choose the propagate (CASCADE) option for the referential trig- gered action (see Section 4.2) on the foreign key in the relation corresponding to the weak entity type, since a weak entity has an existence dependency on its owner entity. This can be used for both ON UPDATE and ON DELETE.
Step 3: Mapping of Binary 1:1 Relationship Types. For each binary 1:1 rela- tionship type R in the ER schema, identify the relations S and T that correspond to the entity types participating in R. There are three possible approaches: (1) the for- eign key approach, (2) the merged relationship approach, and (3) the cross- reference or relationship relation approach. The first approach is the most useful and should be followed unless special conditions exist, as we discuss below.
1. Foreign key approach: Choose one of the relations—S, say—and include as a foreign key in S the primary key of T. It is better to choose an entity type with total participation in R in the role of S. Include all the simple attributes (or simple components of composite attributes) of the 1:1 relationship type R as attributes of S.
In our example, we map the 1:1 relationship type MANAGES from Figure 9.1 by choosing the participating entity type DEPARTMENT to serve in the role of S because its participation in the MANAGES relationship type is total (every department has a manager). We include the primary key of the EMPLOYEE relation as foreign key in the DEPARTMENT relation and rename it Mgr_ssn. We also include the simple attribute Start_date of the MANAGES relationship type in the DEPARTMENT relation and rename it Mgr_start_date (see Figure 9.2).
Note that it is possible to include the primary key of S as a foreign key in T instead. In our example, this amounts to having a foreign key attribute, say Department_managed in the EMPLOYEE relation, but it will have a NULL value for employee tuples who do not manage a department. If only 2 percent of employees manage a department, then 98 percent of the foreign keys would be NULL in this case. Another possibility is to have foreign keys in both rela- tions S and T redundantly, but this creates redundancy and incurs a penalty for consistency maintenance.
2. Merged relation approach: An alternative mapping of a 1:1 relationship type is to merge the two entity types and the relationship into a single rela- tion. This is possible when both participations are total, as this would indicate that the two tables will have the exact same number of tuples at all times.
3. Cross-reference or relationship relation approach: The third option is to set up a third relation R for the purpose of cross-referencing the primary keys of the two relations S and T representing the entity types. As we will see, this approach is required for binary M:N relationships. The relation R is called a relationship relation (or sometimes a lookup table), because each
290 Chapter 9 Relational Database Design by ER- and EER-to-Relational Mapping
tuple in R represents a relationship instance that relates one tuple from S with one tuple from T. The relation R will include the primary key attributes of S and T as foreign keys to S and T. The primary key of R will be one of the two foreign keys, and the other foreign key will be a unique key of R. The drawback is having an extra relation, and requiring an extra join operation when combining related tuples from the tables.
Step 4: Mapping of Binary 1:N Relationship Types. For each regular binary 1:N relationship type R, identify the relation S that represents the participating entity type at the N-side of the relationship type. Include as foreign key in S the primary key of the relation T that represents the other entity type participating in R; we do this because each entity instance on the N-side is related to at most one entity instance on the 1-side of the relationship type. Include any simple attributes (or simple compo- nents of composite attributes) of the 1:N relationship type as attributes of S.
In our example, we now map the 1:N relationship types WORKS_FOR, CONTROLS, and SUPERVISION from Figure 9.1. For WORKS_FOR we include the primary key Dnumber of the DEPARTMENT relation as foreign key in the EMPLOYEE relation and call it Dno. For SUPERVISION we include the primary key of the EMPLOYEE relation as foreign key in the EMPLOYEE relation itself—because the relationship is recur- sive—and call it Super_ssn. The CONTROLS relationship is mapped to the foreign key attribute Dnum of PROJECT, which references the primary key Dnumber of the DEPARTMENT relation. These foreign keys are shown in Figure 9.2.
An alternative approach is to use the relationship relation (cross-reference) option as in the third option for binary 1:1 relationships. We create a separate relation R whose attributes are the primary keys of S and T, which will also be foreign keys to S and T. The primary key of R is the same as the primary key of S. This option can be used if few tuples in S participate in the relationship to avoid excessive NULL val- ues in the foreign key.
Step 5: Mapping of Binary M:N Relationship Types. For each binary M:N relationship type R, create a new relation S to represent R. Include as foreign key attributes in S the primary keys of the relations that represent the participating entity types; their combination will form the primary key of S. Also include any sim- ple attributes of the M:N relationship type (or simple components of composite attributes) as attributes of S. Notice that we cannot represent an M:N relationship type by a single foreign key attribute in one of the participating relations (as we did for 1:1 or 1:N relationship types) because of the M:N cardinality ratio; we must cre- ate a separate relationship relation S.
In our example, we map the M:N relationship type WORKS_ON from Figure 9.1 by creating the relation WORKS_ON in Figure 9.2. We include the primary keys of the PROJECT and EMPLOYEE relations as foreign keys in WORKS_ON and rename them Pno and Essn, respectively. We also include an attribute Hours in WORKS_ON to represent the Hours attribute of the relationship type. The primary key of the WORKS_ON relation is the combination of the foreign key attributes {Essn, Pno}. This relationship relation is shown in Figure 9.3(c).
9.1 Relational Database Design Using ER-to-Relational Mapping 291
The propagate (CASCADE) option for the referential triggered action (see Section 4.2) should be specified on the foreign keys in the relation corresponding to the relationship R, since each relationship instance has an existence dependency on each of the entities it relates. This can be used for both ON UPDATE and ON DELETE.
Notice that we can always map 1:1 or 1:N relationships in a manner similar to M:N relationships by using the cross-reference (relationship relation) approach, as we discussed earlier. This alternative is particularly useful when few relationship instances exist, in order to avoid NULL values in foreign keys. In this case, the pri- mary key of the relationship relation will be only one of the foreign keys that refer- ence the participating entity relations. For a 1:N relationship, the primary key of the relationship relation will be the foreign key that references the entity relation on the N-side. For a 1:1 relationship, either foreign key can be used as the primary key of the relationship relation.
Step 6: Mapping of Multivalued Attributes. For each multivalued attribute A, create a new relation R. This relation R will include an attribute corresponding to A, plus the primary key attribute K—as a foreign key in R—of the relation that repre- sents the entity type or relationship type that has A as a multivalued attribute. The primary key of R is the combination of A and K. If the multivalued attribute is com- posite, we include its simple components.
In our example, we create a relation DEPT_LOCATIONS (see Figure 9.3(d)). The attribute Dlocation represents the multivalued attribute LOCATIONS of DEPARTMENT, while Dnumber—as foreign key—represents the primary key of the DEPARTMENT relation. The primary key of DEPT_LOCATIONS is the combination of {Dnumber, Dlocation}. A separate tuple will exist in DEPT_LOCATIONS for each loca- tion that a department has.
The propagate (CASCADE) option for the referential triggered action (see Section 4.2) should be specified on the foreign key in the relation R corresponding to the multivalued attribute for both ON UPDATE and ON DELETE. We should also note that the key of R when mapping a composite, multivalued attribute requires some analysis of the meaning of the component attributes. In some cases, when a multi- valued attribute is composite, only some of the component attributes are required to be part of the key of R; these attributes are similar to a partial key of a weak entity type that corresponds to the multivalued attribute (see Section 7.5).
Figure 9.2 shows the COMPANY relational database schema obtained with steps 1 through 6, and Figure 3.6 shows a sample database state. Notice that we did not yet discuss the mapping of n-ary relationship types (n > 2) because none exist in Figure 9.1; these are mapped in a similar way to M:N relationship types by including the following additional step in the mapping algorithm.
Step 7: Mapping of N-ary Relationship Types. For each n-ary relationship type R, where n > 2, create a new relation S to represent R. Include as foreign key attributes in S the primary keys of the relations that represent the participating entity types. Also include any simple attributes of the n-ary relationship type (or
292 Chapter 9 Relational Database Design by ER- and EER-to-Relational Mapping
SUPPLIER
Sname
PROJECT
Proj_name
SUPPLY
Sname Proj_name Part_no Quantity
PART
Part_no
. . .
. . .
. . .
Figure 9.4 Mapping the n-ary relationship type SUPPLY from Figure 7.17(a).
simple components of composite attributes) as attributes of S. The primary key of S is usually a combination of all the foreign keys that reference the relations repre- senting the participating entity types. However, if the cardinality constraints on any of the entity types E participating in R is 1, then the primary key of S should not include the foreign key attribute that references the relation E� corresponding to E (see the discussion in Section 7.9.2 concerning constraints on n-ary relationships).
For example, consider the relationship type SUPPLY in Figure 7.17. This can be mapped to the relation SUPPLY shown in Figure 9.4, whose primary key is the com- bination of the three foreign keys {Sname, Part_no, Proj_name}.
9.1.2 Discussion and Summary of Mapping for ER Model Constructs
Table 9.1 summarizes the correspondences between ER and relational model con- structs and constraints.
One of the main points to note in a relational schema, in contrast to an ER schema, is that relationship types are not represented explicitly; instead, they are represented by having two attributes A and B, one a primary key and the other a foreign key (over the same domain) included in two relations S and T. Two tuples in S and T are related when they have the same value for A and B. By using the EQUIJOIN opera- tion (or NATURAL JOIN if the two join attributes have the same name) over S.A and T.B, we can combine all pairs of related tuples from S and T and materialize the relationship. When a binary 1:1 or 1:N relationship type is involved, a single join operation is usually needed. For a binary M:N relationship type, two join operations are needed, whereas for n-ary relationship types, n joins are needed to fully materi- alize the relationship instances.
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Table 9.1 Correspondence between ER and Relational Models
ER MODEL RELATIONAL MODEL
Entity type Entity relation
1:1 or 1:N relationship type Foreign key (or relationship relation)
M:N relationship type Relationship relation and two foreign keys
n-ary relationship type Relationship relation and n foreign keys
Simple attribute Attribute
Composite attribute Set of simple component attributes
Multivalued attribute Relation and foreign key
Value set Domain
Key attribute Primary (or secondary) key
For example, to form a relation that includes the employee name, project name, and hours that the employee works on each project, we need to connect each EMPLOYEE tuple to the related PROJECT tuples via the WORKS_ON relation in Figure 9.2. Hence, we must apply the EQUIJOIN operation to the EMPLOYEE and WORKS_ON relations with the join condition Ssn = Essn, and then apply another EQUIJOIN operation to the resulting relation and the PROJECT relation with join condition Pno = Pnumber. In general, when multiple relationships need to be traversed, numerous join operations must be specified. A relational database user must always be aware of the foreign key attributes in order to use them correctly in combining related tuples from two or more relations. This is sometimes considered to be a drawback of the relational data model, because the foreign key/primary key corre- spondences are not always obvious upon inspection of relational schemas. If an EQUIJOIN is performed among attributes of two relations that do not represent a foreign key/primary key relationship, the result can often be meaningless and may lead to spurious data. For example, the reader can try joining the PROJECT and DEPT_LOCATIONS relations on the condition Dlocation = Plocation and examine the result (see the discussion of spurious tuples in Section 15.1.4).
In the relational schema we create a separate relation for each multivalued attribute. For a particular entity with a set of values for the multivalued attribute, the key attribute value of the entity is repeated once for each value of the multivalued attribute in a separate tuple because the basic relational model does not allow mul- tiple values (a list, or a set of values) for an attribute in a single tuple. For example, because department 5 has three locations, three tuples exist in the DEPT_LOCATIONS relation in Figure 3.6; each tuple specifies one of the locations. In our example, we apply EQUIJOIN to DEPT_LOCATIONS and DEPARTMENT on the Dnumber attribute to get the values of all locations along with other DEPARTMENT attributes. In the resulting relation, the values of the other DEPARTMENT attributes are repeated in separate tuples for every location that a department has.
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The basic relational algebra does not have a NEST or COMPRESS operation that would produce a set of tuples of the form {<‘1’, ‘Houston’>, <‘4’, ‘Stafford’>, <‘5’, {‘Bellaire’, ‘Sugarland’, ‘Houston’}>} from the DEPT_LOCATIONS relation in Figure 3.6. This is a serious drawback of the basic normalized or flat version of the rela- tional model. The object data model and object-relational systems (see Chapter 11) do allow multivalued attributes.
9.2 Mapping EER Model Constructs to Relations
Next, we discuss the mapping of EER model constructs to relations by extending the ER-to-relational mapping algorithm that was presented in Section 9.1.1.
9.2.1 Mapping of Specialization or Generalization There are several options for mapping a number of subclasses that together form a specialization (or alternatively, that are generalized into a superclass), such as the {SECRETARY, TECHNICIAN, ENGINEER} subclasses of EMPLOYEE in Figure 8.4. We can add a further step to our ER-to-relational mapping algorithm from Section 9.1.1, which has seven steps, to handle the mapping of specialization. Step 8, which follows, gives the most common options; other mappings are also possible. We dis- cuss the conditions under which each option should be used. We use Attrs(R) to denote the attributes of relation R, and PK(R) to denote the primary key of R. First we describe the mapping formally, then we illustrate it with examples.
Step 8: Options for Mapping Specialization or Generalization. Convert each specialization with m subclasses {S1, S2, ..., Sm} and (generalized) superclass C, where the attributes of C are {k, a1, ...an} and k is the (primary) key, into relation schemas using one of the following options:
■ Option 8A: Multiple relations—superclass and subclasses. Create a rela- tion L for C with attributes Attrs(L) = {k, a1, ..., an} and PK(L) = k. Create a relation Li for each subclass Si, 1 ≤ i ≤ m, with the attributes Attrs(Li) = {k} ∪ {attributes of Si} and PK(Li) = k. This option works for any specialization (total or partial, disjoint or overlapping).
■ Option 8B: Multiple relations—subclass relations only. Create a relation Li for each subclass Si, 1 ≤ i ≤ m, with the attributes Attrs(Li) = {attributes of Si} ∪ {k, a1, ..., an} and PK(Li) = k. This option only works for a specialization whose subclasses are total (every entity in the superclass must belong to (at least) one of the subclasses). Additionally, it is only recommended if the spe- cialization has the disjointedness constraint (see Section 8.3.1).If the special- ization is overlapping, the same entity may be duplicated in several relations.
■ Option 8C: Single relation with one type attribute. Create a single relation L with attributes Attrs(L) = {k, a1, ..., an} ∪ {attributes of S1} ∪ ... ∪ {attrib- utes of Sm} ∪ {t} and PK(L) = k. The attribute t is called a type (or
SECRETARY
Typing_speed
TECHNICIAN
Tgrade
ENGINEER
Eng_type
CAR
License_plate_no Price Max_speed No_of_passengers
TRUCK
License_plate_no Price No_of_axles Tonnage
EMPLOYEE
Ssn Fname Minit Lname Birth_date Address Typing_speed Tgrade Eng_typeJob_type
PART
Description Mflag Drawing_no Batch_no Pflag List_priceSupplier_nameManufacture_date
Fname Minit Lname Birth_date Address Job_type
EMPLOYEE(a)
(b)
(c)
(d)
Ssn
Ssn Ssn Ssn
Vehicle_id
Vehicle_id
Part_no
9.2 Mapping EER Model Constructs to Relations 295
discriminating) attribute whose value indicates the subclass to which each tuple belongs, if any. This option works only for a specialization whose sub- classes are disjoint, and has the potential for generating many NULL values if many specific attributes exist in the subclasses.
■ Option 8D: Single relation with multiple type attributes. Create a single relation schema L with attributes Attrs(L) = {k, a1, ..., an} ∪ {attributes of S1} ∪ ... ∪ {attributes of Sm} ∪ {t1, t2, ..., tm} and PK(L) = k. Each ti, 1 ≤ i ≤ m, is a Boolean type attribute indicating whether a tuple belongs to subclass Si. This option is used for a specialization whose subclasses are overlapping (but will also work for a disjoint specialization).
Options 8A and 8B can be called the multiple-relation options, whereas options 8C and 8D can be called the single-relation options. Option 8A creates a relation L for the superclass C and its attributes, plus a relation Li for each subclass Si; each Li includes the specific (or local) attributes of Si, plus the primary key of the superclass C, which is propagated to Li and becomes its primary key. It also becomes a foreign key to the superclass relation. An EQUIJOIN operation on the primary key between any Li and L produces all the specific and inherited attributes of the entities in Si. This option is illustrated in Figure 9.5(a) for the EER schema in Figure 8.4. Option 8A works for any constraints on the specialization: disjoint or overlapping, total or partial. Notice that the constraint
π<k>(Li) ⊆ π<k>(L)
must hold for each Li. This specifies a foreign key from each Li to L, as well as an inclusion dependency Li.k < L.k (see Section 16.5).
Figure 9.5 Options for mapping specialization or generalization. (a) Mapping the EER schema in Figure 8.4 using option 8A. (b) Mapping the EER schema in Figure 8.3(b) using option 8B. (c) Mapping the EER schema in Figure 8.4 using option 8C. (d) Mapping Figure 8.5 using option 8D with Boolean type fields Mflag and Pflag.
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In option 8B, the EQUIJOIN operation between each subclass and the superclass is built into the schema and the relation L is done away with, as illustrated in Figure 9.5(b) for the EER specialization in Figure 8.3(b). This option works well only when both the disjoint and total constraints hold. If the specialization is not total, an entity that does not belong to any of the subclasses Si is lost. If the specialization is not disjoint, an entity belonging to more than one subclass will have its inherited attributes from the superclass C stored redundantly in more than one Li. With option 8B, no relation holds all the entities in the superclass C; consequently, we must apply an OUTER UNION (or FULL OUTER JOIN) operation (see Section 6.4) to the Li relations to retrieve all the entities in C. The result of the outer union will be similar to the relations under options 8C and 8D except that the type fields will be missing. Whenever we search for an arbitrary entity in C, we must search all the m relations Li.
Options 8C and 8D create a single relation to represent the superclass C and all its subclasses. An entity that does not belong to some of the subclasses will have NULL values for the specific attributes of these subclasses. These options are not recom- mended if many specific attributes are defined for the subclasses. If few specific sub- class attributes exist, however, these mappings are preferable to options 8A and 8B because they do away with the need to specify EQUIJOIN and OUTER UNION opera- tions; therefore, they can yield a more efficient implementation.
Option 8C is used to handle disjoint subclasses by including a single type (or image or discriminating) attribute t to indicate to which of the m subclasses each tuple belongs; hence, the domain of t could be {1, 2, ..., m}. If the specialization is partial, t can have NULL values in tuples that do not belong to any subclass. If the specializa- tion is attribute-defined, that attribute serves the purpose of t and t is not needed; this option is illustrated in Figure 9.5(c) for the EER specialization in Figure 8.4.
Option 8D is designed to handle overlapping subclasses by including m Boolean type (or flag) fields, one for each subclass. It can also be used for disjoint subclasses. Each type field ti can have a domain {yes, no}, where a value of yes indicates that the tuple is a member of subclass Si. If we use this option for the EER specialization in Figure 8.4, we would include three types attributes—Is_a_secretary, Is_a_engineer, and Is_a_technician—instead of the Job_type attribute in Figure 9.5(c). Notice that it is also possible to create a single type attribute of m bits instead of the m type fields. Figure 9.5(d) shows the mapping of the specialization from Figure 8.5 using option 8D.
When we have a multilevel specialization (or generalization) hierarchy or lattice, we do not have to follow the same mapping option for all the specializations. Instead, we can use one mapping option for part of the hierarchy or lattice and other options for other parts. Figure 9.6 shows one possible mapping into relations for the EER lattice in Figure 8.6. Here we used option 8A for PERSON/{EMPLOYEE, ALUMNUS, STUDENT}, option 8C for EMPLOYEE/{STAFF, FACULTY, STUDENT_ASSISTANT} by including the type attribute Employee_type, and option 8D for STUDENT_ASSISTANT/{RESEARCH_ASSISTANT, TEACHING_ ASSISTANT} by including the type attributes Ta_flag and Ra_flag in EMPLOYEE, STUDENT/
9.2 Mapping EER Model Constructs to Relations 297
EMPLOYEE
Salary Employee_type Position Rank Percent_time Ra_flag Ta_flag Project Course
STUDENT
Major_dept Grad_flag Undergrad_flag Degree_program Class Student_assist_flag
Name Birth_date Sex Address
PERSON
Ssn
ALUMNUS ALUMNUS_DEGREES
Year MajorSsn
Ssn
Ssn
Ssn Degree
Figure 9.6 Mapping the EER specialization lattice in Figure 8.8 using multiple options.
STUDENT_ASSISTANT by including the type attributes Student_assist_flag in STUDENT, and STUDENT/{GRADUATE_STUDENT, UNDERGRADUATE_STUDENT} by including the type attributes Grad_flag and Undergrad_flag in STUDENT. In Figure 9.6, all attributes whose names end with type or flag are type fields.
9.2.2 Mapping of Shared Subclasses (Multiple Inheritance) A shared subclass, such as ENGINEERING_MANAGER in Figure 8.6, is a subclass of several superclasses, indicating multiple inheritance. These classes must all have the same key attribute; otherwise, the shared subclass would be modeled as a category (union type) as we discussed in Section 8.4. We can apply any of the options dis- cussed in step 8 to a shared subclass, subject to the restrictions discussed in step 8 of the mapping algorithm. In Figure 9.6, options 8C and 8D are used for the shared subclass STUDENT_ASSISTANT. Option 8C is used in the EMPLOYEE relation (Employee_type attribute) and option 8D is used in the STUDENT relation (Student_assist_flag attribute).
9.2.3 Mapping of Categories (Union Types) We add another step to the mapping procedure—step 9—to handle categories. A category (or union type) is a subclass of the union of two or more superclasses that can have different keys because they can be of different entity types (see Section 8.4). An example is the OWNER category shown in Figure 8.8, which is a subset of the union of three entity types PERSON, BANK, and COMPANY. The other category in that figure, REGISTERED_VEHICLE, has two superclasses that have the same key attribute.
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Driver_license_no Name Address Owner_id
PERSON
Ssn
BANK
Baddress Owner_idBname
COMPANY
Caddress Owner_idCname
OWNER
Owner_id
REGISTERED_VEHICLE
License_plate_numberVehicle_id
CAR
Cstyle Cmake Cmodel CyearVehicle_id
TRUCK
Tmake Tmodel Tonnage TyearVehicle_id
OWNS
Purchase_date Lien_or_regularOwner_id Vehicle_id
Figure 9.7 Mapping the EER categories (union types) in Figure 8.8 to relations.
Step 9: Mapping of Union Types (Categories). For mapping a category whose defining superclasses have different keys, it is customary to specify a new key attrib- ute, called a surrogate key, when creating a relation to correspond to the category. The keys of the defining classes are different, so we cannot use any one of them exclusively to identify all entities in the category. In our example in Figure 8.8, we create a relation OWNER to correspond to the OWNER category, as illustrated in Figure 9.7, and include any attributes of the category in this relation. The primary key of the OWNER relation is the surrogate key, which we called Owner_id. We also include the surrogate key attribute Owner_id as foreign key in each relation corre- sponding to a superclass of the category, to specify the correspondence in values between the surrogate key and the key of each superclass. Notice that if a particular PERSON (or BANK or COMPANY) entity is not a member of OWNER, it would have a NULL value for its Owner_id attribute in its corresponding tuple in the PERSON (or BANK or COMPANY) relation, and it would not have a tuple in the OWNER relation. It is also recommended to add a type attribute (not shown in Figure 9.7) to the OWNER relation to indicate the particular entity type to which each tuple belongs (PERSON or BANK or COMPANY).
Exercises 299
For a category whose superclasses have the same key, such as VEHICLE in Figure 8.8, there is no need for a surrogate key. The mapping of the REGISTERED_VEHICLE category, which illustrates this case, is also shown in Figure 9.7.
9.3 Summary In Section 9.1, we showed how a conceptual schema design in the ER model can be mapped to a relational database schema. An algorithm for ER-to-relational map- ping was given and illustrated by examples from the COMPANY database. Table 9.1 summarized the correspondences between the ER and relational model constructs and constraints. Next, we added additional steps to the algorithm in Section 9.2 for mapping the constructs from the EER model into the relational model. Similar algorithms are incorporated into graphical database design tools to create a rela- tional schema from a conceptual schema design automatically.
Review Questions 9.1. Discuss the correspondences between the ER model constructs and the rela-
tional model constructs. Show how each ER model construct can be mapped to the relational model and discuss any alternative mappings.
9.2. Discuss the options for mapping EER model constructs to relations.
Exercises 9.3. Try to map the relational schema in Figure 6.14 into an ER schema. This is
part of a process known as reverse engineering, where a conceptual schema is created for an existing implemented database. State any assumptions you make.
9.4. Figure 9.8 shows an ER schema for a database that can be used to keep track of transport ships and their locations for maritime authorities. Map this schema into a relational schema and specify all primary keys and foreign keys.
9.5. Map the BANK ER schema of Exercise 7.23 (shown in Figure 7.21) into a relational schema. Specify all primary keys and foreign keys. Repeat for the AIRLINE schema (Figure 7.20) of Exercise 7.19 and for the other schemas for Exercises 7.16 through 7.24.
9.6. Map the EER diagrams in Figures 8.9 and 8.12 into relational schemas. Justify your choice of mapping options.
9.7. Is it possible to successfully map a binary M:N relationship type without requiring a new relation? Why or why not?
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9.8. Consider the EER diagram in Figure 9.9 for a car dealer.
Map the EER schema into a set of relations. For the VEHICLE to CAR/TRUCK/SUV generalization, consider the four options presented in Section 9.2.1 and show the relational schema design under each of those options.
9.9. Using the attributes you provided for the EER diagram in Exercise 8.27, map the complete schema into a set of relations. Choose an appropriate option out of 8A thru 8D from Section 9.2.1 in doing the mapping of generaliza- tions and defend your choice.
Time_stamp
Longitude
Latitude
Time
Sname
Owner
Date
To nnage
Name
Name
Start_date End_date
HullType1
N
1
N
N 1
N 1
(0,*)
(0,*)
1
(1,1)
N
SHIP_MOVEMENT
HISTORY
SHIP TYPE SHIP_TYPE
HOME_PORT
PORT
PORT_VISIT
STATE/COUNTRY
SEA/OCEAN/LAKE
SHIP_AT _PORT
Pname
Co ntinent
IN
ON
Figure 9.8 An ER schema for a SHIP_TRACKING database.
Laboratory Exercises 9.10. Consider the ER design for the UNIVERSITY database that was modeled
using a tool like ERwin or Rational Rose in Laboratory Exercise 7.31. Using the SQL schema generation feature of the modeling tool, generate the SQL schema for an Oracle database.
9.11. Consider the ER design for the MAIL_ORDER database that was modeled using a tool like ERwin or Rational Rose in Laboratory Exercise 7.32. Using the SQL schema generation feature of the modeling tool, generate the SQL schema for an Oracle database.
9.12. Consider the ER design for the CONFERENCE_REVIEW database that was modeled using a tool like ERwin or Rational Rose in Laboratory Exercise 7.34. Using the SQL schema generation feature of the modeling tool, gener- ate the SQL schema for an Oracle database.
9.13. Consider the EER design for the GRADE_BOOK database that was modeled using a tool like ERwin or Rational Rose in Laboratory Exercise 8.28. Using the SQL schema generation feature of the modeling tool, generate the SQL schema for an Oracle database.
9.14. Consider the EER design for the ONLINE_AUCTION database that was mod- eled using a tool like ERwin or Rational Rose in Laboratory Exercise 8.29. Using the SQL schema generation feature of the modeling tool, generate the SQL schema for an Oracle database.
Laboratory Exercises 301
Name Name
Model
VEHICLE
Price
Date
Engine_size
Tonnage
No_seats
CAR
TRUCK
SUV
d
SALESPERSON CUSTOMER
Vin
Sid Ssn State
Address City
Street
SALE
1 1
N
Figure 9.9 EER diagram for a car dealer
Selected Bibliography The original ER-to-relational mapping algorithm was described in Chen’s classic paper (Chen 1976) that presented the original ER model. Batini et al. (1992) discuss a variety of mapping algorithms from ER and EER models to legacy models and vice versa.
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Practical Database Design Methodology and Use
of UML Diagrams
In this chapter we move from the database design prin-ciples that were presented in Chapters 7 through 9 to examine some of the more practical aspects of database design. We have already described material that is relevant to the design of actual databases for practical real-world applications. This material includes Chapters 7 and 8 on database con- ceptual modeling; Chapters 3 through 6 on the relational model, the SQL language, and relational algebra and calculus; and Chapter 9 on mapping a high-level concep- tual ER or EER schema into a relational schema. We will present additional relevant materials in later chapters, including an overview of programming techniques for relational systems (RDBMSs) in Chapters 13 and 14, and data dependency theory and relational normalization algorithms in Chapters 15 and 16.
The overall database design activity has to undergo a systematic process called the design methodology, whether the target database is managed by an RDBMS, an object database management system (ODBMS, see Chapter 11), an object-relational database management system (ORDBMS, see Chapter 11), or some other type of database management system. Various design methodologies are provided in the database design tools currently supplied by vendors. Popular tools include Oracle Designer and related products in Oracle Developer Suite by Oracle, ERwin and related products by CA, PowerBuilder and PowerDesigner by Sybase, and ER/Studio and related products by Embarcadero Technologies, among many others. Our goal in this chapter is to discuss not one specific methodology but rather data- base design in a broader context, as it is undertaken in large organizations for the design and implementation of applications catering to hundreds or thousands of users.
10chapter 10
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Generally, the design of small databases with perhaps up to 20 users need not be very complicated. But for medium-sized or large databases that serve several diverse application groups, each with dozens or hundreds of users, a systematic approach to the overall database design activity becomes necessary. The sheer size of a populated database does not reflect the complexity of the design; it is the database schema that is the more important focus of database design. Any database with a schema that includes more than 20 entity types and a similar number of relationship types requires a careful design methodology.
Using the term large database for databases with several dozen gigabytes of data and a schema with more than 30 or 40 distinct entity types, we can cover a wide array of databases used in government, industry, and financial and commercial institutions. Service sector industries, including banking, hotels, airlines, insurance, utilities, and communications, use databases for their day-to-day operations 24 hours a day, 7 days a week—known in the industry as 24 by 7 operations. Application systems for these databases are called transaction processing systems due to the large transaction volumes and rates that are required. In this chapter we will concentrate on the database design for such medium- and large-scale databases where transaction processing dominates.
This chapter has a variety of objectives. Section 10.1 discusses the information sys- tem life cycle within organizations with a particular emphasis on the database sys- tem. Section 10.2 highlights the phases of a database design methodology within the organizational context. Section 10.3 introduces some types of UML diagrams and gives details on the notations that are particularly helpful in collecting requirements and performing conceptual and logical design of databases. An illustrative partial example of designing a university database is presented. Section 10.4 introduces the popular software development tool called Rational Rose, which uses UML diagrams as its main specification technique. Features of Rational Rose specific to database requirements modeling and schema design are highlighted. Section 10.5 briefly dis- cusses automated database design tools. Section 10.6 summarizes the chapter.
10.1 The Role of Information Systems in Organizations
10.1.1 The Organizational Context for Using Database Systems
Database systems have become a part of the information systems of many organiza- tions. Historically, information systems were dominated by file systems in the 1960s, but since the early 1970s organizations have gradually moved to database manage- ment systems (DBMSs). To accommodate DBMSs, many organizations have created the position of database administrator (DBA) and database administration depart- ments to oversee and control database life-cycle activities. Similarly, information technology (IT) and information resource management (IRM) departments have
10.1 The Role of Information Systems in Organizations 305
been recognized by large organizations as being key to successful business manage- ment for the following reasons:
■ Data is regarded as a corporate resource, and its management and control is considered central to the effective working of the organization.
■ More functions in organizations are computerized, increasing the need to keep large volumes of data available in an up-to-the-minute current state.
■ As the complexity of the data and applications grows, complex relationships among the data need to be modeled and maintained.
■ There is a tendency toward consolidation of information resources in many organizations.
■ Many organizations are reducing their personnel costs by letting end users perform business transactions. This is evident with travel services, financial services, higher education, government, and many other types of services. This trend was realized early on by online retail goods outlets and customer- to-business electronic commerce, such as Amazon.com and eBay. In these organizations, a publicly accessible and updatable operational database must be designed and made available for the customer transactions.
Many capabilities provided by database systems have made them integral compo- nents in computer-based information systems. The following are some of the key features that they offer:
■ Integrating data across multiple applications into a single database.
■ Support for developing new applications in a short time by using high-level languages like SQL.
■ Providing support for casual access for browsing and querying by managers while supporting major production-level transaction processing for cus- tomers.
From the early 1970s through the mid-1980s, the move was toward creating large centralized repositories of data managed by a single centralized DBMS. Since then, the trend has been toward utilizing distributed systems because of the following developments:
1. Personal computers and database system-like software products such as Excel, Visual FoxPro, Access (Microsoft), and SQL Anywhere (Sybase), and public domain products such as MySQL and PostgreSQL, are being heavily utilized by users who previously belonged to the category of casual and occa- sional database users. Many administrators, secretaries, engineers, scientists, architects, and students belong to this category. As a result, the practice of creating personal databases is gaining popularity. It is sometimes possible to check out a copy of part of a large database from a mainframe computer or a database server, work on it from a personal workstation, and then restore it on the mainframe. Similarly, users can design and create their own databases and then merge them into a larger one.
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2. The advent of distributed and client-server DBMSs (see Chapter 25) is open- ing up the option of distributing the database over multiple computer sys- tems for better local control and faster local processing. At the same time, local users can access remote data using the facilities provided by the DBMS as a client, or through the Web. Application development tools such as PowerBuilder and PowerDesigner (Sybase) and OracleDesigner and Oracle Developer Suite (Oracle) are being used with built-in facilities to link appli- cations to multiple back-end database servers.
3. Many organizations now use data dictionary systems or information repositories, which are mini DBMSs that manage meta-data—that is, data that describes the database structure, constraints, applications, authoriza- tions, users, and so on. These are often used as an integral tool for informa- tion resource management. A useful data dictionary system should store and manage the following types of information:
a. Descriptions of the schemas of the database system.
b. Detailed information on physical database design, such as storage struc- tures, access paths, and file and record sizes.
c. Descriptions of the types of database users, their responsibilities, and their access rights.
d. High-level descriptions of the database transactions and applications and of the relationships of users to transactions.
e. The relationship between database transactions and the data items refer- enced by them. This is useful in determining which transactions are affected when certain data definitions are changed.
f. Usage statistics such as frequencies of queries and transactions and access counts to different portions of the database.
g. The history of any changes made to the database and applications, and documentation that describes the reasons for these changes. This is some- times referred to as data provenance.
This meta-data is available to DBAs, designers, and authorized users as online sys- tem documentation. This improves the control of DBAs over the information sys- tem as well as the users’ understanding and use of the system. The advent of data warehousing technology (see Chapter 29) has highlighted the importance of meta- data.
When designing high-performance transaction processing systems, which require around-the-clock nonstop operation, performance becomes critical. These data- bases are often accessed by hundreds, or thousands, of transactions per minute from remote computers and local terminals. Transaction performance, in terms of the average number of transactions per minute and the average and maximum transac- tion response time, is critical. A careful physical database design that meets the organization’s transaction processing needs is a must in such systems.
Some organizations have committed their information resource management to certain DBMS and data dictionary products. Their investment in the design and
10.1 The Role of Information Systems in Organizations 307
implementation of large and complex systems makes it difficult for them to change to newer DBMS products, which means that the organizations become locked in to their current DBMS system. With regard to such large and complex databases, we cannot overemphasize the importance of a careful design that takes into account the need for possible system modifications—called tuning—to respond to changing requirements. We will discuss tuning in conjunction with query optimization in Chapter 21. The cost can be very high if a large and complex system cannot evolve, and it becomes necessary to migrate to other DBMS products and redesign the whole system.
10.1.2 The Information System Life Cycle In a large organization, the database system is typically part of an information sys- tem (IS), which includes all resources that are involved in the collection, manage- ment, use, and dissemination of the information resources of the organization. In a computerized environment, these resources include the data itself, the DBMS soft- ware, the computer system hardware and storage media, the personnel who use and manage the data (DBA, end users, and so on), the application programs (software) that accesses and updates the data, and the application programmers who develop these applications. Thus the database system is part of a much larger organizational information system.
In this section we examine the typical life cycle of an information system and how the database system fits into this life cycle. The information system life cycle has been called the macro life cycle, whereas the database system life cycle has been referred to as the micro life cycle. The distinction between them is becoming less pronounced for information systems where databases are a major integral compo- nent. The macro life cycle typically includes the following phases:
1. Feasibility analysis. This phase is concerned with analyzing potential appli- cation areas, identifying the economics of information gathering and dis- semination, performing preliminary cost-benefit studies, determining the complexity of data and processes, and setting up priorities among applica- tions.
2. Requirements collection and analysis. Detailed requirements are collected by interacting with potential users and user groups to identify their particu- lar problems and needs. Interapplication dependencies, communication, and reporting procedures are identified.
3. Design. This phase has two aspects: the design of the database system and the design of the application systems (programs) that use and process the database through retrievals and updates.
4. Implementation. The information system is implemented, the database is loaded, and the database transactions are implemented and tested.
5. Validation and acceptance testing. The acceptability of the system in meet- ing users’ requirements and performance criteria is validated. The system is tested against performance criteria and behavior specifications.
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6. Deployment, operation, and maintenance. This may be preceded by con- version of users from an older system as well as by user training. The opera- tional phase starts when all system functions are operational and have been validated. As new requirements or applications crop up, they pass through the previous phases until they are validated and incorporated into the sys- tem. Monitoring of system performance and system maintenance are impor- tant activities during the operational phase.
10.1.3 The Database Application System Life Cycle Activities related to the micro life cycle, which focuses on the database application system, include the following:
1. System definition. The scope of the database system, its users, and its applications are defined. The interfaces for various categories of users, the response time constraints, and storage and processing needs are identified.
2. Database design. A complete logical and physical design of the database system on the chosen DBMS is prepared.
3. Database implementation. This comprises the process of specifying the conceptual, external, and internal database definitions, creating the (empty) database files, and implementing the software applications.
4. Loading or data conversion. The database is populated either by loading the data directly or by converting existing files into the database system for- mat.
5. Application conversion. Any software applications from a previous system are converted to the new system.
6. Testing and validation. The new system is tested and validated. Testing and validation of application programs can be a very involved process, and the techniques that are employed are usually covered in software engineering courses. There are automated tools that assist in this process, but a discus- sion is outside the scope of this textbook.
7. Operation. The database system and its applications are put into opera- tion. Usually, the old and the new systems are operated in parallel for a period of time.
8. Monitoring and maintenance. During the operational phase, the system is constantly monitored and maintained. Growth and expansion can occur in both data content and software applications. Major modifications and reor- ganizations may be needed from time to time.
Activities 2, 3, and 4 are part of the design and implementation phases of the larger information system macro life cycle. Our emphasis in Section 10.2 is on activities 2 and 3, which cover the database design and implementation phases. Most databases in organizations undergo all of the preceding life cycle activities. The conversion activities (4 and 5) are not applicable when both the database and the applications are new. When an organization moves from an established system to a new one,
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activities 4 and 5 tend to be very time-consuming and the effort to accomplish them is often underestimated. In general, there is often feedback among the various steps because new requirements frequently arise at every stage. Figure 10.1 shows the feedback loop affecting the conceptual and logical design phases as a result of sys- tem implementation and tuning.
10.2 The Database Design and Implementation Process
Now, we focus on activities 2 and 3 of the database application system life cycle, which are database design and implementation. The problem of database design can be stated as follows:
Design the logical and physical structure of one or more databases to accommodate the information needs of the users in an organization for a defined set of applications.
Phase 1: Requirements collection and analysis
Phase 2: Conceptual database design
Phase 3: Choice of DBMS
Phase 4: Data model mapping
(logical design)
Phase 5: Physical design
Phase 6: System implementation and tuning
Data content, structure, and constraints
Data requirements
Conceptual Schema design (DBMS-independent)
Logical Schema and view design (DBMS-dependent)
Internal Schema design (DBMS-dependent)
DDL statements SDL statements
Database applications
Processing requirements
Transaction and application design (DBMS-independent)
Transaction and application implementation
Frequencies, performance constraints
Figure 10.1 Phases of database design and implementation for large databases.
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The goals of database design are multiple:
■ Satisfy the information content requirements of the specified users and applications.
■ Provide a natural and easy-to-understand structuring of the information.
■ Support processing requirements and any performance objectives, such as response time, processing time, and storage space.
These goals are very hard to accomplish and measure and they involve an inherent tradeoff: if one attempts to achieve more naturalness and understandability of the model, it may be at the cost of performance. The problem is aggravated because the database design process often begins with informal and incomplete requirements. In contrast, the result of the design activity is a rigidly defined database schema that cannot easily be modified once the database is implemented. We can identify six main phases of the overall database design and implementation process:
1. Requirements collection and analysis
2. Conceptual database design
3. Choice of a DBMS
4. Data model mapping (also called logical database design)
5. Physical database design
6. Database system implementation and tuning
The design process consists of two parallel activities, as illustrated in Figure 10.1. The first activity involves the design of the data content, structure, and constraints of the database; the second relates to the design of database applications. To keep the figure simple, we have avoided showing most of the interactions between these sides, but the two activities are closely intertwined. For example, by analyzing data- base applications, we can identify data items that will be stored in the database. In addition, the physical database design phase, during which we choose the storage structures and access paths of database files, depends on the applications that will use these files for querying and updating. On the other hand, we usually specify the design of database applications by referring to the database schema constructs, which are specified during the first activity. Clearly, these two activities strongly influence one another. Traditionally, database design methodologies have primarily focused on the first of these activities whereas software design has focused on the second; this may be called data-driven versus process-driven design. It now is rec- ognized by database designers and software engineers that the two activities should proceed hand-in-hand, and design tools are increasingly combining them.
The six phases mentioned previously do not typically progress strictly in sequence. In many cases we may have to modify the design from an earlier phase during a later phase. These feedback loops among phases—and also within phases—are com- mon. We show only a couple of feedback loops in Figure 10.1, but many more exist between various phases. We have also shown some interaction between the data and the process sides of the figure; many more interactions exist in reality. Phase 1 in Figure 10.1 involves collecting information about the intended use of the database,
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and Phase 6 concerns database implementation and redesign. The heart of the data- base design process comprises Phases 2, 4, and 5; we briefly summarize these phases:
■ Conceptual database design (Phase 2). The goal of this phase is to produce a conceptual schema for the database that is independent of a specific DBMS. We often use a high-level data model such as the ER or EER model (see Chapters 7 and 8) during this phase. Additionally, we specify as many of the known database applications or transactions as possible, using a nota- tion that is independent of any specific DBMS. Often, the DBMS choice is already made for the organization; the intent of conceptual design is still to keep it as free as possible from implementation considerations.
■ Data model mapping (Phase 4). During this phase, which is also called logical database design, we map (or transform) the conceptual schema from the high-level data model used in Phase 2 into the data model of the chosen DBMS. We can start this phase after choosing a specific type of DBMS—for example, if we decide to use some relational DBMS but have not yet decided on which particular one. We call the latter system-independent (but data model-dependent) logical design. In terms of the three-level DBMS architecture discussed in Chapter 2, the result of this phase is a conceptual schema in the chosen data model. In addition, the design of external schemas (views) for specific applications is often done during this phase.
■ Physical database design (Phase 5). During this phase, we design the spec- ifications for the stored database in terms of physical file storage structures, record placement, and indexes. This corresponds to designing the internal schema in the terminology of the three-level DBMS architecture.
■ Database system implementation and tuning (Phase 6). During this phase, the database and application programs are implemented, tested, and eventually deployed for service. Various transactions and applications are tested individually and then in conjunction with each other. This typically reveals opportunities for physical design changes, data indexing, reorganiza- tion, and different placement of data—an activity referred to as database tuning. Tuning is an ongoing activity—a part of system maintenance that continues for the life cycle of a database as long as the database and applica- tions keep evolving and performance problems are detected.
We discuss each of the six phases of database design in more detail in the following subsections.
10.2.1 Phase 1: Requirements Collection and Analysis1
Before we can effectively design a database, we must know and analyze the expecta- tions of the users and the intended uses of the database in as much detail as possi- ble. This process is called requirements collection and analysis. To specify the requirements, we first identify the other parts of the information system that will
1A part of this section has been contributed by Colin Potts.
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interact with the database system. These include new and existing users and applica- tions, whose requirements are then collected and analyzed. Typically, the following activities are part of this phase:
1. The major application areas and user groups that will use the database or whose work will be affected by it are identified. Key individuals and commit- tees within each group are chosen to carry out subsequent steps of require- ments collection and specification.
2. Existing documentation concerning the applications is studied and ana- lyzed. Other documentation—policy manuals, forms, reports, and organiza- tion charts—is reviewed to determine whether it has any influence on the requirements collection and specification process.
3. The current operating environment and planned use of the information is studied. This includes analysis of the types of transactions and their frequen- cies as well as of the flow of information within the system. Geographic characteristics regarding users, origin of transactions, destination of reports, and so on are studied. The input and output data for the transactions are specified.
4. Written responses to sets of questions are sometimes collected from the potential database users or user groups. These questions involve the users’ priorities and the importance they place on various applications. Key indi- viduals may be interviewed to help in assessing the worth of information and in setting up priorities.
Requirement analysis is carried out for the final users, or customers, of the database system by a team of system analysts or requirement experts. The initial require- ments are likely to be informal, incomplete, inconsistent, and partially incorrect. Therefore, much work needs to be done to transform these early requirements into a specification of the application that can be used by developers and testers as the starting point for writing the implementation and test cases. Because the require- ments reflect the initial understanding of a system that does not yet exist, they will inevitably change. Therefore, it is important to use techniques that help customers converge quickly on the implementation requirements.
There is evidence that customer participation in the development process increases customer satisfaction with the delivered system. For this reason, many practitioners use meetings and workshops involving all stakeholders. One such methodology of refining initial system requirements is called Joint Application Design (JAD). More recently, techniques have been developed, such as Contextual Design, which involve the designers becoming immersed in the workplace in which the application is to be used. To help customer representatives better understand the proposed system, it is common to walk through workflow or transaction scenarios or to create a mock-up rapid prototype of the application.
The preceding modes help structure and refine requirements but leave them still in an informal state. To transform requirements into a better-structured representa- tion, requirements specification techniques are used. These include object-
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oriented analysis (OOA), data flow diagrams (DFDs), and the refinement of appli- cation goals. These methods use diagramming techniques for organizing and pre- senting information-processing requirements. Additional documentation in the form of text, tables, charts, and decision requirements usually accompanies the dia- grams. There are techniques that produce a formal specification that can be checked mathematically for consistency and what-if symbolic analyses. These methods may become standard in the future for those parts of information systems that serve mission-critical functions and which therefore must work as planned. The model- based formal specification methods, of which the Z-notation and methodology is a prominent example, can be thought of as extensions of the ER model and are there- fore the most applicable to information system design.
Some computer-aided techniques—called Upper CASE tools—have been proposed to help check the consistency and completeness of specifications, which are usually stored in a single repository and can be displayed and updated as the design pro- gresses. Other tools are used to trace the links between requirements and other design entities, such as code modules and test cases. Such traceability databases are especially important in conjunction with enforced change-management procedures for systems where the requirements change frequently. They are also used in con- tractual projects where the development organization must provide documentary evidence to the customer that all the requirements have been implemented.
The requirements collection and analysis phase can be quite time-consuming, but it is crucial to the success of the information system. Correcting a requirements error is more expensive than correcting an error made during implementation because the effects of a requirements error are usually pervasive, and much more down- stream work has to be reimplemented as a result. Not correcting a significant error means that the system will not satisfy the customer and may not even be used at all. Requirements gathering and analysis is the subject of entire books.
10.2.2 Phase 2: Conceptual Database Design The second phase of database design involves two parallel activities.2 The first activ- ity, conceptual schema design, examines the data requirements resulting from Phase 1 and produces a conceptual database schema. The second activity, transaction and application design, examines the database applications analyzed in Phase 1 and produces high-level specifications for these applications.
Phase 2a: Conceptual Schema Design. The conceptual schema produced by this phase is usually contained in a DBMS-independent high-level data model for the following reasons:
1. The goal of conceptual schema design is a complete understanding of the database structure, meaning (semantics), interrelationships, and constraints.
2This phase of design is discussed in great detail in the first seven chapters of Batini et al. (1992); we summarize that discussion here.
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This is best achieved independently of a specific DBMS because each DBMS typically has idiosyncrasies and restrictions that should not be allowed to influence the conceptual schema design.
2. The conceptual schema is invaluable as a stable description of the database contents. The choice of DBMS and later design decisions may change with- out changing the DBMS-independent conceptual schema.
3. A good understanding of the conceptual schema is crucial for database users and application designers. Use of a high-level data model that is more expressive and general than the data models of individual DBMSs is there- fore quite important.
4. The diagrammatic description of the conceptual schema can serve as a vehi- cle of communication among database users, designers, and analysts. Because high-level data models usually rely on concepts that are easier to understand than lower-level DBMS-specific data models, or syntactic defini- tions of data, any communication concerning the schema design becomes more exact and more straightforward.
In this phase of database design, it is important to use a conceptual high-level data model with the following characteristics:
1. Expressiveness. The data model should be expressive enough to distin- guish different types of data, relationships, and constraints.
2. Simplicity and understandability. The model should be simple enough for typical nonspecialist users to understand and use its concepts.
3. Minimality. The model should have a small number of basic concepts that are distinct and nonoverlapping in meaning.
4. Diagrammatic representation. The model should have a diagrammatic notation for displaying a conceptual schema that is easy to interpret.
5. Formality. A conceptual schema expressed in the data model must repre- sent a formal unambiguous specification of the data. Hence, the model con- cepts must be defined accurately and unambiguously.
Some of these requirements—the first one in particular—sometimes conflict with the other requirements. Many high-level conceptual models have been proposed for database design (see the Selected Bibliography in Chapter 8). In the following dis- cussion, we will use the terminology of the Enhanced Entity-Relationship (EER) model presented in Chapter 8 and we will assume that it is being used in this phase. Conceptual schema design, including data modeling, is becoming an integral part of object-oriented analysis and design methodologies. The UML has class diagrams that are largely based on extensions of the EER model.
Approaches to Conceptual Schema Design. For conceptual schema design, we must identify the basic components (or constructs) of the schema: the entity types, rela- tionship types, and attributes. We should also specify key attributes, cardinality and participation constraints on relationships, weak entity types, and specialization/ gen- eralization hierarchies/lattices. There are two approaches to designing the conceptual schema, which is derived from the requirements collected during Phase 1.
10.2 The Database Design and Implementation Process 315
The first approach is the centralized (or one shot) schema design approach, in which the requirements of the different applications and user groups from Phase 1 are merged into a single set of requirements before schema design begins. A single schema corresponding to the merged set of requirements is then designed. When many users and applications exist, merging all the requirements can be an arduous and time-consuming task. The assumption is that a centralized authority, the DBA, is responsible for deciding how to merge the requirements and for designing the conceptual schema for the whole database. Once the conceptual schema is designed and finalized, external schemas for the various user groups and applications can be specified by the DBA.
The second approach is the view integration approach, in which the requirements are not merged. Rather a schema (or view) is designed for each user group or appli- cation based only on its own requirements. Thus we develop one high-level schema (view) for each such user group or application. During a subsequent view integra- tion phase, these schemas are merged or integrated into a global conceptual schema for the entire database. The individual views can be reconstructed as exter- nal schemas after view integration.
The main difference between the two approaches lies in the manner and stage in which multiple views or requirements of the many users and applications are recon- ciled and merged. In the centralized approach, the reconciliation is done manually by the DBA staff prior to designing any schemas and is applied directly to the require- ments collected in Phase 1. This places the burden to reconcile the differences and conflicts among user groups on the DBA staff. The problem has been typically dealt with by using external consultants/design experts, who apply their specific methods for resolving these conflicts. Because of the difficulties of managing this task, the view integration approach has been proposed as an alternative technique.
In the view integration approach, each user group or application actually designs its own conceptual (EER) schema from its requirements, with assistance from the DBA staff. Then an integration process is applied to these schemas (views) by the DBA to form the global integrated schema. Although view integration can be done manu- ally, its application to a large database involving dozens of user groups requires a methodology and the use of automated tools. The correspondences among the attributes, entity types, and relationship types in various views must be specified before the integration can be applied. Additionally, problems such as integrating conflicting views and verifying the consistency of the specified interschema corre- spondences must be dealt with.
Strategies for Schema Design. Given a set of requirements, whether for a single user or for a large user community, we must create a conceptual schema that satisfies these requirements. There are various strategies for designing such a schema. Most strategies follow an incremental approach—that is, they start with some important schema constructs derived from the requirements and then they incrementally mod- ify, refine, and build on them. We now discuss some of these strategies:
1. Top-down strategy. We start with a schema containing high-level abstrac- tions and then apply successive top-down refinements. For example, we may
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specify only a few high-level entity types and then, as we specify their attrib- utes, split them into lower-level entity types and specify the relationships. The process of specialization to refine an entity type into subclasses that we illustrated in Sections 8.2 and 8.3 (see Figures 8.1, 8.4, and 8.5) is another activity during a top-down design strategy.
2. Bottom-up strategy. Start with a schema containing basic abstractions and then combine or add to these abstractions. For example, we may start with the database attributes and group these into entity types and relationships. We may add new relationships among entity types as the design progresses. The process of generalizing entity types into higher-level generalized super- classes (see Sections 8.2 and 8.3 and Figure 8.3) is another activity during a bottom-up design strategy.
3. Inside-out strategy. This is a special case of a top-down strategy, where attention is focused on a central set of concepts that are most evident. Modeling then spreads outward by considering new concepts in the vicinity of existing ones. We could specify a few clearly evident entity types in the schema and continue by adding other entity types and relationships that are related to each.
4. Mixed strategy. Instead of following any particular strategy throughout the design, the requirements are partitioned according to a top-down strategy, and part of the schema is designed for each partition according to a bottom- up strategy. The various schema parts are then combined.
Figures 10.2 and 10.3 illustrate some simple examples of top-down and bottom-up refinement, respectively. An example of a top-down refinement primitive is decom- position of an entity type into several entity types. Figure 10.2(a) shows a COURSE being refined into COURSE and SEMINAR, and the TEACHES relationship is corre- spondingly split into TEACHES and OFFERS. Figure 10.2(b) shows a COURSE_OFFERING entity type being refined into two entity types (COURSE and INSTRUCTOR) and a relationship between them. Refinement typically forces a designer to ask more questions and extract more constraints and details: for exam- ple, the (min, max) cardinality ratios between COURSE and INSTRUCTOR are obtained during refinement. Figure 10.3(a) shows the bottom-up refinement prim- itive of generating relationships among the entity types FACULTY and STUDENT. Two relationships are identified: ADVISES and COMMITTEE_CHAIR_OF. The bottom-up refinement using categorization (union type) is illustrated in Figure 10.3(b), where the new concept of VEHICLE_OWNER is discovered from the existing entity types FACULTY, STAFF, and STUDENT; this process of creating a category and the related diagrammatic notation follows what we introduced in Section 8.4.
Schema (View) Integration. For large databases with many expected users and appli- cations, the view integration approach of designing individual schemas and then merging them can be used. Because the individual views can be kept relatively small, design of the schemas is simplified. However, a methodology for integrating the views into a global database schema is needed. Schema integration can be divided into the following subtasks:
10.2 The Database Design and Implementation Process 317
FACULTY
(a)
(b)
COURSETEACHES (1,N)
(1,N)
(1,5)
(1,1)
(1,1)
(1,3)
(1,N)
(1,3)
COURSE
FACULTY
TEACHES
OFFERS
OFFERED_BY
SEMINAR
Name
INSTRUCTOR
InstructorSemesterSec#Course#
COURSE_OFFERING
SemesterSec#Course#
COURSE
Figure 10.2 Examples of top- down refinement. (a) Generating a new entity type. (b) Decomposing an entity type into two entity types and a relationship type.
1. Identifying correspondences and conflicts among the schemas. Because the schemas are designed individually, it is necessary to specify constructs in the schemas that represent the same real-world concept. These correspon- dences must be identified before integration can proceed. During this process, several types of conflicts among the schemas may be discovered:
a. Naming conflicts. These are of two types: synonyms and homonyms. A synonym occurs when two schemas use different names to describe the same concept; for example, an entity type CUSTOMER in one schema may describe the same concept as an entity type CLIENT in another schema. A homonym occurs when two schemas use the same name to describe dif- ferent concepts; for example, an entity type PART may represent computer parts in one schema and furniture parts in another schema.
b. Type conflicts. The same concept may be represented in two schemas by different modeling constructs. For example, the concept of a
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FACULTY(a)
(b)
FACULTY
STUDENTSTUDENT
FACULTY STAFF STUDENT
ADVISES
VEHICLE_OWNER
STAFF STUDENTFACULTY
IS_A_ FACULTY
IS_A_ STAFF
IS_A_ STUDENT
PARKING_DECAL
COMMITTEE_ CHAIR_OF
PARKING_DECAL
Figure 10.3 Examples of bottom-up refinement. (a) Discovering and adding new relationships. (b) Discovering a new category (union type) and relating it.
DEPARTMENT may be an entity type in one schema and an attribute in another.
c. Domain (value set) conflicts. An attribute may have different domains in two schemas. For example, Ssn may be declared as an integer in one schema and as a character string in the other. A conflict of the unit of measure could occur if one schema represented Weight in pounds and the other used kilograms.
d. Conflicts among constraints. Two schemas may impose different con- straints; for example, the key of an entity type may be different in each schema. Another example involves different structural constraints on a relationship such as TEACHES; one schema may represent it as 1:N (a course has one instructor), while the other schema represents it as M:N (a course may have more than one instructor).
10.2 The Database Design and Implementation Process 319
2. Modifying views to conform to one another. Some schemas are modified so that they conform to other schemas more closely. Some of the conflicts identified in the first subtask are resolved during this step.
3. Merging of views. The global schema is created by merging the individual schemas. Corresponding concepts are represented only once in the global schema, and mappings between the views and the global schema are speci- fied. This is the most difficult step to achieve in real-life databases involving dozens or hundreds of entities and relationships. It involves a considerable amount of human intervention and negotiation to resolve conflicts and to settle on the most reasonable and acceptable solutions for a global schema.
4. Restructuring. As a final optional step, the global schema may be analyzed and restructured to remove any redundancies or unnecessary complexity.
Some of these ideas are illustrated by the rather simple example presented in Figures 10.4 and 10.5. In Figure 10.4, two views are merged to create a bibliographic data- base. During identification of correspondences between the two views, we discover that RESEARCHER and AUTHOR are synonyms (as far as this database is con- cerned), as are CONTRIBUTED_BY and WRITTEN_BY. Further, we decide to modify VIEW 1 to include a SUBJECT for ARTICLE, as shown in Figure 10.4, to conform to VIEW 2. Figure 10.5 shows the result of merging MODIFIED VIEW 1 with VIEW 2. We generalize the entity types ARTICLE and BOOK into the entity type PUBLICATION, with their common attribute Title. The relationships CONTRIBUTED_BY and WRITTEN_BY are merged, as are the entity types RESEARCHER and AUTHOR. The attribute Publisher applies only to the entity type BOOK, whereas the attribute Size and the relationship type PUBLISHED_IN apply only to ARTICLE.
This simple example illustrates the complexity of the merging process and how the meaning of the various concepts must be accounted for in simplifying the resultant schema design. For real-life designs, the process of schema integration requires a more disciplined and systematic approach. Several strategies have been proposed for the view integration process (see Figure 10.6):
1. Binary ladder integration. Two schemas that are quite similar are integrated first. The resulting schema is then integrated with another schema, and the process is repeated until all schemas are integrated. The ordering of schemas for integration can be based on some measure of schema similarity. This strat- egy is suitable for manual integration because of its step-by-step approach.
2. N-ary integration. All the views are integrated in one procedure after an analysis and specification of their correspondences. This strategy requires computerized tools for large design problems. Such tools have been built as research prototypes but are not yet commercially available.
3. Binary balanced strategy. Pairs of schemas are integrated first, then the resulting schemas are paired for further integration; this procedure is repeated until a final global schema results.
4. Mixed strategy. Initially, the schemas are partitioned into groups based on their similarity, and each group is integrated separately. The intermediate schemas are grouped again and integrated, and so on.
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Classification_idName
NumberSizeTitle
Jid
NumberVolumeJname
SizeTitle
PublisherTitle
ARTICLE
CONTRIBUTED _BY
BOOK
RESEARCHER
BELONGS_TO
WRITTEN_BY
AUTHOR
Jid
JOURNAL
JOURNAL
ARTICLE
PUBLISHED_IN
PUBLISHED_IN
View 1
View 2
Modified View 1
BELONGS_TO
WRITTEN_BY
AUTHOR
SUBJECT
SUBJECT
Classification_idName
Jname Volume
Figure 10.4 Modifying views to conform before integration.
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NumberJnameVolume
Jid
AUTHOR
PUBLICATION
WRITTEN_BY
BELONGS_TO
PUBLISHED_IN
SUBJECT
Name
Title
Size
d
Publisher
Classification_id
JOURNALBOOK ARTICLE
IS_A_BOOK IS_AN_ARTICLE
Figure 10.5 Integrated schema after merging views 1 and 2.
Integrated schema Integrated schema
Integrated schema Integrated schema
Binary ladder integration N-ary integration
Binary balanced integration Mixed integration
V6
V5V4 V3V2V1
Intermediate lntegrated schemas
V5V4V3 V2V1
V5
V4
V3
V2V1
V5V4V3V2V1
Figure 10.6 Different strategies for the view integration process.
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Phase 2b: Transaction Design. The purpose of Phase 2b, which proceeds in parallel with Phase 2a, is to design the characteristics of known database transac- tions (applications) in a DBMS-independent way. When a database system is being designed, the designers are aware of many known applications (or transactions) that will run on the database once it is implemented. An important part of database design is to specify the functional characteristics of these transactions early on in the design process. This ensures that the database schema will include all the infor- mation required by these transactions. In addition, knowing the relative importance of the various transactions and the expected rates of their invocation plays a crucial part during the physical database design (Phase 5). Usually, not all of the database transactions are known at design time; after the database system is implemented, new transactions are continuously identified and implemented. However, the most important transactions are often known in advance of system implementation and should be specified at an early stage. The informal 80–20 rule typically applies in this context: 80 percent of the workload is represented by 20 percent of the most fre- quently used transactions, which govern the physical database design. In applica- tions that are of the ad hoc querying or batch processing variety, queries and applications that process a substantial amount of data must be identified.
A common technique for specifying transactions at a conceptual level is to identify their input/output and functional behavior. By specifying the input and output parameters (arguments) and the internal functional flow of control, designers can specify a transaction in a conceptual and system-independent way. Transactions usually can be grouped into three categories: (1) retrieval transactions, which are used to retrieve data for display on a screen or for printing of a report; (2) update transactions, which are used to enter new data or to modify existing data in the database; and (3) mixed transactions, which are used for more complex applica- tions that do some retrieval and some update. For example, consider an airline reservations database. A retrieval transaction could first list all morning flights on a given date between two cities. An update transaction could be to book a seat on a particular flight. A mixed transaction may first display some data, such as showing a customer reservation on some flight, and then update the database, such as cancel- ing the reservation by deleting it, or by adding a flight segment to an existing reser- vation. Transactions (applications) may originate in a front-end tool such as PowerBuilder (Sybase), which collect parameters online and then send a transaction to the DBMS as a backend.3
Several techniques for requirements specification include notation for specifying processes, which in this context are more complex operations that can consist of several transactions. Process modeling tools like BPwin as well as workflow model- ing tools are becoming popular to identify information flows in organizations. The UML language, which provides for data modeling via class and object diagrams, has a variety of process modeling diagrams including state transition diagrams, activity diagrams, sequence diagrams, and collaboration diagrams. All of these refer to
3This philosophy has been followed for over 20 years in popular products like CICS, which serves as a tool to generate transactions for legacy DBMSs like IMS.
10.2 The Database Design and Implementation Process 323
activities, events, and operations within the information system, the inputs and out- puts of the processes, the sequencing or synchronization requirements, and other conditions. It is possible to refine these specifications and extract individual trans- actions from them. Other proposals for specifying transactions include TAXIS, GALILEO, and GORDAS (see this chapter’s Selected Bibliography). Some of these have been implemented into prototype systems and tools. Process modeling still remains an active area of research.
Transaction design is just as important as schema design, but it is often considered to be part of software engineering rather than database design. Many current design methodologies emphasize one over the other. One should go through Phases 2a and 2b in parallel, using feedback loops for refinement, until a stable design of schema and transactions is reached.4
10.2.3 Phase 3: Choice of a DBMS The choice of a DBMS is governed by a number of factors—some technical, others economic, and still others concerned with the politics of the organization. The tech- nical factors focus on the suitability of the DBMS for the task at hand. Issues to con- sider are the type of DBMS (relational, object-relational, object, other), the storage structures and access paths that the DBMS supports, the user and programmer interfaces available, the types of high-level query languages, the availability of devel- opment tools, the ability to interface with other DBMSs via standard interfaces, the architectural options related to client-server operation, and so on. Nontechnical factors include the financial status and the support organization of the vendor. In this section we concentrate on discussing the economic and organizational factors that affect the choice of DBMS. The following costs must be considered:
1. Software acquisition cost. This is the up-front cost of buying the software, including programming language options, different interface options (forms, menu, and Web-based graphic user interface (GUI) tools), recov- ery/backup options, special access methods, and documentation. The cor- rect DBMS version for a specific operating system must be selected. Typically, the development tools, design tools, and additional language sup- port are not included in basic pricing.
2. Maintenance cost. This is the recurring cost of receiving standard mainte- nance service from the vendor and for keeping the DBMS version up-to- date.
3. Hardware acquisition cost. New hardware may be needed, such as addi- tional memory, terminals, disk drives and controllers, or specialized DBMS storage and archival storage.
4. Database creation and conversion cost. This is the cost of either creating the database system from scratch or converting an existing system to the new
4High-level transaction modeling is covered in Batini et al. (1992, Chapters 8, 9, and 11). The joint func- tional and data analysis philosophy is advocated throughout that book.
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DBMS software. In the latter case it is customary to operate the existing sys- tem in parallel with the new system until all the new applications are fully implemented and tested. This cost is hard to project and is often underesti- mated.
5. Personnel cost. Acquisition of DBMS software for the first time by an organization is often accompanied by a reorganization of the data processing department. Positions of DBA and staff exist in most companies that have adopted DBMSs.
6. Training cost. Because DBMSs are often complex systems, personnel must often be trained to use and program the DBMS. Training is required at all levels, including programming and application development, physical design, and database administration.
7. Operating cost. The cost of continued operation of the database system is typically not worked into an evaluation of alternatives because it is incurred regardless of the DBMS selected.
The benefits of acquiring a DBMS are not so easy to measure and quantify. A DBMS has several intangible advantages over traditional file systems, such as ease of use, consolidation of company-wide information, wider availability of data, and faster access to information. With Web-based access, certain parts of the data can be made globally accessible to employees as well as external users. More tangible benefits include reduced application development cost, reduced redundancy of data, and better control and security. Although databases have been firmly entrenched in most organizations, the decision of whether to move an application from a file- based to a database-centered approach still comes up. This move is generally driven by the following factors:
1. Data complexity. As data relationships become more complex, the need for a DBMS is greater.
2. Sharing among applications. The need for a DBMS is greater when appli- cations share common data stored redundantly in multiple files.
3. Dynamically evolving or growing data. If the data changes constantly, it is easier to cope with these changes using a DBMS than using a file system.
4. Frequency of ad hoc requests for data. File systems are not at all suitable for ad hoc retrieval of data.
5. Data volume and need for control. The sheer volume of data and the need to control it sometimes demands a DBMS.
It is difficult to develop a generic set of guidelines for adopting a single approach to data management within an organization—whether relational, object-oriented, or object-relational. If the data to be stored in the database has a high level of complex- ity and deals with multiple data types, the typical approach may be to consider an object or object-relational DBMS.5 Also, the benefits of inheritance among classes
5See the discussion in Chapter 11 concerning this issue.
10.2 The Database Design and Implementation Process 325
and the corresponding advantage of reuse favor these approaches. Finally, several economic and organizational factors affect the choice of one DBMS over another:
1. Organization-wide adoption of a certain philosophy. This is often a dom- inant factor affecting the acceptability of a certain data model (for example, relational versus object), a certain vendor, or a certain development method- ology and tools (for example, use of an object-oriented analysis and design tool and methodology may be required of all new applications).
2. Familiarity of personnel with the system. If the programming staff within the organization is familiar with a particular DBMS, it may be favored to reduce training cost and learning time.
3. Availability of vendor services. The availability of vendor assistance in solving problems with the system is important, since moving from a non- DBMS to a DBMS environment is generally a major undertaking and requires much vendor assistance at the start.
Another factor to consider is the DBMS portability among different types of hard- ware. Many commercial DBMSs now have versions that run on many hardware/software configurations (or platforms). The need of applications for backup, recovery, performance, integrity, and security must also be considered. Many DBMSs are currently being designed as total solutions to the information- processing and information resource management needs within organizations. Most DBMS vendors are combining their products with the following options or built-in features:
■ Text editors and browsers
■ Report generators and listing utilities
■ Communication software (often called teleprocessing monitors)
■ Data entry and display features such as forms, screens, and menus with auto- matic editing features
■ Inquiry and access tools that can be used on the World Wide Web (Web- enabling tools)
■ Graphical database design tools
A large amount of third-party software is available that provides added functional- ity to a DBMS in each of the above areas. In rare cases it may be preferable to develop in-house software rather than use a DBMS—for example, if the applica- tions are very well defined and are all known beforehand. Under such circum- stances, an in-house custom-designed system may be appropriate to implement the known applications in the most efficient way. In most cases, however, new applica- tions that were not foreseen at design time come up after system implementation. This is precisely why DBMSs have become very popular: They facilitate the incorpo- ration of new applications with only incremental modifications to the existing design of a database. Such design evolution—or schema evolution—is a feature present to various degrees in commercial DBMSs.
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10.2.4 Phase 4: Data Model Mapping (Logical Database Design)
The next phase of database design is to create a conceptual schema and external schemas in the data model of the selected DBMS by mapping those schemas pro- duced in Phase 2a. The mapping can proceed in two stages:
1. System-independent mapping. In this stage, the mapping does not consider any specific characteristics or special cases that apply to the particular DBMS implementation of the data model. We discussed DBMS-independent map- ping of an ER schema to a relational schema in Section 9.1 and of EER schema constructs to relational schemas in Section 9.2.
2. Tailoring the schemas to a specific DBMS. Different DBMSs implement a data model by using specific modeling features and constraints. We may have to adjust the schemas obtained in step 1 to conform to the specific implementation features of a data model as used in the selected DBMS.
The result of this phase should be DDL (data definition language) statements in the language of the chosen DBMS that specify the conceptual and external level schemas of the database system. But if the DDL statements include some physical design parameters, a complete DDL specification must wait until after the physical database design phase is completed. Many automated CASE (computer-aided soft- ware engineering) design tools (see Section 10.5) can generate DDL for commercial systems from a conceptual schema design.
10.2.5 Phase 5: Physical Database Design Physical database design is the process of choosing specific file storage structures and access paths for the database files to achieve good performance for the various database applications. Each DBMS offers a variety of options for file organizations and access paths. These usually include various types of indexing, clustering of related records on disk blocks, linking related records via pointers, and various types of hashing techniques (see Chapters 17 and 18). Once a specific DBMS is cho- sen, the physical database design process is restricted to choosing the most appro- priate structures for the database files from among the options offered by that DBMS. In this section we give generic guidelines for physical design decisions; they hold for any type of DBMS. The following criteria are often used to guide the choice of physical database design options:
1. Response time. This is the elapsed time between submitting a database transaction for execution and receiving a response. A major influence on response time that is under the control of the DBMS is the database access time for data items referenced by the transaction. Response time is also influenced by factors not under DBMS control, such as system load, operat- ing system scheduling, or communication delays.
2. Space utilization. This is the amount of storage space used by the database files and their access path structures on disk, including indexes and other access paths.
10.2 The Database Design and Implementation Process 327
3. Transaction throughput. This is the average number of transactions that can be processed per minute; it is a critical parameter of transaction systems such as those used for airline reservations or banking. Transaction through- put must be measured under peak conditions on the system.
Typically, average and worst-case limits on the preceding parameters are specified as part of the system performance requirements. Analytical or experimental tech- niques, which can include prototyping and simulation, are used to estimate the average and worst-case values under different physical design decisions to deter- mine whether they meet the specified performance requirements.
Performance depends on record size and number of records in the file. Hence, we must estimate these parameters for each file. Additionally, we should estimate the update and retrieval patterns for the file cumulatively from all the transactions. Attributes used for searching for specific records should have primary access paths and secondary indexes constructed for them. Estimates of file growth, either in the record size because of new attributes or in the number of records, should also be taken into account during physical database design.
The result of the physical database design phase is an initial determination of stor- age structures and access paths for the database files. It is almost always necessary to modify the design on the basis of its observed performance after the database sys- tem is implemented. We include this activity of database tuning in the next phase and cover it in the context of query optimization in Chapter 20.
10.2.6 Phase 6: Database System Implementation and Tuning
After the logical and physical designs are completed, we can implement the database system. This is typically the responsibility of the DBA and is carried out in conjunc- tion with the database designers. Language statements in the DDL, including the SDL (storage definition language) of the selected DBMS, are compiled and used to create the database schemas and (empty) database files. The database can then be loaded (populated) with the data. If data is to be converted from an earlier comput- erized system, conversion routines may be needed to reformat the data for loading into the new database.
Database programs are implemented by the application programmers, by referring to the conceptual specifications of transactions, and then writing and testing pro- gram code with embedded DML (data manipulation language) commands. Once the transactions are ready and the data is loaded into the database, the design and implementation phase is over and the operational phase of the database system begins.
Most systems include a monitoring utility to collect performance statistics, which are kept in the system catalog or data dictionary for later analysis. These include sta- tistics on the number of invocations of predefined transactions or queries, input/output activity against files, counts of file disk pages or index records, and fre-
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quency of index usage. As the database system requirements change, it often becomes necessary to add or remove existing tables and to reorganize some files by changing primary access methods or by dropping old indexes and constructing new ones. Some queries or transactions may be rewritten for better performance. Database tuning continues as long as the database is in existence, as long as per- formance problems are discovered, and while the requirements keep changing (see Chapter 20).
10.3 Use of UML Diagrams as an Aid to Database Design Specification6
10.3.1 UML as a Design Specification Standard There is a need of some standard approach to cover the entire spectrum of require- ments analysis, modeling, design, implementation, and deployment of databases and their applications. One approach that is receiving wide attention and that is also proposed as a standard by the Object Management Group (OMG) is the Unified Modeling Language (UML) approach. It provides a mechanism in the form of dia- grammatic notation and associated language syntax to cover the entire life cycle. Presently, UML can be used by software developers, data modelers, database design- ers, and so on to define the detailed specification of an application. They also use it to specify the environment consisting of users, software, communications, and hardware to implement and deploy the application.
UML combines commonly accepted concepts from many object-oriented (O-O) methods and methodologies (see this chapter’s Selected Bibliography for the con- tributing methodologies that led to UML). It is generic, and is language-independent and platform-independent. Software architects can model any type of application, running on any operating system, programming language, or network, in UML. That has made the approach very widely applicable. Tools like Rational Rose are currently popular for drawing UML diagrams—they enable software developers to develop clear and easy-to-understand models for specifying, visualizing, constructing, and documenting components of software systems. Since the scope of UML extends to software and application development at large, we will not cover all aspects of UML here. Our goal is to show some relevant UML notations that are commonly used in the requirements collection and analysis phase of database design, as well as the con- ceptual design phase (see Phases 1 and 2 in Figure 10.1). A detailed application devel- opment methodology using UML is outside the scope of this book and may be found in various textbooks devoted to object-oriented design, software engineering, and UML (see the Selected Bibliography at the end of this chapter).
6The contribution of Abrar Ul-Haque to the UML and Rational Rose sections is much appreciated.
10.3 Use of UML Diagrams as an Aid to Database Design Specification 329
UML has many types of diagrams. Class diagrams, which can represent the end result of conceptual database design, were discussed in Sections 7.8 and 8.6. To arrive at the class diagrams, the application requirements may be gathered and spec- ified using use case diagrams, sequence diagrams, and statechart diagrams. In the rest of this section we introduce the different types of UML diagrams briefly to give the reader an idea of the scope of UML. Then we describe a small sample applica- tion to illustrate the use of some of these diagrams and show how they lead to the eventual class diagram as the final conceptual database design. The diagrams pre- sented in this section pertain to the standard UML notation and have been drawn using Rational Rose. Section 10.4 is devoted to a general discussion of the use of Rational Rose in database application design.
10.3.2 UML for Database Application Design UML was developed as a software engineering methodology. As we mentioned ear- lier in Section 7.8, most software systems have sizable database components. The database community has started embracing UML, and now some database design- ers and developers are using UML for data modeling as well as for subsequent phases of database design. The advantage of UML is that even though its concepts are based on object-oriented techniques, the resulting models of structure and behavior can be used to design relational, object-oriented, or object-relational databases (see Chapter 11 for definitions of object databases and object-relational databases).
One of the major contributions of the UML approach has been to bring the tradi- tional database modelers, analysts, and designers together with the software applica- tion developers. In Figure 10.1 we showed the phases of database design and implementation and how they apply to these two groups. UML also allows us to do behavioral, functional, and dynamic modeling by introducing various types of dia- grams. This results in a more complete specification/description of the overall data- base application. In the following sections we summarize the different types of UML diagrams and then give an example of the use case, sequence, and statechart diagrams in a sample application.
10.3.3 Different Types of Diagrams in UML UML defines nine types of diagrams divided into these two categories:
■ Structural Diagrams. These describe the structural or static relationships among schema objects, data objects, and software components. They include class diagrams, object diagrams, component diagrams, and deployment dia- grams.
■ Behavioral Diagrams. Their purpose is to describe the behavioral or dynamic relationships among components. They include use case diagrams, sequence diagrams, collaboration diagrams, statechart diagrams, and activ- ity diagrams.
330 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
We introduce the nine types briefly below. The structural diagrams include:
A. Class Diagrams. Class diagrams capture the static structure of the system and act as foundation for other models. They show classes, interfaces, collaborations, dependencies, generalizations, associations, and other relationships. Class diagrams are a very useful way to model the conceptual database schema. We showed exam- ples of class diagrams for the COMPANY database schema in Figure 7.16 and for a generalization hierarchy in Figure 8.10.
Package Diagrams. Package diagrams are a subset of class diagrams. They organize elements of the system into related groups called packages. A package may be a col- lection of related classes and the relationships between them. Package diagrams help minimize dependencies in a system.
B. Object Diagrams. Object diagrams show a set of individual objects and their relationships, and are sometimes referred to as instance diagrams. They give a static view of a system at a particular time and are normally used to test class diagrams for accuracy.
C. Component Diagrams. Component diagrams illustrate the organizations and dependencies among software components. A component diagram typically consists of components, interfaces, and dependency relationships. A component may be a source code component, a runtime component, or an executable component. It is a physical building block in the system and is represented as a rectangle with two small rectangles or tabs overlaid on its left side. An interface is a group of operations used or created by a component and is usually represented by a small circle. Dependency relationship is used to model the relationship between two components and is repre- sented by a dotted arrow pointing from a component to the component it depends on. For databases, component diagrams stand for stored data such as tablespaces or partitions. Interfaces refer to applications that use the stored data.
D. Deployment Diagrams. Deployment diagrams represent the distribution of components (executables, libraries, tables, files) across the hardware topology. They depict the physical resources in a system, including nodes, components, and con- nections, and are basically used to show the configuration of runtime processing elements (the nodes) and the software processes that reside on them (the threads).
Next, we briefly describe the various types of behavioral diagrams and expand on those that are of particular interest.
E. Use Case Diagrams. Use case diagrams are used to model the functional interactions between users and the system. A scenario is a sequence of steps describ- ing an interaction between a user and a system. A use case is a set of scenarios that have a common goal. The use case diagram was introduced by Jacobson7 to visual- ize use cases. A use case diagram shows actors interacting with use cases and can be understood easily without the knowledge of any notation. An individual use case is
7See Jacobson et al. (1992).
10.3 Use of UML Diagrams as an Aid to Database Design Specification 331
Base use case_1
Base use case_2
Use case
Base use case_3
Extended use case
Actor_1
Actor_2 Actor_4
Actor_3
<<Include>>
<<Include>>
<<Extend>>
Included use case
Figure 10.7 The use case diagram notation.
shown as an oval and stands for a specific task performed by the system. An actor, shown with a stick person symbol, represents an external user, which may be a human user, a representative group of users, a certain role of a person in the organ- ization, or anything external to the system (see Figure 10.7). The use case diagram shows possible interactions of the system (in our case, a database system) and describes as use cases the specific tasks the system performs. Since they do not spec- ify any implementation detail and are supposed to be easy to understand, they are used as a vehicle for communicating between the end users and developers to help in easier user validation at an early stage. Test plans can also be described using use case diagrams. Figure 10.7 shows the use case diagram notation. The include rela- tionship is used to factor out some common behavior from two or more of the orig- inal use cases—it is a form of reuse. For example, in a university environment shown in Figure 10.8, the use cases Register for course and Enter grades in which the actors student and professor are involved, include a common use case called Validate user. If a use case incorporates two or more significantly different scenarios, based on circumstances or varying conditions, the extend relationship is used to show the subcases attached to the base case.
Interaction Diagrams. The next two types of UML behavioral diagrams, interaction diagrams, are used to model the dynamic aspects of a system. They consist of a set of messages exchanged between a set of objects. There are two types of interaction diagrams, sequence and collaboration.
F. Sequence Diagrams. Sequence diagrams describe the interactions between various objects over time. They basically give a dynamic view of the system by
332 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
Student
Professor
Register for course
Enter grades
Validate user
Apply for aid
Financial aid officer
<<Include>>
<<Include>>
Figure 10.8 A sample use case diagram for a UNIVERSITY database.
showing the flow of messages between objects. Within the sequence diagram, an object or an actor is shown as a box at the top of a dashed vertical line, which is called the object’s lifeline. For a database, this object is typically something physi- cal—a book in a warehouse that would be represented in the database, an external document or form such as an order form, or an external visual screen—that may be part of a user interface. The lifeline represents the existence of an object over time. Activation, which indicates when an object is performing an action, is represented as a rectangular box on a lifeline. Each message is represented as an arrow between the lifelines of two objects. A message bears a name and may have arguments and control information to explain the nature of the interaction. The order of messages is read from top to bottom. A sequence diagram also gives the option of self-call, which is basically just a message from an object to itself. Condition and Iteration markers can also be shown in sequence diagrams to specify when the message should be sent and to specify the condition to send multiple markers. A return dashed line shows a return from the message and is optional unless it carries a special meaning. Object deletion is shown with a large X. Figure 10.9 explains some of the notation used in sequence diagrams.
G. Collaboration Diagrams. Collaboration diagrams represent interactions among objects as a series of sequenced messages. In collaboration diagrams the emphasis is on the structural organization of the objects that send and receive mes- sages, whereas in sequence diagrams the emphasis is on the time-ordering of the messages. Collaboration diagrams show objects as icons and number the messages; numbered messages represent an ordering. The spatial layout of collaboration dia- grams allows linkages among objects that show their structural relationships. Use of collaboration and sequence diagrams to represent interactions is a matter of choice as they can be used for somewhat similar purposes; we will hereafter use only sequence diagrams.
10.3 Use of UML Diagrams as an Aid to Database Design Specification 333
Object: Class or Actor
Lifeline
Focus of control/activation Message to self
Message
Object: Class or ActorObject: Class or Actor
Object deconstruction termination
Return
Figure 10.9 The sequence diagram notation.
H. Statechart Diagrams. Statechart diagrams describe how an object’s state changes in response to external events.
To describe the behavior of an object, it is common in most object-oriented tech- niques to draw a statechart diagram to show all the possible states an object can get into in its lifetime. The UML statecharts are based on David Harel’s8 statecharts. They show a state machine consisting of states, transitions, events, and actions and are very useful in the conceptual design of the application that works against a data- base of stored objects.
The important elements of a statechart diagram shown in Figure 10.10 are as follows:
■ States. Shown as boxes with rounded corners, they represent situations in the lifetime of an object.
■ Transitions. Shown as solid arrows between the states, they represent the paths between different states of an object. They are labeled by the event- name [guard] /action; the event triggers the transition and the action results from it. The guard is an additional and optional condition that specifies a condition under which the change of state may not occur.
■ Start/Initial State. Shown by a solid circle with an outgoing arrow to a state. ■ Stop/Final State. Shown as a double-lined filled circle with an arrow point-
ing into it from a state.
Statechart diagrams are useful in specifying how an object’s reaction to a message depends on its state. An event is something done to an object such as receiving a message; an action is something that an object does such as sending a message.
8See Harel (1987).
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Start/initial state Transition
State 1 State 2
State 3 State consists of three parts:
Name Activities Embedded machine
Activities and embedded machine are optional
Stop/accepting/final state
Name Do/action
Figure 10.10 The statechart diagram notation.
I. Activity Diagrams. Activity diagrams present a dynamic view of the system by modeling the flow of control from activity to activity. They can be considered as flowcharts with states. An activity is a state of doing something, which could be a real-world process or an operation on some object or class in the database. Typically, activity diagrams are used to model workflow and internal business oper- ations for an application.
10.3.4 A Modeling and Design Example: UNIVERSITY Database
In this section we will briefly illustrate the use of some of the UML diagrams we presented above to design a simple database in a university setting. A large number of details are left out to conserve space; only a stepwise use of these diagrams that leads toward a conceptual design and the design of program components is illus- trated. As we indicated before, the eventual DBMS on which this database gets implemented may be relational, object-oriented, or object-relational. That will not change the stepwise analysis and modeling of the application using the UML diagrams.
Imagine a scenario with students enrolling in courses that are offered by professors. The registrar’s office is in charge of maintaining a schedule of courses in a course catalog. They have the authority to add and delete courses and to do schedule changes. They also set enrollment limits on courses. The financial aid office is in
10.3 Use of UML Diagrams as an Aid to Database Design Specification 335
charge of processing student aid applications for which the students have to apply. Assume that we have to design a database that maintains the data about students, professors, courses, financial aid, and so on. We also want to design some of the applications that enable us to do course registration, financial aid application pro- cessing, and maintaining of the university-wide course catalog by the registrar’s office. The above requirements may be depicted by a series of UML diagrams.
As mentioned previously, one of the first steps involved in designing a database is to gather customer requirements by using use case diagrams. Suppose one of the requirements in the UNIVERSITY database is to allow the professors to enter grades for the courses they are teaching and for the students to be able to register for courses and apply for financial aid. The use case diagram corresponding to these use cases can be drawn as shown in Figure 10.8.
Another helpful element when designing a system is to graphically represent some of the states the system can be in, to visualize the various states the system can be in during the course of an application. For example, in our UNIVERSITY database the various states that the system goes through when the registration for a course with 50 seats is opened can be represented by the statechart diagram in Figure 10.11. This shows the states of a course while enrollment is in process. The first state sets the count of students enrolled to zero. During the enrolling state, the Enroll student transition continues as long as the count of enrolled students is less than 50. When the count reaches 50, the state to close the section is entered. In a real system, addi- tional states and/or transitions could be added to allow a student to drop a section and any other needed actions.
Next, we can design a sequence diagram to visualize the execution of the use cases. For the university database, the sequence diagram corresponds to the use case: student requests to register and selects a particular course to register is shown in Figure
Enroll student [count < 50]
Enroll student/set count = 0Course enrollment Enrolling Entry/register student
Section closing Canceled
Cancel
CancelCancel Count = 50
Exit/closesection
Do/enroll students
Figure 10.11 A sample statechart diagram for the UNIVERSITY database.
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getSeatsLeft
getPreq = true && [getSeatsLeft = True]/update Schedule
:Registration
requestRegistration getCourseListing
selectCourse addCourse getPreReq
:Student
:Catalog :Course :Schedule
Figure 10.12 A sequence diagram for the UNIVERSITY database.
10.12. The catalog is first browsed to get course listings. Then, when the student selects a course to register in, prerequisites and course capacity are checked, and the course is then added to the student’s schedule if the prerequisites are met and there is space in the course.
These UML diagrams are not the complete specification of the UNIVERSITY data- base. There will be other use cases for the various applications of the actors, includ- ing registrar, student, professor, and so on. A complete methodology for how to arrive at the class diagrams from the various diagrams we illustrated in this section is outside our scope here. Design methodologies remain a matter of judgment and personal preferences. However, the designer should make sure that the class dia- gram will account for all the specifications that have been given in the form of the use cases, statechart, and sequence diagrams. The class diagram in Figure 10.13 shows a possible class diagram for this application, with the structural relationships and the operations within the classes. These classes will need to be implemented to develop the UNIVERSITY database, and together with the operations they will implement the complete class schedule/enrollment/aid application. Only some of the attributes and methods (operations) are shown in Figure 10.13. It is likely that these class diagrams will be modified as more details are specified and more func- tions evolve in the UNIVERSITY application.
10.4 Rational Rose: A UML-Based Design Tool 337
REGISTRATION . . .
findCourseAdd()
cancelCourse()
addCourse() viewSchedule()
. . .()
CATALOG . . .
getPreReq()
getSeatsLeft()
getCourseListing() . . .()
FINANCIAL_AID aidType
aidAmount
assignAid()
denyAid()
SCHEDULE . . .
updateSchedule()
showSchedule()
. . .()
STUDENT . . .
requestRegistration()
applyAid()
. . .()
PROFESSOR . . .
enterGrades()
offerCourse()
. . .()
COURSE time
classroom
seats
. . . dropCourse()
addCourse() . . .()
PERSON Name
Ssn
. . .
viewSchedule() . . .()
Figure 10.13 The design of the UNIVERSITY database as a class diagram.
10.4 Rational Rose: A UML-Based Design Tool
10.4.1 Rational Rose for Database Design Rational Rose is one of the modeling tools used in the industry to develop informa- tion systems. It was acquired by IBM in 2003. As we pointed out in the first two sec- tions of this chapter, a database is a central component of most information systems. Rational Rose provides the initial specification in UML that eventually leads to the database development. Many extensions have been made in the latest versions of Rose for data modeling, and now it provides support for conceptual, logical, and physical database modeling and design.
338 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
10.4.2 Rational Rose Data Modeler Rational Rose Data Modeler is a visual modeling tool for designing databases. Because it is UML-based, it provides a common tool and language to bridge the communication gap between database designers and application developers. This makes it possible for database designers, developers, and analysts to work together, capture and share business requirements, and track them as they change through- out the process. Also, by allowing the designers to model and design all specifica- tions on the same platform using the same notation, it improves the design process and reduces the risk of errors.
The process modeling capabilities in Rational Rose allow the modeling of the behavior of database applications as we saw in the short example above, in the form of use cases (Figure 10.8), sequence diagrams (Figure 10.12), and statechart dia- grams (Figure 10.11). There is the additional machinery of collaboration diagrams to show interactions between objects and activity diagrams to model the flow of control, which we did not show in our example. The eventual goal is to generate the database specification and application code as much as possible. The Rose Data Modeler can also capture triggers, stored procedures, and other modeling concepts explicitly in the diagram rather than representing them with hidden tagged values behind the scenes (see Chapter 26 which discusses active databases and triggers). The Rose Data Modeler also provides the capability to forward engineer a database in terms of constantly changing requirements and reverse engineer an existing implemented database into its conceptual design.
10.4.3 Data Modeling Using Rational Rose Data Modeler There are many tools and options available in Rose Data Modeler for data modeling.
Reverse Engineering. Reverse engineering of a database allows the user to create a conceptual data model based on an existing database schema specified in a DDL file. We can use the reverse engineering wizard in Rational Rose Data Modeler for this purpose. The reverse engineering wizard basically reads the schema in the data- base or DDL file and recreates it as a data model. While doing so, it also includes the names of all quoted identifier entities.
Forward Engineering and DDL Generation. We can also create a data model directly from scratch in Rose. Having created the data model,9 we can also use it to generate the DDL for a specific DBMS. There is a forward engineering wizard in the Rose Data Modeler that reads the schema in the data model or reads both the schema in the data model and the tablespaces in the data storage model and gener- ates the appropriate DDL code in a DDL file. The wizard also provides the option of generating a database by executing the generated DDL file.
9The term data model used by Rational Rose Data Modeler corresponds to our notion of an application model or conceptual schema.
10.4 Rational Rose: A UML-Based Design Tool 339
Conceptual Design in UML Notation. Rational Rose allows modeling of data- bases using UML notation. ER diagrams most often used in the conceptual design of databases can be easily built using the UML notation as class diagrams in Rational Rose. For example, the ER schema of our COMPANY database from Chapter 7 can be redrawn in Rose using UML notation as shown in Figure 10.14. The textual specification in Figure 10.14 can be converted to the graphical represen- tation shown in Figure 10.15 by using the data model diagram option in Rose.
Figure 10.15 is similar to Figure 7.16, except that it is using the notation provided by Rational Rose. Hence, it can be considered as an ER diagram using UML notation, with the inclusion of methods and other details. Identifying relationships specify that an object in a child class (DEPENDENT in Figure 10.15) cannot exist without a corresponding parent object in the parent class (EMPLOYEE in Figure 10.15),
Figure 10.14 A logical data model diagram definition in Rational Rose.
340 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
whereas non-identifying relationships specify a regular association (relationship) between two independent classes. It is possible to update the schemas directly in their text or graphical form. For example, if the relationship between the EMPLOYEE and PROJECT called WORKS_ON was deleted, Rose would automati- cally update or delete all the foreign keys in the relevant tables.
EMPLOYEE
Fname: Char(15)
Minit: Char(1)
Lname: Char(15)
Sex: Char(1)
Salary: Integer
Address: Char(20)
Ssn: Integer
Bdate: Date
Number: Integer
Project_number: Integer
Name: Char(15)
Employee_ssn: Integer
<<PK>>PK_T_00()
<<FK>>Employee2()
<<FK>>Employee6()
<<FK>>Employee10()
PK
FK
FK
FK
FK
DEPARTMENT Number: Integer
Name: Char(15)
Location: Char(15)
No_of_employees: Integer
Mgr_ssn: Integer
Mgr_start_date: Date
Ssn: Integer
<<PK>>PK_Department1()
<<FK>>FK_Department7()
<<Unique>>TC_Department24()
FK
PK
DEPENDENT
<<Identifying>> HAS_DEPENDENTS
<<Non-Identifying>> WORKS_ON
<<Non-Identifying>> CONTROLS
<<Non-Identifying>> SUPERVISION
<<Non-Identifying>>
1
0..1*
1
0..*1..*0..*
1
1..*
MANAGES
<<Non-Identifying>>
1..* 1 WORKS_FOR
Name: Char(15)
Sex: Char(1)
Birth_date: Date
Relationship: Char(15)
Ssn: Integer
<<PK>>PK_Dependent3()
<<FK>>FK_Dependent1()
PK
P F K
PROJECT Number: Integer
Name: Char(15)
Location: Char(15)
Department_number: Integer
Hours: Time(2)
<<PK>>PK_Project2()
<<FK>>FK_Project3()
PK
PK
FK
1
Figure 10.15 A graphical data model diagram in Rational Rose for the COMPANY database.
10.4 Rational Rose: A UML-Based Design Tool 341
An important difference in Figure 10.15 from our previous ER notation shown in Chapters 7 and 8 is that foreign key attributes actually appear in the class diagrams in Rational Rose. This is common in several diagrammatic notations to make the conceptual design closer to the way it is realized in the relational model implemen- tation. In Chapters 7 and 8, the conceptual ER and EER diagrams and the UML class diagrams did not include foreign key attributes, which were added to the relational schema during the mapping process (see Chapter 9).
Converting Logical Data Model to Object Model and Vice Versa. Rational Rose Data Modeler also provides the option of converting a logical database design (relational schema) to an object model design (object schema) and vice versa. For example, the logical data model shown in Figure 10.14 can be converted to an object model. This sort of mapping allows a deep understanding of the relationships between the conceptual model and implementation model, and helps in keeping them both up-to-date when changes are made to either model during the develop- ment process. Figure 10.16 shows the Employee table after converting it to a class in an object model. The various tabs in the window can then be used to enter/display different types of information. They include operations, attributes, and relation- ships for that class.
Synchronization between the Conceptual Design and the Actual Database. Rose Data Modeler allows keeping the data model and database imple- mentation synchronized. It allows visualizing both the data model and the database and then, based on the differences, it gives the option to update the model or change the database.
Extensive Domain Support. The Rose Data Modeler allows database designers to create a standard set of user-defined data types (these are similar to domains in
Figure 10.16 The class OM_EMPLOYEE corresponding to the table Employee in Figure 10.14.
342 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
SQL; see Chapter 4) and assign them to any column in the data model. Properties of the domain are then cascaded to assigned columns. These domains can then be maintained by a standards group and deployed to all modelers when they begin cre- ating new models by using the Rational Rose framework.
Easy Communication among Design Teams. As mentioned earlier, using a common tool allows easy communication between teams. In the Rose Data Modeler, an application developer can access both the object and data models and see how they are related, and thus make informed and better choices about how to build data access methods. There is also the option of using Rational Rose Web Publisher to allow the models and the meta-data beneath these models to be avail- able to everyone on the team.
What we have described above is a partial description of the capabilities of the Rational Rose tool as it relates to the conceptual and logical design phases in Figure 10.1. The entire range of UML diagrams we described in Section 10.3 can be devel- oped and maintained in Rose. For further details the reader is referred to the prod- uct literature. Figure 10.17 gives another version of the class diagram in Figure 7.16 drawn using Rational Rose. Figure 10.17 differs from Figure 10.15 in that the for- eign key attributes are not shown explicitly. Hence, Figure 10.17 is using the nota- tions presented in Chapters 7 and 8. Rational Rose allows either option to be used, depending on the preference of the designers.
10.5 Automated Database Design Tools The database design activity predominantly spans Phase 2 (conceptual design), Phase 4 (data model mapping, or logical design), and Phase 5 (physical database design) in the design process that we discussed in Section 10.2. Discussion of Phase 5 is deferred to Chapter 20 after we present storage and indexing techniques, and query optimization. We discussed Phases 2 and 4 in detail with the use of the UML notation in Section 10.3 and pointed out the features of the tool Rational Rose, which supports these phases, in Section 10.4. As we mentioned, Rational Rose is more than just a database design tool. It is a software development tool and does database modeling and schema design in the form of class diagrams as part of its overall object-oriented application development methodology. In this section, we summarize the features and shortcomings of the set of commercial tools that are focused on automating the process of conceptual, logical, and physical design of databases.
When database technology was first introduced, most database design was carried out manually by expert designers, who used their experience and knowledge in the design process. However, at least two factors indicated that some form of automa- tion had to be utilized if possible:
1. As an application involves more and more complexity of data in terms of relationships and constraints, the number of options or different designs to
10.5 Automated Database Design Tools 343
EMPLOYEE Fname
Minit
Lname
Ssn
Bdate Sex
Address
Salary
age()
change_department()
change_projects()
DEPARTMENT
Name
Number
add_employee()
no_of_employee()
change_major
1..n
1
1 0..n
0..n1
1..n
n+supervisee
+supervisor
0..1
1..n
1..n
n
1
0..1
WORKS_FOR
PROJECT
Name
Number
add_employee()
add_project()
change_manager()
MANAGES
Start_date
WORKS ON
Hours
LOCATION
Name
DEPENDENT
Dependent name
Sex
Birth_date
Relationship
Controls
Figure 10.17 The COMPANY data- base class diagram (Figure 7.16) drawn in Rational Rose.
model the same information keeps increasing rapidly. It becomes difficult to deal with this complexity and the corresponding design alternatives manually.
2. The sheer size of some databases runs into hundreds of entity types and rela- tionship types, making the task of manually managing these designs almost impossible. The meta information related to the design process we described in Section 10.2 yields another database that must be created, maintained, and queried as a database in its own right.
The above factors have given rise to many tools that come under the general cate- gory of CASE (computer-aided software engineering) tools for database design. Rational Rose is a good example of a modern CASE tool. Typically these tools con- sist of a combination of the following facilities:
1. Diagramming. This allows the designer to draw a conceptual schema dia- gram in some tool-specific notation. Most notations include entity types (classes), relationship types (associations) that are shown either as separate boxes or simply as directed or undirected lines, cardinality constraints
344 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
shown alongside the lines or in terms of the different types of arrowheads or min/max constraints, attributes, keys, and so on.10 Some tools display inher- itance hierarchies and use additional notation for showing the partial- versus-total and disjoint-versus-overlapping nature of the specialization/ generalization. The diagrams are internally stored as conceptual designs and are available for modification as well as generation of reports, cross- reference listings, and other uses.
2. Model mapping. This implements mapping algorithms similar to the ones we presented in Sections 9.1 and 9.2. The mapping is system-specific—most tools generate schemas in SQL DDL for Oracle, DB2, Informix, Sybase, and other RDBMSs. This part of the tool is most amenable to automation. The designer can further edit the produced DDL files if needed.
3. Design normalization. This utilizes a set of functional dependencies that are supplied at the conceptual design or after the relational schemas are pro- duced during logical design. Then, design decomposition algorithms (see Chapter 16) are applied to decompose existing relations into higher normal- form relations. Generally, many of these tools lack the approach of generat- ing alternative 3NF or BCNF designs (described in Chapter 15) and allowing the designer to select among them based on some criteria like the minimum number of relations or least amount of storage.
Most tools incorporate some form of physical design including the choice of indexes. A whole range of separate tools exists for performance monitoring and measurement. The problem of tuning a design or the database implementation is still mostly handled as a human decision-making activity. Out of the phases of design described in this chapter, one area where there is hardly any commercial tool support is view integration (see Section 10.2.2).
We will not survey database design tools here, but only mention the following char- acteristics that a good design tool should possess:
1. An easy-to-use interface. This is critical because it enables designers to focus on the task at hand, not on understanding the tool. Graphical and point-and-click interfaces are commonly used. A few tools like the SECSI design tool use natural language input. Different interfaces may be tailored to beginners or to expert designers.
2. Analytical components. Tools should provide analytical components for tasks that are difficult to perform manually, such as evaluating physical design alternatives or detecting conflicting constraints among views. This area is weak in most current tools.
3. Heuristic components. Aspects of the design that cannot be precisely quantified can be automated by entering heuristic rules in the design tool to evaluate design alternatives.
10We showed the ER, EER, and UML class diagram notations in Chapters 7 and 8. See Appendix A for an idea of the different types of diagrammatic notations used.
10.6 Summary 345
4. Trade-off analysis. A tool should present the designer with adequate com- parative analysis whenever it presents multiple alternatives to choose from. Tools should ideally incorporate an analysis of a design change at the con- ceptual design level down to physical design. Because of the many alterna- tives possible for physical design in a given system, such tradeoff analysis is difficult to carry out and most current tools avoid it.
5. Display of design results. Design results, such as schemas, are often dis- played in diagrammatic form. Aesthetically pleasing and well laid out dia- grams are not easy to generate automatically. Multipage design layouts that are easy to read are another challenge. Other types of results of design may be shown as tables, lists, or reports that should be easy to interpret.
6. Design verification. This is a highly desirable feature. Its purpose is to ver- ify that the resulting design satisfies the initial requirements. Unless the requirements are captured and internally represented in some analyzable form, the verification cannot be attempted.
Currently there is increasing awareness of the value of design tools, and they are becoming a must for dealing with large database design problems. There is also an increasing awareness that schema design and application design should go hand in hand, and the current trend among CASE tools is to address both areas. The popu- larity of tools such as Rational Rose is due to the fact that it approaches the two arms of the design process shown in Figure 10.1 concurrently, approaching database design and application design as a unified activity. After the acquisition of Rational by IBM in 2003, the Rational suite of tools have been enhanced as XDE (extended development environment) tools. Some vendors like Platinum (CA) provide a tool for data modeling and schema design (ERwin), and another for process modeling and functional design (BPwin). Other tools (for example, SECSI) use expert system technology to guide the design process by including design expertise in the form of rules. Expert system technology is also useful in the requirements collection and analysis phase, which is typically a laborious and frustrating process. The trend is to use both meta-data repositories and design tools to achieve better designs for com- plex databases. Without a claim of being exhaustive, Table 10.1 lists some popular database design and application modeling tools. Companies in the table are listed alphabetically.
10.6 Summary We started this chapter by discussing the role of information systems in organiza- tions; database systems are looked upon as a part of information systems in large- scale applications. We discussed how databases fit within an information system for information resource management in an organization and the life cycle they go through. Then we discussed the six phases of the design process. The three phases commonly included as a part of database design are conceptual design, logical design (data model mapping), and physical design. We also discussed the initial phase of requirements collection and analysis, which is often considered to be a predesign phase. Additionally, at some point during the design, a specific DBMS
346 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
package must be chosen. We discussed some of the organizational criteria that come into play in selecting a DBMS. As performance problems are detected, and as new applications are added, designs have to be modified. The importance of designing both the schema and the applications (or transactions) was highlighted. We dis- cussed different approaches to conceptual schema design and the difference between centralized schema design and the view integration approach.
We introduced UML diagrams as an aid to the specification of database models and designs. We presented the entire range of structural and behavioral diagrams and then we described the notational detail about the following types of diagrams: use case, sequence, and statechart. (Class diagrams have already been discussed in Sections 7.8 and 8.6, respectively.) We showed how a few requirements for the UNI- VERSITY database are specified using these diagrams and can be used to develop the conceptual design of the database. Only illustrative details and not the complete specification were supplied. Then we discussed a specific software development tool—Rational Rose and the Rose Data Modeler—that provides support for the conceptual design and logical design phases of database design. Rose is a much broader tool for design of information systems at large. Finally, we briefly discussed the functionality and desirable features of commercial automated database design tools that are more focused on database design as opposed to Rose. A tabular sum- mary of features was presented.
Table 10.1 Some of the Currently Available Automated Database Design Tools
Company Tool Functionality
Embarcadero ER/Studio Database modeling in ER and IDEF1x Technologies DBArtisan Database administration and space and
security management
Oracle Developer 2000 and Database modeling, application Designer 2000 development
Persistence Inc. PowerTier Mapping from O-O to relational model
Platinum Technology Platinum ModelMart, Data, process, and business component (Computer Associates) ERwin, BPwin, AllFusion modeling
Component Modeler
Popkin Software Telelogic System Architect Data modeling, object modeling, process modeling, structured analysis/design
Rational (IBM) Rational Rose Modeling in UML and application XDE Developer Plus generation in C++ and Java
Resolution Ltd. XCase Conceptual modeling up to code maintenance
Sybase Enterprise Application Suite Data modeling, business logic modeling
Visio Visio Enterprise Data modeling, design and reengineering Visual Basic and Visual C++
Review Questions 10.1. What are the six phases of database design? Discuss each phase.
10.2. Which of the six phases are considered the main activities of the database design process itself? Why?
10.3. Why is it important to design the schemas and applications in parallel?
10.4. Why is it important to use an implementation-independent data model dur- ing conceptual schema design? What models are used in current design tools? Why?
10.5. Discuss the importance of requirements collection and analysis.
10.6. Consider an actual application of a database system of interest. Define the requirements of the different levels of users in terms of data needed, types of queries, and transactions to be processed.
10.7. Discuss the characteristics that a data model for conceptual schema design should possess.
10.8. Compare and contrast the two main approaches to conceptual schema design.
10.9. Discuss the strategies for designing a single conceptual schema from its requirements.
10.10. What are the steps of the view integration approach to conceptual schema design? What are the difficulties during each step?
10.11. How would a view integration tool work? Design a sample modular architec- ture for such a tool.
10.12. What are the different strategies for view integration?
10.13. Discuss the factors that influence the choice of a DBMS package for the information system of an organization.
10.14. What is system-independent data model mapping? How is it different from system-dependent data model mapping?
10.15. What are the important factors that influence physical database design?
10.16. Discuss the decisions made during physical database design.
10.17. Discuss the macro and micro life cycles of an information system.
10.18. Discuss the guidelines for physical database design in RDBMSs.
10.19. Discuss the types of modifications that may be applied to the logical data- base design of a relational database.
10.20. What functions do the typical database design tools provide?
10.21. What type of functionality would be desirable in automated tools to support optimal design of large databases?
Review Questions 347
348 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
10.22. What are the current relational DBMSs that dominate the market? Choose one that you are familiar with and show how it measures up based on the cri- teria laid out in Section 10.2.3?
10.23. A possible DDL corresponding to Figure 3.1 follows:
CREATE TABLE STUDENT ( Name VARCHAR(30) NOT NULL, Ssn CHAR(9) PRIMARY KEY, Home_phone VARCHAR(14), Address VARCHAR(40), Office_phone VARCHAR(14), Age INT, Gpa DECIMAL(4,3)
);
Discuss the following detailed design decisions:
a. The choice of requiring Name to be NON NULL b. Selection of Ssn as the PRIMARY KEY c. Choice of field sizes and precision d. Any modification of the fields defined in this database e. Any constraints on individual fields
10.24. What naming conventions can you develop to help identify foreign keys more efficiently?
10.25. What functions do the typical database design tools provide?
Selected Bibliography There is a vast amount of literature on database design. First we list some of the books that address database design. Batini et al. (1992) is a comprehensive treat- ment of conceptual and logical database design. Wiederhold (1987) covers all phases of database design, with an emphasis on physical design. O’Neil (1994) has a detailed discussion of physical design and transaction issues in reference to com- mercial RDBMSs. A large body of work on conceptual modeling and design was done in the 1980s. Brodie et al. (1984) gives a collection of chapters on conceptual modeling, constraint specification and analysis, and transaction design. Yao (1985) is a collection of works ranging from requirements specification techniques to schema restructuring. Teorey (1998) emphasizes EER modeling and discusses vari- ous aspects of conceptual and logical database design. Hoffer et al. (2009) is a good introduction to the business applications issues of database management.
Navathe and Kerschberg (1986) discuss all phases of database design and point out the role of data dictionaries. Goldfine and Konig (1988) and ANSI (1989) discuss the role of data dictionaries in database design. Rozen and Shasha (1991) and Carlis and March (1984) present different models for the problem of physical database design. Object-oriented analysis and design is discussed in Schlaer and Mellor
Selected Bibliography 349
(1988), Rumbaugh et al. (1991), Martin and Odell (1991), and Jacobson et al. (1992). Recent books by Blaha and Rumbaugh (2005) and Martin and Odell (2008) consolidate the existing techniques in object-oriented analysis and design using UML. Fowler and Scott (2000) is a quick introduction to UML. For a comprehen- sive treatment of UML and its use in the software development process, consult Jacobson et al. (1999) and Rumbaugh et al. (1999).
Requirements collection and analysis is a heavily researched topic. Chatzoglu et al. (1997) and Lubars et al. (1993) present surveys of current practices in requirements capture, modeling, and analysis. Carroll (1995) provides a set of readings on the use of scenarios for requirements gathering in early stages of system development. Wood and Silver (1989) gives a good overview of the official Joint Application Design (JAD) process. Potter et al. (1991) describes the Z-notation and methodol- ogy for formal specification of software. Zave (1997) has classified the research efforts in requirements engineering.
A large body of work has been produced on the problems of schema and view inte- gration, which is becoming particularly relevant now because of the need to inte- grate a variety of existing databases. Navathe and Gadgil (1982) defined approaches to view integration. Schema integration methodologies are compared in Batini et al. (1987). Detailed work on n-ary view integration can be found in Navathe et al. (1986), Elmasri et al. (1986), and Larson et al. (1989). An integration tool based on Elmasri et al. (1986) is described in Sheth et al. (1988). Another view integration system is discussed in Hayne and Ram (1990). Casanova et al. (1991) describes a tool for modular database design. Motro (1987) discusses integration with respect to preexisting databases. The binary balanced strategy to view integration is dis- cussed in Teorey and Fry (1982). A formal approach to view integration, which uses inclusion dependencies, is given in Casanova and Vidal (1982). Ramesh and Ram (1997) describe a methodology for integration of relationships in schemas utilizing the knowledge of integrity constraints; this extends the previous work of Navathe et al. (1984a). Sheth at al. (1993) describe the issues of building global schemas by rea- soning about attribute relationships and entity equivalences. Navathe and Savasere (1996) describe a practical approach to building global schemas based on operators applied to schema components. Santucci (1998) provides a detailed treatment of refinement of EER schemas for integration. Castano et al. (1998) present a compre- hensive survey of conceptual schema analysis techniques.
Transaction design is a relatively less thoroughly researched topic. Mylopoulos et al. (1980) proposed the TAXIS language, and Albano et al. (1985) developed the GALILEO system, both of which are comprehensive systems for specifying transac- tions. The GORDAS language for the ECR model (Elmasri et al. 1985) contains a transaction specification capability. Navathe and Balaraman (1991) and Ngu (1989) discuss transaction modeling in general for semantic data models. Elmagarmid (1992) discusses transaction models for advanced applications. Batini et al. (1992, Chapters 8, 9, and 11) discuss high-level transaction design and joint analysis of data and functions. Shasha (1992) is an excellent source on database tuning.
350 Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
Information about some well-known commercial database design tools can be found at the Websites of the vendors (see company names in Table 10.1). Principles behind automated design tools are discussed in Batini et al. (1992, Chapter 15). The SECSI tool is described in Metais et al. (1998). DKE (1997) is a special issue on nat- ural language issues in databases.
part 4 Object, Object-Relational, and
XML: Concepts, Models, Languages, and Standards
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353
Object and Object-Relational Databases
In this chapter, we discuss the features of object-oriented data models and show how some of these features have been incorporated in relational database systems. Object-oriented databases are now referred to as object databases (ODB) (previously called OODB), and the database systems are referred to as object data management sys- tems (ODMS) (formerly referred to as ODBMS or OODBMS). Traditional data models and systems, such as relational, network, and hierarchical, have been quite successful in developing the database technologies required for many traditional business database applications. However, they have certain shortcomings when more complex database applications must be designed and implemented—for example, databases for engineering design and manufacturing (CAD/CAM and CIM1), scientific experiments, telecommunications, geographic information sys- tems, and multimedia.2 These newer applications have requirements and character- istics that differ from those of traditional business applications, such as more complex structures for stored objects; the need for new data types for storing images, videos, or large textual items; longer-duration transactions; and the need to define nonstandard application-specific operations. Object databases were pro- posed to meet some of the needs of these more complex applications. A key feature of object databases is the power they give the designer to specify both the structure of complex objects and the operations that can be applied to these objects.
11chapter 11
2Multimedia databases must store various types of multimedia objects, such as video, audio, images, graphics, and documents (see Chapter 26).
1Computer-aided design/computer-aided manufacturing and computer-integrated manufacturing.
354 Chapter 11 Object and Object-Relational Databases
Another reason for the creation of object-oriented databases is the vast increase in the use of object-oriented programming languages for developing software applica- tions. Databases are fundamental components in many software systems, and tradi- tional databases are sometimes difficult to use with software applications that are developed in an object-oriented programming language such as C++ or Java. Object databases are designed so they can be directly—or seamlessly—integrated with software that is developed using object-oriented programming languages.
Relational DBMS (RDBMS) vendors have also recognized the need for incorporat- ing features that were proposed for object databases, and newer versions of rela- tional systems have incorporated many of these features. This has led to database systems that are characterized as object-relational or ORDBMSs. The latest version of the SQL standard (2008) for RDBMSs includes many of these features, which were originally known as SQL/Object and they have now been merged into the main SQL specification, known as SQL/Foundation.
Although many experimental prototypes and commercial object-oriented database systems have been created, they have not found widespread use because of the pop- ularity of relational and object-relational systems. The experimental prototypes included the Orion system developed at MCC,3 OpenOODB at Texas Instruments, the Iris system at Hewlett-Packard laboratories, the Ode system at AT&T Bell Labs,4
and the ENCORE/ObServer project at Brown University. Commercially available systems included GemStone Object Server of GemStone Systems, ONTOS DB of Ontos, Objectivity/DB of Objectivity Inc., Versant Object Database and FastObjects by Versant Corporation (and Poet), ObjectStore of Object Design, and Ardent Database of Ardent.5 These represent only a partial list of the experimental proto- types and commercial object-oriented database systems that were created.
As commercial object DBMSs became available, the need for a standard model and language was recognized. Because the formal procedure for approval of standards normally takes a number of years, a consortium of object DBMS vendors and users, called ODMG,6 proposed a standard whose current specification is known as the ODMG 3.0 standard.
Object-oriented databases have adopted many of the concepts that were developed originally for object-oriented programming languages.7 In Section 11.1, we describe the key concepts utilized in many object database systems and that were later incor- porated into object-relational systems and the SQL standard. These include object identity, object structure and type constructors, encapsulation of operations and the definition of methods as part of class declarations, mechanisms for storing objects in
3Microelectronics and Computer Technology Corporation, Austin, Texas. 4Now called Lucent Technologies. 5Formerly O2 of O2 Technology. 6Object Data Management Group. 7Similar concepts were also developed in the fields of semantic data modeling and knowledge represen- tation.
11.1 Overview of Object Database Concepts 355
a database by making them persistent, and type and class hierarchies and inheritance. Then, in Section 11.2 we see how these concepts have been incorporated into the latest SQL standards, leading to object-relational databases. Object features were originally introduced in SQL:1999, and then updated in the latest version (SQL:2008) of the standard. In Section 11.3, we turn our attention to “pure” object database standards by presenting features of the object database standard ODMG 3.0 and the object definition language ODL. Section 11.4 presents an overview of the database design process for object databases. Section 11.5 discusses the object query language (OQL), which is part of the ODMG 3.0 standard. In Section 11.6, we discuss programming language bindings, which specify how to extend object- oriented programming languages to include the features of the object database standard. Section 11.7 summarizes the chapter. Sections 11.5 and 11.6 may be left out if a less thorough introduction to object databases is desired.
11.1 Overview of Object Database Concepts
11.1.1 Introduction to Object-Oriented Concepts and Features The term object-oriented—abbreviated OO or O-O—has its origins in OO pro- gramming languages, or OOPLs. Today OO concepts are applied in the areas of databases, software engineering, knowledge bases, artificial intelligence, and com- puter systems in general. OOPLs have their roots in the SIMULA language, which was proposed in the late 1960s. The programming language Smalltalk, developed at Xerox PARC8 in the 1970s, was one of the first languages to explicitly incorporate additional OO concepts, such as message passing and inheritance. It is known as a pure OO programming language, meaning that it was explicitly designed to be object-oriented. This contrasts with hybrid OO programming languages, which incorporate OO concepts into an already existing language. An example of the latter is C++, which incorporates OO concepts into the popular C programming language.
An object typically has two components: state (value) and behavior (operations). It can have a complex data structure as well as specific operations defined by the pro- grammer.9 Objects in an OOPL exist only during program execution; therefore, they are called transient objects. An OO database can extend the existence of objects so that they are stored permanently in a database, and hence the objects become persistent objects that exist beyond program termination and can be retrieved later and shared by other programs. In other words, OO databases store persistent objects permanently in secondary storage, and allow the sharing of these objects among multiple programs and applications. This requires the incorporation of other well-known features of database management systems, such as indexing mechanisms to efficiently locate the objects, concurrency control to allow object
8Palo Alto Research Center, Palo Alto, California. 9Objects have many other characteristics, as we discuss in the rest of this chapter.
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sharing among concurrent programs, and recovery from failures. An OO database system will typically interface with one or more OO programming languages to provide persistent and shared object capabilities.
The internal structure of an object in OOPLs includes the specification of instance variables, which hold the values that define the internal state of the object. An instance variable is similar to the concept of an attribute in the relational model, except that instance variables may be encapsulated within the object and thus are not necessarily visible to external users. Instance variables may also be of arbitrarily complex data types. Object-oriented systems allow definition of the operations or functions (behavior) that can be applied to objects of a particular type. In fact, some OO models insist that all operations a user can apply to an object must be prede- fined. This forces a complete encapsulation of objects. This rigid approach has been relaxed in most OO data models for two reasons. First, database users often need to know the attribute names so they can specify selection conditions on the attributes to retrieve specific objects. Second, complete encapsulation implies that any simple retrieval requires a predefined operation, thus making ad hoc queries difficult to specify on the fly.
To encourage encapsulation, an operation is defined in two parts. The first part, called the signature or interface of the operation, specifies the operation name and arguments (or parameters). The second part, called the method or body, specifies the implementation of the operation, usually written in some general-purpose pro- gramming language. Operations can be invoked by passing a message to an object, which includes the operation name and the parameters. The object then executes the method for that operation. This encapsulation permits modification of the internal structure of an object, as well as the implementation of its operations, with- out the need to disturb the external programs that invoke these operations. Hence, encapsulation provides a form of data and operation independence (see Chapter 2).
Another key concept in OO systems is that of type and class hierarchies and inheritance. This permits specification of new types or classes that inherit much of their structure and/or operations from previously defined types or classes. This makes it easier to develop the data types of a system incrementally, and to reuse existing type definitions when creating new types of objects.
One problem in early OO database systems involved representing relationships among objects. The insistence on complete encapsulation in early OO data models led to the argument that relationships should not be explicitly represented, but should instead be described by defining appropriate methods that locate related objects. However, this approach does not work very well for complex databases with many relationships because it is useful to identify these relationships and make them visible to users. The ODMG object database standard has recognized this need and it explicitly represents binary relationships via a pair of inverse references, as we will describe in Section 11.3.
Another OO concept is operator overloading, which refers to an operation’s ability to be applied to different types of objects; in such a situation, an operation name may refer to several distinct implementations, depending on the type of object it is
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applied to. This feature is also called operator polymorphism. For example, an opera- tion to calculate the area of a geometric object may differ in its method (implemen- tation), depending on whether the object is of type triangle, circle, or rectangle. This may require the use of late binding of the operation name to the appropriate method at runtime, when the type of object to which the operation is applied becomes known.
In the next several sections, we discuss in some detail the main characteristics of object databases. Section 11.1.2 discusses object identity; Section 11.1.3 shows how the types for complex-structured objects are specified via type constructors; Section 11.1.4 discusses encapsulation and persistence; and Section 11.1.5 presents inheri- tance concepts. Section 11.1.6 discusses some additional OO concepts, and Section 11.1.7 gives a summary of all the OO concepts that we introduced. In Section 11.2, we show how some of these concepts have been incorporated into the SQL:2008 standard for relational databases. Then in Section 11.3, we show how these concepts are realized in the ODMG 3.0 object database standard.
11.1.2 Object Identity, and Objects versus Literals One goal of an ODMS (Object Data Management System) is to maintain a direct correspondence between real-world and database objects so that objects do not lose their integrity and identity and can easily be identified and operated upon. Hence, an ODMS provides a unique identity to each independent object stored in the data- base. This unique identity is typically implemented via a unique, system-generated object identifier (OID). The value of an OID is not visible to the external user, but is used internally by the system to identify each object uniquely and to create and manage inter-object references. The OID can be assigned to program variables of the appropriate type when needed.
The main property required of an OID is that it be immutable; that is, the OID value of a particular object should not change. This preserves the identity of the real-world object being represented. Hence, an ODMS must have some mechanism for generating OIDs and preserving the immutability property. It is also desirable that each OID be used only once; that is, even if an object is removed from the data- base, its OID should not be assigned to another object. These two properties imply that the OID should not depend on any attribute values of the object, since the value of an attribute may be changed or corrected. We can compare this with the relational model, where each relation must have a primary key attribute whose value identifies each tuple uniquely. In the relational model, if the value of the pri- mary key is changed, the tuple will have a new identity, even though it may still rep- resent the same real-world object. Alternatively, a real-world object may have different names for key attributes in different relations, making it difficult to ascer- tain that the keys represent the same real-world object (for example, the object identifier may be represented as Emp_id in one relation and as Ssn in another).
It is inappropriate to base the OID on the physical address of the object in storage, since the physical address can change after a physical reorganization of the database. However, some early ODMSs have used the physical address as the OID to increase
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the efficiency of object retrieval. If the physical address of the object changes, an indirect pointer can be placed at the former address, which gives the new physical location of the object. It is more common to use long integers as OIDs and then to use some form of hash table to map the OID value to the current physical address of the object in storage.
Some early OO data models required that everything—from a simple value to a complex object—was represented as an object; hence, every basic value, such as an integer, string, or Boolean value, has an OID. This allows two identical basic values to have different OIDs, which can be useful in some cases. For example, the integer value 50 can sometimes be used to mean a weight in kilograms and at other times to mean the age of a person. Then, two basic objects with distinct OIDs could be cre- ated, but both objects would represent the integer value 50. Although useful as a theoretical model, this is not very practical, since it leads to the generation of too many OIDs. Hence, most OO database systems allow for the representation of both objects and literals (or values). Every object must have an immutable OID, whereas a literal value has no OID and its value just stands for itself. Thus, a literal value is typically stored within an object and cannot be referenced from other objects. In many systems, complex structured literal values can also be created without having a corresponding OID if needed.
11.1.3 Complex Type Structures for Objects and Literals Another feature of an ODMS (and ODBs in general) is that objects and literals may have a type structure of arbitrary complexity in order to contain all of the necessary information that describes the object or literal. In contrast, in traditional database systems, information about a complex object is often scattered over many relations or records, leading to loss of direct correspondence between a real-world object and its database representation. In ODBs, a complex type may be constructed from other types by nesting of type constructors. The three most basic constructors are atom, struct (or tuple), and collection.
1. One type constructor has been called the atom constructor, although this term is not used in the latest object standard. This includes the basic built-in data types of the object model, which are similar to the basic types in many programming languages: integers, strings, floating point numbers, enumer- ated types, Booleans, and so on. They are called single-valued or atomic types, since each value of the type is considered an atomic (indivisible) sin- gle value.
2. A second type constructor is referred to as the struct (or tuple) constructor. This can create standard structured types, such as the tuples (record types) in the basic relational model. A structured type is made up of several compo- nents, and is also sometimes referred to as a compound or composite type. More accurately, the struct constructor is not considered to be a type, but rather a type generator, because many different structured types can be cre- ated. For example, two different structured types that can be created are: struct Name<FirstName: string, MiddleInitial: char, LastName: string>, and
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struct CollegeDegree<Major: string, Degree: string, Year: date>. To create complex nested type structures in the object model, the collection type con- structors are needed, which we discuss next. Notice that the type construc- tors atom and struct are the only ones available in the original (basic) relational model.
3. Collection (or multivalued) type constructors include the set(T), list(T), bag(T), array(T), and dictionary(K,T) type constructors. These allow part of an object or literal value to include a collection of other objects or values when needed. These constructors are also considered to be type generators because many different types can be created. For example, set(string), set(integer), and set(Employee) are three different types that can be created from the set type constructor. All the elements in a particular collection value must be of the same type. For example, all values in a collection of type set(string) must be string values.
The atom constructor is used to represent all basic atomic values, such as integers, real numbers, character strings, Booleans, and any other basic data types that the system supports directly. The tuple constructor can create structured values and objects of the form <a1:i1, a2:i2, ..., an:in>, where each aj is an attribute name
10 and each ij is a value or an OID.
The other commonly used constructors are collectively referred to as collection types, but have individual differences among them. The set constructor will create objects or literals that are a set of distinct elements {i1, i2, ..., in}, all of the same type. The bag constructor (sometimes called a multiset) is similar to a set except that the elements in a bag need not be distinct. The list constructor will create an ordered list [i1, i2, ..., in] of OIDs or values of the same type. A list is similar to a bag except that the elements in a list are ordered, and hence we can refer to the first, second, or jth element. The array constructor creates a single-dimensional array of elements of the same type. The main difference between array and list is that a list can have an arbitrary number of elements whereas an array typically has a maximum size. Finally, the dictionary constructor creates a collection of two tuples (K, V), where the value of a key K can be used to retrieve the corresponding value V.
The main characteristic of a collection type is that its objects or values will be a collection of objects or values of the same type that may be unordered (such as a set or a bag) or ordered (such as a list or an array). The tuple type constructor is often called a structured type, since it corresponds to the struct construct in the C and C++ programming languages.
An object definition language (ODL)11 that incorporates the preceding type con- structors can be used to define the object types for a particular database application. In Section 11.3 we will describe the standard ODL of ODMG, but first we introduce
10Also called an instance variable name in OO terminology. 11This corresponds to the DDL (data definition language) of the database system (see Chapter 2).
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Figure 11.1 Specifying the object types EMPLOYEE, DATE, and DEPARTMENT using type constructors.
the concepts gradually in this section using a simpler notation. The type construc- tors can be used to define the data structures for an OO database schema. Figure 11.1 shows how we may declare EMPLOYEE and DEPARTMENT types.
In Figure 11.1, the attributes that refer to other objects—such as Dept of EMPLOYEE or Projects of DEPARTMENT—are basically OIDs that serve as references to other objects to represent relationships among the objects. For example, the attribute Dept of EMPLOYEE is of type DEPARTMENT, and hence is used to refer to a specific DEPARTMENT object (the DEPARTMENT object where the employee works). The value of such an attribute would be an OID for a specific DEPARTMENT object. A binary relationship can be represented in one direction, or it can have an inverse ref- erence. The latter representation makes it easy to traverse the relationship in both directions. For example, in Figure 11.1 the attribute Employees of DEPARTMENT has as its value a set of references (that is, a set of OIDs) to objects of type EMPLOYEE; these are the employees who work for the DEPARTMENT. The inverse is the reference attribute Dept of EMPLOYEE. We will see in Section 11.3 how the ODMG standard allows inverses to be explicitly declared as relationship attributes to ensure that inverse references are consistent.
define type EMPLOYEE tuple ( Fname: string;
Minit: char; Lname: string; Ssn: string; Birth_date: DATE; Address: string; Sex: char; Salary: float; Supervisor: EMPLOYEE; Dept: DEPARTMENT;
define type DATE tuple ( Year: integer;
Month: integer; Day: integer; );
define type DEPARTMENT tuple ( Dname: string;
Dnumber: integer; Mgr: tuple ( Manager: EMPLOYEE;
Start_date: DATE; ); Locations: set(string); Employees: set(EMPLOYEE); Projects: set(PROJECT); );
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11.1.4 Encapsulation of Operations and Persistence of Objects
Encapsulation of Operations. The concept of encapsulation is one of the main characteristics of OO languages and systems. It is also related to the concepts of abstract data types and information hiding in programming languages. In traditional database models and systems this concept was not applied, since it is customary to make the structure of database objects visible to users and external programs. In these traditional models, a number of generic database operations are applicable to objects of all types. For example, in the relational model, the operations for selecting, inserting, deleting, and modifying tuples are generic and may be applied to any rela- tion in the database. The relation and its attributes are visible to users and to exter- nal programs that access the relation by using these operations. The concepts of encapsulation is applied to database objects in ODBs by defining the behavior of a type of object based on the operations that can be externally applied to objects of that type. Some operations may be used to create (insert) or destroy (delete) objects; other operations may update the object state; and others may be used to retrieve parts of the object state or to apply some calculations. Still other operations may perform a combination of retrieval, calculation, and update. In general, the implementation of an operation can be specified in a general-purpose programming language that provides flexibility and power in defining the operations.
The external users of the object are only made aware of the interface of the opera- tions, which defines the name and arguments (parameters) of each operation. The implementation is hidden from the external users; it includes the definition of any hidden internal data structures of the object and the implementation of the opera- tions that access these structures. The interface part of an operation is sometimes called the signature, and the operation implementation is sometimes called the method.
For database applications, the requirement that all objects be completely encapsu- lated is too stringent. One way to relax this requirement is to divide the structure of an object into visible and hidden attributes (instance variables). Visible attributes can be seen by and are directly accessible to the database users and programmers via the query language. The hidden attributes of an object are completely encapsulated and can be accessed only through predefined operations. Most ODMSs employ high-level query languages for accessing visible attributes. In Section 11.5 we will describe the OQL query language that is proposed as a standard query language for ODBs.
The term class is often used to refer to a type definition, along with the definitions of the operations for that type.12 Figure 11.2 shows how the type definitions in Figure 11.1 can be extended with operations to define classes. A number of operations are
12This definition of class is similar to how it is used in the popular C++ programming language. The ODMG standard uses the word interface in addition to class (see Section 11.3). In the EER model, the term class was used to refer to an object type, along with the set of all objects of that type (see Chapter 8).
Figure 11.2 Adding operations to the definitions of EMPLOYEE and DEPARTMENT.
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declared for each class, and the signature (interface) of each operation is included in the class definition. A method (implementation) for each operation must be defined elsewhere using a programming language. Typical operations include the object con- structor operation (often called new), which is used to create a new object, and the destructor operation, which is used to destroy (delete) an object. A number of object modifier operations can also be declared to modify the states (values) of various attributes of an object. Additional operations can retrieve information about the object.
An operation is typically applied to an object by using the dot notation. For exam- ple, if d is a reference to a DEPARTMENT object, we can invoke an operation such as no_of_emps by writing d.no_of_emps. Similarly, by writing d.destroy_dept, the object
define class EMPLOYEE type tuple ( Fname: string;
Minit: char; Lname: string; Ssn: string; Birth_date: DATE; Address: string; Sex: char; Salary: float; Supervisor: EMPLOYEE; Dept: DEPARTMENT; );
operations age: integer; create_emp: EMPLOYEE; destroy_emp: boolean;
end EMPLOYEE;
define class DEPARTMENT type tuple ( Dname: string;
Dnumber: integer; Mgr: tuple ( Manager: EMPLOYEE;
Start_date: DATE; ); Locations: set (string); Employees: set (EMPLOYEE); Projects set(PROJECT); );
operations no_of_emps: integer; create_dept: DEPARTMENT; destroy_dept: boolean; assign_emp(e: EMPLOYEE): boolean; (* adds an employee to the department *) remove_emp(e: EMPLOYEE): boolean; (* removes an employee from the department *)
end DEPARTMENT;
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referenced by d is destroyed (deleted). The only exception is the constructor opera- tion, which returns a reference to a new DEPARTMENT object. Hence, it is customary in some OO models to have a default name for the constructor operation that is the name of the class itself, although this was not used in Figure 11.2.13 The dot nota- tion is also used to refer to attributes of an object—for example, by writing d.Dnumber or d.Mgr_Start_date.
Specifying Object Persistence via Naming and Reachability. An ODBS is often closely coupled with an object-oriented programming language (OOPL). The OOPL is used to specify the method (operation) implementations as well as other application code. Not all objects are meant to be stored permanently in the data- base. Transient objects exist in the executing program and disappear once the pro- gram terminates. Persistent objects are stored in the database and persist after program termination. The typical mechanisms for making an object persistent are naming and reachability.
The naming mechanism involves giving an object a unique persistent name within a particular database. This persistent object name can be given via a specific state- ment or operation in the program, as shown in Figure 11.3. The named persistent objects are used as entry points to the database through which users and applica- tions can start their database access. Obviously, it is not practical to give names to all objects in a large database that includes thousands of objects, so most objects are made persistent by using the second mechanism, called reachability. The reachabil- ity mechanism works by making the object reachable from some other persistent object. An object B is said to be reachable from an object A if a sequence of refer- ences in the database lead from object A to object B.
If we first create a named persistent object N, whose state is a set (or possibly a bag) of objects of some class C, we can make objects of C persistent by adding them to the set, thus making them reachable from N. Hence, N is a named object that defines a persistent collection of objects of class C. In the object model standard, N is called the extent of C (see Section 11.3).
For example, we can define a class DEPARTMENT_SET (see Figure 11.3) whose objects are of type set(DEPARTMENT).14 We can create an object of type DEPARTMENT_SET, and give it a persistent name ALL_DEPARTMENTS, as shown in Figure 11.3. Any DEPARTMENT object that is added to the set of ALL_DEPARTMENTS by using the add_dept operation becomes persistent by virtue of its being reachable from ALL_DEPARTMENTS. As we will see in Section 11.3, the ODMG ODL standard gives the schema designer the option of naming an extent as part of class definition.
Notice the difference between traditional database models and ODBs in this respect.
13Default names for the constructor and destructor operations exist in the C++ programming language. For example, for class EMPLOYEE, the default constructor name is EMPLOYEE and the default destruc- tor name is ~EMPLOYEE. It is also common to use the new operation to create new objects. 14As we will see in Section 11.3, the ODMG ODL syntax uses set<DEPARTMENT> instead of set(DEPARTMENT).
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Figure 11.3 Creating persistent objects by naming and reachability.
define class DEPARTMENT_SET type set (DEPARTMENT); operations add_dept(d: DEPARTMENT): boolean;
(* adds a department to the DEPARTMENT_SET object *) remove_dept(d: DEPARTMENT): boolean;
(* removes a department from the DEPARTMENT_SET object *) create_dept_set: DEPARTMENT_SET; destroy_dept_set: boolean;
end DEPARTMENT_SET; ... persistent name ALL_DEPARTMENTS: DEPARTMENT_SET; (* ALL_DEPARTMENTS is a persistent named object of type DEPARTMENT_SET *) ... d:= create_dept; (* create a new DEPARTMENT object in the variable d *) ... b:= ALL_DEPARTMENTS.add_dept(d); (* make d persistent by adding it to the persistent set ALL_DEPARTMENTS *)
In traditional database models, such as the relational model, all objects are assumed to be persistent. Hence, when a table such as EMPLOYEE is created in a relational database, it represents both the type declaration for EMPLOYEE and a persistent set of all EMPLOYEE records (tuples). In the OO approach, a class declaration of EMPLOYEE specifies only the type and operations for a class of objects. The user must separately define a persistent object of type set(EMPLOYEE) or bag(EMPLOYEE) whose value is the collection of references (OIDs) to all persistent EMPLOYEE objects, if this is desired, as shown in Figure 11.3.15 This allows transient and persistent objects to follow the same type and class declarations of the ODL and the OOPL. In general, it is possible to define several persistent collections for the same class definition, if desired.
11.1.5 Type Hierarchies and Inheritance
Simplified Model for Inheritance. Another main characteristic of ODBs is that they allow type hierarchies and inheritance. We use a simple OO model in this sec- tion—a model in which attributes and operations are treated uniformly—since both attributes and operations can be inherited. In Section 11.3, we will discuss the inheritance model of the ODMG standard, which differs from the model discussed here because it distinguishes between two types of inheritance. Inheritance allows the definition of new types based on other predefined types, leading to a type (or class) hierarchy.
15Some systems, such as POET, automatically create the extent for a class.
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A type is defined by assigning it a type name, and then defining a number of attrib- utes (instance variables) and operations (methods) for the type.16 In the simplified model we use in this section, the attributes and operations are together called functions, since attributes resemble functions with zero arguments. A function name can be used to refer to the value of an attribute or to refer to the resulting value of an operation (method). We use the term function to refer to both attrib- utes and operations, since they are treated similarly in a basic introduction to inher- itance.17
A type in its simplest form has a type name and a list of visible (public) functions. When specifying a type in this section, we use the following format, which does not specify arguments of functions, to simplify the discussion:
TYPE_NAME: function, function, ..., function
For example, a type that describes characteristics of a PERSON may be defined as follows:
PERSON: Name, Address, Birth_date, Age, Ssn
In the PERSON type, the Name, Address, Ssn, and Birth_date functions can be imple- mented as stored attributes, whereas the Age function can be implemented as an operation that calculates the Age from the value of the Birth_date attribute and the current date.
The concept of subtype is useful when the designer or user must create a new type that is similar but not identical to an already defined type. The subtype then inher- its all the functions of the predefined type, which is referred to as the supertype. For example, suppose that we want to define two new types EMPLOYEE and STUDENT as follows:
EMPLOYEE: Name, Address, Birth_date, Age, Ssn, Salary, Hire_date, Seniority STUDENT: Name, Address, Birth_date, Age, Ssn, Major, Gpa
Since both STUDENT and EMPLOYEE include all the functions defined for PERSON plus some additional functions of their own, we can declare them to be subtypes of PERSON. Each will inherit the previously defined functions of PERSON—namely, Name, Address, Birth_date, Age, and Ssn. For STUDENT, it is only necessary to define the new (local) functions Major and Gpa, which are not inherited. Presumably, Major can be defined as a stored attribute, whereas Gpa may be implemented as an opera- tion that calculates the student’s grade point average by accessing the Grade values that are internally stored (hidden) within each STUDENT object as hidden attributes. For EMPLOYEE, the Salary and Hire_date functions may be stored attributes, whereas Seniority may be an operation that calculates Seniority from the value of Hire_date.
16In this section we will use the terms type and class as meaning the same thing—namely, the attributes and operations of some type of object. 17We will see in Section 11.3 that types with functions are similar to the concept of interfaces as used in ODMG ODL.
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Therefore, we can declare EMPLOYEE and STUDENT as follows:
EMPLOYEE subtype-of PERSON: Salary, Hire_date, Seniority STUDENT subtype-of PERSON: Major, Gpa
In general, a subtype includes all of the functions that are defined for its supertype plus some additional functions that are specific only to the subtype. Hence, it is pos- sible to generate a type hierarchy to show the supertype/subtype relationships among all the types declared in the system.
As another example, consider a type that describes objects in plane geometry, which may be defined as follows:
GEOMETRY_OBJECT: Shape, Area, Reference_point
For the GEOMETRY_OBJECT type, Shape is implemented as an attribute (its domain can be an enumerated type with values ‘triangle’, ‘rectangle’, ‘circle’, and so on), and Area is a method that is applied to calculate the area. Reference_point speci- fies the coordinates of a point that determines the object location. Now suppose that we want to define a number of subtypes for the GEOMETRY_OBJECT type, as follows:
RECTANGLE subtype-of GEOMETRY_OBJECT: Width, Height TRIANGLE S subtype-of GEOMETRY_OBJECT: Side1, Side2, Angle CIRCLE subtype-of GEOMETRY_OBJECT: Radius
Notice that the Area operation may be implemented by a different method for each subtype, since the procedure for area calculation is different for rectangles, triangles, and circles. Similarly, the attribute Reference_point may have a different meaning for each subtype; it might be the center point for RECTANGLE and CIRCLE objects, and the vertex point between the two given sides for a TRIANGLE object.
Notice that type definitions describe objects but do not generate objects on their own. When an object is created, typically it belongs to one or more of these types that have been declared. For example, a circle object is of type CIRCLE and GEOMETRY_OBJECT (by inheritance). Each object also becomes a member of one or more persistent collections of objects (or extents), which are used to group together collections of objects that are persistently stored in the database.
Constraints on Extents Corresponding to a Type Hierarchy. In most ODBs, an extent is defined to store the collection of persistent objects for each type or sub- type. In this case, the constraint is that every object in an extent that corresponds to a subtype must also be a member of the extent that corresponds to its supertype. Some OO database systems have a predefined system type (called the ROOT class or the OBJECT class) whose extent contains all the objects in the system.18
Classification then proceeds by assigning objects into additional subtypes that are meaningful to the application, creating a type hierarchy (or class hierarchy) for the system. All extents for system- and user-defined classes are subsets of the extent cor-
18This is called OBJECT in the ODMG model (see Section 11.3).
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responding to the class OBJECT, directly or indirectly. In the ODMG model (see Section 11.3), the user may or may not specify an extent for each class (type), depending on the application.
An extent is a named persistent object whose value is a persistent collection that holds a collection of objects of the same type that are stored permanently in the database. The objects can be accessed and shared by multiple programs. It is also possible to create a transient collection, which exists temporarily during the execu- tion of a program but is not kept when the program terminates. For example, a transient collection may be created in a program to hold the result of a query that selects some objects from a persistent collection and copies those objects into the transient collection. The program can then manipulate the objects in the transient collection, and once the program terminates, the transient collection ceases to exist. In general, numerous collections—transient or persistent—may contain objects of the same type.
The inheritance model discussed in this section is very simple. As we will see in Section 11.3, the ODMG model distinguishes between type inheritance—called interface inheritance and denoted by a colon (:)—and the extent inheritance con- straint—denoted by the keyword EXTEND.
11.1.6 Other Object-Oriented Concepts
Polymorphism of Operations (Operator Overloading). Another characteris- tic of OO systems in general is that they provide for polymorphism of operations, which is also known as operator overloading. This concept allows the same operator name or symbol to be bound to two or more different implementations of the operator, depending on the type of objects to which the operator is applied. A simple example from programming languages can illustrate this concept. In some languages, the operator symbol “+” can mean different things when applied to operands (objects) of different types. If the operands of “+” are of type integer, the operation invoked is integer addition. If the operands of “+” are of type floating point, the operation invoked is floating point addition. If the operands of “+” are of type set, the operation invoked is set union. The compiler can determine which operation to execute based on the types of operands supplied.
In OO databases, a similar situation may occur. We can use the GEOMETRY_OBJECT example presented in Section 11.1.5 to illustrate operation polymorphism19 in ODB.
In this example, the function Area is declared for all objects of type GEOMETRY_OBJECT. However, the implementation of the method for Area may differ for each subtype of GEOMETRY_OBJECT. One possibility is to have a general implementation for calculating the area of a generalized GEOMETRY_OBJECT (for
19In programming languages, there are several kinds of polymorphism. The interested reader is referred to the Selected Bibliography at the end of this chapter for works that include a more thorough discus- sion.
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example, by writing a general algorithm to calculate the area of a polygon) and then to rewrite more efficient algorithms to calculate the areas of specific types of geo- metric objects, such as a circle, a rectangle, a triangle, and so on. In this case, the Area function is overloaded by different implementations.
The ODMS must now select the appropriate method for the Area function based on the type of geometric object to which it is applied. In strongly typed systems, this can be done at compile time, since the object types must be known. This is termed early (or static) binding. However, in systems with weak typing or no typing (such as Smalltalk and LISP), the type of the object to which a function is applied may not be known until runtime. In this case, the function must check the type of object at runtime and then invoke the appropriate method. This is often referred to as late (or dynamic) binding.
Multiple Inheritance and Selective Inheritance. Multiple inheritance occurs when a certain subtype T is a subtype of two (or more) types and hence inherits the functions (attributes and methods) of both supertypes. For example, we may create a subtype ENGINEERING_MANAGER that is a subtype of both MANAGER and ENGINEER. This leads to the creation of a type lattice rather than a type hierarchy. One problem that can occur with multiple inheritance is that the supertypes from which the subtype inherits may have distinct functions of the same name, creating an ambiguity. For example, both MANAGER and ENGINEER may have a function called Salary. If the Salary function is implemented by different methods in the MANAGER and ENGINEER supertypes, an ambiguity exists as to which of the two is inherited by the subtype ENGINEERING_MANAGER. It is possible, however, that both ENGINEER and MANAGER inherit Salary from the same supertype (such as EMPLOYEE) higher up in the lattice. The general rule is that if a function is inherited from some common supertype, then it is inherited only once. In such a case, there is no ambiguity; the problem only arises if the functions are distinct in the two supertypes.
There are several techniques for dealing with ambiguity in multiple inheritance. One solution is to have the system check for ambiguity when the subtype is created, and to let the user explicitly choose which function is to be inherited at this time. A second solution is to use some system default. A third solution is to disallow multi- ple inheritance altogether if name ambiguity occurs, instead forcing the user to change the name of one of the functions in one of the supertypes. Indeed, some OO systems do not permit multiple inheritance at all. In the object database standard (see Section 11.3), multiple inheritance is allowed for operation inheritance of interfaces, but is not allowed for EXTENDS inheritance of classes.
Selective inheritance occurs when a subtype inherits only some of the functions of a supertype. Other functions are not inherited. In this case, an EXCEPT clause may be used to list the functions in a supertype that are not to be inherited by the sub- type. The mechanism of selective inheritance is not typically provided in ODBs, but it is used more frequently in artificial intelligence applications.20
20In the ODMG model, type inheritance refers to inheritance of operations only, not attributes (see Section 11.3).
11.2 Object-Relational Features: Object Database Extensions to SQL 369
11.1.7 Summary of Object Database Concepts To conclude this section, we give a summary of the main concepts used in ODBs and object-relational systems:
■ Object identity. Objects have unique identities that are independent of their attribute values and are generated by the ODMS.
■ Type constructors. Complex object structures can be constructed by apply- ing in a nested manner a set of basic constructors, such as tuple, set, list, array, and bag.
■ Encapsulation of operations. Both the object structure and the operations that can be applied to individual objects are included in the type definitions.
■ Programming language compatibility. Both persistent and transient objects are handled seamlessly. Objects are made persistent by being reach- able from a persistent collection (extent) or by explicit naming.
■ Type hierarchies and inheritance. Object types can be specified by using a type hierarchy, which allows the inheritance of both attributes and methods (operations) of previously defined types. Multiple inheritance is allowed in some models.
■ Extents. All persistent objects of a particular type can be stored in an extent. Extents corresponding to a type hierarchy have set/subset constraints enforced on their collections of persistent objects.
■ Polymorphism and operator overloading. Operations and method names can be overloaded to apply to different object types with different imple- mentations.
In the following sections we show how these concepts are realized in the SQL stan- dard (Section 11.2) and the ODMG standard (Section 11.3).
11.2 Object-Relational Features: Object Database Extensions to SQL
We introduced SQL as the standard language for RDBMSs in Chapters 4 and 5. As we discussed, SQL was first specified by Chamberlin and Boyce (1974) and under- went enhancements and standardization in 1989 and 1992. The language continued its evolution with a new standard, initially called SQL3 while being developed, and later known as SQL:99 for the parts of SQL3 that were approved into the standard. Starting with the version of SQL known as SQL3, features from object databases were incorporated into the SQL standard. At first, these extensions were known as SQL/Object, but later they were incorporated in the main part of SQL, known as SQL/Foundation. We will use that latest standard, SQL:2008, in our presentation of the object features of SQL, even though this may not yet have been realized in com- mercial DBMSs that follow SQL. We will also discuss how the object features of SQL evolved to their latest manifestation in SQL:2008.
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The relational model with object database enhancements is sometimes referred to as the object-relational model. Additional revisions were made to SQL in 2003 and 2006 to add features related to XML (see Chapter 12).
The following are some of the object database features that have been included in SQL:
■ Some type constructors have been added to specify complex objects. These include the row type, which corresponds to the tuple (or struct) constructor. An array type for specifying collections is also provided. Other collection type constructors, such as set, list, and bag constructors, were not part of the original SQL/Object specifications but were later included in the standard.
■ A mechanism for specifying object identity through the use of reference type is included.
■ Encapsulation of operations is provided through the mechanism of user- defined types (UDTs) that may include operations as part of their declara- tion. These are somewhat similar to the concept of abstract data types that were developed in programming languages. In addition, the concept of user- defined routines (UDRs) allows the definition of general methods (opera- tions).
■ Inheritance mechanisms are provided using the keyword UNDER.
We now discuss each of these concepts in more detail. In our discussion, we will refer to the example in Figure 11.4.
11.2.1 User-Defined Types and Complex Structures for Objects To allow the creation of complex-structured objects, and to separate the declaration of a type from the creation of a table, SQL now provides user-defined types (UDTs). In addition, four collection types have been included to allow for multival- ued types and attributes in order to specify complex-structured objects rather than just simple (flat) records. The user will create the UDTs for a particular application as part of the database schema. A UDT may be specified in its simplest form using the following syntax:
CREATE TYPE TYPE_NAME AS (<component declarations>);
Figure 11.4 illustrates some of the object concepts in SQL. We will explain the exam- ples in this figure gradually as we explain the concepts. First, a UDT can be used as either the type for an attribute or as the type for a table. By using a UDT as the type for an attribute within another UDT, a complex structure for objects (tuples) in a table can be created, much like that achieved by nesting type constructors. This is similar to using the struct type constructor of Section 11.1.3. For example, in Figure 11.4(a), the UDT STREET_ADDR_TYPE is used as the type for the STREET_ADDR attribute in the UDT USA_ADDR_TYPE. Similarly, the UDT USA_ADDR_TYPE is in turn used as the type for the ADDR attribute in the UDT PERSON_TYPE in Figure 11.4(b). If a UDT does not have any operations, as in the examples in Figure 11.4(a), it is possible to use the concept of ROW TYPE to directly create a structured attribute
11.2 Object-Relational Features: Object Database Extensions to SQL 371
Figure 11.4 Illustrating some of the object features of SQL. (a) Using UDTs as types for attributes such as Address and Phone, (b) Specifying UDT for PERSON_TYPE, (c) Specifying UDTs for STUDENT_TYPE and EMPLOYEE_TYPE as two sub- types of PERSON_TYPE
(a) CREATE TYPE STREET_ADDR_TYPE AS ( NUMBER VARCHAR (5), STREET_NAME VARCHAR (25), APT_NO VARCHAR (5), SUITE_NO VARCHAR (5)
); CREATE TYPE USA_ADDR_TYPE AS (
STREET_ADDR STREET_ADDR_TYPE, CITY VARCHAR (25), ZIP VARCHAR (10)
); CREATE TYPE USA_PHONE_TYPE AS (
PHONE_TYPE VARCHAR (5), AREA_CODE CHAR (3), PHONE_NUM CHAR (7)
);
(b) CREATE TYPE PERSON_TYPE AS ( NAME VARCHAR (35), SEX CHAR, BIRTH_DATE DATE, PHONES USA_PHONE_TYPE ARRAY [4], ADDR USA_ADDR_TYPE
INSTANTIABLE NOT FINAL REF IS SYSTEM GENERATED INSTANCE METHOD AGE() RETURNS INTEGER; CREATE INSTANCE METHOD AGE() RETURNS INTEGER
FOR PERSON_TYPE BEGIN
RETURN /* CODE TO CALCULATE A PERSON’S AGE FROM TODAY’S DATE AND SELF.BIRTH_DATE */
END; );
(c) CREATE TYPE GRADE_TYPE AS ( COURSENO CHAR (8), SEMESTER VARCHAR (8), YEAR CHAR (4), GRADE CHAR
); CREATE TYPE STUDENT_TYPE UNDER PERSON_TYPE AS (
MAJOR_CODE CHAR (4), STUDENT_ID CHAR (12), DEGREE VARCHAR (5), TRANSCRIPT GRADE_TYPE ARRAY [100] (continues)
INSTANTIABLE NOT FINAL INSTANCE METHOD GPA() RETURNS FLOAT; CREATE INSTANCE METHOD GPA() RETURNS FLOAT
FOR STUDENT_TYPE BEGIN
RETURN /* CODE TO CALCULATE A STUDENT’S GPA FROM SELF.TRANSCRIPT */
END; ); CREATE TYPE EMPLOYEE_TYPE UNDER PERSON_TYPE AS (
JOB_CODE CHAR (4), SALARY FLOAT, SSN CHAR (11)
INSTANTIABLE NOT FINAL ); CREATE TYPE MANAGER_TYPE UNDER EMPLOYEE_TYPE AS (
DEPT_MANAGED CHAR (20) INSTANTIABLE );
(d) CREATE TABLE PERSON OF PERSON_TYPE REF IS PERSON_ID SYSTEM GENERATED;
CREATE TABLE EMPLOYEE OF EMPLOYEE_TYPE UNDER PERSON;
CREATE TABLE MANAGER OF MANAGER_TYPE UNDER EMPLOYEE;
CREATE TABLE STUDENT OF STUDENT_TYPE UNDER PERSON;
(e) CREATE TYPE COMPANY_TYPE AS ( COMP_NAME VARCHAR (20), LOCATION VARCHAR (20));
CREATE TYPE EMPLOYMENT_TYPE AS ( Employee REF (EMPLOYEE_TYPE) SCOPE (EMPLOYEE), Company REF (COMPANY_TYPE) SCOPE (COMPANY) );
CREATE TABLE COMPANY OF COMPANY_TYPE ( REF IS COMP_ID SYSTEM GENERATED, PRIMARY KEY (COMP_NAME) );
CREATE TABLE EMPLOYMENT OF EMPLOYMENT_TYPE;
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Figure 11.4 (continued) Illustrating some of the object features of SQL. (c) (continued) Specifying UDTs for STUDENT_TYPE and EMPLOYEE_TYPE as two subtypes of PERSON_TYPE, (d) Creating tables based on some of the UDTs, and illustrating table inheritance, (e) Specifying relation- ships using REF and SCOPE.
11.2 Object-Relational Features: Object Database Extensions to SQL 373
by using the keyword ROW. For example, we could use the following instead of declaring STREET_ADDR_TYPE as a separate type as in Figure 11.4(a):
CREATE TYPE USA_ADDR_TYPE AS ( STREET_ADDR ROW ( NUMBER VARCHAR (5),
STREET_NAME VARCHAR (25), APT_NO VARCHAR (5), SUITE_NO VARCHAR (5) ),
CITY VARCHAR (25), ZIP VARCHAR (10)
);
To allow for collection types in order to create complex-structured objects, four constructors are now included in SQL: ARRAY, MULTISET, LIST, and SET. These are similar to the type constructors discussed in Section 11.1.3. In the initial specifica- tion of SQL/Object, only the ARRAY type was specified, since it can be used to sim- ulate the other types, but the three additional collection types were included in the latest version of the SQL standard. In Figure 11.4(b), the PHONES attribute of PERSON_TYPE has as its type an array whose elements are of the previously defined UDT USA_PHONE_TYPE. This array has a maximum of four elements, meaning that we can store up to four phone numbers per person. An array can also have no maximum number of elements if desired.
An array type can have its elements referenced using the common notation of square brackets. For example, PHONES[1] refers to the first location value in a PHONES attribute (see Figure 11.4(b)). A built-in function CARDINALITY can return the cur- rent number of elements in an array (or any other collection type). For example, PHONES[CARDINALITY (PHONES)] refers to the last element in the array.
The commonly used dot notation is used to refer to components of a ROW TYPE or a UDT. For example, ADDR.CITY refers to the CITY component of an ADDR attribute (see Figure 11.4(b)).
11.2.2 Object Identifiers Using Reference Types Unique system-generated object identifiers can be created via the reference type in the latest version of SQL. For example, in Figure 11.4(b), the phrase:
REF IS SYSTEM GENERATED
indicates that whenever a new PERSON_TYPE object is created, the system will assign it a unique system-generated identifier. It is also possible not to have a system-generated object identifier and use the traditional keys of the basic relational model if desired.
In general, the user can specify that system-generated object identifiers for the indi- vidual rows in a table should be created. By using the syntax:
REF IS <OID_ATTRIBUTE> <VALUE_GENERATION_METHOD> ;
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the user declares that the attribute named <OID_ATTRIBUTE> will be used to iden- tify individual tuples in the table. The options for <VALUE_GENERATION _METHOD> are SYSTEM GENERATED or DERIVED. In the former case, the system will automatically generate a unique identifier for each tuple. In the latter case, the traditional method of using the user-provided primary key value to identify tuples is applied.
11.2.3 Creating Tables Based on the UDTs For each UDT that is specified to be instantiable via the phrase INSTANTIABLE (see Figure 11.4(b)), one or more tables may be created. This is illustrated in Figure 11.4(d), where we create a table PERSON based on the PERSON_TYPE UDT. Notice that the UDTs in Figure 11.4(a) are noninstantiable, and hence can only be used as types for attributes, but not as a basis for table creation. In Figure 11.4(b), the attrib- ute PERSON_ID will hold the system-generated object identifier whenever a new PERSON record (object) is created and inserted in the table.
11.2.4 Encapsulation of Operations In SQL, a user-defined type can have its own behavioral specification by specifying methods (or operations) in addition to the attributes. The general form of a UDT specification with methods is as follows:
CREATE TYPE <TYPE-NAME> ( <LIST OF COMPONENT ATTRIBUTES AND THEIR TYPES> <DECLARATION OF FUNCTIONS (METHODS)>
);
For example, in Figure 11.4(b), we declared a method Age() that calculates the age of an individual object of type PERSON_TYPE.
The code for implementing the method still has to be written. We can refer to the method implementation by specifying the file that contains the code for the method, or we can write the actual code within the type declaration itself (see Figure 11.4(b)).
SQL provides certain built-in functions for user-defined types. For a UDT called TYPE_T, the constructor function TYPE_T( ) returns a new object of that type. In the new UDT object, every attribute is initialized to its default value. An observer function A is implicitly created for each attribute A to read its value. Hence, A(X) or X.A returns the value of attribute A of TYPE_T if X is of type TYPE_T. A mutator function for updating an attribute sets the value of the attribute to a new value. SQL allows these functions to be blocked from public use; an EXECUTE privilege is needed to have access to these functions.
In general, a UDT can have a number of user-defined functions associated with it. The syntax is
INSTANCE METHOD <NAME> (<ARGUMENT_LIST>) RETURNS <RETURN_TYPE>;
11.2 Object-Relational Features: Object Database Extensions to SQL 375
Two types of functions can be defined: internal SQL and external. Internal functions are written in the extended PSM language of SQL (see Chapter 13). External func- tions are written in a host language, with only their signature (interface) appearing in the UDT definition. An external function definition can be declared as follows:
DECLARE EXTERNAL <FUNCTION_NAME> <SIGNATURE> LANGUAGE <LANGUAGE_NAME>;
Attributes and functions in UDTs are divided into three categories:
■ PUBLIC (visible at the UDT interface) ■ PRIVATE (not visible at the UDT interface) ■ PROTECTED (visible only to subtypes)
It is also possible to define virtual attributes as part of UDTs, which are computed and updated using functions.
11.2.5 Specifying Inheritance and Overloading of Functions Recall that we already discussed many of the principles of inheritance in Section 11.1.5. SQL has rules for dealing with type inheritance (specified via the UNDER keyword). In general, both attributes and instance methods (operations) are inher- ited. The phrase NOT FINAL must be included in a UDT if subtypes are allowed to be created under that UDT (see Figure 11.4(a) and (b), where PERSON_TYPE, STUDENT_TYPE, and EMPLOYEE_TYPE are declared to be NOT FINAL). Associated with type inheritance are the rules for overloading of function implementations and for resolution of function names. These inheritance rules can be summarized as follows:
■ All attributes are inherited. ■ The order of supertypes in the UNDER clause determines the inheritance
hierarchy. ■ An instance of a subtype can be used in every context in which a supertype
instance is used. ■ A subtype can redefine any function that is defined in its supertype, with the
restriction that the signature be the same. ■ When a function is called, the best match is selected based on the types of all
arguments. ■ For dynamic linking, the runtime types of parameters is considered.
Consider the following examples to illustrate type inheritance, which are illustrated in Figure 11.4(c). Suppose that we want to create two subtypes of PERSON_TYPE: EMPLOYEE_TYPE and STUDENT_TYPE. In addition, we also create a subtype MANAGER_TYPE that inherits all the attributes (and methods) of EMPLOYEE_TYPE but has an additional attribute DEPT_MANAGED. These subtypes are shown in Figure 11.4(c).
In general, we specify the local attributes and any additional specific methods for the subtype, which inherits the attributes and operations of its supertype.
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Another facility in SQL is table inheritance via the supertable/subtable facility. This is also specified using the keyword UNDER (see Figure 11.4(d)). Here, a new record that is inserted into a subtable, say the MANAGER table, is also inserted into its supertables EMPLOYEE and PERSON. Notice that when a record is inserted in MANAGER, we must provide values for all its inherited attributes. INSERT, DELETE, and UPDATE operations are appropriately propagated.
11.2.6 Specifying Relationships via Reference A component attribute of one tuple may be a reference (specified using the key- word REF) to a tuple of another (or possibly the same) table. An example is shown in Figure 11.4(e).
The keyword SCOPE specifies the name of the table whose tuples can be referenced by the reference attribute. Notice that this is similar to a foreign key, except that the system-generated value is used rather than the primary key value.
SQL uses a dot notation to build path expressions that refer to the component attributes of tuples and row types. However, for an attribute whose type is REF, the dereferencing symbol –> is used. For example, the query below retrieves employees working in the company named ‘ABCXYZ’ by querying the EMPLOYMENT table:
SELECT E.Employee–>NAME FROM EMPLOYMENT AS E WHERE E.Company–>COMP_NAME = ‘ABCXYZ’;
In SQL, –> is used for dereferencing and has the same meaning assigned to it in the C programming language. Thus, if r is a reference to a tuple and a is a component attribute in that tuple, then r –> a is the value of attribute a in that tuple.
If several relations of the same type exist, SQL provides the SCOPE keyword by which a reference attribute may be made to point to a tuple within a specific table of that type.
11.3 The ODMG Object Model and the Object Definition Language ODL
As we discussed in the introduction to Chapter 4, one of the reasons for the success of commercial relational DBMSs is the SQL standard. The lack of a standard for ODMSs for several years may have caused some potential users to shy away from converting to this new technology. Subsequently, a consortium of ODMS vendors and users, called ODMG (Object Data Management Group), proposed a standard that is known as the ODMG-93 or ODMG 1.0 standard. This was revised into ODMG 2.0, and later to ODMG 3.0. The standard is made up of several parts, including the object model, the object definition language (ODL), the object query language (OQL), and the bindings to object-oriented programming languages.
In this section, we describe the ODMG object model and the ODL. In Section 11.4, we discuss how to design an ODB from an EER conceptual schema. We will give an
11.3 The ODMG Object Model and the Object Definition Language ODL 377
overview of OQL in Section 11.5, and the C++ language binding in Section 11.6. Examples of how to use ODL, OQL, and the C++ language binding will use the UNIVERSITY database example introduced in Chapter 8. In our description, we will follow the ODMG 3.0 object model as described in Cattell et al. (2000).21 It is important to note that many of the ideas embodied in the ODMG object model are based on two decades of research into conceptual modeling and object databases by many researchers.
The incorporation of object concepts into the SQL relational database standard, leading to object-relational technology, was presented in Section 11.2.
11.3.1 Overview of the Object Model of ODMG The ODMG object model is the data model upon which the object definition lan- guage (ODL) and object query language (OQL) are based. It is meant to provide a standard data model for object databases, just as SQL describes a standard data model for relational databases. It also provides a standard terminology in a field where the same terms were sometimes used to describe different concepts. We will try to adhere to the ODMG terminology in this chapter. Many of the concepts in the ODMG model have already been discussed in Section 11.1, and we assume the reader has read this section. We will point out whenever the ODMG terminology differs from that used in Section 11.1.
Objects and Literals. Objects and literals are the basic building blocks of the object model. The main difference between the two is that an object has both an object identifier and a state (or current value), whereas a literal has a value (state) but no object identifier.22 In either case, the value can have a complex structure. The object state can change over time by modifying the object value. A literal is basically a constant value, possibly having a complex structure, but it does not change.
An object has five aspects: identifier, name, lifetime, structure, and creation.
1. The object identifier is a unique system-wide identifier (or Object_id).23
Every object must have an object identifier.
2. Some objects may optionally be given a unique name within a particular ODMS—this name can be used to locate the object, and the system should return the object given that name.24 Obviously, not all individual objects will have unique names. Typically, a few objects, mainly those that hold col- lections of objects of a particular object type—such as extents—will have a name. These names are used as entry points to the database; that is, by locating these objects by their unique name, the user can then locate other objects that are referenced from these objects. Other important objects in
21The earlier versions of the object model were published in 1993 and 1997. 22We will use the terms value and state interchangeably here. 23This corresponds to the OID of Section 11.1.2. 24This corresponds to the naming mechanism for persistence, described in Section 11.1.4.
the application may also have unique names, and it is possible to give more than one name to an object. All names within a particular ODMS must be unique.
3. The lifetime of an object specifies whether it is a persistent object (that is, a database object) or transient object (that is, an object in an executing pro- gram that disappears after the program terminates). Lifetimes are indepen- dent of types—that is, some objects of a particular type may be transient whereas others may be persistent.
4. The structure of an object specifies how the object is constructed by using the type constructors. The structure specifies whether an object is atomic or not. An atomic object refers to a single object that follows a user-defined type, such as Employee or Department. If an object is not atomic, then it will be composed of other objects. For example, a collection object is not an atomic object, since its state will be a collection of other objects.25 The term atomic object is different from how we defined the atom constructor in Section 11.1.3, which referred to all values of built-in data types. In the ODMG model, an atomic object is any individual user-defined object. All values of the basic built-in data types are considered to be literals.
5. Object creation refers to the manner in which an object can be created. This is typically accomplished via an operation new for a special Object_Factory interface. We shall describe this in more detail later in this section.
In the object model, a literal is a value that does not have an object identifier. However, the value may have a simple or complex structure. There are three types of literals: atomic, structured, and collection.
1. Atomic literals26 correspond to the values of basic data types and are prede- fined. The basic data types of the object model include long, short, and unsigned integer numbers (these are specified by the keywords long, short, unsigned long, and unsigned short in ODL), regular and double precision floating point numbers (float, double), Boolean values (boolean), single characters (char), character strings (string), and enumeration types (enum), among others.
2. Structured literals correspond roughly to values that are constructed using the tuple constructor described in Section 11.1.3. The built-in structured lit- erals include Date, Interval, Time, and Timestamp (see Figure 11.5(b)). Additional user-defined structured literals can be defined as needed by each application.27 User-defined structures are created using the STRUCT key- word in ODL, as in the C and C++ programming languages.
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25In the ODMG model, atomic objects do not correspond to objects whose values are basic data types. All basic values (integers, reals, and so on) are considered literals. 26The use of the word atomic in atomic literal corresponds to the way we used atom constructor in Section 11.1.3. 27The structures for Date, Interval, Time, and Timestamp can be used to create either literal values or objects with identifiers.
Figure 11.5 Overview of the interface defini- tions for part of the ODMG object model. (a) The basic Object inter- face, inherited by all objects, (b) Some standard interfaces for structured literals
11.3 The ODMG Object Model and the Object Definition Language ODL 379
(a) interface Object { ... boolean same_as(in object other_object); object copy(); void delete();
};
(b) Class Date : Object { enum Weekday
{ Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday };
enum Month { January, February, March, April, May, June,
July, August, September, October, November, December };
unsigned short year(); unsigned short month(); unsigned short day(); ... boolean is_equal(in Date other_date); boolean is_greater(in Date other_date); ... };
Class Time : Object { ... unsigned short hour(); unsigned short minute(); unsigned short second(); unsigned short millisecond(); ... boolean is_equal(in Time a_time); boolean is_greater(in Time a_time); ... Time add_interval(in Interval an_interval); Time subtract_interval(in Interval an_interval); Interval subtract_time(in Time other_time); };
class Timestamp : Object { ... unsigned short year(); unsigned short month(); unsigned short day(); unsigned short hour(); unsigned short minute(); unsigned short second(); unsigned short millisecond(); ... Timestamp plus(in Interval an_interval); (continues)
Timestamp minus(in Interval an_interval); boolean is_equal(in Timestamp a_timestamp); boolean is_greater(in Timestamp a_timestamp); ... }; class Interval : Object { unsigned short day(); unsigned short hour(); unsigned short minute(); unsigned short second(); unsigned short millisecond(); ... Interval plus(in Interval an_interval); Interval minus(in Interval an_interval); Interval product(in long a_value); Interval quotient(in long a_value); boolean is_equal(in interval an_interval); boolean is_greater(in interval an_interval); ... };
(c) interface Collection : Object { ... exception ElementNotFound{ Object element; }; unsigned long cardinality(); boolean is_empty(); ... boolean contains_element(in Object element); void insert_element(in Object element); void remove_element(in Object element)
raises(ElementNotFound); iterator create_iterator(in boolean stable); ... };
interface Iterator { exception NoMoreElements(); ... boolean at_end(); void reset(); Object get_element() raises(NoMoreElements); void next_position() raises(NoMoreElements); ... };
interface set : Collection { set create_union(in set other_set); ... boolean is_subset_of(in set other_set); ... };
interface bag : Collection { unsigned long occurrences_of(in Object element);
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Figure 11.5 (continued) Overview of the inter- face definitions for part of the ODMG object model. (b) (continued) Some standard interfaces for structured literals, (c) Interfaces for collections and iterators.
bag create_union(in Bag other_bag); ... };
interface list : Collection { exception lnvalid_lndex{unsigned_long index; ); void remove_element_at(in unsigned long index)
raises(lnvalidlndex); Object retrieve_element_at(in unsigned long index)
raises(lnvalidlndex); void replace_element_at(in Object element, in unsigned long index)
raises(lnvalidlndex); void insert_element_after(in Object element, in unsigned long index)
raises(lnvalidlndex); ... void insert_element_first(in Object element); ... void remove_first_element() raises(ElementNotFound); ... Object retrieve_first_element() raises(ElementNotFound); ... list concat(in list other_list); void append(in list other_list);
}; interface array : Collection {
exception lnvalid_lndex{unsigned_long index; }; exception lnvalid_Size{unsigned_long size; }; void remove_element_at(in unsigned long index)
raises(InvalidIndex); Object retrieve_element_at(in unsigned long index)
raises(InvalidIndex); void replace_element_at(in unsigned long index, in Object element)
raises(InvalidIndex); void resize(in unsigned long new_size)
raises(InvalidSize); }; struct association { Object key; Object value; }; interface dictionary : Collection {
exception DuplicateName{string key; }; exception KeyNotFound{Object key; }; void bind(in Object key, in Object value)
raises(DuplicateName); void unbind(in Object key) raises(KeyNotFound); Object lookup(in Object key) raises(KeyNotFound); boolean contains_key(in Object key);
};
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3. Collection literals specify a literal value that is a collection of objects or val- ues but the collection itself does not have an Object_id. The collections in the object model can be defined by the type generators set<T>, bag<T>, list<T>, and array<T>, where T is the type of objects or values in the collection.28
Another collection type is dictionary<K, V>, which is a collection of associa- tions <K, V>, where each K is a key (a unique search value) associated with a value V; this can be used to create an index on a collection of values V.
Figure 11.5 gives a simplified view of the basic types and type generators of the object model. The notation of ODMG uses three concepts: interface, literal, and class. Following the ODMG terminology, we use the word behavior to refer to operations and state to refer to properties (attributes and relationships). An interface specifies only behavior of an object type and is typically noninstantiable (that is, no objects are created corresponding to an interface). Although an interface may have state properties (attributes and relationships) as part of its specifications, these cannot be inherited from the interface. Hence, an interface serves to define operations that can be inherited by other interfaces, as well as by classes that define the user-defined objects for a particular application. A class specifies both state (attributes) and behavior (operations) of an object type, and is instantiable. Hence, database and application objects are typically created based on the user-specified class declara- tions that form a database schema. Finally, a literal declaration specifies state but no behavior. Thus, a literal instance holds a simple or complex structured value but has neither an object identifier nor encapsulated operations.
Figure 11.5 is a simplified version of the object model. For the full specifications, see Cattell et al. (2000). We will describe some of the constructs shown in Figure 11.5 as we describe the object model. In the object model, all objects inherit the basic inter- face operations of Object, shown in Figure 11.5(a); these include operations such as copy (creates a new copy of the object), delete (deletes the object), and same_as (compares the object’s identity to another object).29 In general, operations are applied to objects using the dot notation. For example, given an object O, to com- pare it with another object P, we write
O.same_as(P)
The result returned by this operation is Boolean and would be true if the identity of P is the same as that of O, and false otherwise. Similarly, to create a copy P of object O, we write
P = O.copy()
An alternative to the dot notation is the arrow notation: O–>same_as(P) or O–>copy().
28These are similar to the corresponding type constructors described in Section 11.1.3. 29Additional operations are defined on objects for locking purposes, which are not shown in Figure 11.5. We discuss locking concepts for databases in Chapter 22.
11.3 The ODMG Object Model and the Object Definition Language ODL 383
11.3.2 Inheritance in the Object Model of ODMG In the ODMG object model, two types of inheritance relationships exist: behavior- only inheritance and state plus behavior inheritance. Behavior inheritance is also known as ISA or interface inheritance, and is specified by the colon (:) notation.30
Hence, in the ODMG object model, behavior inheritance requires the supertype to be an interface, whereas the subtype could be either a class or another interface.
The other inheritance relationship, called EXTENDS inheritance, is specified by the keyword extends. It is used to inherit both state and behavior strictly among classes, so both the supertype and the subtype must be classes. Multiple inheritance via extends is not permitted. However, multiple inheritance is allowed for behavior inheritance via the colon (:) notation. Hence, an interface may inherit behavior from several other interfaces. A class may also inherit behavior from several inter- faces via colon (:) notation, in addition to inheriting behavior and state from at most one other class via extends. In Section 11.3.4 we will give examples of how these two inheritance relationships—“:” and extends—may be used.
11.3.3 Built-in Interfaces and Classes in the Object Model Figure 11.5 shows the built-in interfaces and classes of the object model. All inter- faces, such as Collection, Date, and Time, inherit the basic Object interface. In the object model, there is a distinction between collection objects, whose state contains multiple objects or literals, versus atomic (and structured) objects, whose state is an individual object or literal. Collection objects inherit the basic Collection interface shown in Figure 11.5(c), which shows the operations for all collection objects. Given a collection object O, the O.cardinality() operation returns the number of ele- ments in the collection. The operation O.is_empty() returns true if the collection O is empty, and returns false otherwise. The operations O.insert_element(E) and O.remove_element(E) insert or remove an element E from the collection O. Finally, the operation O.contains_element(E) returns true if the collection O includes ele- ment E, and returns false otherwise. The operation I = O.create_iterator() creates an iterator object I for the collection object O, which can iterate over each element in the collection. The interface for iterator objects is also shown in Figure 11.5(c). The I.reset() operation sets the iterator at the first element in a collection (for an unordered collection, this would be some arbitrary element), and I.next_position() sets the iterator to the next element. The I.get_element() retrieves the current ele- ment, which is the element at which the iterator is currently positioned.
The ODMG object model uses exceptions for reporting errors or particular condi- tions. For example, the ElementNotFound exception in the Collection interface would
30The ODMG report also calls interface inheritance as type/subtype, is-a, and generalization/specializa- tion relationships, although, in the literature these terms have been used to describe inheritance of both state and operations (see Chapter 8 and Section 11.1).
384 Chapter 11 Object and Object-Relational Databases
be raised by the O.remove_element(E) operation if E is not an element in the collec- tion O. The NoMoreElements exception in the iterator interface would be raised by the I.next_position() operation if the iterator is currently positioned at the last ele- ment in the collection, and hence no more elements exist for the iterator to point to.
Collection objects are further specialized into set, list, bag, array, and dictionary, which inherit the operations of the Collection interface. A set<T> type generator can be used to create objects such that the value of object O is a set whose elements are of type T. The Set interface includes the additional operation P = O.create_union(S) (see Figure 11.5(c)), which returns a new object P of type set<T> that is the union of the two sets O and S. Other operations similar to create_union (not shown in Figure 11.5(c)) are create_intersection(S) and create_difference(S). Operations for set com- parison include the O.is_subset_of(S) operation, which returns true if the set object O is a subset of some other set object S, and returns false otherwise. Similar opera- tions (not shown in Figure 11.5(c)) are is_proper_subset_of(S), is_superset_of(S), and is_proper_superset_of(S). The bag<T> type generator allows duplicate elements in the collection and also inherits the Collection interface. It has three operations— create_union(b), create_intersection(b), and create_difference(b)—that all return a new object of type bag<T>.
A list<T> object type inherits the Collection operations and can be used to create col- lections where the order of the elements is important. The value of each such object O is an ordered list whose elements are of type T. Hence, we can refer to the first, last, and ith element in the list. Also, when we add an element to the list, we must specify the position in the list where the element is inserted. Some of the list operations are shown in Figure 11.5(c). If O is an object of type list<T>, the operation O.insert_element_first(E) inserts the element E before the first element in the list O, so that E becomes the first element in the list. A similar operation (not shown) is O.insert_element_last(E). The operation O.insert_element_after(E, I) in Figure 11.5(c) inserts the element E after the ith element in the list O and will raise the exception InvalidIndex if no ith element exists in O. A similar operation (not shown) is O.insert_element_before(E, I). To remove elements from the list, the operations are E = O.remove_first_element(), E = O.remove_last_element(), and E = O.remove_element _at(I); these operations remove the indicated element from the list and return the element as the operation’s result. Other operations retrieve an element without removing it from the list. These are E = O.retrieve_first_element(), E = O.retrieve _last_element(), and E = O.retrieve_element_at(I). Also, two operations to manipulate lists are defined. They are P = O.concat(I), which creates a new list P that is the con- catenation of lists O and I (the elements in list O followed by those in list I), and O.append(I), which appends the elements of list I to the end of list O (without creat- ing a new list object).
The array<T> object type also inherits the Collection operations, and is similar to list. Specific operations for an array object O are O.replace_element_at(I, E), which replaces the array element at position I with element E; E = O.remove_element_at(I), which retrieves the ith element and replaces it with a NULL value; and
11.3 The ODMG Object Model and the Object Definition Language ODL 385
E = O.retrieve_element_at(I), which simply retrieves the ith element of the array. Any of these operations can raise the exception InvalidIndex if I is greater than the array’s size. The operation O.resize(N) changes the number of array elements to N.
The last type of collection objects are of type dictionary<K,V>. This allows the cre- ation of a collection of association pairs <K,V>, where all K (key) values are unique. This allows for associative retrieval of a particular pair given its key value (similar to an index). If O is a collection object of type dictionary<K,V>, then O.bind(K,V) binds value V to the key K as an association <K,V> in the collection, whereas O.unbind(K) removes the association with key K from O, and V = O.lookup(K) returns the value V associated with key K in O. The latter two operations can raise the exception KeyNotFound. Finally, O.contains_key(K) returns true if key K exists in O, and returns false otherwise.
Figure 11.6 is a diagram that illustrates the inheritance hierarchy of the built-in con- structs of the object model. Operations are inherited from the supertype to the sub- type. The collection interfaces described above are not directly instantiable; that is, one cannot directly create objects based on these interfaces. Rather, the interfaces can be used to generate user-defined collection types—of type set, bag, list, array, or dictionary—for a particular database application. If an attribute or class has a collec- tion type, say a set, then it will inherit the operations of the set interface. For exam- ple, in a UNIVERSITY database application, the user can specify a type for set<STUDENT>, whose state would be sets of STUDENT objects. The programmer can then use the operations for set<T> to manipulate an instance of type set<STUDENT>. Creating application classes is typically done by utilizing the object definition language ODL (see Section 11.3.6).
It is important to note that all objects in a particular collection must be of the same type. Hence, although the keyword any appears in the specifications of collection interfaces in Figure 11.5(c), this does not mean that objects of any type can be inter- mixed within the same collection. Rather, it means that any type can be used when specifying the type of elements for a particular collection (including other collec- tion types!).
Collection
Object
Iterator Date IntervalTime
set list bag dictionary
Timestamp
array
Figure 11.6 Inheritance hierarchy for the built-in interfaces of the object model.
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11.3.4 Atomic (User-Defined) Objects The previous section described the built-in collection types of the object model. Now we discuss how object types for atomic objects can be constructed. These are specified using the keyword class in ODL. In the object model, any user-defined object that is not a collection object is called an atomic object.31
For example, in a UNIVERSITY database application, the user can specify an object type (class) for STUDENT objects. Most such objects will be structured objects; for example, a STUDENT object will have a complex structure, with many attributes, relationships, and operations, but it is still considered atomic because it is not a col- lection. Such a user-defined atomic object type is defined as a class by specifying its properties and operations. The properties define the state of the object and are fur- ther distinguished into attributes and relationships. In this subsection, we elabo- rate on the three types of components—attributes, relationships, and operations—that a user-defined object type for atomic (structured) objects can include. We illustrate our discussion with the two classes EMPLOYEE and DEPARTMENT shown in Figure 11.7.
An attribute is a property that describes some aspect of an object. Attributes have values (which are typically literals having a simple or complex structure) that are stored within the object. However, attribute values can also be Object_ids of other objects. Attribute values can even be specified via methods that are used to calculate the attribute value. In Figure 11.732 the attributes for EMPLOYEE are Name, Ssn, Birth_date, Sex, and Age, and those for DEPARTMENT are Dname, Dnumber, Mgr, Locations, and Projs. The Mgr and Projs attributes of DEPARTMENT have complex structure and are defined via struct, which corresponds to the tuple constructor of Section 11.1.3. Hence, the value of Mgr in each DEPARTMENT object will have two components: Manager, whose value is an Object_id that references the EMPLOYEE object that manages the DEPARTMENT, and Start_date, whose value is a date. The locations attribute of DEPARTMENT is defined via the set constructor, since each DEPARTMENT object can have a set of locations.
A relationship is a property that specifies that two objects in the database are related. In the object model of ODMG, only binary relationships (see Section 7.4) are explicitly represented, and each binary relationship is represented by a pair of inverse references specified via the keyword relationship. In Figure 11.7, one relationship exists that relates each EMPLOYEE to the DEPARTMENT in which he or she works— the Works_for relationship of EMPLOYEE. In the inverse direction, each DEPARTMENT is related to the set of EMPLOYEES that work in the DEPARTMENT— the Has_emps relationship of DEPARTMENT. The keyword inverse specifies that these two properties define a single conceptual relationship in inverse directions.33
31As mentioned earlier, this definition of atomic object in the ODMG object model is different from the definition of atom constructor given in Section 11.1.3, which is the definition used in much of the object- oriented database literature. 32We are using the Object Definition Language (ODL) notation in Figure 11.7, which will be discussed in more detail in Section 11.3.6. 33Section 7.4 discusses how a relationship can be represented by two attributes in inverse directions.
11.3 The ODMG Object Model and the Object Definition Language ODL 387
Figure 11.7 The attributes, relationships, and operations in a class definition.
By specifying inverses, the database system can maintain the referential integrity of the relationship automatically. That is, if the value of Works_for for a particular EMPLOYEE E refers to DEPARTMENT D, then the value of Has_emps for DEPARTMENT D must include a reference to E in its set of EMPLOYEE references. If the database designer desires to have a relationship to be represented in only one direction, then it has to be modeled as an attribute (or operation). An example is the Manager component of the Mgr attribute in DEPARTMENT.
In addition to attributes and relationships, the designer can include operations in object type (class) specifications. Each object type can have a number of operation signatures, which specify the operation name, its argument types, and its returned value, if applicable. Operation names are unique within each object type, but they can be overloaded by having the same operation name appear in distinct object types. The operation signature can also specify the names of exceptions that can
class EMPLOYEE ( extent ALL_EMPLOYEES
key Ssn ) {
attribute string Name; attribute string Ssn; attribute date Birth_date; attribute enum Gender{M, F} Sex; attribute short Age; relationship DEPARTMENT Works_for
inverse DEPARTMENT::Has_emps; void reassign_emp(in string New_dname)
raises(dname_not_valid); }; class DEPARTMENT ( extent ALL_DEPARTMENTS
key Dname, Dnumber ) {
attribute string Dname; attribute short Dnumber; attribute struct Dept_mgr {EMPLOYEE Manager, date Start_date}
Mgr; attribute set<string> Locations; attribute struct Projs {string Proj_name, time Weekly_hours)
Projs; relationship set<EMPLOYEE> Has_emps inverse EMPLOYEE::Works_for; void add_emp(in string New_ename) raises(ename_not_valid); void change_manager(in string New_mgr_name; in date
Start_date); };
occur during operation execution. The implementation of the operation will include the code to raise these exceptions. In Figure 11.7 the EMPLOYEE class has one operation: reassign_emp, and the DEPARTMENT class has two operations: add_emp and change_manager.
11.3.5 Extents, Keys, and Factory Objects In the ODMG object model, the database designer can declare an extent (using the keyword extent) for any object type that is defined via a class declaration. The extent is given a name, and it will contain all persistent objects of that class. Hence, the extent behaves as a set object that holds all persistent objects of the class. In Figure 11.7 the EMPLOYEE and DEPARTMENT classes have extents called ALL_EMPLOYEES and ALL_DEPARTMENTS, respectively. This is similar to creating two objects—one of type set<EMPLOYEE> and the second of type set<DEPARTMENT>—and making them persistent by naming them ALL_EMPLOYEES and ALL_DEPARTMENTS. Extents are also used to automatically enforce the set/subset relationship between the extents of a supertype and its subtype. If two classes A and B have extents ALL_A and ALL_B, and class B is a subtype of class A (that is, class B extends class A), then the collection of objects in ALL_B must be a subset of those in ALL_A at any point. This constraint is automatically enforced by the database system.
A class with an extent can have one or more keys. A key consists of one or more properties (attributes or relationships) whose values are constrained to be unique for each object in the extent. For example, in Figure 11.7 the EMPLOYEE class has the Ssn attribute as key (each EMPLOYEE object in the extent must have a unique Ssn value), and the DEPARTMENT class has two distinct keys: Dname and Dnumber (each DEPARTMENT must have a unique Dname and a unique Dnumber). For a com- posite key34 that is made of several properties, the properties that form the key are contained in parentheses. For example, if a class VEHICLE with an extent ALL_VEHICLES has a key made up of a combination of two attributes State and License_number, they would be placed in parentheses as (State, License_number) in the key declaration.
Next, we present the concept of factory object—an object that can be used to gen- erate or create individual objects via its operations. Some of the interfaces of factory objects that are part of the ODMG object model are shown in Figure 11.8. The interface ObjectFactory has a single operation, new(), which returns a new object with an Object_id. By inheriting this interface, users can create their own factory interfaces for each user-defined (atomic) object type, and the programmer can implement the operation new differently for each type of object. Figure 11.8 also shows a DateFactory interface, which has additional operations for creating a new calendar_date, and for creating an object whose value is the current_date, among other operations (not shown in Figure 11.8). As we can see, a factory object basically provides the constructor operations for new objects.
388 Chapter 11 Object and Object-Relational Databases
34A composite key is called a compound key in the ODMG report.
11.3 The ODMG Object Model and the Object Definition Language ODL 389
Figure 11.8 Interfaces to illustrate factory objects and database objects.
interface ObjectFactory { Object new();
};
interface SetFactory : ObjectFactory { Set new_of_size(in long size);
};
interface ListFactory : ObjectFactory { List new_of_size(in long size);
};
interface ArrayFactory : ObjectFactory { Array new_of_size(in long size);
};
interface DictionaryFactory : ObjectFactory { Dictionary new_of_size(in long size);
};
interface DateFactory : ObjectFactory { exception InvalidDate{}; ... Date calendar_date( in unsigned short year,
in unsigned short month, in unsigned short day )
raises(InvalidDate); ... Date current();
};
interface DatabaseFactory { Database new();
};
interface Database { ... void open(in string database_name)
raises(DatabaseNotFound, DatabaseOpen); void close() raises(DatabaseClosed, ...); void bind(in Object an_object, in string name)
raises(DatabaseClosed, ObjectNameNotUnique, ...); Object unbind(in string name)
raises(DatabaseClosed, ObjectNameNotFound, ...); Object Iookup(in string object_name)
raises(DatabaseClosed, ObjectNameNotFound, ...); ... };
390 Chapter 11 Object and Object-Relational Databases
Finally, we discuss the concept of a database. Because an ODBMS can create many different databases, each with its own schema, the ODMG object model has inter- faces for DatabaseFactory and Database objects, as shown in Figure 11.8. Each data- base has its own database name, and the bind operation can be used to assign individual unique names to persistent objects in a particular database. The lookup operation returns an object from the database that has the specified object_name, and the unbind operation removes the name of a persistent named object from the database.
11.3.6 The Object Definition Language ODL After our overview of the ODMG object model in the previous section, we now show how these concepts can be utilized to create an object database schema using the object definition language ODL.35
The ODL is designed to support the semantic constructs of the ODMG object model and is independent of any particular programming language. Its main use is to create object specifications—that is, classes and interfaces. Hence, ODL is not a full programming language. A user can specify a database schema in ODL inde- pendently of any programming language, and then use the specific language bind- ings to specify how ODL constructs can be mapped to constructs in specific programming languages, such as C++, Smalltalk, and Java. We will give an overview of the C++ binding in Section 11.6.
Figure 11.9(b) shows a possible object schema for part of the UNIVERSITY database, which was presented in Chapter 8. We will describe the concepts of ODL using this example, and the one in Figure 11.11. The graphical notation for Figure 11.9(b) is shown in Figure 11.9(a) and can be considered as a variation of EER diagrams (see Chapter 8) with the added concept of interface inheritance but without several EER concepts, such as categories (union types) and attributes of relationships.
Figure 11.10 shows one possible set of ODL class definitions for the UNIVERSITY database. In general, there may be several possible mappings from an object schema diagram (or EER schema diagram) into ODL classes. We will discuss these options further in Section 11.4.
Figure 11.10 shows the straightforward way of mapping part of the UNIVERSITY database from Chapter 8. Entity types are mapped into ODL classes, and inheri- tance is done using extends. However, there is no direct way to map categories (union types) or to do multiple inheritance. In Figure 11.10 the classes PERSON, FACULTY, STUDENT, and GRAD_STUDENT have the extents PERSONS, FACULTY, STUDENTS, and GRAD_STUDENTS, respectively. Both FACULTY and STUDENT extends PERSON and GRAD_STUDENT extends STUDENT. Hence, the collection of STUDENTS (and the collection of FACULTY) will be constrained to be a subset of the
35The ODL syntax and data types are meant to be compatible with the Interface Definition language (IDL) of CORBA (Common Object Request Broker Architecture), with extensions for relationships and other database concepts.
(a)
(b)
Person-IFInterface
STUDENTClass
PERSON
Works_in
Has_faculty
Has_majors DEPARTMENT
GRAD_STUDENT
Registered_in FACULTY STUDENT
Advisor
Committee
Advises
COURSE
Offered_by Majors_in
Completed_sections
Has_sections
Students
Of_course
Offers
SECTION
Registered_students
On_committee_of
CURR_SECTION
Relationships
1:1
1:N
M:N
Inheritance
Interface(is-a) inheritance using “:”
Class inheritance using extends
11.3 The ODMG Object Model and the Object Definition Language ODL 391
Figure 11.9 An example of a database schema. (a) Graphical notation for representing ODL schemas. (b) A graphical object database schema for part of the UNIVERSITY database (GRADE and DEGREE classes are not shown).
collection of PERSONs at any time. Similarly, the collection of GRAD_STUDENTs will be a subset of STUDENTs. At the same time, individual STUDENT and FACULTY objects will inherit the properties (attributes and relationships) and operations of PERSON, and individual GRAD_STUDENT objects will inherit those of STUDENT.
The classes DEPARTMENT, COURSE, SECTION, and CURR_SECTION in Figure 11.10 are straightforward mappings of the corresponding entity types in Figure
392 Chapter 11 Object and Object-Relational Databases
Figure 11.10 Possible ODL schema for the UNIVERSITY database in Figure 11.8(b).
class PERSON ( extent PERSONS
key Ssn ) { attribute struct Pname { string Fname,
string Mname, string Lname } Name;
attribute string Ssn; attribute date Birth_date; attribute enum Gender{M, F} Sex; attribute struct Address { short No,
string Street, short Apt_no, string City, string State, short Zip } Address;
short Age(); }; class FACULTY extends PERSON ( extent FACULTY ) { attribute string Rank;
attribute float Salary; attribute string Office; attribute string Phone; relationship DEPARTMENT Works_in inverse DEPARTMENT::Has faculty; relationship set<GRAD_STUDENT> Advises inverse GRAD_STUDENT::Advisor; relationship set<GRAD_STUDENT> On_committee_of inverse GRAD_STUDENT::Committee; void give_raise(in float raise); void promote(in string new rank); };
class GRADE ( extent GRADES ) {
attribute enum GradeValues{A,B,C,D,F,l, P} Grade; relationship SECTION Section inverse SECTION::Students; relationship STUDENT Student inverse STUDENT::Completed_sections; };
class STUDENT extends PERSON ( extent STUDENTS ) { attribute string Class;
attribute DEPARTMENT Minors_in; relationship DEPARTMENT Majors_in inverse DEPARTMENT::Has_majors; relationship set<GRADE> Completed_sections inverse GRADE::Student; relationship set<CURR_SECTION> Registered_in INVERSE CURR_SECTION::Registered_students; void change_major(in string dname) raises(dname_not_valid); float gpa(); void register(in short secno) raises(section_not_valid); void assign_grade(in short secno; IN GradeValue grade)
raises(section_not_valid,grade_not_valid); };
class DEGREE { attribute string College;
attribute string Degree; attribute string Year; };
class GRAD_STUDENT extends STUDENT ( extent GRAD_STUDENTS ) { attribute set<DEGREE> Degrees;
relationship FACULTY Advisor inverse FACULTY::Advises; relationship set<FACULTY> Committee inverse FACULTY::On_committee_of; void assign_advisor(in string Lname; in string Fname)
raises(facuIty_not_valid); void assign_committee_member(in string Lname; in string Fname)
raises(facuIty_not_valid); }; class DEPARTMENT ( extent DEPARTMENTS
key Dname ) { attribute string Dname;
attribute string Dphone; attribute string Doffice; attribute string College; attribute FACULTY Chair; relationship set<FACULTY> Has_faculty inverse FACULTY::Works_in; relationship set<STUDENT> Has_majors inverse STUDENT::Majors_in; relationship set<COURSE> Offers inverse COURSE::Offered_by; };
class COURSE ( extent COURSES
key Cno ) { attribute string Cname;
attribute string Cno; attribute string Description; relationship set<SECTION> Has_sections inverse SECTION::Of_course; relationship <DEPARTMENT> Offered_by inverse DEPARTMENT::Offers; };
class SECTION ( extent SECTIONS ) { attribute short Sec_no;
attribute string Year; attribute enum Quarter{Fall, Winter, Spring, Summer}
Qtr; relationship set<GRADE> Students inverse GRADE::Section; relationship course Of_course inverse COURSE::Has_sections; };
class CURR_SECTION extends SECTION ( extent CURRENT_SECTIONS ) { relationship set<STUDENT> Registered_students
inverse STUDENT::Registered_in void register_student(in string Ssn)
raises(student_not_valid, section_full); };
11.3 The ODMG Object Model and the Object Definition Language ODL 393
Figure 11.11 An illustration of inter- face inheritance via “:”. (a) Graphical schema representation, (b) Corresponding interface and class definitions in ODL.
(a)
(b) interface GeometryObject { attribute enum Shape{RECTANGLE, TRIANGLE, CIRCLE, ... }
Shape; attribute struct Point {short x, short y} Reference_point; float perimeter(); float area(); void translate(in short x_translation; in short y_translation); void rotate(in float angle_of_rotation); };
class RECTANGLE : GeometryObject ( extent RECTANGLES ) { attribute struct Point {short x, short y} Reference_point;
attribute short Length; attribute short Height; attribute float Orientation_angle; };
class TRIANGLE : GeometryObject ( extent TRIANGLES ) { attribute struct Point {short x, short y} Reference_point;
attribute short Side_1; attribute short Side_2; attribute float Side1_side2_angle; attribute float Side1_orientation_angle; };
class CIRCLE : GeometryObject ( extent CIRCLES ) { attribute struct Point {short x, short y} Reference_point;
attribute short Radius; }; ...
TRIANGLE
GeometryObject
CIRCLERECTANGLE . . .
11.9(b). However, the class GRADE requires some explanation. The GRADE class corresponds to the M:N relationship between STUDENT and SECTION in Figure 11.9(b). The reason it was made into a separate class (rather than as a pair of inverse relationships) is because it includes the relationship attribute Grade.36
Hence, the M:N relationship is mapped to the class GRADE, and a pair of 1:N rela- tionships, one between STUDENT and GRADE and the other between SECTION and
394 Chapter 11 Object and Object-Relational Databases
36We will discuss alternative mappings for attributes of relationships in Section 11.4.
11.4 Object Database Conceptual Design 395
GRADE.37 These relationships are represented by the following relationship proper- ties: Completed_sections of STUDENT; Section and Student of GRADE; and Students of SECTION (see Figure 11.10). Finally, the class DEGREE is used to represent the com- posite, multivalued attribute degrees of GRAD_STUDENT (see Figure 8.10).
Because the previous example does not include any interfaces, only classes, we now utilize a different example to illustrate interfaces and interface (behavior) inheri- tance. Figure 11.11(a) is part of a database schema for storing geometric objects. An interface GeometryObject is specified, with operations to calculate the perimeter and area of a geometric object, plus operations to translate (move) and rotate an object. Several classes (RECTANGLE, TRIANGLE, CIRCLE, ...) inherit the GeometryObject interface. Since GeometryObject is an interface, it is noninstantiable—that is, no objects can be created based on this interface directly. However, objects of type RECTANGLE, TRIANGLE, CIRCLE, ... can be created, and these objects inherit all the operations of the GeometryObject interface. Note that with interface inheritance, only operations are inherited, not properties (attributes, relationships). Hence, if a property is needed in the inheriting class, it must be repeated in the class definition, as with the Reference_point attribute in Figure 11.11(b). Notice that the inherited operations can have different implementations in each class. For example, the implementations of the area and perimeter operations may be different for RECTANGLE, TRIANGLE, and CIRCLE.
Multiple inheritance of interfaces by a class is allowed, as is multiple inheritance of interfaces by another interface. However, with the extends (class) inheritance, mul- tiple inheritance is not permitted. Hence, a class can inherit via extends from at most one class (in addition to inheriting from zero or more interfaces).
11.4 Object Database Conceptual Design Section 11.4.1 discusses how object database (ODB) design differs from relational database (RDB) design. Section 11.4.2 outlines a mapping algorithm that can be used to create an ODB schema, made of ODMG ODL class definitions, from a con- ceptual EER schema.
11.4.1 Differences between Conceptual Design of ODB and RDB
One of the main differences between ODB and RDB design is how relationships are handled. In ODB, relationships are typically handled by having relationship proper- ties or reference attributes that include OID(s) of the related objects. These can be considered as OID references to the related objects. Both single references and collec- tions of references are allowed. References for a binary relationship can be declared
37This is similar to how an M:N relationship is mapped in the relational model (see Section 9.1) and in the legacy network model (see Appendix E).
in a single direction, or in both directions, depending on the types of access expected. If declared in both directions, they may be specified as inverses of one another, thus enforcing the ODB equivalent of the relational referential integrity constraint.
In RDB, relationships among tuples (records) are specified by attributes with matching values. These can be considered as value references and are specified via foreign keys, which are values of primary key attributes repeated in tuples of the ref- erencing relation. These are limited to being single-valued in each record because multivalued attributes are not permitted in the basic relational model. Thus, M:N relationships must be represented not directly, but as a separate relation (table), as discussed in Section 9.1.
Mapping binary relationships that contain attributes is not straightforward in ODBs, since the designer must choose in which direction the attributes should be included. If the attributes are included in both directions, then redundancy in stor- age will exist and may lead to inconsistent data. Hence, it is sometimes preferable to use the relational approach of creating a separate table by creating a separate class to represent the relationship. This approach can also be used for n-ary relationships, with degree n > 2.
Another major area of difference between ODB and RDB design is how inheritance is handled. In ODB, these structures are built into the model, so the mapping is achieved by using the inheritance constructs, such as derived (:) and extends. In relational design, as we discussed in Section 9.2, there are several options to choose from since no built-in construct exists for inheritance in the basic relational model. It is important to note, though, that object-relational and extended-relational sys- tems are adding features to model these constructs directly as well as to include operation specifications in abstract data types (see Section 11.2).
The third major difference is that in ODB design, it is necessary to specify the oper- ations early on in the design since they are part of the class specifications. Although it is important to specify operations during the design phase for all types of data- bases, it may be delayed in RDB design as it is not strictly required until the imple- mentation phase.
There is a philosophical difference between the relational model and the object model of data in terms of behavioral specification. The relational model does not mandate the database designers to predefine a set of valid behaviors or operations, whereas this is a tacit requirement in the object model. One of the claimed advan- tages of the relational model is the support of ad hoc queries and transactions, whereas these are against the principle of encapsulation.
In practice, it is becoming commonplace to have database design teams apply object-based methodologies at early stages of conceptual design so that both the structure and the use or operations of the data are considered, and a complete spec- ification is developed during conceptual design. These specifications are then mapped into relational schemas, constraints, and behavioral artifacts such as trig- gers or stored procedures (see Sections 5.2 and 13.4).
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11.4.2 Mapping an EER Schema to an ODB Schema It is relatively straightforward to design the type declarations of object classes for an ODBMS from an EER schema that contains neither categories nor n-ary relation- ships with n > 2. However, the operations of classes are not specified in the EER dia- gram and must be added to the class declarations after the structural mapping is completed. The outline of the mapping from EER to ODL is as follows:
Step 1. Create an ODL class for each EER entity type or subclass. The type of the ODL class should include all the attributes of the EER class.38 Multivalued attributes are typically declared by using the set, bag, or list constructors.39 If the values of the multivalued attribute for an object should be ordered, the list constructor is chosen; if duplicates are allowed, the bag constructor should be chosen; otherwise, the set constructor is chosen. Composite attributes are mapped into a tuple constructor (by using a struct declaration in ODL).
Declare an extent for each class, and specify any key attributes as keys of the extent. (This is possible only if an extent facility and key constraint declarations are avail- able in the ODBMS.)
Step 2. Add relationship properties or reference attributes for each binary relation- ship into the ODL classes that participate in the relationship. These may be created in one or both directions. If a binary relationship is represented by references in both directions, declare the references to be relationship properties that are inverses of one another, if such a facility exists.40 If a binary relationship is represented by a reference in only one direction, declare the reference to be an attribute in the refer- encing class whose type is the referenced class name.
Depending on the cardinality ratio of the binary relationship, the relationship prop- erties or reference attributes may be single-valued or collection types. They will be single-valued for binary relationships in the 1:1 or N:1 directions; they are collec- tion types (set-valued or list-valued41) for relationships in the 1:N or M:N direc- tion. An alternative way to map binary M:N relationships is discussed in step 7.
If relationship attributes exist, a tuple constructor (struct) can be used to create a structure of the form <reference, relationship attributes>, which may be included instead of the reference attribute. However, this does not allow the use of the inverse constraint. Additionally, if this choice is represented in both directions, the attribute values will be represented twice, creating redundancy.
38This implicitly uses a tuple constructor at the top level of the type declaration, but in general, the tuple constructor is not explicitly shown in the ODL class declarations. 39Further analysis of the application domain is needed to decide which constructor to use because this information is not available from the EER schema. 40The ODL standard provides for the explicit definition of inverse relationships. Some ODBMS products may not provide this support; in such cases, programmers must maintain every relationship explicitly by coding the methods that update the objects appropriately. 41The decision whether to use set or list is not available from the EER schema and must be determined from the requirements.
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Step 3. Include appropriate operations for each class. These are not available from the EER schema and must be added to the database design by referring to the origi- nal requirements. A constructor method should include program code that checks any constraints that must hold when a new object is created. A destructor method should check any constraints that may be violated when an object is deleted. Other methods should include any further constraint checks that are relevant.
Step 4. An ODL class that corresponds to a subclass in the EER schema inherits (via extends) the type and methods of its superclass in the ODL schema. Its specific (noninherited) attributes, relationship references, and operations are specified, as discussed in steps 1, 2, and 3.
Step 5. Weak entity types can be mapped in the same way as regular entity types. An alternative mapping is possible for weak entity types that do not participate in any relationships except their identifying relationship; these can be mapped as though they were composite multivalued attributes of the owner entity type, by using the set<struct<... >> or list<struct<... >> constructors. The attributes of the weak entity are included in the struct<... > construct, which corresponds to a tuple constructor. Attributes are mapped as discussed in steps 1 and 2.
Step 6. Categories (union types) in an EER schema are difficult to map to ODL. It is possible to create a mapping similar to the EER-to-relational mapping (see Section 9.2) by declaring a class to represent the category and defining 1:1 relationships between the category and each of its superclasses. Another option is to use a union type, if it is available.
Step 7. An n-ary relationship with degree n > 2 can be mapped into a separate class, with appropriate references to each participating class. These references are based on mapping a 1:N relationship from each class that represents a participating entity type to the class that represents the n-ary relationship. An M:N binary relationship, especially if it contains relationship attributes, may also use this mapping option, if desired.
The mapping has been applied to a subset of the UNIVERSITY database schema in Figure 8.10 in the context of the ODMG object database standard. The mapped object schema using the ODL notation is shown in Figure 11.10.
11.5 The Object Query Language OQL The object query language OQL is the query language proposed for the ODMG object model. It is designed to work closely with the programming languages for which an ODMG binding is defined, such as C++, Smalltalk, and Java. Hence, an OQL query embedded into one of these programming languages can return objects that match the type system of that language. Additionally, the implementations of class operations in an ODMG schema can have their code written in these program- ming languages. The OQL syntax for queries is similar to the syntax of the relational standard query language SQL, with additional features for ODMG concepts, such as object identity, complex objects, operations, inheritance, polymorphism, and rela- tionships.
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In Section 11.5.1 we will discuss the syntax of simple OQL queries and the concept of using named objects or extents as database entry points. Then, in Section 11.5.2 we will discuss the structure of query results and the use of path expressions to tra- verse relationships among objects. Other OQL features for handling object identity, inheritance, polymorphism, and other object-oriented concepts are discussed in Section 11.5.3. The examples to illustrate OQL queries are based on the UNIVERSITY database schema given in Figure 11.10.
11.5.1 Simple OQL Queries, Database Entry Points, and Iterator Variables
The basic OQL syntax is a select ... from ... where ... structure, as it is for SQL. For example, the query to retrieve the names of all departments in the college of ‘Engineering’ can be written as follows:
Q0: select D.Dname from D in DEPARTMENTS where D.College = ‘Engineering’;
In general, an entry point to the database is needed for each query, which can be any named persistent object. For many queries, the entry point is the name of the extent of a class. Recall that the extent name is considered to be the name of a persistent object whose type is a collection (in most cases, a set) of objects from the class. Looking at the extent names in Figure 11.10, the named object DEPARTMENTS is of type set<DEPARTMENT>; PERSONS is of type set<PERSON>; FACULTY is of type set<FACULTY>; and so on.
The use of an extent name—DEPARTMENTS in Q0—as an entry point refers to a persistent collection of objects. Whenever a collection is referenced in an OQL query, we should define an iterator variable42—D in Q0—that ranges over each object in the collection. In many cases, as in Q0, the query will select certain objects from the collection, based on the conditions specified in the where clause. In Q0, only persistent objects D in the collection of DEPARTMENTS that satisfy the condi- tion D.College = ‘Engineering’ are selected for the query result. For each selected object D, the value of D.Dname is retrieved in the query result. Hence, the type of the result for Q0 is bag<string> because the type of each Dname value is string (even though the actual result is a set because Dname is a key attribute). In general, the result of a query would be of type bag for select ... from ... and of type set for select distinct ... from ... , as in SQL (adding the keyword distinct eliminates duplicates).
Using the example in Q0, there are three syntactic options for specifying iterator variables:
D in DEPARTMENTS DEPARTMENTS D DEPARTMENTS AS D
42This is similar to the tuple variables that range over tuples in SQL queries.
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We will use the first construct in our examples.43
The named objects used as database entry points for OQL queries are not limited to the names of extents. Any named persistent object, whether it refers to an atomic (single) object or to a collection object, can be used as a database entry point.
11.5.2 Query Results and Path Expressions In general, the result of a query can be of any type that can be expressed in the ODMG object model. A query does not have to follow the select ... from ... where ... structure; in the simplest case, any persistent name on its own is a query, whose result is a reference to that persistent object. For example, the query
Q1: DEPARTMENTS;
returns a reference to the collection of all persistent DEPARTMENT objects, whose type is set<DEPARTMENT>. Similarly, suppose we had given (via the database bind operation, see Figure 11.8) a persistent name CS_DEPARTMENT to a single DEPARTMENT object (the Computer Science department); then, the query
Q1A: CS_DEPARTMENT;
returns a reference to that individual object of type DEPARTMENT. Once an entry point is specified, the concept of a path expression can be used to specify a path to related attributes and objects. A path expression typically starts at a persistent object name, or at the iterator variable that ranges over individual objects in a collection. This name will be followed by zero or more relationship names or attribute names connected using the dot notation. For example, referring to the UNIVERSITY data- base in Figure 11.10, the following are examples of path expressions, which are also valid queries in OQL:
Q2: CS_DEPARTMENT.Chair; Q2A: CS_DEPARTMENT.Chair.Rank; Q2B: CS_DEPARTMENT.Has_faculty;
The first expression Q2 returns an object of type FACULTY, because that is the type of the attribute Chair of the DEPARTMENT class. This will be a reference to the FACULTY object that is related to the DEPARTMENT object whose persistent name is CS_DEPARTMENT via the attribute Chair; that is, a reference to the FACULTY object who is chairperson of the Computer Science department. The second expression Q2A is similar, except that it returns the Rank of this FACULTY object (the Computer Science chair) rather than the object reference; hence, the type returned by Q2A is string, which is the data type for the Rank attribute of the FACULTY class.
Path expressions Q2 and Q2A return single values, because the attributes Chair (of DEPARTMENT) and Rank (of FACULTY) are both single-valued and they are applied to a single object. The third expression, Q2B, is different; it returns an object of type set<FACULTY> even when applied to a single object, because that is the type of the
43Note that the latter two options are similar to the syntax for specifying tuple variables in SQL queries.
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relationship Has_faculty of the DEPARTMENT class. The collection returned will include references to all FACULTY objects that are related to the DEPARTMENT object whose persistent name is CS_DEPARTMENT via the relationship Has_faculty; that is, references to all FACULTY objects who are working in the Computer Science depart- ment. Now, to return the ranks of Computer Science faculty, we cannot write
Q3�: CS_DEPARTMENT.Has_faculty.Rank;
because it is not clear whether the object returned would be of type set<string> or bag<string> (the latter being more likely, since multiple faculty may share the same rank). Because of this type of ambiguity problem, OQL does not allow expressions such as Q3�. Rather, one must use an iterator variable over any collections, as in Q3A or Q3B below:
Q3A: select F.Rank from F in CS_DEPARTMENT.Has_faculty;
Q3B: select distinct F.Rank from F in CS_DEPARTMENT.Has_faculty;
Here, Q3A returns bag<string> (duplicate rank values appear in the result), whereas Q3B returns set<string> (duplicates are eliminated via the distinct keyword). Both Q3A and Q3B illustrate how an iterator variable can be defined in the from clause to range over a restricted collection specified in the query. The variable F in Q3A and Q3B ranges over the elements of the collection CS_DEPARTMENT.Has_faculty, which is of type set<FACULTY>, and includes only those faculty who are members of the Computer Science department.
In general, an OQL query can return a result with a complex structure specified in the query itself by utilizing the struct keyword. Consider the following examples:
Q4: CS_DEPARTMENT.Chair.Advises;
Q4A: select struct ( name: struct (last_name: S.name.Lname, first_name: S.name.Fname),
degrees:( select struct (deg: D.Degree, yr: D.Year, college: D.College)
from D in S.Degrees )) from S in CS_DEPARTMENT.Chair.Advises;
Here, Q4 is straightforward, returning an object of type set<GRAD_STUDENT> as its result; this is the collection of graduate students who are advised by the chair of the Computer Science department. Now, suppose that a query is needed to retrieve the last and first names of these graduate students, plus the list of previous degrees of each. This can be written as in Q4A, where the variable S ranges over the collec- tion of graduate students advised by the chairperson, and the variable D ranges over the degrees of each such student S. The type of the result of Q4A is a collection of (first-level) structs where each struct has two components: name and degrees.44
44As mentioned earlier, struct corresponds to the tuple constructor discussed in Section 11.1.3.
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The name component is a further struct made up of last_name and first_name, each being a single string. The degrees component is defined by an embedded query and is itself a collection of further (second level) structs, each with three string compo- nents: deg, yr, and college.
Note that OQL is orthogonal with respect to specifying path expressions. That is, attributes, relationships, and operation names (methods) can be used interchange- ably within the path expressions, as long as the type system of OQL is not compro- mised. For example, one can write the following queries to retrieve the grade point average of all senior students majoring in Computer Science, with the result ordered by GPA, and within that by last and first name:
Q5A: select struct ( last_name: S.name.Lname, first_name: S.name.Fname, gpa: S.gpa )
from S in CS_DEPARTMENT.Has_majors where S.Class = ‘senior’ order by gpa desc, last_name asc, first_name asc;
Q5B: select struct ( last_name: S.name.Lname, first_name: S.name.Fname, gpa: S.gpa )
from S in STUDENTS where S.Majors_in.Dname = ‘Computer Science’ and
S.Class = ‘senior’ order by gpa desc, last_name asc, first_name asc;
Q5A used the named entry point CS_DEPARTMENT to directly locate the reference to the Computer Science department and then locate the students via the relation- ship Has_majors, whereas Q5B searches the STUDENTS extent to locate all students majoring in that department. Notice how attribute names, relationship names, and operation (method) names are all used interchangeably (in an orthogonal manner) in the path expressions: gpa is an operation; Majors_in and Has_majors are relation- ships; and Class, Name, Dname, Lname, and Fname are attributes. The implementa- tion of the gpa operation computes the grade point average and returns its value as a float type for each selected STUDENT.
The order by clause is similar to the corresponding SQL construct, and specifies in which order the query result is to be displayed. Hence, the collection returned by a query with an order by clause is of type list.
11.5.3 Other Features of OQL
Specifying Views as Named Queries. The view mechanism in OQL uses the concept of a named query. The define keyword is used to specify an identifier of the named query, which must be a unique name among all named objects, class names, method names, and function names in the schema. If the identifier has the same name as an existing named query, then the new definition replaces the previous def- inition. Once defined, a query definition is persistent until it is redefined or deleted. A view can also have parameters (arguments) in its definition.
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For example, the following view V1 defines a named query Has_minors to retrieve the set of objects for students minoring in a given department:
V1: define Has_minors(Dept_name) as select S from S in STUDENTS where S.Minors_in.Dname = Dept_name;
Because the ODL schema in Figure 11.10 only provided a unidirectional Minors_in attribute for a STUDENT, we can use the above view to represent its inverse without having to explicitly define a relationship. This type of view can be used to represent inverse relationships that are not expected to be used frequently. The user can now utilize the above view to write queries such as
Has_minors(‘Computer Science’);
which would return a bag of students minoring in the Computer Science depart- ment. Note that in Figure 11.10, we defined Has_majors as an explicit relationship, presumably because it is expected to be used more often.
Extracting Single Elements from Singleton Collections. An OQL query will, in general, return a collection as its result, such as a bag, set (if distinct is specified), or list (if the order by clause is used). If the user requires that a query only return a sin- gle element, there is an element operator in OQL that is guaranteed to return a sin- gle element E from a singleton collection C that contains only one element. If C contains more than one element or if C is empty, then the element operator raises an exception. For example, Q6 returns the single object reference to the Computer Science department:
Q6: element ( select D from D in DEPARTMENTS where D.Dname = ‘Computer Science’ );
Since a department name is unique across all departments, the result should be one department. The type of the result is D:DEPARTMENT.
Collection Operators (Aggregate Functions, Quantifiers). Because many query expressions specify collections as their result, a number of operators have been defined that are applied to such collections. These include aggregate operators as well as membership and quantification (universal and existential) over a collection.
The aggregate operators (min, max, count, sum, avg) operate over a collection.45 The operator count returns an integer type. The remaining aggregate operators (min, max, sum, avg) return the same type as the type of the operand collection. Two examples follow. The query Q7 returns the number of students minoring in Computer Science and Q8 returns the average GPA of all seniors majoring in Computer Science.
45These correspond to aggregate functions in SQL.
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Q7: count ( S in Has_minors(‘Computer Science’));
Q8: avg ( select S.Gpa from S in STUDENTS where S.Majors_in.Dname = ‘Computer Science’ and
S.Class = ‘Senior’);
Notice that aggregate operations can be applied to any collection of the appropriate type and can be used in any part of a query. For example, the query to retrieve all department names that have more than 100 majors can be written as in Q9:
Q9: select D.Dname from D in DEPARTMENTS where count (D.Has_majors) > 100;
The membership and quantification expressions return a Boolean type—that is, true or false. Let V be a variable, C a collection expression, B an expression of type Boolean (that is, a Boolean condition), and E an element of the type of elements in collection C. Then:
(E in C) returns true if element E is a member of collection C. (for all V in C : B) returns true if all the elements of collection C satisfy B. (exists V in C : B) returns true if there is at least one element in C satisfying B.
To illustrate the membership condition, suppose we want to retrieve the names of all students who completed the course called ‘Database Systems I’. This can be writ- ten as in Q10, where the nested query returns the collection of course names that each STUDENT S has completed, and the membership condition returns true if ‘Database Systems I’ is in the collection for a particular STUDENT S:
Q10: select S.name.Lname, S.name.Fname from S in STUDENTS where ‘Database Systems I’ in
( select C.Section.Of_course.Cname from C in S.Completed_sections);
Q10 also illustrates a simpler way to specify the select clause of queries that return a collection of structs; the type returned by Q10 is bag<struct(string, string)>.
One can also write queries that return true/false results. As an example, let us assume that there is a named object called JEREMY of type STUDENT. Then, query Q11 answers the following question: Is Jeremy a Computer Science minor? Similarly, Q12 answers the question Are all Computer Science graduate students advised by Computer Science faculty? Both Q11 and Q12 return true or false, which are inter- preted as yes or no answers to the above questions:
Q11: JEREMY in Has_minors(‘Computer Science’);
Q12: for all G in ( select S
from S in GRAD_STUDENTS where S.Majors_in.Dname = ‘Computer Science’ )
: G.Advisor in CS_DEPARTMENT.Has_faculty;
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Note that query Q12 also illustrates how attribute, relationship, and operation inheritance applies to queries. Although S is an iterator that ranges over the extent GRAD_STUDENTS, we can write S.Majors_in because the Majors_in relationship is inherited by GRAD_STUDENT from STUDENT via extends (see Figure 11.10). Finally, to illustrate the exists quantifier, query Q13 answers the following question: Does any graduate Computer Science major have a 4.0 GPA? Here, again, the operation gpa is inherited by GRAD_STUDENT from STUDENT via extends.
Q13: exists G in ( select S
from S in GRAD_STUDENTS where S.Majors_in.Dname = ‘Computer Science’ )
: G.Gpa = 4;
Ordered (Indexed) Collection Expressions. As we discussed in Section 11.3.3, collections that are lists and arrays have additional operations, such as retrieving the ith, first, and last elements. Additionally, operations exist for extracting a subcollec- tion and concatenating two lists. Hence, query expressions that involve lists or arrays can invoke these operations. We will illustrate a few of these operations using sample queries. Q14 retrieves the last name of the faculty member who earns the highest salary:
Q14: first ( select struct(facname: F.name.Lname, salary: F.Salary) from F in FACULTY order by salary desc );
Q14 illustrates the use of the first operator on a list collection that contains the salaries of faculty members sorted in descending order by salary. Thus, the first ele- ment in this sorted list contains the faculty member with the highest salary. This query assumes that only one faculty member earns the maximum salary. The next query, Q15, retrieves the top three Computer Science majors based on GPA.
Q15: ( select struct( last_name: S.name.Lname, first_name: S.name.Fname, gpa: S.Gpa )
from S in CS_DEPARTMENT.Has_majors order by gpa desc ) [0:2];
The select-from-order-by query returns a list of Computer Science students ordered by GPA in descending order. The first element of an ordered collection has an index position of 0, so the expression [0:2] returns a list containing the first, second, and third elements of the select ... from ... order by ... result.
The Grouping Operator. The group by clause in OQL, although similar to the corresponding clause in SQL, provides explicit reference to the collection of objects within each group or partition. First we give an example, and then we describe the general form of these queries.
Q16 retrieves the number of majors in each department. In this query, the students are grouped into the same partition (group) if they have the same major; that is, the
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same value for S.Majors_in.Dname:
Q16: ( select struct( dept_name, number_of_majors: count (partition) ) from S in STUDENTS group by dept_name: S.Majors_in.Dname;
The result of the grouping specification is of type set<struct(dept_name: string, parti- tion: bag<struct(S:STUDENT>)>), which contains a struct for each group (partition) that has two components: the grouping attribute value (dept_name) and the bag of the STUDENT objects in the group (partition). The select clause returns the grouping attribute (name of the department), and a count of the number of elements in each partition (that is, the number of students in each department), where partition is the keyword used to refer to each partition. The result type of the select clause is set<struct(dept_name: string, number_of_majors: integer)>. In general, the syntax for the group by clause is
group by F1: E1, F2: E2, ..., Fk: Ek
where F1: E1, F2: E2, ..., Fk: Ek is a list of partitioning (grouping) attributes and each partitioning attribute specification Fi: Ei defines an attribute (field) name Fi and an expression Ei. The result of applying the grouping (specified in the group by clause) is a set of structures:
set<struct(F1: T1, F2: T2, ..., Fk: Tk, partition: bag<B>)>
where Ti is the type returned by the expression Ei, partition is a distinguished field name (a keyword), and B is a structure whose fields are the iterator variables (S in Q16) declared in the from clause having the appropriate type.
Just as in SQL, a having clause can be used to filter the partitioned sets (that is, select only some of the groups based on group conditions). In Q17, the previous query is modified to illustrate the having clause (and also shows the simplified syntax for the select clause). Q17 retrieves for each department having more than 100 majors, the average GPA of its majors. The having clause in Q17 selects only those partitions (groups) that have more than 100 elements (that is, departments with more than 100 students).
Q17: select dept_name, avg_gpa: avg ( select P.gpa from P in partition) from S in STUDENTS group by dept_name: S.Majors_in.Dname having count (partition) > 100;
Note that the select clause of Q17 returns the average GPA of the students in the partition. The expression
select P.Gpa from P in partition
returns a bag of student GPAs for that partition. The from clause declares an iterator variable P over the partition collection, which is of type bag<struct(S: STUDENT)>. Then the path expression P.gpa is used to access the GPA of each student in the partition.
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11.6 Overview of the C++ Language Binding in the ODMG Standard
The C++ language binding specifies how ODL constructs are mapped to C++ con- structs. This is done via a C++ class library that provides classes and operations that implement the ODL constructs. An object manipulation language (OML) is needed to specify how database objects are retrieved and manipulated within a C++ pro- gram, and this is based on the C++ programming language syntax and semantics. In addition to the ODL/OML bindings, a set of constructs called physical pragmas are defined to allow the programmer some control over physical storage issues, such as clustering of objects, utilizing indexes, and memory management.
The class library added to C++ for the ODMG standard uses the prefix d_ for class declarations that deal with database concepts.46 The goal is that the programmer should think that only one language is being used, not two separate languages. For the programmer to refer to database objects in a program, a class D_Ref<T> is defined for each database class T in the schema. Hence, program variables of type D_Ref<T> can refer to both persistent and transient objects of class T.
In order to utilize the various built-in types in the ODMG object model such as col- lection types, various template classes are specified in the library. For example, an abstract class D_Object<T> specifies the operations to be inherited by all objects. Similarly, an abstract class D_Collection<T> specifies the operations of collections. These classes are not instantiable, but only specify the operations that can be inher- ited by all objects and by collection objects, respectively. A template class is specified for each type of collection; these include D_Set<T>, D_List<T>, D_Bag<T>, D_Varray<T>, and D_Dictionary<T>, and correspond to the collection types in the object model (see Section 11.3.1). Hence, the programmer can create classes of types such as D_Set<D_Ref<STUDENT>> whose instances would be sets of refer- ences to STUDENT objects, or D_Set<string> whose instances would be sets of strings. Additionally, a class d_Iterator corresponds to the Iterator class of the object model.
The C++ ODL allows a user to specify the classes of a database schema using the constructs of C++ as well as the constructs provided by the object database library. For specifying the data types of attributes,47 basic types such as d_Short (short inte- ger), d_Ushort (unsigned short integer), d_Long (long integer), and d_Float (floating point number) are provided. In addition to the basic data types, several structured literal types are provided to correspond to the structured literal types of the ODMG object model. These include d_String, d_Interval, d_Date, d_Time, and d_Timestamp (see Figure 11.5(b)).
46Presumably, d_ stands for database classes. 47That is, member variables in object-oriented programming terminology.
408 Chapter 11 Object and Object-Relational Databases
To specify relationships, the keyword rel_ is used within the prefix of type names; for example, by writing
d_Rel_Ref <DEPARTMENT, Has_majors> Majors_in;
in the STUDENT class, and
d_Rel_Set<STUDENT, Majors_in> Has_majors;
in the DEPARTMENT class, we are declaring that Majors_in and Has_majors are rela- tionship properties that are inverses of one another and hence represent a 1:N binary relationship between DEPARTMENT and STUDENT.
For the OML, the binding overloads the operation new so that it can be used to cre- ate either persistent or transient objects. To create persistent objects, one must pro- vide the database name and the persistent name of the object. For example, by writing
D_Ref<STUDENT> S = new(DB1, ‘John_Smith’) STUDENT;
the programmer creates a named persistent object of type STUDENT in database DB1 with persistent name John_Smith. Another operation, delete_object() can be used to delete objects. Object modification is done by the operations (methods) defined in each class by the programmer.
The C++ binding also allows the creation of extents by using the library class d_Extent. For example, by writing
D_Extent<PERSON> ALL_PERSONS(DB1);
the programmer would create a named collection object ALL_PERSONS—whose type would be D_Set<PERSON>—in the database DB1 that would hold persistent objects of type PERSON. However, key constraints are not supported in the C++ binding, and any key checks must be programmed in the class methods.48 Also, the C++ binding does not support persistence via reachability; the object must be stati- cally declared to be persistent at the time it is created.
11.7 Summary In this chapter, we started in Section 11.1 with an overview of the concepts utilized in object databases, and discussed how these concepts were derived from general object-oriented principles. The main concepts we discussed were: object identity and identifiers; encapsulation of operations; inheritance; complex structure of objects through nesting of type constructors; and how objects are made persistent. Then in Section 11.2, we showed how many of these concepts were incorporated into the relational model and the SQL standard, leading to expanded relational database functionality. These systems have been called object-relational databases.
48We have only provided a brief overview of the C++ binding. For full details, see Cattell and Barry eds. (2000), Ch. 5.
Review Questions 409
We then discussed the ODMG 3.0 standard for object databases. We started by describing the various constructs of the object model in Sction 11.3. The various built-in types, such as Object, Collection, Iterator, set, list, and so on were described by their interfaces, which specify the built-in operations of each type. These built-in types are the foundation upon which the object definition language (ODL) and object query language (OQL) are based. We also described the difference between objects, which have an ObjectId, and literals, which are values with no OID. Users can declare classes for their application that inherit operations from the appropriate built-in interfaces. Two types of properties can be specified in a user-defined class— attributes and relationships—in addition to the operations that can be applied to objects of the class. The ODL allows users to specify both interfaces and classes, and permits two different types of inheritance—interface inheritance via “:” and class inheritance via extends. A class can have an extent and keys. A description of ODL followed, and an example database schema for the UNIVERSITY database was used to illustrate the ODL constructs.
Following the description of the ODMG object model, we described a general tech- nique for designing object database schemas in Section 11.4. We discussed how object databases differ from relational databases in three main areas: references to represent relationships, inclusion of operations, and inheritance. Finally, we showed how to map a conceptual database design in the EER model to the constructs of object databases.
In Section 11.5, we presented an overview of the object query language (OQL). The OQL follows the concept of orthogonality in constructing queries, meaning that an operation can be applied to the result of another operation as long as the type of the result is of the correct input type for the operation. The OQL syntax follows many of the constructs of SQL but includes additional concepts such as path expressions, inheritance, methods, relationships, and collections. Examples of how to use OQL over the UNIVERSITY database were given.
Next we gave an overview of the C++ language binding in Section 11.6, which extends C++ class declarations with the ODL type constructors, but permits seam- less integration of C++ with the ODBMS.
In 1997 Sun endorsed the ODMG API (Application Program Interface). O2 tech- nologies was the first corporation to deliver an ODMG-compliant DBMS. Many ODBMS vendors, including Object Design (now eXcelon), Gemstone Systems, POET Software, and Versant Object Technology, have endorsed the ODMG standard.
Review Questions 11.1. What are the origins of the object-oriented approach?
11.2. What primary characteristics should an OID possess?
11.3. Discuss the various type constructors. How are they used to create complex object structures?
410 Chapter 11 Object and Object-Relational Databases
11.4. Discuss the concept of encapsulation, and tell how it is used to create abstract data types.
11.5. Explain what the following terms mean in object-oriented database termi- nology: method, signature, message, collection, extent.
11.6. What is the relationship between a type and its subtype in a type hierarchy? What is the constraint that is enforced on extents corresponding to types in the type hierarchy?
11.7. What is the difference between persistent and transient objects? How is per- sistence handled in typical OO database systems?
11.8. How do regular inheritance, multiple inheritance, and selective inheritance differ?
11.9. Discuss the concept of polymorphism/operator overloading.
11.10. Discuss how each of the following features is realized in SQL 2008: object identifier.; type inheritance, encapsulation of operations, and complex object structures.
11.11. In the traditional relational model, creating a table defined both the table type (schema or attributes) and the table itself (extension or set of current tuples). How can these two concepts be separated in SQL 2008?
11.12. Describe the rules of inheritance in SQL 2008.
11.13. What are the differences and similarities between objects and literals in the ODMG object model?
11.14. List the basic operations of the following built-in interfaces of the ODMG object model: Object, Collection, Iterator, Set, List, Bag, Array, and Dictionary.
11.15. Describe the built-in structured literals of the ODMG object model and the operations of each.
11.16. What are the differences and similarities of attribute and relationship prop- erties of a user-defined (atomic) class?
11.17. What are the differences and similarities of class inhertance via extends and interface inheritance via “:”in the ODMG object model?
11.18. Discuss how persistence is specified in the ODMG object model in the C++ binding.
11.19. Why are the concepts of extents and keys important in database applica- tions?
11.20. Describe the following OQL concepts: database entry points, path expres- sions, iterator variables, named queries (views), aggregate functions, grouping, and quantifiers.
Exercises 411
11.21. What is meant by the type orthogonality of OQL?
11.22. Discuss the general principles behind the C++ binding of the ODMG stan- dard.
11.23. What are the main differences between designing a relational database and an object database?
11.24. Describe the steps of the algorithm for object database design by EER-to-OO mapping.
Exercises 11.25. Convert the example of GEOMETRY_OBJECTs given in Section 11.1.5 from
the functional notation to the notation given in Figure 11.2 that distin- guishes between attributes and operations. Use the keyword INHERIT to show that one class inherits from another class.
11.26. Compare inheritance in the EER model (see Chapter 8) to inheritance in the OO model described in Section 11.1.5.
11.27. Consider the UNIVERSITY EER schema in Figure 8.10. Think of what opera- tions are needed for the entity types/classes in the schema. Do not consider constructor and destructor operations.
11.28. Consider the COMPANY ER schema in Figure 7.2. Think of what operations are needed for the entity types/classes in the schema. Do not consider con- structor and destructor operations.
11.29. Design an OO schema for a database application that you are interested in. Construct an EER schema for the application, and then create the corre- sponding classes in ODL. Specify a number of methods for each class, and then specify queries in OQL for your database application.
11.30. Consider the AIRPORT database described in Exercise 8.21. Specify a number of operations/methods that you think should be applicable to that applica- tion. Specify the ODL classes and methods for the database.
11.31. Map the COMPANY ER schema in Figure 7.2 into ODL classes. Include appropriate methods for each class.
11.32. Specify in OQL the queries in the exercises of Chapters 7 and 8 that apply to the COMPANY database.
11.33. Using search engines and other sources, determine to what extent the vari- ous commercial ODBMS products are compliant with the ODMG 3.0 standard.
412 Chapter 11 Object and Object-Relational Databases
Selected Bibliography Object-oriented database concepts are an amalgam of concepts from OO program- ming languages and from database systems and conceptual data models. A number of textbooks describe OO programming languages—for example, Stroustrup (1997) for C++, and Goldberg and Robson (1989) for Smalltalk. Books by Cattell (1994) and Lausen and Vossen (1997) describe OO database concepts. Other books on OO models include a detailed description of the experimental OODBMS devel- oped at Microelectronic Computer Corporation called ORION and related OO topics by Kim and Lochovsky (1989). Bancilhon et al. (1992) describes the story of building the O2 OODBMS with a detailed discussion of design decisions and lan- guage implementation. Dogac et al. (1994) provides a thorough discussion on OO database topics by experts at a NATO workshop.
There is a vast bibliography on OO databases, so we can only provide a representa- tive sample here. The October 1991 issue of CACM and the December 1990 issue of IEEE Computer describe OO database concepts and systems. Dittrich (1986) and Zaniolo et al. (1986) survey the basic concepts of OO data models. An early paper on OO database system implementation is Baroody and DeWitt (1981). Su et al. (1988) presents an OO data model that was used in CAD/CAM applications. Gupta and Horowitz (1992) discusses OO applications to CAD, Network Management, and other areas. Mitschang (1989) extends the relational algebra to cover complex objects. Query languages and graphical user interfaces for OO are described in Gyssens et al. (1990), Kim (1989), Alashqur et al. (1989), Bertino et al. (1992), Agrawal et al. (1990), and Cruz (1992).
The Object-Oriented Manifesto by Atkinson et al. (1990) is an interesting article that reports on the position by a panel of experts regarding the mandatory and optional features of OO database management. Polymorphism in databases and OO programming languages is discussed in Osborn (1989), Atkinson and Buneman (1987), and Danforth and Tomlinson (1988). Object identity is discussed in Abiteboul and Kanellakis (1989). OO programming languages for databases are dis- cussed in Kent (1991). Object constraints are discussed in Delcambre et al. (1991) and Elmasri, James and Kouramajian (1993). Authorization and security in OO databases are examined in Rabitti et al. (1991) and Bertino (1992).
Cattell and Barry (2000) describes the ODMG 3.0 standard, which is described in this chapter, and Cattell et al. (1993) and Cattell et al. (1997) describe the earlier ver- sions of the standard. Bancilhon and Ferrari (1995) give a tutorial presentation of the important aspects of the ODMG standard. Several books describe the CORBA architecture—for example, Baker (1996).
The O2 system is described in Deux et al. (1991), and Bancilhon et al. (1992) includes a list of references to other publications describing various aspects of O2. The O2 model was formalized in Velez et al. (1989). The ObjectStore system is described in Lamb et al. (1991). Fishman et al. (1987) and Wilkinson et al. (1990) discuss IRIS, an object-oriented DBMS developed at Hewlett-Packard laboratories.
Maier et al. (1986) and Butterworth et al. (1991) describe the design of GEM- STONE. The ODE system developed at AT&T Bell Labs is described in Agrawal and Gehani (1989). The ORION system developed at MCC is described in Kim et al. (1990). Morsi et al. (1992) describes an OO testbed.
Cattell (1991) surveys concepts from both relational and object databases and dis- cusses several prototypes of object-based and extended relational database systems. Alagic (1997) points out discrepancies between the ODMG data model and its lan- guage bindings and proposes some solutions. Bertino and Guerrini (1998) propose an extension of the ODMG model for supporting composite objects. Alagic (1999) presents several data models belonging to the ODMG family.
Selected Bibliography 413
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415
XML: Extensible Markup Language
Many electronic commerce (e-commerce) andother Internet applications provide Web inter- faces to access information stored in one or more databases. These databases are often referred to as data sources. It is common to use two-tier and three-tier client/server architectures for Internet applications (see Section 2.5). In some cases, other variations of the client/server model are used. E-commerce and other Internet database applications are designed to interact with the user through Web interfaces that display Web pages. The common method of specifying the contents and for- matting of Web pages is through the use of hypertext documents. There are various languages for writing these documents, the most common being HTML (HyperText Markup Language). Although HTML is widely used for formatting and structuring Web documents, it is not suitable for specifying structured data that is extracted from databases. A new language—namely, XML (Extensible Markup Language)—has emerged as the standard for structuring and exchanging data over the Web. XML can be used to provide information about the structure and meaning of the data in the Web pages rather than just specifying how the Web pages are formatted for dis- play on the screen. The formatting aspects are specified separately—for example, by using a formatting language such as XSL (Extensible Stylesheet Language) or a transformation language such as XSLT (Extensible Stylesheet Language for Transformations or simply XSL Transformations). Recently, XML has also been proposed as a possible model for data storage and retrieval, although only a few experimental database systems based on XML have been developed so far.
Basic HTML is useful for generating static Web pages with fixed text and other objects, but most e-commerce applications require Web pages that provide interac- tive features with the user. For example, consider the case of an airline customer who wants to check the arrival time and gate information of a particular flight. The user may enter information such as a date and flight number in certain form fields
12chapter 12
416 Chapter 12 XML: Extensible Markup Language
of the Web page. The Web program must first submit a query to the airline database to retrieve this information, and then display it. Such Web pages, where part of the information is extracted from databases or other data sources are called dynamic Web pages, because the data extracted and displayed each time will be for different flights and dates.
In this chapter, we will focus on describing the XML data model and its associated languages, and how data extracted from relational databases can be formatted as XML documents to be exchanged over the Web. Section 12.1 discusses the differ- ence between structured, semistructured, and unstructured data. Section 12.2 pre- sents the XML data model, which is based on tree (hierarchical) structures as compared to the flat relational data model structures. In Section 12.3, we focus on the structure of XML documents, and the languages for specifying the structure of these documents such as DTD (Document Type Definition) and XML Schema. Section 12.4 shows the relationship between XML and relational databases. Section 12.5 describes some of the languages associated with XML, such as XPath and XQuery. Section 12.6 discusses how data extracted from relational databases can be formatted as XML documents. Finally, Section 12.7 is the chapter summary.
12.1 Structured, Semistructured, and Unstructured Data
The information stored in databases is known as structured data because it is rep- resented in a strict format. For example, each record in a relational database table— such as each of the tables in the COMPANY database in Figure 3.6—follows the same format as the other records in that table. For structured data, it is common to care- fully design the database schema using techniques such as those described in Chapters 7 and 8 in order to define the database structure. The DBMS then checks to ensure that all data follows the structures and constraints specified in the schema.
However, not all data is collected and inserted into carefully designed structured databases. In some applications, data is collected in an ad hoc manner before it is known how it will be stored and managed. This data may have a certain structure, but not all the information collected will have the identical structure. Some attrib- utes may be shared among the various entities, but other attributes may exist only in a few entities. Moreover, additional attributes can be introduced in some of the newer data items at any time, and there is no predefined schema. This type of data is known as semistructured data. A number of data models have been introduced for representing semistructured data, often based on using tree or graph data structures rather than the flat relational model structures.
A key difference between structured and semistructured data concerns how the schema constructs (such as the names of attributes, relationships, and entity types) are handled. In semistructured data, the schema information is mixed in with the data values, since each data object can have different attributes that are not known in advance. Hence, this type of data is sometimes referred to as self-describing data. Consider the following example. We want to collect a list of bibliographic references
12.1 Structured, Semistructured, and Unstructured Data 417
LocationNumber
Project Project
Company projects
Name
‘Bellaire’1‘Product X’
Worker Worker
HoursLast_ name
Ssn HoursFirst_
name Ssn
32.5‘Smith’‘123456789’ 20.0‘Joyce’‘435435435’
Figure 12.1 Representing semistructured data as a graph.
related to a certain research project. Some of these may be books or technical reports, others may be research articles in journals or conference proceedings, and still others may refer to complete journal issues or conference proceedings. Clearly, each of these may have different attributes and different types of information. Even for the same type of reference—say, conference articles—we may have different information. For example, one article citation may be quite complete, with full information about author names, title, proceedings, page numbers, and so on, whereas another citation may not have all the information available. New types of bibliographic sources may appear in the future—for instance, references to Web pages or to conference tutori- als—and these may have new attributes that describe them.
Semistructured data may be displayed as a directed graph, as shown in Figure 12.1. The information shown in Figure 12.1 corresponds to some of the structured data shown in Figure 3.6. As we can see, this model somewhat resembles the object model (see Section 11.1.3) in its ability to represent complex objects and nested structures. In Figure 12.1, the labels or tags on the directed edges represent the schema names: the names of attributes, object types (or entity types or classes), and relationships. The internal nodes represent individual objects or composite attrib- utes. The leaf nodes represent actual data values of simple (atomic) attributes.
There are two main differences between the semistructured model and the object model that we discussed in Chapter 11:
1. The schema information—names of attributes, relationships, and classes (object types) in the semistructured model is intermixed with the objects and their data values in the same data structure.
2. In the semistructured model, there is no requirement for a predefined schema to which the data objects must conform, although it is possible to define a schema if necessary.
418 Chapter 12 XML: Extensible Markup Language
Figure 12.2 Part of an HTML document representing unstructured data.
<HTML> <HEAD> ... </HEAD> <BODY>
<H1>List of company projects and the employees in each project</H1> <H2>The ProductX project:</H2> <TABLE width=“100%” border=0 cellpadding=0 cellspacing=0>
<TR> <TD width=“50%”><FONT size=“2” face=“Arial”>John Smith:</FONT></TD> <TD>32.5 hours per week</TD>
</TR> <TR>
<TD width=“50%”><FONT size=“2” face=“Arial”>Joyce English:</FONT></TD> <TD>20.0 hours per week</TD>
</TR> </TABLE> <H2>The ProductY project:</H2> <TABLE width=“100%” border=0 cellpadding=0 cellspacing=0>
<TR> <TD width=“50%”><FONT size=“2” face=“Arial”>John Smith:</FONT></TD> <TD>7.5 hours per week</TD>
</TR> <TR>
<TD width=“50%”><FONT size=“2” face=“Arial”>Joyce English:</FONT></TD> <TD>20.0 hours per week</TD>
</TR> <TR>
<TD width= “50%”><FONT size=“2” face=“Arial”>Franklin Wong:</FONT></TD> <TD>10.0 hours per week</TD>
</TR> </TABLE>
... </BODY>
</HTML>
In addition to structured and semistructured data, a third category exists, known as unstructured data because there is very limited indication of the type of data. A typical example is a text document that contains information embedded within it. Web pages in HTML that contain some data are considered to be unstructured data. Consider part of an HTML file, shown in Figure 12.2. Text that appears between angled brackets, <...>, is an HTML tag. A tag with a slash, </...>, indicates an end tag, which represents the ending of the effect of a matching start tag. The tags mark
12.1 Structured, Semistructured, and Unstructured Data 419
up the document1 in order to instruct an HTML processor how to display the text between a start tag and a matching end tag. Hence, the tags specify document for- matting rather than the meaning of the various data elements in the document. HTML tags specify information, such as font size and style (boldface, italics, and so on), color, heading levels in documents, and so on. Some tags provide text structur- ing in documents, such as specifying a numbered or unnumbered list or a table. Even these structuring tags specify that the embedded textual data is to be displayed in a certain manner, rather than indicating the type of data represented in the table.
HTML uses a large number of predefined tags, which are used to specify a variety of commands for formatting Web documents for display. The start and end tags spec- ify the range of text to be formatted by each command. A few examples of the tags shown in Figure 12.2 follow:
■ The <HTML> ... </HTML> tags specify the boundaries of the document. ■ The document header information—within the <HEAD> ... </HEAD>
tags—specifies various commands that will be used elsewhere in the docu- ment. For example, it may specify various script functions in a language such as JavaScript or PERL, or certain formatting styles (fonts, paragraph styles, header styles, and so on) that can be used in the document. It can also specify a title to indicate what the HTML file is for, and other similar infor- mation that will not be displayed as part of the document.
■ The body of the document—specified within the <BODY> ... </BODY> tags—includes the document text and the markup tags that specify how the text is to be formatted and displayed. It can also include references to other objects, such as images, videos, voice messages, and other documents.
■ The <H1> ... </H1> tags specify that the text is to be displayed as a level 1 heading. There are many heading levels (<H2>, <H3>, and so on), each dis- playing text in a less prominent heading format.
■ The <TABLE> ... </TABLE> tags specify that the following text is to be dis- played as a table. Each table row in the table is enclosed within <TR> ... </TR> tags, and the individual table data elements in a row are displayed within <TD> ... </TD> tags.2
■ Some tags may have attributes, which appear within the start tag and describe additional properties of the tag.3
In Figure 12.2, the <TABLE> start tag has four attributes describing various charac- teristics of the table. The following <TD> and <FONT> start tags have one and two attributes, respectively.
HTML has a very large number of predefined tags, and whole books are devoted to describing how to use these tags. If designed properly, HTML documents can be
1That is why it is known as HyperText Markup Language. 2<TR> stands for table row and <TD> stands for table data. 3This is how the term attribute is used in document markup languages, which differs from how it is used in database models.
420 Chapter 12 XML: Extensible Markup Language
formatted so that humans are able to easily understand the document contents, and are able to navigate through the resulting Web documents. However, the source HTML text documents are very difficult to interpret automatically by computer pro- grams because they do not include schema information about the type of data in the documents. As e-commerce and other Internet applications become increasingly automated, it is becoming crucial to be able to exchange Web documents among various computer sites and to interpret their contents automatically. This need was one of the reasons that led to the development of XML. In addition, an extendible version of HTML called XHTML was developed that allows users to extend the tags of HTML for different applications, and allows an XHTML file to be interpreted by standard XML processing programs. Our discussion will focus on XML only.
The example in Figure 12.2 illustrates a static HTML page, since all the information to be displayed is explicitly spelled out as fixed text in the HTML file. In many cases, some of the information to be displayed may be extracted from a database. For example, the project names and the employees working on each project may be extracted from the database in Figure 3.6 through the appropriate SQL query. We may want to use the same HTML formatting tags for displaying each project and the employees who work on it, but we may want to change the particular projects (and employees) being displayed. For example, we may want to see a Web page displaying the information for ProjectX, and then later a page displaying the information for ProjectY. Although both pages are displayed using the same HTML formatting tags, the actual data items displayed will be different. Such Web pages are called dynamic, since the data parts of the page may be different each time it is displayed, even though the display appearance is the same.
12.2 XML Hierarchical (Tree) Data Model We now introduce the data model used in XML. The basic object in XML is the XML document. Two main structuring concepts are used to construct an XML doc- ument: elements and attributes. It is important to note that the term attribute in XML is not used in the same manner as is customary in database terminology, but rather as it is used in document description languages such as HTML and SGML.4
Attributes in XML provide additional information that describes elements, as we will see. There are additional concepts in XML, such as entities, identifiers, and ref- erences, but first we concentrate on describing elements and attributes to show the essence of the XML model.
Figure 12.3 shows an example of an XML element called <Projects>. As in HTML, elements are identified in a document by their start tag and end tag. The tag names are enclosed between angled brackets < ... >, and end tags are further identified by a slash, </ ... >.5
4SGML (Standard Generalized Markup Language) is a more general language for describing documents and provides capabilities for specifying new tags. However, it is more complex than HTML and XML. 5The left and right angled bracket characters (< and >) are reserved characters, as are the ampersand (&), apostrophe (’), and single quotation mark (‘). To include them within the text of a document, they must be encoded with escapes as <, >, &, ', and ", respectively.
12.2 XML Hierarchical (Tree) Data Model 421
Figure 12.3 A complex XML element called <Projects>.
<?xml version= “1.0” standalone=“yes”?> <Projects>
<Project> <Name>ProductX</Name> <Number>1</Number> <Location>Bellaire</Location> <Dept_no>5</Dept_no> <Worker>
<Ssn>123456789</Ssn> <Last_name>Smith</Last_name> <Hours>32.5</Hours>
</Worker> <Worker>
<Ssn>453453453</Ssn> <First_name>Joyce</First_name> <Hours>20.0</Hours>
</Worker> </Project> <Project>
<Name>ProductY</Name> <Number>2</Number> <Location>Sugarland</Location> <Dept_no>5</Dept_no> <Worker>
<Ssn>123456789</Ssn> <Hours>7.5</Hours>
</Worker> <Worker>
<Ssn>453453453</Ssn> <Hours>20.0</Hours>
</Worker> <Worker>
<Ssn>333445555</Ssn> <Hours>10.0</Hours>
</Worker> </Project>
... </Projects>
Complex elements are constructed from other elements hierarchically, whereas simple elements contain data values. A major difference between XML and HTML is that XML tag names are defined to describe the meaning of the data elements in the document, rather than to describe how the text is to be displayed. This makes it possible to process the data elements in the XML document automatically by com- puter programs. Also, the XML tag (element) names can be defined in another doc- ument, known as the schema document, to give a semantic meaning to the tag names
422 Chapter 12 XML: Extensible Markup Language
that can be exchanged among multiple users. In HTML, all tag names are prede- fined and fixed; that is why they are not extendible.
It is straightforward to see the correspondence between the XML textual representa- tion shown in Figure 12.3 and the tree structure shown in Figure 12.1. In the tree representation, internal nodes represent complex elements, whereas leaf nodes rep- resent simple elements. That is why the XML model is called a tree model or a hierarchical model. In Figure 12.3, the simple elements are the ones with the tag names <Name>, <Number>, <Location>, <Dept_no>, <Ssn>, <Last_name>, <First_name>, and <Hours>. The complex elements are the ones with the tag names <Projects>, <Project>, and <Worker>. In general, there is no limit on the levels of nesting of elements.
It is possible to characterize three main types of XML documents:
■ Data-centric XML documents. These documents have many small data items that follow a specific structure and hence may be extracted from a structured database. They are formatted as XML documents in order to exchange them over or display them on the Web. These usually follow a predefined schema that defines the tag names.
■ Document-centric XML documents. These are documents with large amounts of text, such as news articles or books. There are few or no struc- tured data elements in these documents.
■ Hybrid XML documents. These documents may have parts that contain structured data and other parts that are predominantly textual or unstruc- tured. They may or may not have a predefined schema.
XML documents that do not follow a predefined schema of element names and cor- responding tree structure are known as schemaless XML documents. It is impor- tant to note that data-centric XML documents can be considered either as semistructured data or as structured data as defined in Section 12.1. If an XML doc- ument conforms to a predefined XML schema or DTD (see Section 12.3), then the document can be considered as structured data. On the other hand, XML allows documents that do not conform to any schema; these would be considered as semistructured data and are schemaless XML documents. When the value of the standalone attribute in an XML document is yes, as in the first line in Figure 12.3, the document is standalone and schemaless.
XML attributes are generally used in a manner similar to how they are used in HTML (see Figure 12.2), namely, to describe properties and characteristics of the elements (tags) within which they appear. It is also possible to use XML attributes to hold the values of simple data elements; however, this is generally not recom- mended. An exception to this rule is in cases that need to reference another element in another part of the XML document. To do this, it is common to use attribute val- ues in one element as the references. This resembles the concept of foreign keys in relational databases, and is a way to get around the strict hierarchical model that the XML tree model implies. We discuss XML attributes further in Section 12.3 when we discuss XML schema and DTD.
12.3 XML Documents, DTD, and XML Schema 423
12.3 XML Documents, DTD, and XML Schema
12.3.1 Well-Formed and Valid XML Documents and XML DTD
In Figure 12.3, we saw what a simple XML document may look like. An XML docu- ment is well formed if it follows a few conditions. In particular, it must start with an XML declaration to indicate the version of XML being used as well as any other rel- evant attributes, as shown in the first line in Figure 12.3. It must also follow the syn- tactic guidelines of the tree data model. This means that there should be a single root element, and every element must include a matching pair of start and end tags within the start and end tags of the parent element. This ensures that the nested ele- ments specify a well-formed tree structure.
A well-formed XML document is syntactically correct. This allows it to be processed by generic processors that traverse the document and create an internal tree repre- sentation. A standard model with an associated set of API (application program- ming interface) functions called DOM (Document Object Model) allows programs to manipulate the resulting tree representation corresponding to a well-formed XML document. However, the whole document must be parsed beforehand when using DOM in order to convert the document to that standard DOM internal data structure representation. Another API called SAX (Simple API for XML) allows processing of XML documents on the fly by notifying the processing program through callbacks whenever a start or end tag is encountered. This makes it easier to process large documents and allows for processing of so-called streaming XML documents, where the processing program can process the tags as they are encoun- tered. This is also known as event-based processing.
A well-formed XML document can be schemaless; that is, it can have any tag names for the elements within the document. In this case, there is no predefined set of ele- ments (tag names) that a program processing the document knows to expect. This gives the document creator the freedom to specify new elements, but limits the pos- sibilities for automatically interpreting the meaning or semantics of the elements within the document.
A stronger criterion is for an XML document to be valid. In this case, the document must be well formed, and it must follow a particular schema. That is, the element names used in the start and end tag pairs must follow the structure specified in a separate XML DTD (Document Type Definition) file or XML schema file. We first discuss XML DTD here, and then we give an overview of XML schema in Section 12.3.2. Figure 12.4 shows a simple XML DTD file, which specifies the elements (tag names) and their nested structures. Any valid documents conforming to this DTD should follow the specified structure. A special syntax exists for specifying DTD files, as illustrated in Figure 12.4. First, a name is given to the root tag of the docu- ment, which is called Projects in the first line in Figure 12.4. Then the elements and their nested structure are specified.
424 Chapter 12 XML: Extensible Markup Language
Figure 12.4 An XML DTD file called Projects.
When specifying elements, the following notation is used:
■ A * following the element name means that the element can be repeated zero or more times in the document. This kind of element is known as an optional multivalued (repeating) element.
■ A + following the element name means that the element can be repeated one or more times in the document. This kind of element is a required multival- ued (repeating) element.
■ A ? following the element name means that the element can be repeated zero or one times. This kind is an optional single-valued (nonrepeating) element.
■ An element appearing without any of the preceding three symbols must appear exactly once in the document. This kind is a required single-valued (nonrepeating) element.
■ The type of the element is specified via parentheses following the element. If the parentheses include names of other elements, these latter elements are the children of the element in the tree structure. If the parentheses include the keyword #PCDATA or one of the other data types available in XML DTD, the element is a leaf node. PCDATA stands for parsed character data, which is roughly similar to a string data type.
■ The list of attributes that can appear within an element can also be specified via the keyword !ATTLIST. In Figure 12.3, the Project element has an attribute ProjId. If the type of an attribute is ID, then it can be referenced from another attribute whose type is IDREF within another element. Notice that attributes can also be used to hold the values of simple data elements of type #PCDATA.
■ Parentheses can be nested when specifying elements. ■ A bar symbol ( e1 | e2 ) specifies that either e1 or e2 can appear in the docu-
ment.
<!DOCTYPE Projects [ <!ELEMENT Projects (Project+)> <!ELEMENT Project (Name, Number, Location, Dept_no?, Workers)
<!ATTLIST Project ProjId ID #REQUIRED>
> <!ELEMENT Name (#PCDATA)> <!ELEMENT Number (#PCDATA) <!ELEMENT Location (#PCDATA)> <!ELEMENT Dept_no (#PCDATA)> <!ELEMENT Workers (Worker*)> <!ELEMENT Worker (Ssn, Last_name?, First_name?, Hours)> <!ELEMENT Ssn (#PCDATA)> <!ELEMENT Last_name (#PCDATA)> <!ELEMENT First_name (#PCDATA)> <!ELEMENT Hours (#PCDATA)>
] >
12.3 XML Documents, DTD, and XML Schema 425
We can see that the tree structure in Figure 12.1 and the XML document in Figure 12.3 conform to the XML DTD in Figure 12.4. To require that an XML document be checked for conformance to a DTD, we must specify this in the declaration of the document. For example, we could change the first line in Figure 12.3 to the following:
<?xml version=“1.0” standalone=“no”?> <!DOCTYPE Projects SYSTEM “proj.dtd”>
When the value of the standalone attribute in an XML document is “no”, the docu- ment needs to be checked against a separate DTD document or XML schema docu- ment (see below). The DTD file shown in Figure 12.4 should be stored in the same file system as the XML document, and should be given the file name proj.dtd. Alternatively, we could include the DTD document text at the beginning of the XML document itself to allow the checking.
Although XML DTD is quite adequate for specifying tree structures with required, optional, and repeating elements, and with various types of attributes, it has several limitations. First, the data types in DTD are not very general. Second, DTD has its own special syntax and thus requires specialized processors. It would be advanta- geous to specify XML schema documents using the syntax rules of XML itself so that the same processors used for XML documents could process XML schema descriptions. Third, all DTD elements are always forced to follow the specified ordering of the document, so unordered elements are not permitted. These draw- backs led to the development of XML schema, a more general but also more com- plex language for specifying the structure and elements of XML documents.
12.3.2 XML Schema The XML schema language is a standard for specifying the structure of XML docu- ments. It uses the same syntax rules as regular XML documents, so that the same processors can be used on both. To distinguish the two types of documents, we will use the term XML instance document or XML document for a regular XML docu- ment, and XML schema document for a document that specifies an XML schema. Figure 12.5 shows an XML schema document corresponding to the COMPANY database shown in Figures 3.5 and 7.2. Although it is unlikely that we would want to display the whole database as a single document, there have been proposals to store data in native XML format as an alternative to storing the data in relational data- bases. The schema in Figure 12.5 would serve the purpose of specifying the struc- ture of the COMPANY database if it were stored in a native XML system. We discuss this topic further in Section 12.4.
As with XML DTD, XML schema is based on the tree data model, with elements and attributes as the main structuring concepts. However, it borrows additional concepts from database and object models, such as keys, references, and identifiers. Here we describe the features of XML schema in a step-by-step manner, referring to the sam- ple XML schema document in Figure 12.5 for illustration. We introduce and describe some of the schema concepts in the order in which they are used in Figure 12.5.
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Figure 12.5 An XML schema file called company.
<?xml version=“1.0” encoding=“UTF-8” ?> <xsd:schema xmlns:xsd=“http://www.w3.org/2001/XMLSchema”>
<xsd:annotation> <xsd:documentation xml:lang=“en”>Company Schema (Element Approach) - Prepared by Babak
Hojabri</xsd:documentation> </xsd:annotation>
<xsd:element name=“company”> <xsd:complexType>
<xsd:sequence> <xsd:element name=“department” type=“Department” minOccurs=“0” maxOccurs= “unbounded” /> <xsd:element name=“employee” type=“Employee” minOccurs=“0” maxOccurs= “unbounded”>
<xsd:unique name=“dependentNameUnique”> <xsd:selector xpath=“employeeDependent” /> <xsd:field xpath=“dependentName” />
</xsd:unique> </xsd:element> <xsd:element name=“project” type=“Project” minOccurs=“0” maxOccurs=“unbounded” />
</xsd:sequence> </xsd:complexType> <xsd:unique name=“departmentNameUnique”>
<xsd:selector xpath=“department” /> <xsd:field xpath=“departmentName” />
</xsd:unique> <xsd:unique name=“projectNameUnique”>
<xsd:selector xpath=“project” /> <xsd:field xpath=“projectName” />
</xsd:unique> <xsd:key name=“projectNumberKey”>
<xsd:selector xpath=“project” /> <xsd:field xpath=“projectNumber” />
</xsd:key> <xsd:key name=“departmentNumberKey”>
<xsd:selector xpath=“department” /> <xsd:field xpath=“departmentNumber” />
</xsd:key> <xsd:key name=“employeeSSNKey”>
<xsd:selector xpath=“employee” /> <xsd:field xpath=“employeeSSN” />
</xsd:key> <xsd:keyref name=“departmentManagerSSNKeyRef” refer=“employeeSSNKey”>
<xsd:selector xpath=“department” /> <xsd:field xpath=“departmentManagerSSN” />
</xsd:keyref> <xsd:keyref name=“employeeDepartmentNumberKeyRef”
refer=“departmentNumberKey”> <xsd:selector xpath=“employee” />
<xsd:field xpath=“employeeDepartmentNumber” /> </xsd:keyref> <xsd:keyref name=“employeeSupervisorSSNKeyRef” refer=“employeeSSNKey”>
<xsd:selector xpath=“employee” /> <xsd:field xpath=“employeeSupervisorSSN” />
</xsd:keyref> <xsd:keyref name=“projectDepartmentNumberKeyRef” refer=“departmentNumberKey”>
<xsd:selector xpath=“project” /> <xsd:field xpath=“projectDepartmentNumber” />
</xsd:keyref> <xsd:keyref name=“projectWorkerSSNKeyRef” refer=“employeeSSNKey”>
<xsd:selector xpath=“project/projectWorker” /> <xsd:field xpath=“SSN” />
</xsd:keyref> <xsd:keyref name=“employeeWorksOnProjectNumberKeyRef”
refer=“projectNumberKey”> <xsd:selector xpath=“employee/employeeWorksOn” /> <xsd:field xpath=“projectNumber” />
</xsd:keyref> </xsd:element> <xsd:complexType name=“Department”>
<xsd:sequence> <xsd:element name=“departmentName” type=“xsd:string” /> <xsd:element name=“departmentNumber” type=“xsd:string” /> <xsd:element name=“departmentManagerSSN” type=“xsd:string” /> <xsd:element name=“departmentManagerStartDate” type=“xsd:date” /> <xsd:element name=“departmentLocation” type=“xsd:string” minOccurs=“0” maxOccurs=“unbounded” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“Employee”>
<xsd:sequence> <xsd:element name=“employeeName” type=“Name” /> <xsd:element name=“employeeSSN” type=“xsd:string” /> <xsd:element name=“employeeSex” type=“xsd:string” /> <xsd:element name=“employeeSalary” type=“xsd:unsignedInt” /> <xsd:element name=“employeeBirthDate” type=“xsd:date” /> <xsd:element name=“employeeDepartmentNumber” type=“xsd:string” /> <xsd:element name=“employeeSupervisorSSN” type=“xsd:string” /> <xsd:element name=“employeeAddress” type=“Address” /> <xsd:element name=“employeeWorksOn” type=“WorksOn” minOccurs=“1” maxOccurs=“unbounded” /> <xsd:element name=“employeeDependent” type=“Dependent” minOccurs=“0” maxOccurs=“unbounded” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“Project”>
<xsd:sequence> <xsd:element name=“projectName” type=“xsd:string” /> <xsd:element name=“projectNumber” type=“xsd:string” /> <xsd:element name=“projectLocation” type=“xsd:string” />
12.3 XML Documents, DTD, and XML Schema 427
(continues)
Figure 12.5 (continued) An XML schema called company.
<xsd:element name=“projectDepartmentNumber” type=“xsd:string” /> <xsd:element name=“projectWorker” type=“Worker” minOccurs=“1” maxOccurs=“unbounded” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“Dependent”>
<xsd:sequence> <xsd:element name=“dependentName” type=“xsd:string” /> <xsd:element name=“dependentSex” type=“xsd:string” /> <xsd:element name=“dependentBirthDate” type=“xsd:date” /> <xsd:element name=“dependentRelationship” type=“xsd:string” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“Address”>
<xsd:sequence> <xsd:element name=“number” type=“xsd:string” /> <xsd:element name=“street” type=“xsd:string” /> <xsd:element name=“city” type=“xsd:string” /> <xsd:element name=“state” type=“xsd:string” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“Name”>
<xsd:sequence> <xsd:element name=“firstName” type=“xsd:string” /> <xsd:element name=“middleName” type=“xsd:string” /> <xsd:element name=“lastName” type=“xsd:string” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“Worker”>
<xsd:sequence> <xsd:element name=“SSN” type=“xsd:string” /> <xsd:element name=“hours” type=“xsd:float” />
</xsd:sequence> </xsd:complexType> <xsd:complexType name=“WorksOn”>
<xsd:sequence> <xsd:element name=“projectNumber” type=“xsd:string” /> <xsd:element name=“hours” type=“xsd:float” />
</xsd:sequence> </xsd:complexType> </xsd:schema>
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1. Schema descriptions and XML namespaces. It is necessary to identify the specific set of XML schema language elements (tags) being used by specify- ing a file stored at a Web site location. The second line in Figure 12.5 specifies
12.3 XML Documents, DTD, and XML Schema 429
the file used in this example, which is http://www.w3.org/2001/XMLSchema. This is a commonly used standard for XML schema commands. Each such definition is called an XML namespace, because it defines the set of com- mands (names) that can be used. The file name is assigned to the variable xsd (XML schema description) using the attribute xmlns (XML namespace), and this variable is used as a prefix to all XML schema commands (tag names). For example, in Figure 12.5, when we write xsd:element or xsd:sequence, we are referring to the definitions of the element and sequence tags as defined in the file http://www.w3.org/2001/XMLSchema.
2. Annotations, documentation, and language used. The next couple of lines in Figure 12.5 illustrate the XML schema elements (tags) xsd:annotation and xsd:documentation, which are used for providing comments and other descriptions in the XML document. The attribute xml:lang of the xsd:documentation element specifies the language being used, where en stands for the English language.
3. Elements and types. Next, we specify the root element of our XML schema. In XML schema, the name attribute of the xsd:element tag specifies the ele- ment name, which is called company for the root element in our example (see Figure 12.5). The structure of the company root element can then be speci- fied, which in our example is xsd:complexType. This is further specified to be a sequence of departments, employees, and projects using the xsd:sequence structure of XML schema. It is important to note here that this is not the only way to specify an XML schema for the COMPANY database. We will dis- cuss other options in Section 12.6.
4. First-level elements in the COMPANY database. Next, we specify the three first-level elements under the company root element in Figure 12.5. These elements are named employee, department, and project, and each is specified in an xsd:element tag. Notice that if a tag has only attributes and no further subelements or data within it, it can be ended with the backslash symbol (/>) directly instead of having a separate matching end tag. These are called empty elements; examples are the xsd:element elements named department and project in Figure 12.5.
5. Specifying element type and minimum and maximum occurrences. In XML schema, the attributes type, minOccurs, and maxOccurs in the xsd:element tag specify the type and multiplicity of each element in any doc- ument that conforms to the schema specifications. If we specify a type attrib- ute in an xsd:element, the structure of the element must be described separately, typically using the xsd:complexType element of XML schema. This is illustrated by the employee, department, and project elements in Figure 12.5. On the other hand, if no type attribute is specified, the element structure can be defined directly following the tag, as illustrated by the company root ele- ment in Figure 12.5. The minOccurs and maxOccurs tags are used for specify- ing lower and upper bounds on the number of occurrences of an element in
430 Chapter 12 XML: Extensible Markup Language
any XML document that conforms to the schema specifications. If they are not specified, the default is exactly one occurrence. These serve a similar role to the *, +, and ? symbols of XML DTD.
6. Specifying keys. In XML schema, it is possible to specify constraints that correspond to unique and primary key constraints in a relational database (see Section 3.2.2), as well as foreign keys (or referential integrity) con- straints (see Section 3.2.4). The xsd:unique tag specifies elements that corre- spond to unique attributes in a relational database. We can give each such uniqueness constraint a name, and we must specify xsd:selector and xsd:field tags for it to identify the element type that contains the unique element and the element name within it that is unique via the xpath attribute. This is illus- trated by the departmentNameUnique and projectNameUnique elements in Figure 12.5. For specifying primary keys, the tag xsd:key is used instead of xsd:unique, as illustrated by the projectNumberKey, departmentNumberKey, and employeeSSNKey elements in Figure 12.5. For specifying foreign keys, the tag xsd:keyref is used, as illustrated by the six xsd:keyref elements in Figure 12.5. When specifying a foreign key, the attribute refer of the xsd:keyref tag specifies the referenced primary key, whereas the tags xsd:selector and xsd:field specify the referencing element type and foreign key (see Figure 12.5).
7. Specifying the structures of complex elements via complex types. The next part of our example specifies the structures of the complex elements Department, Employee, Project, and Dependent, using the tag xsd:complexType (see Figure 12.5 on page 428). We specify each of these as a sequence of subelements corresponding to the database attributes of each entity type (see Figure 3.7) by using the xsd:sequence and xsd:element tags of XML schema. Each element is given a name and type via the attributes name and type of xsd:element. We can also specify minOccurs and maxOccurs attributes if we need to change the default of exactly one occurrence. For (optional) data- base attributes where null is allowed, we need to specify minOccurs = 0, whereas for multivalued database attributes we need to specify maxOccurs = “unbounded” on the corresponding element. Notice that if we were not going to specify any key constraints, we could have embedded the subelements within the parent element definitions directly without having to specify complex types. However, when unique, primary key and foreign key con- straints need to be specified; we must define complex types to specify the ele- ment structures.
8. Composite (compound) attributes. Composite attributes from Figure 7.2 are also specified as complex types in Figure 12.7, as illustrated by the Address, Name, Worker, and WorksOn complex types. These could have been directly embedded within their parent elements.
This example illustrates some of the main features of XML schema. There are other features, but they are beyond the scope of our presentation. In the next section, we discuss the different approaches to creating XML documents from relational data- bases and storing XML documents.
12.4 Storing and Extracting XML Documents from Databases 431
12.4 Storing and Extracting XML Documents from Databases
Several approaches to organizing the contents of XML documents to facilitate their subsequent querying and retrieval have been proposed. The following are the most common approaches:
1. Using a DBMS to store the documents as text. A relational or object DBMS can be used to store whole XML documents as text fields within the DBMS records or objects. This approach can be used if the DBMS has a special module for document processing, and would work for storing schemaless and document-centric XML documents.
2. Using a DBMS to store the document contents as data elements. This approach would work for storing a collection of documents that follow a specific XML DTD or XML schema. Because all the documents have the same structure, one can design a relational (or object) database to store the leaf-level data elements within the XML documents. This approach would require mapping algorithms to design a database schema that is compatible with the XML document structure as specified in the XML schema or DTD and to recreate the XML documents from the stored data. These algorithms can be implemented either as an internal DBMS module or as separate mid- dleware that is not part of the DBMS.
3. Designing a specialized system for storing native XML data. A new type of database system based on the hierarchical (tree) model could be designed and implemented. Such systems are being called Native XML DBMSs. The system would include specialized indexing and querying techniques, and would work for all types of XML documents. It could also include data com- pression techniques to reduce the size of the documents for storage. Tamino by Software AG and the Dynamic Application Platform of eXcelon are two popular products that offer native XML DBMS capability. Oracle also offers a native XML storage option.
4. Creating or publishing customized XML documents from preexisting relational databases. Because there are enormous amounts of data already stored in relational databases, parts of this data may need to be formatted as documents for exchanging or displaying over the Web. This approach would use a separate middleware software layer to handle the conversions needed between the XML documents and the relational database. Section 12.6 dis- cusses this approach, in which data-centric XML documents are extracted from existing databases, in more detail. In particular, we show how tree structured documents can be created from graph-structured databases. Section 12.6.2 discusses the problem of cycles and how to deal with it.
All of these approaches have received considerable attention. We focus on the fourth approach in Section 12.6, because it gives a good conceptual understanding of the differences between the XML tree data model and the traditional database models
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based on flat files (relational model) and graph representations (ER model). But first we give an overview of XML query languages in Section 12.5.
12.5 XML Languages There have been several proposals for XML query languages, and two query language standards have emerged. The first is XPath, which provides language constructs for specifying path expressions to identify certain nodes (elements) or attributes within an XML document that match specific patterns. The second is XQuery, which is a more general query language. XQuery uses XPath expressions but has additional constructs. We give an overview of each of these languages in this section. Then we discuss some additional languages related to HTML in Section 12.5.3.
12.5.1 XPath: Specifying Path Expressions in XML An XPath expression generally returns a sequence of items that satisfy a certain pat- tern as specified by the expression. These items are either values (from leaf nodes) or elements or attributes. The most common type of XPath expression returns a col- lection of element or attribute nodes that satisfy certain patterns specified in the expression. The names in the XPath expression are node names in the XML docu- ment tree that are either tag (element) names or attribute names, possibly with additional qualifier conditions to further restrict the nodes that satisfy the pattern. Two main separators are used when specifying a path: single slash (/) and double slash (//). A single slash before a tag specifies that the tag must appear as a direct child of the previous (parent) tag, whereas a double slash specifies that the tag can appear as a descendant of the previous tag at any level. Let us look at some examples of XPath as shown in Figure 12.6.
The first XPath expression in Figure 12.6 returns the company root node and all its descendant nodes, which means that it returns the whole XML document. We should note that it is customary to include the file name in the XPath query. This allows us to specify any local file name or even any path name that specifies a file on the Web. For example, if the COMPANY XML document is stored at the location
www.company.com/info.XML
then the first XPath expression in Figure 12.6 can be written as
doc(www.company.com/info.XML)/company
This prefix would also be included in the other examples of XPath expressions.
Figure 12.6 Some examples of XPath expressions on XML documents that follow the XML schema file company in Figure 12.5.
1. /company
2. /company/department
3. //employee [employeeSalary gt 70000]/employeeName
4. /company/employee [employeeSalary gt 70000]/employeeName
5. /company/project/projectWorker [hours ge 20.0]
12.5 XML Languages 433
The second example in Figure 12.6 returns all department nodes (elements) and their descendant subtrees. Note that the nodes (elements) in an XML document are ordered, so the XPath result that returns multiple nodes will do so in the same order in which the nodes are ordered in the document tree.
The third XPath expression in Figure 12.6 illustrates the use of //, which is conve- nient to use if we do not know the full path name we are searching for, but do know the name of some tags of interest within the XML document. This is particularly useful for schemaless XML documents or for documents with many nested levels of nodes.6
The expression returns all employeeName nodes that are direct children of an employee node, such that the employee node has another child element employeeSalary whose value is greater than 70000. This illustrates the use of qualifier conditions, which restrict the nodes selected by the XPath expression to those that satisfy the con- dition. XPath has a number of comparison operations for use in qualifier conditions, including standard arithmetic, string, and set comparison operations.
The fourth XPath expression in Figure 12.6 should return the same result as the pre- vious one, except that we specified the full path name in this example. The fifth expression in Figure 12.6 returns all projectWorker nodes and their descendant nodes that are children under a path /company/project and have a child node hours with a value greater than 20.0 hours.
When we need to include attributes in an XPath expression, the attribute name is prefixed by the @ symbol to distinguish it from element (tag) names. It is also pos- sible to use the wildcard symbol *, which stands for any element, as in the following example, which retrieves all elements that are child elements of the root, regardless of their element type. When wildcards are used, the result can be a sequence of dif- ferent types of items.
/company/*
The examples above illustrate simple XPath expressions, where we can only move down in the tree structure from a given node. A more general model for path expressions has been proposed. In this model, it is possible to move in multiple directions from the current node in the path expression. These are known as the axes of an XPath expression. Our examples above used only three of these axes: child of the current node (/), descendent or self at any level of the current node (//), and attribute of the current node (@). Other axes include parent, ancestor (at any level), previous sibling (any node at same level to the left in the tree), and next sibling (any node at the same level to the right in the tree). These axes allow for more complex path expressions.
The main restriction of XPath path expressions is that the path that specifies the pat- tern also specifies the items to be retrieved. Hence, it is difficult to specify certain conditions on the pattern while separately specifying which result items should be
6We use the terms node, tag, and element interchangeably here.
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retrieved. The XQuery language separates these two concerns, and provides more powerful constructs for specifying queries.
12.5.2 XQuery: Specifying Queries in XML XPath allows us to write expressions that select items from a tree-structured XML document. XQuery permits the specification of more general queries on one or more XML documents. The typical form of a query in XQuery is known as a FLWR expression, which stands for the four main clauses of XQuery and has the following form:
FOR <variable bindings to individual nodes (elements)> LET <variable bindings to collections of nodes (elements)> WHERE <qualifier conditions> RETURN <query result specification>
There can be zero or more instances of the FOR clause, as well as of the LET clause in a single XQuery. The WHERE clause is optional, but can appear at most once, and the RETURN clause must appear exactly once. Let us illustrate these clauses with the fol- lowing simple example of an XQuery.
LET $d := doc(www.company.com/info.xml) FOR $x IN $d/company/project[projectNumber = 5]/projectWorker,
$y IN $d/company/employee WHERE $x/hours gt 20.0 AND $y.ssn = $x.ssn RETURN <res> $y/employeeName/firstName, $y/employeeName/lastName,
$x/hours </res>
1. Variables are prefixed with the $ sign. In the above example, $d, $x, and $y are variables.
2. The LET clause assigns a variable to a particular expression for the rest of the query. In this example, $d is assigned to the document file name. It is possi- ble to have a query that refers to multiple documents by assigning multiple variables in this way.
3. The FOR clause assigns a variable to range over each of the individual items in a sequence. In our example, the sequences are specified by path expres- sions. The $x variable ranges over elements that satisfy the path expression $d/company/project[projectNumber = 5]/projectWorker. The $y variable ranges over elements that satisfy the path expression $d/company/employee. Hence, $x ranges over projectWorker elements, whereas $y ranges over employee ele- ments.
4. The WHERE clause specifies additional conditions on the selection of items. In this example, the first condition selects only those projectWorker elements that satisfy the condition (hours gt 20.0). The second condition specifies a join condition that combines an employee with a projectWorker only if they have the same ssn value.
5. Finally, the RETURN clause specifies which elements or attributes should be retrieved from the items that satisfy the query conditions. In this example, it
12.5 XML Languages 435
will return a sequence of elements each containing <firstName, lastName, hours> for employees who work more that 20 hours per week on project number 5.
Figure 12.7 includes some additional examples of queries in XQuery that can be specified on an XML instance documents that follow the XML schema document in Figure 12.5. The first query retrieves the first and last names of employees who earn more than $70,000. The variable $x is bound to each employeeName element that is a child of an employee element, but only for employee elements that satisfy the quali- fier that their employeeSalary value is greater than $70,000. The result retrieves the firstName and lastName child elements of the selected employeeName elements. The second query is an alternative way of retrieving the same elements retrieved by the first query.
The third query illustrates how a join operation can be performed by using more than one variable. Here, the $x variable is bound to each projectWorker element that is a child of project number 5, whereas the $y variable is bound to each employee ele- ment. The join condition matches ssn values in order to retrieve the employee names. Notice that this is an alternative way of specifying the same query in our ear- lier example, but without the LET clause.
XQuery has very powerful constructs to specify complex queries. In particular, it can specify universal and existential quantifiers in the conditions of a query, aggregate functions, ordering of query results, selection based on position in a sequence, and even conditional branching. Hence, in some ways, it qualifies as a full-fledged pro- gramming language.
This concludes our brief introduction to XQuery. The interested reader is referred to www.w3.org, which contains documents describing the latest standards related to XML and XQuery. The next section briefly discusses some additional languages and protocols related to XML.
Figure 12.7 Some examples of XQuery queries on XML documents that follow the XML schema file company in Figure 12.5.
1. FOR $x IN doc(www.company.com/info.xml) //employee [employeeSalary gt 70000]/employeeName RETURN <res> $x/firstName, $x/lastName </res>
2. FOR $x IN doc(www.company.com/info.xml)/company/employee WHERE $x/employeeSalary gt 70000 RETURN <res> $x/employeeName/firstName, $x/employeeName/lastName </res>
3. FOR $x IN doc(www.company.com/info.xml)/company/project[projectNumber = 5]/projectWorker, $y IN doc(www.company.com/info.xml)/company/employee WHERE $x/hours gt 20.0 AND $y.ssn = $x.ssn RETURN <res> $y/employeeName/firstName, $y/employeeName/lastName, $x/hours </res>
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12.5.3 Other Languages and Protocols Related to XML There are several other languages and protocols related to XML technology. The long-term goal of these and other languages and protocols is to provide the technol- ogy for realization of the Semantic Web, where all information in the Web can be intelligently located and processed.
■ The Extensible Stylesheet Language (XSL) can be used to define how a doc- ument should be rendered for display by a Web browser.
■ The Extensible Stylesheet Language for Transformations (XSLT) can be used to transform one structure into a different structure. Hence, it can convert documents from one form to another.
■ The Web Services Description Language (WSDL) allows for the description of Web Services in XML. This makes the Web Service available to users and programs over the Web.
■ The Simple Object Access Protocol (SOAP) is a platform-independent and programming language-independent protocol for messaging and remote procedure calls.
■ The Resource Description Framework (RDF) provides languages and tools for exchanging and processing of meta-data (schema) descriptions and spec- ifications over the Web.
12.6 Extracting XML Documents from Relational Databases
12.6.1 Creating Hierarchical XML Views over Flat or Graph-Based Data
This section discusses the representational issues that arise when converting data from a database system into XML documents. As we have discussed, XML uses a hierarchical (tree) model to represent documents. The database systems with the most widespread use follow the flat relational data model. When we add referential integrity constraints, a relational schema can be considered to be a graph structure (for example, see Figure 3.7). Similarly, the ER model represents data using graph- like structures (for example, see Figure 7.2). We saw in Chapter 9 that there are straightforward mappings between the ER and relational models, so we can concep- tually represent a relational database schema using the corresponding ER schema. Although we will use the ER model in our discussion and examples to clarify the conceptual differences between tree and graph models, the same issues apply to converting relational data to XML.
We will use the simplified UNIVERSITY ER schema shown in Figure 12.8 to illustrate our discussion. Suppose that an application needs to extract XML documents for student, course, and grade information from the UNIVERSITY database. The data needed for these documents is contained in the database attributes of the entity
12.6 Extracting XML Documents from Relational Databases 437
Name
S-D
Students
Courses
Instructors
Major dept Department
1 1
1
1
N
N
S-S C-S S-1
D-1
D-C
DEPARTMENT
COURSE
SECTION
Name
Ssn
N N
M 1
Class
YearNumber Qtr
Grade
STUDENT
Sections completed
Sections taught
N N Students attended Instructors
NameSsn Name
Number
Rank
SalaryINSTRUCTOR
Department
Course
Sections
Figure 12.8 An ER schema diagram for a simplified UNIVERSITY database.
types COURSE, SECTION, and STUDENT from Figure 12.8, and the relationships S-S and C-S between them. In general, most documents extracted from a database will only use a subset of the attributes, entity types, and relationships in the database. In this example, the subset of the database that is needed is shown in Figure 12.9.
At least three possible document hierarchies can be extracted from the database subset in Figure 12.9. First, we can choose COURSE as the root, as illustrated in Figure 12.10. Here, each course entity has the set of its sections as subelements, and each section has its students as subelements. We can see one consequence of model- ing the information in a hierarchical tree structure. If a student has taken multiple sections, that student’s information will appear multiple times in the document— once under each section. A possible simplified XML schema for this view is shown in Figure 12.11. The Grade database attribute in the S-S relationship is migrated to the STUDENT element. This is because STUDENT becomes a child of SECTION in this hierarchy, so each STUDENT element under a specific SECTION element can have a specific grade in that section. In this document hierarchy, a student taking more than one section will have several replicas, one under each section, and each replica will have the specific grade given in that particular section.
438 Chapter 12 XML: Extensible Markup Language
1
Number
Sections
Name COURSE
1
Number
Students attended
Qtr
YearSECTION
N
N
Name
Ssn
Grade
Class STUDENT
Figure 12.10 Hierarchical (tree) view with COURSE as the root.
Figure 12.11 XML schema document with course as the root.
<xsd:element name=“root”> <xsd:sequence> <xsd:element name=“course” minOccurs=“0” maxOccurs=“unbounded”>
<xsd:sequence> <xsd:element name=“cname” type=“xsd:string” /> <xsd:element name=“cnumber” type=“xsd:unsignedInt” /> <xsd:element name=“section” minOccurs=“0” maxOccurs=“unbounded”>
<xsd:sequence> <xsd:element name=“secnumber” type=“xsd:unsignedInt” /> <xsd:element name=“year” type=“xsd:string” /> <xsd:element name=“quarter” type=“xsd:string” /> (continues)
S-D
Ssn
Name
Class
STUDENT Sections completed
M N N 1
Number
Year Qtr
SECTION
Number
Name
COURSES-D Students attended
Course Sections
Grade
Figure 12.9 Subset of the UNIVERSITY database schema needed for XML document extraction.
12.6 Extracting XML Documents from Relational Databases 439
1
Ssn
Sections completed
NameSTUDENT
1
Number
Qtr
Year SECTION
1
N
Grade
Class
COURSE
Course_number
Course_name
Figure 12.12 Hierarchical (tree) view with STUDENT as the root.
Figure 12.11 (continued) XML schema document with course as the root.
<xsd:element name=“student” minOccurs=“0” maxOccurs=“unbounded”> <xsd:sequence>
<xsd:element name=“ssn” type=“xsd:string” /> <xsd:element name=“sname” type=“xsd:string” /> <xsd:element name=“class” type=“xsd:string” /> <xsd:element name=“grade” type=“xsd:string” />
</xsd:sequence> </xsd:element>
</xsd:sequence> </xsd:element>
</xsd:sequence> </xsd:element> </xsd:sequence> </xsd:element>
In the second hierarchical document view, we can choose STUDENT as root (Figure 12.12). In this hierar- chical view, each student has a set of sections as its child elements, and each section is related to one course as its child, because the relationship between SECTION and COURSE is N:1. Thus, we can merge the COURSE and SECTION elements in this view, as shown in Figure 12.12. In addition, the GRADE data- base attribute can be migrated to the SECTION element. In this hierarchy, the combined COURSE/SECTION information is replicated under each student who completed the section. A possible simplified XML schema for this view is shown in Figure 12.13.
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Figure 12.13 XML schema document with student as the root.
1
Ssn Students attended
Name STUDENT
1
Number
Qtr
Year SECTION
1
N
Grade
Class
COURSE
Course_number
Course_name
Figure 12.14 Hierarchical (tree) view with SECTION as the root.
<xsd:element name=”root”> <xsd:sequence> <xsd:element name=”student” minOccurs=”0” maxOccurs=”unbounded”>
<xsd:sequence> <xsd:element name=”ssn” type=”xsd:string” /> <xsd:element name=”sname” type=”xsd:string” /> <xsd:element name=”class” type=”xsd:string” /> <xsd:element name=”section” minOccurs=”0” maxOccurs=”unbounded”>
<xsd:sequence> <xsd:element name=”secnumber” type=”xsd:unsignedInt” /> <xsd:element name=”year” type=”xsd:string” /> <xsd:element name=”quarter” type=”xsd:string” /> <xsd:element name=”cnumber” type=”xsd:unsignedInt” /> <xsd:element name=”cname” type=”xsd:string” /> <xsd:element name=”grade” type=”xsd:string” />
</xsd:sequence> </xsd:element>
</xsd:sequence> </xsd:element> </xsd:sequence> </xsd:element>
The third possible way is to choose SECTION as the root, as shown in Figure 12.14. Similar to the second hierarchical view, the COURSE information can be merged into the SECTION element. The GRADE database attribute can be migrated to the STUDENT element. As we can see, even in this simple example, there can be numer- ous hierarchical document views, each corresponding to a different root and a dif- ferent XML document structure.
12.6 Extracting XML Documents from Relational Databases 441
COURSE
INSTRUCTOR
1 1 N N
1 1NN
(a) (b)
STUDENT
DEPARTMENTSECTION COURSE
INSTRUCTOR INSTRUCTOR1
STUDENT
DEPARTMENTSECTION
(c)
STUDENT
DEPARTMENTSECTION
INSTRUCTOR COURSE1 INSTRUCTOR1 COURSE
1 M
N N
Figure 12.15 Converting a graph with cycles into a hierarchical (tree) structure.
12.6.2 Breaking Cycles to Convert Graphs into Trees In the previous examples, the subset of the database of interest had no cycles. It is possible to have a more complex subset with one or more cycles, indicating multiple relationships among the entities. In this case, it is more difficult to decide how to create the document hierarchies. Additional duplication of entities may be needed to represent the multiple relationships. We will illustrate this with an example using the ER schema in Figure 12.8.
Suppose that we need the information in all the entity types and relationships in Figure 12.8 for a particular XML document, with STUDENT as the root element. Figure 12.15 illustrates how a possible hierarchical tree structure can be created for this document. First, we get a lattice with STUDENT as the root, as shown in Figure 12.15(a). This is not a tree structure because of the cycles. One way to break the cycles is to replicate the entity types involved in the cycles. First, we replicate INSTRUCTOR as shown in Figure 12.15(b), calling the replica to the right INSTRUCTOR1. The INSTRUCTOR replica on the left represents the relationship between instructors and the sections they teach, whereas the INSTRUCTOR1 replica on the right represents the relationship between instructors and the department each works in. After this, we still have the cycle involving COURSE, so we can repli- cate COURSE in a similar manner, leading to the hierarchy shown in Figure 12.15(c). The COURSE1 replica to the left represents the relationship between courses and their sections, whereas the COURSE replica to the right represents the relationship between courses and the department that offers each course.
In Figure 12.15(c), we have converted the initial graph to a hierarchy. We can do fur- ther merging if desired (as in our previous example) before creating the final hierar- chy and the corresponding XML schema structure.
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12.6.3 Other Steps for Extracting XML Documents from Databases
In addition to creating the appropriate XML hierarchy and corresponding XML schema document, several other steps are needed to extract a particular XML docu- ment from a database:
1. It is necessary to create the correct query in SQL to extract the desired infor- mation for the XML document.
2. Once the query is executed, its result must be restructured from the flat rela- tional form to the XML tree structure.
3. The query can be customized to select either a single object or multiple objects into the document. For example, in the view in Figure 12.13, the query can select a single student entity and create a document correspond- ing to that single student, or it may select several—or even all—of the stu- dents and create a document with multiple students.
12.7 Summary This chapter provided an overview of the XML standard for representing and exchanging data over the Internet. First we discussed some of the differences between various types of data, classifying three main types: structured, semi-struc- tured, and unstructured. Structured data is stored in traditional databases. Semistructured data mixes data types names and data values, but the data does not all have to follow a fixed predefined structure. Unstructured data refers to informa- tion displayed on the Web, specified via HTML, where information on the types of data items is missing. We described the XML standard and its tree-structured (hier- archical) data model, and discussed XML documents and the languages for specify- ing the structure of these documents, namely, XML DTD (Document Type Definition) and XML schema. We gave an overview of the various approaches for storing XML documents, whether in their native (text) format, in a compressed form, or in relational and other types of databases. Finally, we gave an overview of the XPath and XQuery languages proposed for querying XML data, and discussed the mapping issues that arise when it is necessary to convert data stored in tradi- tional relational databases into XML documents.
Review Questions 12.1. What are the differences between structured, semistructured, and unstruc-
tured data?
12.2. Under which of the categories in 12.1 do XML documents fall? What about self-describing data?
12.3. What are the differences between the use of tags in XML versus HTML?
12.4. What is the difference between data-centric and document-centric XML documents?
12.5. What is the difference between attributes and elements in XML? List some of the important attributes used to specify elements in XML schema.
12.6. What is the difference between XML schema and XML DTD?
Exercises 12.7. Create part of an XML instance document to correspond to the data stored
in the relational database shown in Figure 3.6 such that the XML document conforms to the XML schema document in Figure 12.5.
12.8. Create XML schema documents and XML DTDs to correspond to the hier- archies shown in Figures 12.14 and 12.15(c).
12.9. Consider the LIBRARY relational database schema in Figure 4.6. Create an XML schema document that corresponds to this database schema.
12.10. Specify the following views as queries in XQuery on the company XML schema shown in Figure 12.5.
a. A view that has the department name, manager name, and manager salary for every department.
b. A view that has the employee name, supervisor name, and employee salary for each employee who works in the Research department.
c. A view that has the project name, controlling department name, number of employees, and total hours worked per week on the project for each project.
d. A view that has the project name, controlling department name, number of employees, and total hours worked per week on the project for each project with more than one employee working on it.
Selected Bibliography There are so many articles and books on various aspects of XML that it would be impossible to make even a modest list. We will mention one book: Chaudhri, Rashid, and Zicari, eds. (2003). This book discusses various aspects of XML and contains a list of some references to XML research and practice.
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part 5 Database Programming
Techniques
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447
Introduction to SQL Programming Techniques
In Chapters 4 and 5, we described several aspects of theSQL language, which is the standard for relational databases. We described the SQL statements for data definition, schema modifica- tion, queries, views, and updates. We also described how various constraints on the database contents, such as key and referential integrity constraints, are specified.
In this chapter and the next, we discuss some of the methods that have been devel- oped for accessing databases from programs. Most database access in practical applications is accomplished through software programs that implement database applications. This software is usually developed in a general-purpose programming language such as Java, C/C++/C#, COBOL, or some other programming language. In addition, many scripting languages, such as PHP and JavaScript, are also being used for programming of database access within Web applications. In this chapter, we focus on how databases can be accessed from the traditional programming lan- guages C/C++ and Java, whereas in the next chapter we introduce how databases are accessed from scripting languages such as PHP and JavaScript. Recall from Section 2.3.1 that when database statements are included in a program, the general-purpose programming language is called the host language, whereas the database language— SQL, in our case—is called the data sublanguage. In some cases, special database programming languages are developed specifically for writing database applications. Although many of these were developed as research prototypes, some notable data- base programming languages have widespread use, such as Oracle’s PL/SQL (Programming Language/SQL).
It is important to note that database programming is a very broad topic. There are whole textbooks devoted to each database programming technique and how that technique is realized in a specific system. New techniques are developed all the time,
13chapter 13
448 Chapter 13 Introduction to SQL Programming Techniques
and changes to existing techniques are incorporated into newer system versions and languages. An additional difficulty in presenting this topic is that although there are SQL standards, these standards themselves are continually evolving, and each DBMS vendor may have some variations from the standard. Because of this, we have chosen to give an introduction to some of the main types of database pro- gramming techniques and to compare these techniques, rather than study one par- ticular method or system in detail. The examples we give serve to illustrate the main differences that a programmer would face when using each of these database pro- gramming techniques. We will try to use the SQL standards in our examples rather than describe a specific system. When using a specific system, the materials in this chapter can serve as an introduction, but should be augmented with the system manuals or with books describing the specific system.
We start our presentation of database programming in Section 13.1 with an overview of the different techniques developed for accessing a database from pro- grams. Then, in Section 13.2, we discuss the rules for embedding SQL statements into a general-purpose programming language, generally known as embedded SQL. This section also briefly discusses dynamic SQL, in which queries can be dynami- cally constructed at runtime, and presents the basics of the SQLJ variation of embedded SQL that was developed specifically for the programming language Java. In Section 13.3, we discuss the technique known as SQL/CLI (Call Level Interface), in which a library of procedures and functions is provided for accessing the data- base. Various sets of library functions have been proposed. The SQL/CLI set of functions is the one given in the SQL standard. Another library of functions is ODBC (Open Data Base Connectivity). We do not describe ODBC because it is con- sidered to be the predecessor to SQL/CLI. A third library of functions—which we do describe—is JDBC; this was developed specifically for accessing databases from Java. In Section 13.4 we discuss SQL/PSM (Persistent Stored Modules), which is a part of the SQL standard that allows program modules—procedures and func- tions—to be stored by the DBMS and accessed through SQL. We briefly compare the three approaches to database programming in Section 13.5, and provide a chap- ter summary in Section 13.6.
13.1 Database Programming: Techniques and Issues
We now turn our attention to the techniques that have been developed for accessing databases from programs and, in particular, to the issue of how to access SQL data- bases from application programs. Our presentation of SQL in Chapters 4 and 5 focused on the language constructs for various database operations—from schema definition and constraint specification to querying, updating, and specifying views. Most database systems have an interactive interface where these SQL commands can be typed directly into a monitor for execution by the database system. For example, in a computer system where the Oracle RDBMS is installed, the command SQLPLUS starts the interactive interface. The user can type SQL commands or queries directly over several lines, ended by a semicolon and the Enter key (that is,
13.1 Database Programming: Techniques and Issues 449
“; <cr>"). Alternatively, a file of commands can be created and executed through the interactive interface by typing @<filename>. The system will execute the com- mands written in the file and display the results, if any.
The interactive interface is quite convenient for schema and constraint creation or for occasional ad hoc queries. However, in practice, the majority of database inter- actions are executed through programs that have been carefully designed and tested. These programs are generally known as application programs or database applica- tions, and are used as canned transactions by the end users, as discussed in Section 1.4.3. Another common use of database programming is to access a database through an application program that implements a Web interface, for example, when making airline reservations or online purchases. In fact, the vast majority of Web electronic commerce applications include some database access commands. Chapter 14 gives an overview of Web database programming using PHP, a scripting language that has recently become widely used.
In this section, first we give an overview of the main approaches to database pro- gramming. Then we discuss some of the problems that occur when trying to access a database from a general-purpose programming language, and the typical sequence of commands for interacting with a database from a software program.
13.1.1 Approaches to Database Programming Several techniques exist for including database interactions in application pro- grams. The main approaches for database programming are the following:
1. Embedding database commands in a general-purpose programming lan- guage. In this approach, database statements are embedded into the host programming language, but they are identified by a special prefix. For exam- ple, the prefix for embedded SQL is the string EXEC SQL, which precedes all SQL commands in a host language program.1 A precompiler or preproccessor scans the source program code to identify database state- ments and extract them for processing by the DBMS. They are replaced in the program by function calls to the DBMS-generated code. This technique is generally referred to as embedded SQL.
2. Using a library of database functions. A library of functions is made avail- able to the host programming language for database calls. For example, there could be functions to connect to a database, execute a query, execute an update, and so on. The actual database query and update commands and any other necessary information are included as parameters in the function calls. This approach provides what is known as an application programming interface (API) for accessing a database from application programs.
3. Designing a brand-new language. A database programming language is designed from scratch to be compatible with the database model and query language. Additional programming structures such as loops and conditional
1Other prefixes are sometimes used, but this is the most common.
450 Chapter 13 Introduction to SQL Programming Techniques
statements are added to the database language to convert it into a full- fledged programming language. An example of this approach is Oracle’s PL/SQL.
In practice, the first two approaches are more common, since many applications are already written in general-purpose programming languages but require some data- base access. The third approach is more appropriate for applications that have intensive database interaction. One of the main problems with the first two approaches is impedance mismatch, which does not occur in the third approach.
13.1.2 Impedance Mismatch Impedance mismatch is the term used to refer to the problems that occur because of differences between the database model and the programming language model. For example, the practical relational model has three main constructs: columns (attributes) and their data types, rows (also referred to as tuples or records), and tables (sets or multisets of records). The first problem that may occur is that the data types of the programming language differ from the attribute data types that are available in the data model. Hence, it is necessary to have a binding for each host programming language that specifies for each attribute type the compatible pro- gramming language types. A different binding is needed for each programming lan- guage because different languages have different data types. For example, the data types available in C/C++ and Java are different, and both differ from the SQL data types, which are the standard data types for relational databases.
Another problem occurs because the results of most queries are sets or multisets of tuples (rows), and each tuple is formed of a sequence of attribute values. In the pro- gram, it is often necessary to access the individual data values within individual tuples for printing or processing. Hence, a binding is needed to map the query result data structure, which is a table, to an appropriate data structure in the programming language. A mechanism is needed to loop over the tuples in a query result in order to access a single tuple at a time and to extract individual values from the tuple. The extracted attribute values are typically copied to appropriate program variables for further processing by the program. A cursor or iterator variable is typically used to loop over the tuples in a query result. Individual values within each tuple are then extracted into distinct program variables of the appropriate type.
Impedance mismatch is less of a problem when a special database programming language is designed that uses the same data model and data types as the database model. One example of such a language is Oracle’s PL/SQL. The SQL standard also has a proposal for such a database programming language, known as SQL/PSM. For object databases, the object data model (see Chapter 11) is quite similar to the data model of the Java programming language, so the impedance mismatch is greatly reduced when Java is used as the host language for accessing a Java-compatible object database. Several database programming languages have been implemented as research prototypes (see the Selected Bibliography).
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13.1.3 Typical Sequence of Interaction in Database Programming
When a programmer or software engineer writes a program that requires access to a database, it is quite common for the program to be running on one computer sys- tem while the database is installed on another. Recall from Section 2.5 that a com- mon architecture for database access is the client/server model, where a client program handles the logic of a software application, but includes some calls to one or more database servers to access or update the data.2 When writing such a pro- gram, a common sequence of interaction is the following:
1. When the client program requires access to a particular database, the pro- gram must first establish or open a connection to the database server. Typically, this involves specifying the Internet address (URL) of the machine where the database server is located, plus providing a login account name and password for database access.
2. Once the connection is established, the program can interact with the data- base by submitting queries, updates, and other database commands. In gen- eral, most types of SQL statements can be included in an application program.
3. When the program no longer needs access to a particular database, it should terminate or close the connection to the database.
A program can access multiple databases if needed. In some database programming approaches, only one connection can be active at a time, whereas in other approaches multiple connections can be established simultaneously.
In the next three sections, we discuss examples of each of the three main approaches to database programming. Section 13.2 describes how SQL is embedded into a pro- gramming language. Section 13.3 discusses how function calls are used to access the database, and Section 13.4 discusses an extension to SQL called SQL/PSM that allows general-purpose programming constructs for defining modules (procedures and functions) that are stored within the database system.3 Section 13.5 compares these approaches.
13.2 Embedded SQL, Dynamic SQL, and SQLJ In this section, we give an overview of the technique for how SQL statements can be embedded in a general-purpose programming language. We focus on two lan- guages: C and Java. The examples used with the C language, known as embedded
2As we discussed in Section 2.5, there are two-tier and three-tier architectures; to keep our discussion simple, we will assume a two-tier client/server architecture here. 3SQL/PSM illustrates how typical general-purpose programming language constructs—such as loops and conditional structures—can be incorporated into SQL.
452 Chapter 13 Introduction to SQL Programming Techniques
SQL, are presented in Sections 13.2.1 through 13.2.3, and can be adapted to other programming languages. The examples using Java, known as SQLJ, are presented in Sections 13.2.4 and 13.2.5. In this embedded approach, the programming language is called the host language. Most SQL statements—including data or constraint definitions, queries, updates, or view definitions—can be embedded in a host lan- guage program.
13.2.1 Retrieving Single Tuples with Embedded SQL To illustrate the concepts of embedded SQL, we will use C as the host programming language.4 When using C as the host language, an embedded SQL statement is dis- tinguished from programming language statements by prefixing it with the key- words EXEC SQL so that a preprocessor (or precompiler) can separate embedded SQL statements from the host language code. The SQL statements within a program are terminated by a matching END-EXEC or by a semicolon (;). Similar rules apply to embedding SQL in other programming languages.
Within an embedded SQL command, we may refer to specially declared C program variables. These are called shared variables because they are used in both the C pro- gram and the embedded SQL statements. Shared variables are prefixed by a colon (:) when they appear in an SQL statement. This distinguishes program variable names from the names of database schema constructs such as attributes (column names) and relations (table names). It also allows program variables to have the same names as attribute names, since they are distinguishable by the colon (:) prefix in the SQL statement. Names of database schema constructs—such as attributes and rela- tions—can only be used within the SQL commands, but shared program variables can be used elsewhere in the C program without the colon (:) prefix.
Suppose that we want to write C programs to process the COMPANY database in Figure 3.5. We need to declare program variables to match the types of the database attributes that the program will process. The programmer can choose the names of the program variables; they may or may not have names that are identical to their corresponding database attributes. We will use the C program variables declared in Figure 13.1 for all our examples and show C program segments without variable declarations. Shared variables are declared within a declare section in the program, as shown in Figure 13.1 (lines 1 through 7).5 A few of the common bindings of C types to SQL types are as follows. The SQL types INTEGER, SMALLINT, REAL, and DOUBLE are mapped to the C types long, short, float, and double, respectively. Fixed-length and varying-length strings (CHAR[i], VARCHAR[i]) in SQL can be mapped to arrays of characters (char [i+1], varchar [i+1]) in C that are one character longer than the SQL type because strings in C are terminated by a NULL
4Our discussion here also applies to the C++ programming language, since we do not use any of the object-oriented features, but focus on the database programming mechanism. 5We use line numbers in our code segments for easy reference; these numbers are not part of the actual code.
13.2 Embedded SQL, Dynamic SQL, and SQLJ 453
Figure 13.1 C program variables used in the embedded SQL examples E1 and E2.
0) int loop ; 1) EXEC SQL BEGIN DECLARE SECTION ; 2) varchar dname [16], fname [16], lname [16], address [31] ; 3) char ssn [10], bdate [11], sex [2], minit [2] ; 4) float salary, raise ; 5) int dno, dnumber ; 6) int SQLCODE ; char SQLSTATE [6] ; 7) EXEC SQL END DECLARE SECTION ;
character (\0), which is not part of the character string itself.6 Although varchar is not a standard C data type, it is permitted when C is used for SQL database pro- gramming.
Notice that the only embedded SQL commands in Figure 13.1 are lines 1 and 7, which tell the precompiler to take note of the C variable names between BEGIN DECLARE and END DECLARE because they can be included in embedded SQL state- ments—as long as they are preceded by a colon (:). Lines 2 through 5 are regular C program declarations. The C program variables declared in lines 2 through 5 corre- spond to the attributes of the EMPLOYEE and DEPARTMENT tables from the COMPANY database in Figure 3.5 that was declared by the SQL DDL in Figure 4.1. The variables declared in line 6—SQLCODE and SQLSTATE—are used to communi- cate errors and exception conditions between the database system and the executing program. Line 0 shows a program variable loop that will not be used in any embed- ded SQL statement, so it is declared outside the SQL declare section.
Connecting to the Database. The SQL command for establishing a connection to a database has the following form:
CONNECT TO <server name>AS <connection name> AUTHORIZATION <user account name and password> ;
In general, since a user or program can access several database servers, several con- nections can be established, but only one connection can be active at any point in time. The programmer or user can use the <connection name> to change from the currently active connection to a different one by using the following command:
SET CONNECTION <connection name> ;
Once a connection is no longer needed, it can be terminated by the following com- mand:
DISCONNECT <connection name> ;
In the examples in this chapter, we assume that the appropriate connection has already been established to the COMPANY database, and that it is the currently active connection.
6SQL strings can also be mapped to char* types in C.
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Communicating between the Program and the DBMS Using SQLCODE and SQLSTATE. The two special communication variables that are used by the DBMS to communicate exception or error conditions to the program are SQLCODE and SQLSTATE. The SQLCODE variable shown in Figure 13.1 is an integer variable. After each database command is executed, the DBMS returns a value in SQLCODE. A value of 0 indicates that the statement was executed successfully by the DBMS. If SQLCODE > 0 (or, more specifically, if SQLCODE = 100), this indicates that no more data (records) are available in a query result. If SQLCODE < 0, this indicates some error has occurred. In some systems—for example, in the Oracle RDBMS— SQLCODE is a field in a record structure called SQLCA (SQL communication area), so it is referenced as SQLCA.SQLCODE. In this case, the definition of SQLCA must be included in the C program by including the following line:
EXEC SQL include SQLCA ;
In later versions of the SQL standard, a communication variable called SQLSTATE was added, which is a string of five characters. A value of ‘00000’ in SQLSTATE indi- cates no error or exception; other values indicate various errors or exceptions. For example, ‘02000’ indicates ‘no more data’ when using SQLSTATE. Currently, both SQLSTATE and SQLCODE are available in the SQL standard. Many of the error and exception codes returned in SQLSTATE are supposed to be standardized for all SQL vendors and platforms,7 whereas the codes returned in SQLCODE are not standard- ized but are defined by the DBMS vendor. Hence, it is generally better to use SQLSTATE because this makes error handling in the application programs inde- pendent of a particular DBMS. As an exercise, the reader should rewrite the exam- ples given later in this chapter using SQLSTATE instead of SQLCODE.
Example of Embedded SQL Programming. Our first example to illustrate embedded SQL programming is a repeating program segment (loop) that takes as input a Social Security number of an employee and prints some information from the corresponding EMPLOYEE record in the database. The C program code is shown as program segment E1 in Figure 13.2. The program reads (inputs) an Ssn value and then retrieves the EMPLOYEE tuple with that Ssn from the database via the embed- ded SQL command. The INTO clause (line 5) specifies the program variables into which attribute values from the database record are retrieved. C program variables in the INTO clause are prefixed with a colon (:), as we discussed earlier. The INTO clause can be used in this way only when the query result is a single record; if multi- ple records are retrieved, an error will be generated. We will see how multiple records are handled in Section 13.2.2.
Line 7 in E1 illustrates the communication between the database and the program through the special variable SQLCODE. If the value returned by the DBMS in SQLCODE is 0, the previous statement was executed without errors or exception conditions. Line 7 checks this and assumes that if an error occurred, it was because
7In particular, SQLSTATE codes starting with the characters 0 through 4 or A through H are supposed to be standardized, whereas other values can be implementation-defined.
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Figure 13.2 Program segment E1, a C program segment with embedded SQL.
//Program Segment E1: 0) loop = 1 ; 1) while (loop) { 2) prompt("Enter a Social Security Number: ", ssn) ; 3) EXEC SQL 4) select Fname, Minit, Lname, Address, Salary 5) into :fname, :minit, :lname, :address, :salary 6) from EMPLOYEE where Ssn = :ssn ; 7) if (SQLCODE == 0) printf(fname, minit, lname, address, salary) 8) else printf("Social Security Number does not exist: ", ssn) ; 9) prompt("More Social Security Numbers (enter 1 for Yes, 0 for No): ", loop) ; 10) }
no EMPLOYEE tuple existed with the given Ssn; therefore it outputs a message to that effect (line 8).
In E1 a single record is selected by the embedded SQL query (because Ssn is a key attribute of EMPLOYEE);. When a single record is retrieved, the programmer can assign its attribute values directly to C program variables in the INTO clause, as in line 5. In general, an SQL query can retrieve many tuples. In that case, the C pro- gram will typically go through the retrieved tuples and process them one at a time. The concept of a cursor is used to allow tuple-at-a-time processing of a query result by the host language program. We describe cursors next.
13.2.2 Retrieving Multiple Tuples with Embedded SQL Using Cursors
We can think of a cursor as a pointer that points to a single tuple (row) from the result of a query that retrieves multiple tuples. The cursor is declared when the SQL query command is declared in the program. Later in the program, an OPEN CUR- SOR command fetches the query result from the database and sets the cursor to a position before the first row in the result of the query. This becomes the current row for the cursor. Subsequently, FETCH commands are issued in the program; each FETCH moves the cursor to the next row in the result of the query, making it the cur- rent row and copying its attribute values into the C (host language) program vari- ables specified in the FETCH command by an INTO clause. The cursor variable is basically an iterator that iterates (loops) over the tuples in the query result—one tuple at a time.
To determine when all the tuples in the result of the query have been processed, the communication variable SQLCODE (or, alternatively, SQLSTATE) is checked. If a FETCH command is issued that results in moving the cursor past the last tuple in the result of the query, a positive value (SQLCODE > 0) is returned in SQLCODE, indi- cating that no data (tuple) was found (or the string ‘02000’ is returned in SQLSTATE). The programmer uses this to terminate a loop over the tuples in the query result. In general, numerous cursors can be opened at the same time. A
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CLOSE CURSOR command is issued to indicate that we are done with processing the result of the query associated with that cursor.
An example of using cursors to process a query result with multiple records is shown in Figure 13.3, where a cursor called EMP is declared in line 4. The EMP cur- sor is associated with the SQL query declared in lines 5 through 6, but the query is not executed until the OPEN EMP command (line 8) is processed. The OPEN <cursor name> command executes the query and fetches its result as a table into the program workspace, where the program can loop through the individual rows (tuples) by subsequent FETCH <cursor name> commands (line 9). We assume that appropriate C program variables have been declared as in Figure 13.1. The program segment in E2 reads (inputs) a department name (line 0), retrieves the matching department number from the database (lines 1 to 3), and then retrieves the employ- ees who work in that department via the declared EMP cursor. A loop (lines 10 to 18) iterates over each record in the query result, one at a time, and prints the employee name. The program then reads (inputs) a raise amount for that employee (line 12) and updates the employee’s salary in the database by the raise amount that was provided (lines 14 to 16).
This example also illustrates how the programmer can update database records. When a cursor is defined for rows that are to be modified (updated), we must add
Figure 13.3 Program segment E2, a C program segment that uses cursors with embedded SQL for update purposes.
//Program Segment E2: 0) prompt("Enter the Department Name: ", dname) ; 1) EXEC SQL 2) select Dnumber into :dnumber 3) from DEPARTMENT where Dname = :dname ; 4) EXEC SQL DECLARE EMP CURSOR FOR 5) select Ssn, Fname, Minit, Lname, Salary 6) from EMPLOYEE where Dno = :dnumber 7) FOR UPDATE OF Salary ; 8) EXEC SQL OPEN EMP ; 9) EXEC SQL FETCH from EMP into :ssn, :fname, :minit, :lname, :salary ; 10) while (SQLCODE == 0) { 11) printf("Employee name is:", Fname, Minit, Lname) ; 12) prompt("Enter the raise amount: ", raise) ; 13) EXEC SQL 14) update EMPLOYEE 15) set Salary = Salary + :raise 16) where CURRENT OF EMP ; 17) EXEC SQL FETCH from EMP into :ssn, :fname, :minit, :lname, :salary ; 18) } 19) EXEC SQL CLOSE EMP ;
13.2 Embedded SQL, Dynamic SQL, and SQLJ 457
the clause FOR UPDATE OF in the cursor declaration and list the names of any attributes that will be updated by the program. This is illustrated in line 7 of code segment E2. If rows are to be deleted, the keywords FOR UPDATE must be added without specifying any attributes. In the embedded UPDATE (or DELETE) com- mand, the condition WHERE CURRENT OF<cursor name> specifies that the cur- rent tuple referenced by the cursor is the one to be updated (or deleted), as in line 16 of E2.
Notice that declaring a cursor and associating it with a query (lines 4 through 7 in E2) does not execute the query; the query is executed only when the OPEN <cursor name> command (line 8) is executed. Also notice that there is no need to include the FOR UPDATE OF clause in line 7 of E2 if the results of the query are to be used for retrieval purposes only (no update or delete).
General Options for a Cursor Declaration. Several options can be specified when declaring a cursor. The general form of a cursor declaration is as follows:
DECLARE <cursor name> [ INSENSITIVE ] [ SCROLL ] CURSOR [ WITH HOLD ] FOR <query specification> [ ORDER BY <ordering specification> ] [ FOR READ ONLY | FOR UPDATE [ OF <attribute list> ] ] ;
We already briefly discussed the options listed in the last line. The default is that the query is for retrieval purposes (FOR READ ONLY). If some of the tuples in the query result are to be updated, we need to specify FOR UPDATE OF <attribute list> and list the attributes that may be updated. If some tuples are to be deleted, we need to spec- ify FOR UPDATE without any attributes listed.
When the optional keyword SCROLL is specified in a cursor declaration, it is possi- ble to position the cursor in other ways than for purely sequential access. A fetch orientation can be added to the FETCH command, whose value can be one of NEXT, PRIOR, FIRST, LAST, ABSOLUTE i, and RELATIVE i. In the latter two commands, i must evaluate to an integer value that specifies an absolute tuple position within the query result (for ABSOLUTE i), or a tuple position relative to the current cursor position (for RELATIVE i). The default fetch orientation, which we used in our exam- ples, is NEXT. The fetch orientation allows the programmer to move the cursor around the tuples in the query result with greater flexibility, providing random access by position or access in reverse order. When SCROLL is specified on the cur- sor, the general form of a FETCH command is as follows, with the parts in square brackets being optional:
FETCH [ [ <fetch orientation> ] FROM ] <cursor name> INTO <fetch target list> ;
The ORDER BY clause orders the tuples so that the FETCH command will fetch them in the specified order. It is specified in a similar manner to the corresponding clause for SQL queries (see Section 4.3.6). The last two options when declaring a cursor (INSENSITIVE and WITH HOLD) refer to transaction characteristics of database pro- grams, which we will discuss in Chapter 21.
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Figure 13.4 Program segment E3, a C program segment that uses dynamic SQL for updating a table.
//Program Segment E3: 0) EXEC SQL BEGIN DECLARE SECTION ; 1) varchar sqlupdatestring [256] ; 2) EXEC SQL END DECLARE SECTION ;
... 3) prompt("Enter the Update Command: ", sqlupdatestring) ; 4) EXEC SQL PREPARE sqlcommand FROM :sqlupdatestring ; 5) EXEC SQL EXECUTE sqlcommand ;
...
13.2.3 Specifying Queries at Runtime Using Dynamic SQL In the previous examples, the embedded SQL queries were written as part of the host program source code. Hence, any time we want to write a different query, we must modify the program code, and go through all the steps involved (compiling, debugging, testing, and so on). In some cases, it is convenient to write a program that can execute different SQL queries or updates (or other operations) dynamically at runtime. For example, we may want to write a program that accepts an SQL query typed from the monitor, executes it, and displays its result, such as the interactive interfaces available for most relational DBMSs. Another example is when a user- friendly interface generates SQL queries dynamically for the user based on point- and-click operations on a graphical schema (for example, a QBE-like interface; see Appendix C). In this section, we give a brief overview of dynamic SQL, which is one technique for writing this type of database program, by giving a simple example to illustrate how dynamic SQL can work. In Section 13.3, we will describe another approach for dealing with dynamic queries.
Program segment E3 in Figure 13.4 reads a string that is input by the user (that string should be an SQL update command) into the string program variable sqlupdatestring in line 3. It then prepares this as an SQL command in line 4 by associating it with the SQL variable sqlcommand. Line 5 then executes the command. Notice that in this case no syntax check or other types of checks on the command are possible at compile time, since the SQL command is not available until runtime. This contrasts with our previous examples of embedded SQL, where the query could be checked at compile time because its text was in the program source code.
Although including a dynamic update command is relatively straightforward in dynamic SQL, a dynamic query is much more complicated. This is because usually we do not know the types or the number of attributes to be retrieved by the SQL query when we are writing the program. A complex data structure is sometimes needed to allow for different numbers and types of attributes in the query result if no prior information is known about the dynamic query. Techniques similar to those that we discuss in Section 13.3 can be used to assign query results (and query parameters) to host program variables.
In E3, the reason for separating PREPARE and EXECUTE is that if the command is to be executed multiple times in a program, it can be prepared only once. Preparing the command generally involves syntax and other types of checks by the system, as
13.2 Embedded SQL, Dynamic SQL, and SQLJ 459
well as generating the code for executing it. It is possible to combine the PREPARE and EXECUTE commands (lines 4 and 5 in E3) into a single statement by writing
EXEC SQL EXECUTE IMMEDIATE :sqlupdatestring ;
This is useful if the command is to be executed only once. Alternatively, the pro- grammer can separate the two statements to catch any errors after the PREPARE statement, if any.
13.2.4 SQLJ: Embedding SQL Commands in Java In the previous subsections, we gave an overview of how SQL commands can be embedded in a traditional programming language, using the C language in our examples. We now turn our attention to how SQL can be embedded in an object- oriented programming language,8 in particular, the Java language. SQLJ is a stan- dard that has been adopted by several vendors for embedding SQL in Java. Historically, SQLJ was developed after JDBC, which is used for accessing SQL data- bases from Java using function calls. We discuss JDBC in Section 13.3.2. In this sec- tion, we focus on SQLJ as it is used in the Oracle RDBMS. An SQLJ translator will generally convert SQL statements into Java, which can then be executed through the JDBC interface. Hence, it is necessary to install a JDBC driver when using SQLJ.9 In this section, we focus on how to use SQLJ concepts to write embedded SQL in a Java program.
Before being able to process SQLJ with Java in Oracle, it is necessary to import sev- eral class libraries, shown in Figure 13.5. These include the JDBC and IO classes (lines 1 and 2), plus the additional classes listed in lines 3, 4, and 5. In addition, the program must first connect to the desired database using the function call getConnection, which is one of the methods of the oracle class in line 5 of Figure
Figure 13.5 Importing classes needed for including SQLJ in Java programs in Oracle, and establishing a connection and default context.
1) import java.sql.* ; 2) import java.io.* ; 3) import sqlj.runtime.* ; 4) import sqlj.runtime.ref.* ; 5) import oracle.sqlj.runtime.* ;
... 6) DefaultContext cntxt = 7) oracle.getConnection("<url name>", "<user name>", "<password>", true) ; 8) DefaultContext.setDefaultContext(cntxt) ;
...
8This section assumes familiarity with object-oriented concepts (see Chapter 11) and basic JAVA con- cepts. 9We discuss JDBC drivers in Section 13.3.2.
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Figure 13.6 Java program vari- ables used in SQLJ examples J1 and J2.
1) string dname, ssn , fname, fn, lname, ln, bdate, address ;
2) char sex, minit, mi ; 3) double salary, sal ; 4) integer dno, dnumber ;
13.5. The format of this function call, which returns an object of type default context,10 is as follows:
public static DefaultContext getConnection(String url, String user, String password,
Boolean autoCommit) throws SQLException ;
For example, we can write the statements in lines 6 through 8 in Figure 13.5 to con- nect to an Oracle database located at the url <url name> using the login of <user name> and <password> with automatic commitment of each command,11 and then set this connection as the default context for subsequent commands.
In the following examples, we will not show complete Java classes or programs since it is not our intention to teach Java. Rather, we will show program segments that illustrate the use of SQLJ. Figure 13.6 shows the Java program variables used in our examples. Program segment J1 in Figure 13.7 reads an employee’s Ssn and prints some of the employee’s information from the database.
Notice that because Java already uses the concept of exceptions for error handling, a special exception called SQLException is used to return errors or exception con- ditions after executing an SQL database command. This plays a similar role to SQLCODE and SQLSTATE in embedded SQL. Java has many types of predefined exceptions. Each Java operation (function) must specify the exceptions that can be thrown—that is, the exception conditions that may occur while executing the Java code of that operation. If a defined exception occurs, the system transfers control to the Java code specified for exception handling. In J1, exception handling for an SQLException is specified in lines 7 and 8. In Java, the following structure
try {<operation>} catch (<exception>) {<exception handling code>} <continuation code>
is used to deal with exceptions that occur during the execution of <operation>. If no exception occurs, the <continuation code> is processed directly. Exceptions
10A default context, when set, applies to subsequent commands in the program until it is changed. 11Automatic commitment roughly means that each command is applied to the database after it is exe- cuted. The alternative is that the programmer wants to execute several related database commands and then commit them together. We discuss commit concepts in Chapter 21 when we describe database transactions.
13.2 Embedded SQL, Dynamic SQL, and SQLJ 461
Figure 13.7 Program segment J1, a Java program seg- ment with SQLJ.
//Program Segment J1: 1) ssn = readEntry("Enter a Social Security Number: ") ; 2) try { 3) #sql { select Fname, Minit, Lname, Address, Salary 4) into :fname, :minit, :lname, :address, :salary 5) from EMPLOYEE where Ssn = :ssn} ; 6) } catch (SQLException se) { 7) System.out.println("Social Security Number does not exist: " + ssn) ; 8) Return ; 9) } 10) System.out.println(fname + " " + minit + " " + lname + " " + address
+ " " + salary)
that can be thrown by the code in a particular operation should be specified as part of the operation declaration or interface—for example, in the following format:
<operation return type> <operation name> (<parameters>) throws SQLException, IOException ;
In SQLJ, the embedded SQL commands within a Java program are preceded by #sql, as illustrated in J1 line 3, so that they can be identified by the preprocessor. The #sql is used instead of the keywords EXEC SQL that are used in embedded SQL with the C programming language (see Section 13.2.1). SQLJ uses an INTO clause— similar to that used in embedded SQL—to return the attribute values retrieved from the database by an SQL query into Java program variables. The program vari- ables are preceded by colons (:) in the SQL statement, as in embedded SQL.
In J1 a single tuple is retrieved by the embedded SQLJ query; that is why we are able to assign its attribute values directly to Java program variables in the INTO clause in line 4 in Figure 13.7. For queries that retrieve many tuples, SQLJ uses the concept of an iterator, which is similar to a cursor in embedded SQL.
13.2.5 Retrieving Multiple Tuples in SQLJ Using Iterators In SQLJ, an iterator is a type of object associated with a collection (set or multiset) of records in a query result.12 The iterator is associated with the tuples and attrib- utes that appear in a query result. There are two types of iterators:
1. A named iterator is associated with a query result by listing the attribute names and types that appear in the query result. The attribute names must correspond to appropriately declared Java program variables, as shown in Figure 13.6.
2. A positional iterator lists only the attribute types that appear in the query result.
12We discussed iterators in more detail in Chapter 11 when we presented object database concepts.
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Figure 13.8 Program segment J2A, a Java program segment that uses a named iterator to print employee information in a particular department.
//Program Segment J2A: 0) dname = readEntry("Enter the Department Name: ") ; 1) try { 2) #sql { select Dnumber into :dnumber 3) from DEPARTMENT where Dname = :dname} ; 4) } catch (SQLException se) { 5) System.out.println("Department does not exist: " + dname) ; 6) Return ; 7) } 8) System.out.printline("Employee information for Department: " + dname) ; 9) #sql iterator Emp(String ssn, String fname, String minit, String lname,
double salary) ; 10) Emp e = null ; 11) #sql e = { select ssn, fname, minit, lname, salary 12) from EMPLOYEE where Dno = :dnumber} ; 13) while (e.next()) { 14) System.out.printline(e.ssn + " " + e.fname + " " + e.minit + " " +
e.lname + " " + e.salary) ; 15) } ; 16) e.close() ;
In both cases, the list should be in the same order as the attributes that are listed in the SELECT clause of the query. However, looping over a query result is different for the two types of iterators, as we shall see. First, we show an example of using a named iterator in Figure 13.8, program segment J2A. Line 9 in Figure 13.8 shows how a named iterator type Emp is declared. Notice that the names of the attributes in a named iterator type must match the names of the attributes in the SQL query result. Line 10 shows how an iterator object e of type Emp is created in the program and then associated with a query (lines 11 and 12).
When the iterator object is associated with a query (lines 11 and 12 in Figure 13.8), the program fetches the query result from the database and sets the iterator to a position before the first row in the result of the query. This becomes the current row for the iterator. Subsequently, next operations are issued on the iterator object; each next moves the iterator to the next row in the result of the query, making it the cur- rent row. If the row exists, the operation retrieves the attribute values for that row into the corresponding program variables. If no more rows exist, the next opera- tion returns NULL, and can thus be used to control the looping. Notice that the named iterator does not need an INTO clause, because the program variables corre- sponding to the retrieved attributes are already specified when the iterator type is declared (line 9 in Figure 13.8).
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In Figure 13.8, the command (e.next()) in line 13 performs two functions: It gets the next tuple in the query result and controls the while loop. Once the program is done with processing the query result, the command e.close() (line 16) closes the iterator.
Next, consider the same example using positional iterators as shown in Figure 13.9 (program segment J2B). Line 9 in Figure 13.9 shows how a positional iterator type Emppos is declared. The main difference between this and the named iterator is that there are no attribute names (corresponding to program variable names) in the positional iterator—only attribute types. This can provide more flexibility, but makes the processing of the query result slightly more complex. The attribute types must still must be compatible with the attribute types in the SQL query result and in the same order. Line 10 shows how a positional iterator object e of type Emppos is created in the program and then associated with a query (lines 11 and 12).
The positional iterator behaves in a manner that is more similar to embedded SQL (see Section 13.2.2). A FETCH <iterator variable> INTO <program variables> command is needed to get the next tuple in a query result. The first time fetch is executed, it gets the first tuple (line 13 in Figure 13.9). Line 16 gets the next tuple until no more tuples exist in the query result. To control the loop, a positional itera- tor function e.endFetch() is used. This function is set to a value of TRUE when the iterator is initially associated with an SQL query (line 11), and is set to FALSE
Figure 13.9 Program segment J2B, a Java program segment that uses a positional iterator to print employee information in a particular department.
//Program Segment J2B: 0) dname = readEntry("Enter the Department Name: ") ; 1) try { 2) #sql { select Dnumber into :dnumber 3) from DEPARTMENT where Dname = :dname} ; 4) } catch (SQLException se) { 5) System.out.println("Department does not exist: " + dname) ; 6) Return ; 7) } 8) System.out.printline("Employee information for Department: " + dname) ; 9) #sql iterator Emppos(String, String, String, String, double) ; 10) Emppos e = null ; 11) #sql e = { select ssn, fname, minit, lname, salary 12) from EMPLOYEE where Dno = :dnumber} ; 13) #sql { fetch :e into :ssn, :fn, :mi, :ln, :sal} ; 14) while (!e.endFetch()) { 15) System.out.printline(ssn + " " + fn + " " + mi + " " + ln + " " + sal) ; 16) #sql { fetch :e into :ssn, :fn, :mi, :ln, :sal} ; 17) } ; 18) e.close() ;
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each time a fetch command returns a valid tuple from the query result. It is set to TRUE again when a fetch command does not find any more tuples. Line 14 shows how the looping is controlled by negation.
13.3 Database Programming with Function Calls: SQL/CLI and JDBC
Embedded SQL (see Section 13.2) is sometimes referred to as a static database pro- gramming approach because the query text is written within the program source code and cannot be changed without recompiling or reprocessing the source code. The use of function calls is a more dynamic approach for database programming than embedded SQL. We already saw one dynamic database programming tech- nique—dynamic SQL—in Section 13.2.3. The techniques discussed here provide another approach to dynamic database programming. A library of functions, also known as an application programming interface (API), is used to access the data- base. Although this provides more flexibility because no preprocessor is needed, one drawback is that syntax and other checks on SQL commands have to be done at runtime. Another drawback is that it sometimes requires more complex program- ming to access query results because the types and numbers of attributes in a query result may not be known in advance.
In this section, we give an overview of two function call interfaces. We first discuss the SQL Call Level Interface (SQL/CLI), which is part of the SQL standard. This was developed as a follow-up to the earlier technique known as ODBC (Open Database Connectivity). We use C as the host language in our SQL/CLI examples. Then we give an overview of JDBC, which is the call function interface for accessing databases from Java. Although it is commonly assumed that JDBC stands for Java Database Connectivity, JDBC is just a registered trademark of Sun Microsystems, not an acronym.
The main advantage of using a function call interface is that it makes it easier to access multiple databases within the same application program, even if they are stored under different DBMS packages. We discuss this further in Section 13.3.2 when we discuss Java database programming with JDBC, although this advantage also applies to database programming with SQL/CLI and ODBC (see Section 13.3.1).
13.3.1 Database Programming with SQL/CLI Using C as the Host Language
Before using the function calls in SQL/CLI, it is necessary to install the appropriate library packages on the database server. These packages are obtained from the ven- dor of the DBMS being used. We now give an overview of how SQL/CLI can be used in a C program.13 We will illustrate our presentation with the sample program seg- ment CLI1 shown in Figure 13.10.
13Our discussion here also applies to the C++ programming language, since we do not use any of the object-oriented features but focus on the database programming mechanism.
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When using SQL/CLI, the SQL statements are dynamically created and passed as string parameters in the function calls. Hence, it is necessary to keep track of the information about host program interactions with the database in runtime data structures because the database commands are processed at runtime. The informa- tion is kept in four types of records, represented as structs in C data types. An environment record is used as a container to keep track of one or more database connections and to set environment information. A connection record keeps track of the information needed for a particular database connection. A statement record keeps track of the information needed for one SQL statement. A description record keeps track of the information about tuples or parameters—for example, the num- ber of attributes and their types in a tuple, or the number and types of parameters in a function call. This is needed when the programmer does not know this informa- tion about the query when writing the program. In our examples, we assume that the programmer knows the exact query, so we do not show any description records.
Each record is accessible to the program through a C pointer variable—called a handle to the record. The handle is returned when a record is first created. To create a record and return its handle, the following SQL/CLI function is used:
SQLAllocHandle(<handle_type>, <handle_1>, <handle_2>)
Figure 13.10 Program segment CLI1, a C program segment with SQL/CLI.
//Program CLI1: 0) #include sqlcli.h ; 1) void printSal() { 2) SQLHSTMT stmt1 ; 3) SQLHDBC con1 ; 4) SQLHENV env1 ; 5) SQLRETURN ret1, ret2, ret3, ret4 ; 6) ret1 = SQLAllocHandle(SQL_HANDLE_ENV, SQL_NULL_HANDLE, &env1) ; 7) if (!ret1) ret2 = SQLAllocHandle(SQL_HANDLE_DBC, env1, &con1) else exit ; 8) if (!ret2) ret3 = SQLConnect(con1, "dbs", SQL_NTS, "js", SQL_NTS, "xyz",
SQL_NTS) else exit ; 9) if (!ret3) ret4 = SQLAllocHandle(SQL_HANDLE_STMT, con1, &stmt1) else exit ; 10) SQLPrepare(stmt1, "select Lname, Salary from EMPLOYEE where Ssn = ?",
SQL_NTS) ; 11) prompt("Enter a Social Security Number: ", ssn) ; 12) SQLBindParameter(stmt1, 1, SQL_CHAR, &ssn, 9, &fetchlen1) ; 13) ret1 = SQLExecute(stmt1) ; 14) if (!ret1) { 15) SQLBindCol(stmt1, 1, SQL_CHAR, &lname, 15, &fetchlen1) ; 16) SQLBindCol(stmt1, 2, SQL_FLOAT, &salary, 4, &fetchlen2) ; 17) ret2 = SQLFetch(stmt1) ; 18) if (!ret2) printf(ssn, lname, salary) 19) else printf("Social Security Number does not exist: ", ssn) ; 20) } 21) }
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In this function, the parameters are as follows:
■ <handle_type> indicates the type of record being created. The possible val- ues for this parameter are the keywords SQL_HANDLE_ENV, SQL_HANDLE_DBC, SQL_HANDLE_STMT, or SQL_HANDLE_DESC, for an envi- ronment, connection, statement, or description record, respectively.
■ <handle_1> indicates the container within which the new handle is being created. For example, for a connection record this would be the environment within which the connection is being created, and for a statement record this would be the connection for that statement.
■ <handle_2> is the pointer (handle) to the newly created record of type <handle_type>.
When writing a C program that will include database calls through SQL/CLI, the following are the typical steps that are taken. We illustrate the steps by referring to the example CLI1 in Figure 13.10, which reads a Social Security number of an employee and prints the employee’s last name and salary.
1. The library of functions comprising SQL/CLI must be included in the C pro- gram. This is called sqlcli.h, and is included using line 0 in Figure 13.10.
2. Declare handle variables of types SQLHSTMT, SQLHDBC, SQLHENV, and SQLHDESC for the statements, connections, environments, and descriptions needed in the program, respectively (lines 2 to 4).14 Also declare variables of type SQLRETURN (line 5) to hold the return codes from the SQL/CLI func- tion calls. A return code of 0 (zero) indicates successful execution of the func- tion call.
3. An environment record must be set up in the program using SQLAllocHandle. The function to do this is shown in line 6. Because an environment record is not contained in any other record, the parameter <handle_1> is the NULL handle SQL_NULL_HANDLE (NULL pointer) when creating an environment. The handle (pointer) to the newly created environ- ment record is returned in variable env1 in line 6.
4. A connection record is set up in the program using SQLAllocHandle. In line 7, the connection record created has the handle con1 and is contained in the environment env1. A connection is then established in con1 to a particular server database using the SQLConnect function of SQL/CLI (line 8). In our example, the database server name we are connecting to is dbs and the account name and password for login are js and xyz, respectively.
5. A statement record is set up in the program using SQLAllocHandle. In line 9, the statement record created has the handle stmt1 and uses the connec- tion con1.
6. The statement is prepared using the SQL/CLI function SQLPrepare. In line 10, this assigns the SQL statement string (the query in our example) to the
14To keep our presentation simple, we will not show description records here.
13.3 Database Programming with Function Calls: SQL/CLI and JDBC 467
statement handle stmt1. The question mark (?) symbol in line 10 represents a statement parameter, which is a value to be determined at runtime—typ- ically by binding it to a C program variable. In general, there could be several parameters in a statement string. They are distinguished by the order of appearance of the question marks in the statement string (the first ? repre- sents parameter 1, the second ? represents parameter 2, and so on). The last parameter in SQLPrepare should give the length of the SQL statement string in bytes, but if we enter the keyword SQL_NTS, this indicates that the string holding the query is a NULL-terminated string so that SQL can calcu- late the string length automatically. This use of SQL_NTS also applies to other string parameters in the function calls in our examples.
7. Before executing the query, any parameters in the query string should be bound to program variables using the SQL/CLI function SQLBindParameter. In Figure 13.10, the parameter (indicated by ?) to the prepared query referenced by stmt1 is bound to the C program variable ssn in line 12. If there are n parameters in the SQL statement, we should have n SQLBindParameter function calls, each with a different parameter position (1, 2, ..., n).
8. Following these preparations, we can now execute the SQL statement refer- enced by the handle stmt1 using the function SQLExecute (line 13). Notice that although the query will be executed in line 13, the query results have not yet been assigned to any C program variables.
9. In order to determine where the result of the query is returned, one common technique is the bound columns approach. Here, each column in a query result is bound to a C program variable using the SQLBindCol function. The columns are distinguished by their order of appearance in the SQL query. In Figure 13.10 lines 15 and 16, the two columns in the query (Lname and Salary) are bound to the C program variables lname and salary, respectively.15
10. Finally, in order to retrieve the column values into the C program variables, the function SQLFetch is used (line 17). This function is similar to the FETCH command of embedded SQL. If a query result has a collection of tuples, each SQLFetch call gets the next tuple and returns its column values into the bound program variables. SQLFetch returns an exception (nonzero) code if there are no more tuples in the query result.16
15An alternative technique known as unbound columns uses different SQL/CLI functions, namely SQLGetCol or SQLGetData, to retrieve columns from the query result without previously binding them; these are applied after the SQLFetch command in line 17. 16If unbound program variables are used, SQLFetch returns the tuple into a temporary program area. Each subsequent SQLGetCol (or SQLGetData) returns one attribute value in order. Basically, for each row in the query result, the program should iterate over the attribute values (columns) in that row. This is useful if the number of columns in the query result is variable.
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Figure 13.11 Program segment CLI2, a C program segment that uses SQL/CLI for a query with a collection of tuples in its result.
//Program Segment CLI2: 0) #include sqlcli.h ; 1) void printDepartmentEmps() { 2) SQLHSTMT stmt1 ; 3) SQLHDBC con1 ; 4) SQLHENV env1 ; 5) SQLRETURN ret1, ret2, ret3, ret4 ; 6) ret1 = SQLAllocHandle(SQL_HANDLE_ENV, SQL_NULL_HANDLE, &env1) ; 7) if (!ret1) ret2 = SQLAllocHandle(SQL_HANDLE_DBC, env1, &con1) else exit ; 8) if (!ret2) ret3 = SQLConnect(con1, "dbs", SQL_NTS, "js", SQL_NTS, "xyz",
SQL_NTS) else exit ; 9) if (!ret3) ret4 = SQLAllocHandle(SQL_HANDLE_STMT, con1, &stmt1) else exit ; 10) SQLPrepare(stmt1, "select Lname, Salary from EMPLOYEE where Dno = ?",
SQL_NTS) ; 11) prompt("Enter the Department Number: ", dno) ; 12) SQLBindParameter(stmt1, 1, SQL_INTEGER, &dno, 4, &fetchlen1) ; 13) ret1 = SQLExecute(stmt1) ; 14) if (!ret1) { 15) SQLBindCol(stmt1, 1, SQL_CHAR, &lname, 15, &fetchlen1) ; 16) SQLBindCol(stmt1, 2, SQL_FLOAT, &salary, 4, &fetchlen2) ; 17) ret2 = SQLFetch(stmt1) ; 18) while (!ret2) { 19) printf(lname, salary) ; 20) ret2 = SQLFetch(stmt1) ; 21) } 22) } 23) }
As we can see, using dynamic function calls requires a lot of preparation to set up the SQL statements and to bind statement parameters and query results to the appropriate program variables.
In CLI1 a single tuple is selected by the SQL query. Figure 13.11 shows an example of retrieving multiple tuples. We assume that appropriate C program variables have been declared as in Figure 13.1. The program segment in CLI2 reads (inputs) a department number and then retrieves the employees who work in that depart- ment. A loop then iterates over each employee record, one at a time, and prints the employee’s last name and salary.
13.3 Database Programming with Function Calls: SQL/CLI and JDBC 469
13.3.2 JDBC: SQL Function Calls for Java Programming We now turn our attention to how SQL can be called from the Java object-oriented programming language.17 The function libraries for this access are known as JDBC.18 The Java programming language was designed to be platform indepen- dent—that is, a program should be able to run on any type of computer system that has a Java interpreter installed. Because of this portability, many RDBMS vendors provide JDBC drivers so that it is possible to access their systems via Java programs. A JDBC driver is basically an implementation of the function calls specified in the JDBC application programming interface (API) for a particular vendor’s RDBMS. Hence, a Java program with JDBC function calls can access any RDBMS that has a JDBC driver available.
Because Java is object-oriented, its function libraries are implemented as classes. Before being able to process JDBC function calls with Java, it is necessary to import the JDBC class libraries, which are called java.sql.*. These can be downloaded and installed via the Web.19
JDBC is designed to allow a single Java program to connect to several different data- bases. These are sometimes called the data sources accessed by the Java program. These data sources could be stored using RDBMSs from different vendors and could reside on different machines. Hence, different data source accesses within the same Java program may require JDBC drivers from different vendors. To achieve this flex- ibility, a special JDBC class called the driver manager class is employed, which keeps track of the installed drivers. A driver should be registered with the driver manager before it is used. The operations (methods) of the driver manager class include getDriver, registerDriver, and deregisterDriver. These can be used to add and remove drivers dynamically. Other functions set up and close connections to data sources, as we will see.
To load a JDBC driver explicitly, the generic Java function for loading a class can be used. For example, to load the JDBC driver for the Oracle RDBMS, the following command can be used:
Class.forName("oracle.jdbc.driver.OracleDriver")
This will register the driver with the driver manager and make it available to the program. It is also possible to load and register the driver(s) needed in the com- mand line that runs the program, for example, by including the following in the command line:
-Djdbc.drivers = oracle.jdbc.driver
17This section assumes familiarity with object-oriented concepts (see Chapter 11) and basic Java con- cepts. 18As we mentioned earlier, JDBC is a registered trademark of Sun Microsystems, although it is com- monly thought to be an acronym for Java Database Connectivity. 19These are available from several Web sites—for example, at http://industry.java.sun.com/products/ jdbc/drivers.
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Figure 13.12 Program segment JDBC1, a Java program segment with JDBC.
//Program JDBC1: 0) import java.io.* ; 1) import java.sql.*
... 2) class getEmpInfo { 3) public static void main (String args []) throws SQLException, IOException { 4) try { Class.forName("oracle.jdbc.driver.OracleDriver") 5) } catch (ClassNotFoundException x) { 6) System.out.println ("Driver could not be loaded") ; 7) } 8) String dbacct, passwrd, ssn, lname ; 9) Double salary ; 10) dbacct = readentry("Enter database account:") ; 11) passwrd = readentry("Enter password:") ; 12) Connection conn = DriverManager.getConnection 13) ("jdbc:oracle:oci8:" + dbacct + "/" + passwrd) ; 14) String stmt1 = "select Lname, Salary from EMPLOYEE where Ssn = ?" ; 15) PreparedStatement p = conn.prepareStatement(stmt1) ; 16) ssn = readentry("Enter a Social Security Number: ") ; 17) p.clearParameters() ; 18) p.setString(1, ssn) ; 19) ResultSet r = p.executeQuery() ; 20) while (r.next()) { 21) lname = r.getString(1) ; 22) salary = r.getDouble(2) ; 23) system.out.printline(lname + salary) ; 24) } } 25) }
The following are typical steps that are taken when writing a Java application pro- gram with database access through JDBC function calls. We illustrate the steps by referring to the example JDBC1 in Figure 13.12, which reads a Social Security num- ber of an employee and prints the employee’s last name and salary.
1. The JDBC library of classes must be imported into the Java program. These classes are called java.sql.*, and can be imported using line 1 in Figure 13.12. Any additional Java class libraries needed by the program must also be imported.
2. Load the JDBC driver as discussed previously (lines 4 to 7). The Java excep- tion in line 5 occurs if the driver is not loaded successfully.
3. Create appropriate variables as needed in the Java program (lines 8 and 9).
13.3 Database Programming with Function Calls: SQL/CLI and JDBC 471
4. The Connection object. A connection object is created using the getConnection function of the DriverManager class of JDBC. In lines 12 and 13, the Connection object is created by using the function call getConnection(urlstring), where urlstring has the form
jdbc:oracle:<driverType>:<dbaccount>/<password>
An alternative form is
getConnection(url, dbaccount, password)
Various properties can be set for a connection object, but they are mainly related to transactional properties, which we discuss in Chapter 21.
5. The Statement object. A statement object is created in the program. In JDBC, there is a basic statement class, Statement, with two specialized sub- classes: PreparedStatement and CallableStatement. The example in Figure 13.12 illustrates how PreparedStatement objects are created and used. The next example (Figure 13.13) illustrates the other type of
Figure 13.13 Program segment JDBC2, a Java program segment that uses JDBC for a query with a collection of tuples in its result.
//Program Segment JDBC2: 0) import java.io.* ; 1) import java.sql.*
... 2) class printDepartmentEmps { 3) public static void main (String args [])
throws SQLException, IOException { 4) try { Class.forName("oracle.jdbc.driver.OracleDriver") 5) } catch (ClassNotFoundException x) { 6) System.out.println ("Driver could not be loaded") ; 7) } 8) String dbacct, passwrd, lname ; 9) Double salary ; 10) Integer dno ; 11) dbacct = readentry("Enter database account:") ; 12) passwrd = readentry("Enter password:") ; 13) Connection conn = DriverManager.getConnection 14) ("jdbc:oracle:oci8:" + dbacct + "/" + passwrd) ; 15) dno = readentry("Enter a Department Number: ") ; 16) String q = "select Lname, Salary from EMPLOYEE where Dno = " +
dno.tostring() ; 17) Statement s = conn.createStatement() ; 18) ResultSet r = s.executeQuery(q) ; 19) while (r.next()) { 20) lname = r.getString(1) ; 21) salary = r.getDouble(2) ; 22) system.out.printline(lname + salary) ; 23) } } 24) }
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Statement objects. In line 14 in Figure 13.12, a query string with a single parameter—indicated by the ? symbol—is created in the string variable stmt1. In line 15, an object p of type PreparedStatement is created based on the query string in stmt1 and using the connection object conn. In gen- eral, the programmer should use PreparedStatement objects if a query is to be executed multiple times, since it would be prepared, checked, and com- piled only once, thus saving this cost for the additional executions of the query.
6. Setting the statement parameters. The question mark (?) symbol in line 14 represents a statement parameter, which is a value to be determined at run- time, typically by binding it to a Java program variable. In general, there could be several parameters, distinguished by the order of appearance of the question marks within the statement string (first ? represents parameter 1, second ? represents parameter 2, and so on), as we discussed previously.
7. Before executing a PreparedStatement query, any parameters should be bound to program variables. Depending on the type of the parameter, differ- ent functions such as setString, setInteger, setDouble, and so on are applied to the PreparedStatement object to set its parameters. The appro- priate function should be used to correspond to the data type of the param- eter being set. In Figure 13.12, the parameter (indicated by ?) in object p is bound to the Java program variable ssn in line 18. The function setString is used because ssn is a string variable. If there are n parameters in the SQL statement, we should have n set... functions, each with a different param- eter position (1, 2, ..., n). Generally, it is advisable to clear all parameters before setting any new values (line 17).
8. Following these preparations, we can now execute the SQL statement refer- enced by the object p using the function executeQuery (line 19). There is a generic function execute in JDBC, plus two specialized functions: executeUpdate and executeQuery. executeUpdate is used for SQL insert, delete, or update statements, and returns an integer value indicating the number of tuples that were affected. executeQuery is used for SQL retrieval statements, and returns an object of type ResultSet, which we dis- cuss next.
9. The ResultSet object. In line 19, the result of the query is returned in an object r of type ResultSet. This resembles a two-dimensional array or a table, where the tuples are the rows and the attributes returned are the columns. A ResultSet object is similar to a cursor in embedded SQL and an iterator in SQLJ. In our example, when the query is executed, r refers to a tuple before the first tuple in the query result. The r.next() function (line 20) moves to the next tuple (row) in the ResultSet object and returns NULL if there are no more objects. This is used to control the looping. The pro- grammer can refer to the attributes in the current tuple using various get... functions that depend on the type of each attribute (for example, getString, getInteger, getDouble, and so on). The programmer can either use the attribute positions (1, 2) or the actual attribute names
13.4 Database Stored Procedures and SQL/PSM 473
("Lname", "Salary") with the get... functions. In our examples, we used the positional notation in lines 21 and 22.
In general, the programmer can check for SQL exceptions after each JDBC function call. We did not do this to simplify the examples.
Notice that JDBC does not distinguish between queries that return single tuples and those that return multiple tuples, unlike some of the other techniques. This is justi- fiable because a single tuple result set is just a special case.
In example JDBC1, a single tuple is selected by the SQL query, so the loop in lines 20 to 24 is executed at most once. The example shown in Figure 13.13 illustrates the retrieval of multiple tuples. The program segment in JDBC2 reads (inputs) a department number and then retrieves the employees who work in that depart- ment. A loop then iterates over each employee record, one at a time, and prints the employee’s last name and salary. This example also illustrates how we can execute a query directly, without having to prepare it as in the previous example. This tech- nique is preferred for queries that will be executed only once, since it is simpler to program. In line 17 of Figure 13.13, the programmer creates a Statement object (instead of PreparedStatement, as in the previous example) without associating it with a particular query string. The query string q is passed to the statement object s when it is executed in line 18.
This concludes our brief introduction to JDBC. The interested reader is referred to the Web site http://java.sun.com/docs/books/tutorial/jdbc/, which contains many further details about JDBC.
13.4 Database Stored Procedures and SQL/PSM
This section introduces two additional topics related to database programming. In Section 13.4.1, we discuss the concept of stored procedures, which are program modules that are stored by the DBMS at the database server. Then in Section 13.4.2 we discuss the extensions to SQL that are specified in the standard to include general-purpose programming constructs in SQL. These extensions are known as SQL/PSM (SQL/Persistent Stored Modules) and can be used to write stored proce- dures. SQL/PSM also serves as an example of a database programming language that extends a database model and language—namely, SQL—with some program- ming constructs, such as conditional statements and loops.
13.4.1 Database Stored Procedures and Functions In our presentation of database programming techniques so far, there was an implicit assumption that the database application program was running on a client machine, or more likely at the application server computer in the middle-tier of a three-tier client-server architecture (see Section 2.5.4 and Figure 2.7). In either case, the machine where the program is executing is different from the machine on which
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the database server—and the main part of the DBMS software package—is located. Although this is suitable for many applications, it is sometimes useful to create data- base program modules—procedures or functions—that are stored and executed by the DBMS at the database server. These are historically known as database stored procedures, although they can be functions or procedures. The term used in the SQL standard for stored procedures is persistent stored modules because these programs are stored persistently by the DBMS, similarly to the persistent data stored by the DBMS.
Stored procedures are useful in the following circumstances:
■ If a database program is needed by several applications, it can be stored at the server and invoked by any of the application programs. This reduces duplication of effort and improves software modularity.
■ Executing a program at the server can reduce data transfer and communica- tion cost between the client and server in certain situations.
■ These procedures can enhance the modeling power provided by views by allowing more complex types of derived data to be made available to the database users. Additionally, they can be used to check for complex con- straints that are beyond the specification power of assertions and triggers.
In general, many commercial DBMSs allow stored procedures and functions to be written in a general-purpose programming language. Alternatively, a stored proce- dure can be made of simple SQL commands such as retrievals and updates. The general form of declaring stored procedures is as follows:
CREATE PROCEDURE <procedure name> (<parameters>) <local declarations> <procedure body> ;
The parameters and local declarations are optional, and are specified only if needed. For declaring a function, a return type is necessary, so the declaration form is
CREATE FUNCTION <function name> (<parameters>) RETURNS <return type> <local declarations> <function body> ;
If the procedure (or function) is written in a general-purpose programming lan- guage, it is typical to specify the language as well as a file name where the program code is stored. For example, the following format can be used:
CREATE PROCEDURE <procedure name> (<parameters>) LANGUAGE <programming language name> EXTERNAL NAME <file path name> ;
In general, each parameter should have a parameter type that is one of the SQL data types. Each parameter should also have a parameter mode, which is one of IN, OUT, or INOUT. These correspond to parameters whose values are input only, output (returned) only, or both input and output, respectively.
13.4 Database Stored Procedures and SQL/PSM 475
Because the procedures and functions are stored persistently by the DBMS, it should be possible to call them from the various SQL interfaces and programming techniques. The CALL statement in the SQL standard can be used to invoke a stored procedure—either from an interactive interface or from embedded SQL or SQLJ. The format of the statement is as follows:
CALL <procedure or function name> (<argument list>) ;
If this statement is called from JDBC, it should be assigned to a statement object of type CallableStatement (see Section 13.3.2).
13.4.2 SQL/PSM: Extending SQL for Specifying Persistent Stored Modules
SQL/PSM is the part of the SQL standard that specifies how to write persistent stored modules. It includes the statements to create functions and procedures that we described in the previous section. It also includes additional programming con- structs to enhance the power of SQL for the purpose of writing the code (or body) of stored procedures and functions.
In this section, we discuss the SQL/PSM constructs for conditional (branching) statements and for looping statements. These will give a flavor of the type of con- structs that SQL/PSM has incorporated;20 then we give an example to illustrate how these constructs can be used.
The conditional branching statement in SQL/PSM has the following form:
IF <condition> THEN <statement list> ELSEIF <condition> THEN <statement list> ... ELSEIF <condition> THEN <statement list> ELSE <statement list>
END IF ;
Consider the example in Figure 13.14, which illustrates how the conditional branch structure can be used in an SQL/PSM function. The function returns a string value (line 1) describing the size of a department within a company based on the number of employees. There is one IN integer parameter, deptno, which gives a department number. A local variable NoOfEmps is declared in line 2. The query in lines 3 and 4 returns the number of employees in the department, and the conditional branch in lines 5 to 8 then returns one of the values {‘HUGE’, ‘LARGE’, ‘MEDIUM’, ‘SMALL’} based on the number of employees.
SQL/PSM has several constructs for looping. There are standard while and repeat looping structures, which have the following forms:
20We only give a brief introduction to SQL/PSM here. There are many other features in the SQL/PSM standard.
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Figure 13.14 Declaring a function in SQL/PSM.
//Function PSM1: 0) CREATE FUNCTION Dept_size(IN deptno INTEGER) 1) RETURNS VARCHAR [7] 2) DECLARE No_of_emps INTEGER ; 3) SELECT COUNT(*) INTO No_of_emps 4) FROM EMPLOYEE WHERE Dno = deptno ; 5) IF No_of_emps > 100 THEN RETURN "HUGE" 6) ELSEIF No_of_emps > 25 THEN RETURN "LARGE" 7) ELSEIF No_of_emps > 10 THEN RETURN "MEDIUM" 8) ELSE RETURN "SMALL" 9) END IF ;
WHILE <condition> DO <statement list>
END WHILE ; REPEAT
<statement list> UNTIL <condition> END REPEAT ;
There is also a cursor-based looping structure. The statement list in such a loop is executed once for each tuple in the query result. This has the following form:
FOR <loop name> AS <cursor name> CURSOR FOR <query> DO <statement list>
END FOR ;
Loops can have names, and there is a LEAVE <loop name> statement to break a loop when a condition is satisfied. SQL/PSM has many other features, but they are out- side the scope of our presentation.
13.5 Comparing the Three Approaches In this section, we briefly compare the three approaches for database programming and discuss the advantages and disadvantages of each approach.
1. Embedded SQL Approach. The main advantage of this approach is that the query text is part of the program source code itself, and hence can be checked for syntax errors and validated against the database schema at com- pile time. This also makes the program quite readable, as the queries are readily visible in the source code. The main disadvantages are the loss of flex- ibility in changing the query at runtime, and the fact that all changes to queries must go through the whole recompilation process. In addition, because the queries are known beforehand, the choice of program variables to hold the query results is a simple task, and so the programming of the application is generally easier. However, for complex applications where
13.6 Summary 477
queries have to be generated at runtime, the function call approach will be more suitable.
2. Library of Function Calls Approach. This approach provides more flexibil- ity in that queries can be generated at runtime if needed. However, this leads to more complex programming, as program variables that match the columns in the query result may not be known in advance. Because queries are passed as statement strings within the function calls, no checking can be done at compile time. All syntax checking and query validation has to be done at runtime, and the programmer must check and account for possible additional runtime errors within the program code.
3. Database Programming Language Approach. This approach does not suf- fer from the impedance mismatch problem, as the programming language data types are the same as the database data types. However, programmers must learn a new programming language rather than use a language they are already familiar with. In addition, some database programming languages are vendor-specific, whereas general-purpose programming languages can easily work with systems from multiple vendors.
13.6 Summary In this chapter we presented additional features of the SQL database language. In particular, we presented an overview of the most important techniques for database programming in Section 13.1. Then we discussed the various approaches to data- base application programming in Sections 13.2 to 13.4.
In Section 13.2, we discussed the general technique known as embedded SQL, where the queries are part of the program source code. A precompiler is typically used to extract SQL commands from the program for processing by the DBMS, and replacing them with function calls to the DBMS compiled code. We presented an overview of embedded SQL, using the C programming language as host language in our examples. We also discussed the SQLJ technique for embedding SQL in Java programs. The concepts of cursor (for embedded SQL) and iterator (for SQLJ) were presented and illustrated by examples to show how they are used for looping over the tuples in a query result, and extracting the attribute value into program vari- ables for further processing.
In Section 13.3, we discussed how function call libraries can be used to access SQL databases. This technique is more dynamic than embedding SQL, but requires more complex programming because the attribute types and number in a query result may be determined at runtime. An overview of the SQL/CLI standard was pre- sented, with examples using C as the host language. We discussed some of the func- tions in the SQL/CLI library, how queries are passed as strings, how query parameters are assigned at runtime, and how results are returned to program vari- ables. We then gave an overview of the JDBC class library, which is used with Java, and discussed some of its classes and operations. In particular, the ResultSet class is used to create objects that hold the query results, which can then be iterated over
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by the next() operation. The get and set functions for retrieving attribute values and setting parameter values were also discussed.
In Section 13.4 we gave a brief overview of stored procedures, and discussed SQL/PSM as an example of a database programming language. Finally, we briefly compared the three approaches in Section 13.5. It is important to note that we chose to give a comparative overview of the three main approaches to database program- ming, since studying a particular approach in depth is a topic that is worthy of its own textbook.
Review Questions 13.1. What is ODBC? How is it related to SQL/CLI?
13.2. What is JDBC? Is it an example of embedded SQL or of using function calls?
13.3. List the three main approaches to database programming. What are the advantages and disadvantages of each approach?
13.4. What is the impedance mismatch problem? Which of the three program- ming approaches minimizes this problem?
13.5. Describe the concept of a cursor and how it is used in embedded SQL.
13.6. What is SQLJ used for? Describe the two types of iterators available in SQLJ.
Exercises 13.7. Consider the database shown in Figure 1.2, whose schema is shown in Figure
2.1. Write a program segment to read a student’s name and print his or her grade point average, assuming that A=4, B=3, C=2, and D=1 points. Use embedded SQL with C as the host language.
13.8. Repeat Exercise 13.7, but use SQLJ with Java as the host language.
13.9. Consider the library relational database schema in Figure 4.6. Write a pro- gram segment that retrieves the list of books that became overdue yesterday and that prints the book title and borrower name for each. Use embedded SQL with C as the host language.
13.10. Repeat Exercise 13.9, but use SQLJ with Java as the host language.
13.11. Repeat Exercises 13.7 and 13.9, but use SQL/CLI with C as the host language.
13.12. Repeat Exercises 13.7 and 13.9, but use JDBC with Java as the host language.
13.13. Repeat Exercise 13.7, but write a function in SQL/PSM.
13.14. Create a function in PSM that computes the median salary for the EMPLOYEE table shown in Figure 3.5.
Selected Bibliography 479
Selected Bibliography There are many books that describe various aspects of SQL database programming. For example, Sunderraman (2007) describes programming on the Oracle 10g DBMS and Reese (1997) focuses on JDBC and Java programming. Many Web resources are also available.
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Web Database Programming Using PHP
In the previous chapter, we gave an overview of data-base programming techniques using traditional pro- gramming languages, and we used the Java and C programming languages in our examples. We now turn our attention to how databases are accessed from scripting languages. Many electronic commerce (e-commerce) and other Internet applica- tions that provide Web interfaces to access information stored in one or more data- bases use scripting languages. These languages are often used to generate HTML documents, which are then displayed by the Web browser for interaction with the user.
In Chapter 12, we gave an overview of the XML language for data representation and exchange on the Web, and discussed some of the ways in which it can be used. We introduced HTML and discussed how it differs from XML. Basic HTML is use- ful for generating static Web pages with fixed text and other objects, but most e- commerce applications require Web pages that provide interactive features with the user. For example, consider the case of an airline customer who wants to check the arrival time and gate information of a particular flight. The user may enter informa- tion such as a date and flight number in certain form fields of the Web page. The Web program must first submit a query to the airline database to retrieve this infor- mation, and then display it. Such Web pages, where part of the information is extracted from databases or other data sources, are called dynamic Web pages. The data extracted and displayed each time will be for different flights and dates.
There are various techniques for programming dynamic features into Web pages. We will focus on one technique here, which is based on using the PHP open source scripting language. PHP has recently experienced widespread use. The interpreters for PHP are provided free of charge, and are written in the C language so they are
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available on most computer platforms. A PHP interpreter provides a Hypertext Preprocessor, which will execute PHP commands in a text file and create the desired HTML file. To access databases, a library of PHP functions needs to be included in the PHP interpreter as we will discuss in Section 14.3. PHP programs are executed on the Web server computer. This is in contrast to some scripting languages, such as JavaScript, that are executed on the client computer.
This chapter is organized as follows. Section 14.1 gives a simple example to illustrate how PHP can be used. Section 14.2 gives a general overview of the PHP language, and how it is used to program some basic functions for interactive Web pages. Section 14.3 focuses on using PHP to interact with SQL databases through a library of functions known as PEAR DB. Finally, Section 14.4 contains a chapter summary.
14.1 A Simple PHP Example PHP is an open source general-purpose scripting language. The interpreter engine for PHP is written in the C programming language so it can be used on nearly all types of computers and operating systems. PHP usually comes installed with the UNIX operating system. For computer platforms with other operating systems such as Windows, Linux, or Mac OS, the PHP interpreter can be downloaded from: http://www.php.net. As with other scripting languages, PHP is particularly suited for manipulation of text pages, and in particular for manipulating dynamic HTML pages at the Web server computer. This is in contrast to JavaScript, which is down- loaded with the Web pages to execute on the client computer.
PHP has libraries of functions for accessing databases stored under various types of relational database systems such as Oracle, MySQL, SQLServer, and any system that supports the ODBC standard (see Chapter 13). Under the three-tier architecture (see Chapter 2), the DBMS would reside at the bottom-tier database server. PHP would run at the middle-tier Web server, where the PHP program commands would manipulate the HTML files to create the customized dynamic Web pages. The HTML is then sent to the client tier for display and interaction with the user.
Consider the example shown in Figure 14.1(a), which prompts a user to enter the first and last name and then prints a welcome message to that user. The line num- bers are not part of the program code; they are used below for explanation purposes only:
1. Suppose that the file containing PHP script in program segment P1 is stored in the following Internet location: http://www.myserver.com/example/ greeting.php. Then if a user types this address in the browser, the PHP inter- preter would start interpreting the code and produce the form shown in Figure 14.1(b). We will explain how that happens as we go over the lines in code segment P1.
2. Line 0 shows the PHP start tag <?php, which indicates to the PHP inter- preter engine that it should process all subsequent text lines until it encoun- ters the PHP end tag ?>, shown on line 16. Text outside of these tags is
Enter your name:
SUBMIT NAME SUBMIT NAME
Enter your name: (b) (c)
(d)
John Smith
Welcome, John Smith
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Figure 14.1 (a) PHP program segment for entering a greeting, (b) Initial form displayed by PHP program segment, (c) User enters name John Smith, (d) Form prints welcome message for John Smith.
(a) //Program Segment P1:
0) <?php 1) // Printing a welcome message if the user submitted their name
// through the HTML form 2) if ($_POST['user_name']) { 3) print("Welcome, ") ; 4) print($_POST['user_name']); 5) } 6) else { 7) // Printing the form to enter the user name since no name has
// been entered yet 8) print <<<_HTML_ 9) <FORM method="post" action="$_SERVER['PHP_SELF']">
10) Enter your name: <input type="text" name="user_name"> 11) <BR/> 12) <INPUT type="submit" value="SUBMIT NAME"> 13) </FORM> 14) _HTML_; 15) } 16) ?>
printed as is. This allows PHP code segments to be included within a larger HTML file. Only the sections in the file between <?php and ?> are processed by the PHP preprocessor.
3. Line 1 shows one way of posting comments in a PHP program on a single line started by //. Single-line comments can also be started with #, and end at the end of the line in which they are entered. Multiple line comments start with /* and end with */.
4. The auto-global predefined PHP variable $_POST (line 2) is an array that holds all the values entered through form parameters. Arrays in PHP are dynamic arrays, with no fixed number of elements. They can be numerically indexed arrays whose indexes (positions) are numbered (0, 1, 2, ...), or they
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can be associative arrays whose indexes can be any string values. For example, an associative array indexed based on color can have the indexes {“red”, “blue”, “green”}. In this example, $_POST is associatively indexed by the name of the posted value user_name that is specified in the name attribute of the input tag on line 10. Thus $_POST['user_name'] will contain the value typed in by the user. We will discuss PHP arrays further in Section 14.2.2.
5. When the Web page at http://www.myserver.com/example/greeting.php is first opened, the if condition in line 2 will evaluate to false because there is no value yet in $_POST['user_name']. Hence, the PHP interpreter will process lines 6 through 15, which create the text for an HTML file that dis- plays the form shown in Figure 14.1(b). This is then displayed at the client side by the Web browser.
6. Line 8 shows one way of creating long text strings in an HTML file. We will discuss other ways to specify strings later in this section. All text between an opening <<<_HTML_ and a closing _HTML_; is printed into the HTML file as is. The closing _HTML_; must be alone on a separate line. Thus, the text added to the HTML file sent to the client will be the text between lines 9 and 13. This includes HTML tags to create the form shown in Figure 14.1(b).
7. PHP variable names start with a $ sign and can include characters, num- bers, and the underscore character _. The PHP auto-global (predefined) variable $_SERVER (line 9) is an array that includes information about the local server. The element $_SERVER['PHP_SELF'] in the array is the path name of the PHP file currently being executed on the server. Thus, the action attribute of the form tag (line 9) instructs the PHP interpreter to reprocess the same file, once the form parameters are entered by the user.
8. Once the user types the name John Smith in the text box and clicks on the SUBMIT NAME button (Figure 14.1(c)), program segment P1 is reprocessed. This time, $_POST['user_name'] will include the string "John Smith", so lines 3 and 4 will now be placed in the HTML file sent to the client, which displays the message in Figure 14.1(d).
As we can see from this example, the PHP program can create two different HTML commands depending on whether the user just started or whether they had already submitted their name through the form. In general, a PHP program can create numerous variations of HTML text in an HTML file at the server depending on the particular conditional paths taken in the program. Hence, the HTML sent to the client will be different depending on the interaction with the user. This is one way in which PHP is used to create dynamic Web pages.
14.2 Overview of Basic Features of PHP In this section we give an overview of a few of the features of PHP that are useful in creating interactive HTML pages. Section 14.3 will focus on how PHP programs can access databases for querying and updating. We cannot give a comprehensive dis-
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cussion on PHP as there are whole books devoted to this subject. Rather, we focus on illustrating certain features of PHP that are particularly suited for creating dynamic Web pages that contain database access commands. This section covers some PHP concepts and features that will be needed when we discuss database access in Section 14.3.
14.2.1 PHP Variables, Data Types, and Programming Constructs
PHP variable names start with the $ symbol and can include characters, letters, and the underscore character (_). No other special characters are permitted. Variable names are case sensitive, and the first character cannot be a number. Variables are not typed. The values assigned to the variables determine their type. In fact, the same variable can change its type once a new value is assigned to it. Assignment is via the = operator.
Since PHP is directed toward text processing, there are several different types of string values. There are also many functions available for processing strings. We only discuss some basic properties of string values and variables here. Figure 14.2 illustrates some string values. There are three main ways to express strings and text:
1. Single-quoted strings. Enclose the string between single quotes, as in lines 0, 1, and 2. If a single quote is needed within the string, use the escape char- acter (\) (see line 2).
2. Double-quoted strings. Enclose strings between double quotes as in line 7. In this case, variable names appearing within the string are replaced by the values that are currently stored in these variables. The interpreter identifies variable names within double-quoted strings by their initial character $ and replaces them with the value in the variable. This is known as interpolating variables within strings. Interpolation does not occur in single-quoted strings.
Figure 14.2 Illustrating basic PHP string and text values.
0) print 'Welcome to my Web site.'; 1) print 'I said to him, "Welcome Home"'; 2) print 'We\'ll now visit the next Web site'; 3) printf('The cost is $%.2f and the tax is $%.2f',
$cost, $tax) ; 4) print strtolower('AbCdE'); 5) print ucwords(strtolower('JOHN smith')); 6) print 'abc' . 'efg' 7) print "send your email reply to: $email_address" 8) print <<<FORM_HTML 9) <FORM method="post" action="$_SERVER['PHP_SELF']">
10) Enter your name: <input type="text" name="user_name"> 11) FORM_HTML
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3. Here documents. Enclose a part of a document between a <<<DOCNAME and end it with a single line containing the document name DOCNAME. DOCNAME can be any string as long as it used both to start and end the here document. This is illustrated in lines 8 through 11 in Figure 14.2. Variables are also interpolated by replacing them with their string values if they appear inside here documents. This feature is used in a similar way to double-quoted strings, but it is more convenient for multiple-line text.
4. Single and double quotes. Single and double quotes used by PHP to enclose strings should be straight quotes ("") on both sides of the string. The text editor that creates these quotes should not produce curly opening and clos- ing quotes (“”) around the string.
There is also a string concatenate operator specified by the period (.) symbol, as illustrated in line 6 of Figure 14.2. There are many string functions. We only illus- trate a couple of them here. The function strtolower changes the alphabetic char- acters in the string to all lowercase, while the function ucwords capitalizes all the words in a string. These are illustrated in lines 4 and 5 in Figure 14.2.
The general rule is to use single-quoted strings for literal strings that contain no PHP program variables and the other two types (double-quoted strings and here documents) when the values from variables need to be interpolated into the string. For large blocks of multiline text, the program should use the here documents style for strings.
PHP also has numeric data types for integers and floating points and generally fol- lows the rules of the C programming language for processing these types. Numbers can be formatted for printing into strings by specifying the number of digits that follow the decimal point. A variation of the print function called printf (print formatted) allows formatting of numbers within a string as illustrated in line 3 of Figure 14.2.
There are the standard programming language constructs of for-loops, while-loops, and conditional if-statements. They are generally similar to their C language coun- terparts. We will not discuss them here. Similarly, any value evaluates to true if used as a Boolean expression except for numeric zero (0) and blank string, which evaluate to false. There are also literal true and false values that can be assigned. The compar- ison operators also generally follow C language rules. They are == (equal), != (not equal), > (greater than), >= (greater than or equal), < (less than), and <= (less than or equal).
14.2.2 PHP Arrays Arrays are very important in PHP, since they allow lists of elements. They are used frequently in forms that employ pull-down menus. A single-dimensional array is used to hold the list of choices in the pull-down menu. For database query results, two-dimensional arrays are used with the first dimension representing rows of a table and the second dimension representing columns (attributes) within a row.
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Figure 14.3 Illustrating basic PHP array processing.
0) $teaching = array('Database' => 'Smith', 'OS' => 'Carrick', 'Graphics' => 'Kam');
1) $teaching['Graphics'] = 'Benson'; $teaching['Data Mining'] = 'Kam'; 2) sort($teaching); 3) foreach ($teaching as $key => $value) { 4) print " $key : $value\n";} 5) $courses = array('Database', 'OS', 'Graphics', 'Data Mining'); 6) $alt_row_color = array('blue', 'yellow'); 7) for ($i = 0, $num = count($courses); i < $num; $i++) { 8) print '<TR bgcolor="' . $alt_row_color[$i % 2] . '">'; 9) print "<TD>Course $i is</TD><TD>$course[$i]</TD></TR>\n";
10) }
There are two main types of arrays: numeric and associative. We discuss each of these in the context of single-dimensional arrays next.
A numeric array associates a numeric index (or position or sequence number) with each element in the array. Indexes are integer numbers that start at zero and grow incrementally. An element in the array is referenced through its index. An associative array provides pairs of (key => value) elements. The value of an element is referenced through its key, and all key values in a particular array must be unique. The element values can be strings or integers, or they can be arrays themselves, thus leading to higher dimensional arrays.
Figure 14.3 gives two examples of array variables: $teaching and $courses. The first array $teaching is associative (see line 0 in Figure 14.3), and each element associates a course name (as key) with the name of the course instructor (as value). There are three elements in this array. Line 1 shows how the array may be updated. The first command in line 1 assigns a new instructor to the course ‘Graphics’ by updating its value. Since the key value ‘Graphics’ already exists in the array, no new element is created but the existing value is updated. The second command creates a new element since the key value ‘Data Mining’ did not exist in the array before. New elements are added at the end of the array.
If we only provide values (no keys) as array elements, the keys are automatically numeric and numbered 0, 1, 2, .... This is illustrated in line 5 of Figure 14.3, by the $courses array. Both associative and numeric arrays have no size limits. If some value of another data type, say an integer, is assigned to a PHP variable that was holding an array, the variable now holds the integer value and the array contents are lost. Basically, most variables can be assigned to values of any data type at any time.
There are several different techniques for looping through arrays in PHP. We illus- trate two of these techniques in Figure 14.3. Lines 3 and 4 show one method of looping through all the elements in an array using the foreach construct, and
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printing the key and value of each element on a separate line. Lines 7 through 10 show how a traditional for-loop construct can be used. A built-in function count (line 7) returns the current number of elements in the array, which is assigned to the variable $num and used to control ending the loop.
The example in lines 7 through 10 also illustrates how an HTML table can be dis- played with alternating row colors, by setting the two colors in an array $alt_row_color (line 8). Each time through the loop, the remainder function $i % 2 switches from one row (index 0) to the next (index 1) (see line 8). The color is assigned to the HTML bgcolor attribute of the <TR> (table row) tag.
The count function (line 7) returns the current number of elements in the array. The sort function (line 2) sorts the array based on the element values in it (not the keys). For associative arrays, each key remains associated with the same element value after sorting. This does not occur when sorting numeric arrays. There are many other functions that can be applied to PHP arrays, but a full discussion is out- side the scope of our presentation.
14.2.3 PHP Functions As with other programming languages, functions can be defined in PHP to better structure a complex program and to share common sections of code that can be reused by multiple applications. The newer version of PHP, PHP5, also has object- oriented features, but we will not discuss these here as we are focusing on the basics of PHP. Basic PHP functions can have arguments that are passed by value. Global variables can be accessed within functions. Standard scope rules apply to variables that appear within a function and within the code that calls the function.
We now give two simple examples to illustrate basic PHP functions. In Figure 14.4, we show how we could rewrite the code segment P1 from Figure 14.1(a) using func- tions. The code segment P1� in Figure 14.4 has two functions: display_welcome() (lines 0 to 3) and display_empty_form() (lines 5 to 13). Neither of these func- tions has arguments nor do they have return values. Lines 14 through 19 show how we can call these functions to produce the same effect as the segment of code P1 in Figure 14.1(a). As we can see in this example, functions can be used just to make the PHP code better structured and easier to follow.
A second example is shown in Figure 14.5. Here we are using the $teaching array introduced in Figure 14.3. The function course_instructor() in lines 0 to 8 in Figure 14.5 has two arguments: $course (a string holding a course name) and $teaching_assignments (an associative array holding course assignments, simi- lar to the $teaching array shown in Figure 14.3). The function finds the name of the instructor who teaches a particular course. Lines 9 to 14 in Figure 14.5 show how this function may be used.
The function call in line 11 would return the string: Smith is teaching Database, because the array entry with the key ‘Database’ has the value ‘Smith’ for instructor. On the other hand, the function call on line 13 would return the string: there is no Computer Architecture course because there is no entry in the array with the key
14.2 Overview of Basic Features of PHP 489
Figure 14.4 Rewriting program segment P1 as P1� using functions.
//Program Segment P1': 0) function display_welcome() { 1) print("Welcome, ") ; 2) print($_POST['user_name']); 3) } 4) 5) function display_empty_form(); { 6) print <<<_HTML_ 7) <FORM method="post" action="$_SERVER['PHP_SELF']"> 8) Enter your name: <INPUT type="text" name="user_name"> 9) <BR/> 10) <INPUT type="submit" value="Submit name"> 11) </FORM> 12) _HTML_; 13) } 14) if ($_POST['user_name']) { 15) display_welcome(); 16) } 17) else { 18) display_empty_form(); 19) }
Figure 14.5 Illustrating a function with arguments and return value.
0) function course_instructor ($course, $teaching_assignments) { 1) if (array_key_exists($course, $teaching_assignments)) { 2) $instructor = $teaching_assignments[$course]; 3) RETURN "$instructor is teaching $course"; 4) } 5) else { 6) RETURN "there is no $course course"; 7) } 8) } 9) $teaching = array('Database' => 'Smith', 'OS' => 'Carrick',
'Graphics' => 'Kam'); 10) $teaching['Graphics'] = 'Benson'; $teaching['Data Mining'] = 'Kam'; 11) $x = course_instructor('Database', $teaching); 12) print($x); 13) $x = course_instructor('Computer Architecture', $teaching); 14) print($x);
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‘Computer Architecture’. A few comments about this example and about PHP func- tions in general:
■ The built-in PHP array function array_key_exists($k, $a) returns true if the value in variable $k exists as a key in the associative array in the variable $a. In our example, it checks whether the $course value provided exists as a key in the array $teaching_assignments (line 1 in Figure 14.5).
■ Function arguments are passed by value. Hence, in this example, the calls in lines 11 and 13 could not change the array $teaching provided as argument for the call. The values provided in the arguments are passed (copied) to the function arguments when the function is called.
■ Return values of a function are placed after the RETURN keyword. A function can return any type. In this example, it returns a string type. Two different strings can be returned in our example, depending on whether the $course key value provided exists in the array or not.
■ Scope rules for variable names apply as in other programming languages. Global variables outside of the function cannot be used unless they are referred to using the built-in PHP array $GLOBALS. Basically, $GLOBALS['abc'] will access the value in a global variable $abc defined outside the function. Otherwise, variables appearing inside a function are local even if there is a global variable with the same name.
The previous discussion gives a brief overview of PHP functions. Many details are not discussed since it is not our goal to present PHP in detail.
14.2.4 PHP Server Variables and Forms There are a number of built-in entries in a PHP auto-global built-in array variable called $_SERVER that can provide the programmer with useful information about the server where the PHP interpreter is running, as well as other information. These may be needed when constructing the text in an HTML document (for example, see line 7 in Figure 14.4). Here are some of these entries:
1. $_SERVER['SERVER_NAME']. This provides the Web site name of the server computer where the PHP interpreter is running. For example, if the PHP interpreter is running on the Web site http://www.uta.edu, then this string would be the value in $_SERVER['SERVER_NAME'].
2. $_SERVER['REMOTE_ADDRESS']. This is the IP (Internet Protocol) address of the client user computer that is accessing the server, for example 129.107.61.8.
3. $_SERVER['REMOTE_HOST']. This is the Web site name of the client user computer, for example abc.uta.edu. In this case, the server will need to trans- late the name into an IP address to access the client.
4. $_SERVER['PATH_INFO']. This is the part of the URL address that comes after a backslash (/) at the end of the URL.
14.3 Overview of PHP Database Programming 491
5. $_SERVER['QUERY_STRING']. This provides the string that holds parame- ters in a URL after a question mark (?) at the end of the URL. This can hold search parameters, for example.
6. $_SERVER['DOCUMENT_ROOT']. This is the root directory that holds the files on the Web server that are accessible to client users.
These and other entries in the $_SERVER array are usually needed when creating the HTML file to be sent for display.
Another important PHP auto-global built-in array variable is called $_POST. This provides the programmer with input values submitted by the user through HTML forms specified in the HTML <INPUT> tag and other similar tags. For example, in Figure 14.4 line 14, the variable $_POST['user_name'] provides the programmer with the value typed in by the user in the HTML form specified via the <INPUT> tag on line 8. The keys to this array are the names of the various input parameters pro- vided via the form, for example by using the name attribute of the HTML <INPUT> tag as on line 8. When users enter data through forms, the data values can be stored in this array.
14.3 Overview of PHP Database Programming There are various techniques for accessing a database through a programming lan- guage. We discussed some of the techniques in Chapter 13, in the overviews of how to access an SQL database using the C and Java programming languages. In particu- lar, we discussed embedded SQL, JDBC, SQL/CLI (similar to ODBC), and SQLJ. In this section we give an overview of how to access the database using the script lan- guage PHP, which is quite suitable for creating Web interfaces for searching and updating databases, as well as dynamic Web pages.
There is a PHP database access function library that is part of PHP Extension and Application Repository (PEAR), which is a collection of several libraries of func- tions for enhancing PHP. The PEAR DB library provides functions for database access. Many database systems can be accessed from this library, including Oracle, MySQL, SQLite, and Microsoft SQLServer, among others.
We will discuss several functions that are part of PEAR DB in the context of some examples. Section 14.3.1 shows how to connect to a database using PHP. Section 14.3.2 discusses how data collected from HTML forms can be used to insert a new record in a database table (relation). Section 14.3.3 shows how retrieval queries can be executed and have their results displayed within a dynamic Web page.
14.3.1 Connecting to a Database To use the database functions in a PHP program, the PEAR DB library module called DB.php must be loaded. In Figure 14.6, this is done in line 0 of the example. The DB library functions can now be accessed using DB::<function_name>. The function for connecting to a database is called DB::connect('string') where the
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Figure 14.6 Connecting to a database, creating a table, and inserting a record.
0) require 'DB.php'; 1) $d = DB::connect('oci8://acct1:[email protected]/db1'); 2) if (DB::isError($d)) { die("cannot connect – " . $d->getMessage());}
... 3) $q = $d->query("CREATE TABLE EMPLOYEE 4) (Emp_id INT, 5) Name VARCHAR(15), 6) Job VARCHAR(10), 7) Dno INT)" ); 8) if (DB::isError($q)) { die("table creation not successful – " .
$q->getMessage()); } ...
9) $d->setErrorHandling(PEAR_ERROR_DIE); ...
10) $eid = $d->nextID('EMPLOYEE'); 11) $q = $d->query("INSERT INTO EMPLOYEE VALUES 12) ($eid, $_POST['emp_name'], $_POST['emp_job'], $_POST['emp_dno'])" );
... 13) $eid = $d->nextID('EMPLOYEE'); 14) $q = $d->query('INSERT INTO EMPLOYEE VALUES (?, ?, ?, ?)', 15) array($eid, $_POST['emp_name'], $_POST['emp_job'], $_POST['emp_dno']) );
string argument specifies the database information. The format for 'string' is:
<DBMS software>://<user account>:<password>@<database server>
In Figure 14.6, line 1 connects to the database that is stored using Oracle (specified via the string oci8). The <DBMS software> portion of the 'string' specifies the particular DBMS software package being connected to. Some of the DBMS software packages that are accessible through PEAR DB are:
■ MySQL. Specified as mysql for earlier versions and mysqli for later versions starting with version 4.1.2.
■ Oracle. Specified as oc8i for versions 7, 8, and 9. This is used in line 1 of Figure 14.6.
■ SQLite. Specified as sqlite. ■ Microsoft SQL Server. Specified as mssql. ■ Mini SQL. Specified as msql. ■ Informix. Specified as ifx. ■ Sybase. Specified as sybase. ■ Any ODBC-compliant system. Specified as odbc.
The above is not a comprehensive list.
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Following the <DB software> in the string argument passed to DB::connect is the separator :// followed by the user account name <user account> followed by the separator : and the account password <password>. These are followed by the separator @ and the server name and directory <database server> where the database is stored.
In line 1 of Figure 14.6, the user is connecting to the server at www.host.com/db1 using the account name acct1 and password pass12 stored under the Oracle DBMS oci8. The whole string is passed using DB::connect. The connection infor- mation is kept in the database connection variable $d, which is used whenever an operation to this particular database is applied.
Line 2 in Figure 14.6 shows how to check whether the connection to the database was established successfully or not. PEAR DB has a function DB::isError, which can determine whether any database access operation was successful or not. The argument to this function is the database connection variable ($d in this example). In general, the PHP programmer can check after every database call to determine whether the last database operation was successful or not, and terminate the pro- gram (using the die function) if it was not successful. An error message is also returned from the database via the operation $d->get_message(). This can also be displayed as shown in line 2 of Figure 14.6.
In general, most SQL commands can be sent to the database once a connection is established via the query function. The function $d->query takes an SQL com- mand as its string argument and sends it to the database server for execution. In Figure 14.6, lines 3 to 7 send a CREATE TABLE command to create a table called EMPLOYEE with four attributes. Whenever a query is executed, the result of the query is assigned to a query variable, which is called $q in our example. Line 8 checks whether the query was executed successfully or not.
The PHP PEAR DB library offers an alternative to having to check for errors after every database command. The function
$d–>setErrorHandling(PEAR_ERROR_DIE)
will terminate the program and print the default error messages if any subsequent errors occur when accessing the database through connection $d (see line 9 in Figure 14.6).
14.3.2 Collecting Data from Forms and Inserting Records It is common in database applications to collect information through HTML or other types of Web forms. For example, when purchasing an airline ticket or apply- ing for a credit card, the user has to enter personal information such as name, address, and phone number. This information is typically collected and stored in a database record on a database server.
Lines 10 through 12 in Figure 14.6 illustrate how this may be done. In this example, we omitted the code for creating the form and collecting the data, which can be a variation of the example in Figure 14.1. We assume that the user entered valid values
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in the input parameters called emp_name, emp_job, and emp_dno. These would be accessible via the PHP auto-global array $_POST as discussed at the end of Section 14.2.4.
In the SQL INSERT command shown on lines 11 and 12 in Figure 14.6, the array entries $POST['emp_name'], $POST['emp_job'], and $POST['emp_dno'] will hold the values collected from the user through the input form of HTML. These are then inserted as a new employee record in the EMPLOYEE table.
This example also illustrates another feature of PEAR DB. It is common in some applications to create a unique record identifier for each new record inserted into the database.1
PHP has a function $d–>nextID to create a sequence of unique values for a partic- ular table. In our example, the field Emp_id of the EMPLOYEE table (see Figure 14.6, line 4) is created for this purpose. Line 10 shows how to retrieve the next unique value in the sequence for the EMPLOYEE table and insert it as part of the new record in lines 11 and 12.
The code for insert in lines 10 to 12 in Figure 14.6 may allow malicious strings to be entered that can alter the INSERT command. A safer way to do inserts and other queries is through the use of placeholders (specified by the ? symbol). An example is illustrated in lines 13 to 15, where another record is to be inserted. In this form of the $d->query() function, there are two arguments. The first argument is the SQL statement, with one or more ? symbols (placeholders). The second argument is an array, whose element values will be used to replace the placeholders in the order they are specified.
14.3.3 Retrieval Queries from Database Tables We now give three examples of retrieval queries through PHP, shown in Figure 14.7. The first few lines 0 to 3 establish a database connection $d and set the error han- dling to the default, as we discussed in the previous section. The first query (lines 4 to 7) retrieves the name and department number of all employee records. The query variable $q is used to refer to the query result. A while-loop to go over each row in the result is shown in lines 5 to 7. The function $q->fetchRow() in line 5 serves to retrieve the next record in the query result and to control the loop. The looping starts at the first record.
The second query example is shown in lines 8 to 13 and illustrates a dynamic query. In this query, the conditions for selection of rows are based on values input by the user. Here we want to retrieve the names of employees who have a specific job and work for a particular department. The particular job and department number are entered through a form in the array variables $POST['emp_job'] and
1This would be similar to the system-generated OID discussed in Chapter 11 for object and object- relational database systems.
14.3 Overview of PHP Database Programming 495
Figure 14.7 Illustrating database retrieval queries.
0) require 'DB.php'; 1) $d = DB::connect('oci8://acct1:[email protected]/dbname'); 2) if (DB::isError($d)) { die("cannot connect – " . $d->getMessage()); } 3) $d->setErrorHandling(PEAR_ERROR_DIE);
... 4) $q = $d->query('SELECT Name, Dno FROM EMPLOYEE'); 5) while ($r = $q->fetchRow()) { 6) print "employee $r[0] works for department $r[1] \n" ; 7) }
... 8) $q = $d->query('SELECT Name FROM EMPLOYEE WHERE Job = ? AND Dno = ?', 9) array($_POST['emp_job'], $_POST['emp_dno']) );
10) print "employees in dept $_POST['emp_dno'] whose job is $_POST['emp_job']: \n"
11) while ($r = $q->fetchRow()) { 12) print "employee $r[0] \n" ; 13) }
... 14) $allresult = $d->getAll('SELECT Name, Job, Dno FROM EMPLOYEE'); 15) foreach ($allresult as $r) { 16) print "employee $r[0] has job $r[1] and works for department $r[2] \n" ; 17) }
...
$POST['emp_dno']. If the user had entered ‘Engineer’ for the job and 5 for the department number, the query would select the names of all engineers who worked in department 5. As we can see, this is a dynamic query whose results differ depend- ing on the choices that the user enters as input. We used two ? placeholders in this example, as discussed at the end of Section 14.3.2.
The last query (lines 14 to 17) shows an alternative way of specifying a query and looping over its rows. In this example, the function $d=>getAll holds all the records in a query result in a single variable, called $allresult. To loop over the individual records, a foreach loop can be used, with the row variable $r iterating over each row in $allresult.2
As we can see, PHP is suited for both database access and creating dynamic Web pages.
2The $r variable is similar to the cursors and iterator variables discussed in Chapters 11 and 13.
496 Chapter 14 Web Database Programming Using PHP
14.4 Summary In this chapter, we gave an overview of how to convert some structured data from databases into elements to be entered or displayed on a Web page. We focused on the PHP scripting language, which is becoming very popular for Web database pro- gramming. Section 14.1 presented some PHP basics for Web programming through a simple example. Section 14.2 gave some of the basics of the PHP language, includ- ing its array and string data types that are used extensively. Section 14.3 presented an overview of how PHP can be used to specify various types of database com- mands, including creating tables, inserting new records, and retrieving database records. PHP runs at the server computer in comparison to some other scripting languages that run on the client computer.
We gave only a very basic introduction to PHP. There are many books as well as many Web sites devoted to introductory and advanced PHP programming. Many libraries of functions also exist for PHP, as it is an open source product.
Review Questions 14.1. Why are scripting languages popular for programming Web applications?
Where in the three-tier architecture does a PHP program execute? Where does a JavaScript program execute?
14.2. What type of programming language is PHP?
14.3. Discuss the different ways of specifying strings in PHP.
14.4. Discuss the different types of arrays in PHP.
14.5. What are PHP auto-global variables? Give some examples of PHP auto- global arrays, and discuss how each is typically used.
14.6. What is PEAR? What is PEAR DB?
14.7. Discuss the main functions for accessing a database in PEAR DB, and how each is used.
14.8. Discuss the different ways for looping over a query result in PHP.
14.9. What are placeholders? How are they used in PHP database programming?
Selected Bibliography 497
Exercises 14.10. Consider the LIBRARY database schema shown in Figure 4.6. Write PHP
code to create the tables of this schema.
14.11. Write a PHP program that creates Web forms for entering the information about a new BORROWER entity. Repeat for a new BOOK entity.
14.12. Write PHP Web interfaces for the queries specified in Exercise 6.18.
Selected Bibliography There are many sources for PHP programming, both in print and on the Web. We give two books as examples. A very good introduction to PHP is given in Sklar (2005). For advanced Web site development, the book by Schlossnagle (2005) pro- vides many detailed examples.
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part 6 Database Design Theory
and Normalization
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501
Basics of Functional Dependencies and Normalization
for Relational Databases
In Chapters 3 through 6, we presented various aspectsof the relational model and the languages associated with it. Each relation schema consists of a number of attributes, and the relational database schema consists of a number of relation schemas. So far, we have assumed that attributes are grouped to form a relation schema by using the common sense of the database designer or by mapping a database schema design from a conceptual data model such as the ER or Enhanced-ER (EER) data model. These models make the designer identify entity types and relationship types and their respective attrib- utes, which leads to a natural and logical grouping of the attributes into relations when the mapping procedures discussed in Chapter 9 are followed. However, we still need some formal way of analyzing why one grouping of attributes into a rela- tion schema may be better than another. While discussing database design in Chapters 7 through 10, we did not develop any measure of appropriateness or goodness to measure the quality of the design, other than the intuition of the designer. In this chapter we discuss some of the theory that has been developed with the goal of evaluating relational schemas for design quality—that is, to measure for- mally why one set of groupings of attributes into relation schemas is better than another.
There are two levels at which we can discuss the goodness of relation schemas. The first is the logical (or conceptual) level—how users interpret the relation schemas and the meaning of their attributes. Having good relation schemas at this level enables users to understand clearly the meaning of the data in the relations, and hence to formulate their queries correctly. The second is the implementation (or
15chapter 15
502 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
physical storage) level—how the tuples in a base relation are stored and updated. This level applies only to schemas of base relations—which will be physically stored as files—whereas at the logical level we are interested in schemas of both base rela- tions and views (virtual relations). The relational database design theory developed in this chapter applies mainly to base relations, although some criteria of appropri- ateness also apply to views, as shown in Section 15.1.
As with many design problems, database design may be performed using two approaches: bottom-up or top-down. A bottom-up design methodology (also called design by synthesis) considers the basic relationships among individual attrib- utes as the starting point and uses those to construct relation schemas. This approach is not very popular in practice1 because it suffers from the problem of having to collect a large number of binary relationships among attributes as the starting point. For practical situations, it is next to impossible to capture binary relationships among all such pairs of attributes. In contrast, a top-down design methodology (also called design by analysis) starts with a number of groupings of attributes into relations that exist together naturally, for example, on an invoice, a form, or a report. The relations are then analyzed individually and collectively, lead- ing to further decomposition until all desirable properties are met. The theory described in this chapter is applicable to both the top-down and bottom-up design approaches, but is more appropriate when used with the top-down approach.
Relational database design ultimately produces a set of relations. The implicit goals of the design activity are information preservation and minimum redundancy. Information is very hard to quantify—hence we consider information preservation in terms of maintaining all concepts, including attribute types, entity types, and relationship types as well as generalization/specialization relationships, which are described using a model such as the EER model. Thus, the relational design must preserve all of these concepts, which are originally captured in the conceptual design after the conceptual to logical design mapping. Minimizing redundancy implies minimizing redundant storage of the same information and reducing the need for multiple updates to maintain consistency across multiple copies of the same information in response to real-world events that require making an update.
We start this chapter by informally discussing some criteria for good and bad rela- tion schemas in Section 15.1. In Section 15.2, we define the concept of functional dependency, a formal constraint among attributes that is the main tool for formally measuring the appropriateness of attribute groupings into relation schemas. In Section 15.3, we discuss normal forms and the process of normalization using func- tional dependencies. Successive normal forms are defined to meet a set of desirable constraints expressed using functional dependencies. The normalization procedure consists of applying a series of tests to relations to meet these increasingly stringent requirements and decompose the relations when necessary. In Section 15.4, we dis-
1An exception in which this approach is used in practice is based on a model called the binary relational model. An example is the NIAM methodology (Verheijen and VanBekkum, 1982).
15.1 Informal Design Guidelines for Relation Schemas 503
cuss more general definitions of normal forms that can be directly applied to any given design and do not require step-by-step analysis and normalization. Sections 15.5 to 15.7 discuss further normal forms up to the fifth normal form. In Section 15.6 we introduce the multivalued dependency (MVD), followed by the join dependency (JD) in Section 15.7. Section 15.8 summarizes the chapter.
Chapter 16 continues the development of the theory related to the design of good relational schemas. We discuss desirable properties of relational decomposition— nonadditive join property and functional dependency preservation property. A general algorithm that tests whether or not a decomposition has the nonadditive (or lossless) join property (Algorithm 16.3 is also presented). We then discuss properties of functional dependencies and the concept of a minimal cover of dependencies. We consider the bottom-up approach to database design consisting of a set of algo- rithms to design relations in a desired normal form. These algorithms assume as input a given set of functional dependencies and achieve a relational design in a tar- get normal form while adhering to the above desirable properties. In Chapter 16 we also define additional types of dependencies that further enhance the evaluation of the goodness of relation schemas.
If Chapter 16 is not covered in a course, we recommend a quick introduction to the desirable properties of decomposition and the discussion of Property NJB in Section 16.2.
15.1 Informal Design Guidelines for Relation Schemas
Before discussing the formal theory of relational database design, we discuss four informal guidelines that may be used as measures to determine the quality of relation schema design:
■ Making sure that the semantics of the attributes is clear in the schema
■ Reducing the redundant information in tuples
■ Reducing the NULL values in tuples
■ Disallowing the possibility of generating spurious tuples
These measures are not always independent of one another, as we will see.
15.1.1 Imparting Clear Semantics to Attributes in Relations Whenever we group attributes to form a relation schema, we assume that attributes belonging to one relation have certain real-world meaning and a proper interpreta- tion associated with them. The semantics of a relation refers to its meaning result- ing from the interpretation of attribute values in a tuple. In Chapter 3 we discussed how a relation can be interpreted as a set of facts. If the conceptual design described in Chapters 7 and 8 is done carefully and the mapping procedure in Chapter 9 is fol- lowed systematically, the relational schema design should have a clear meaning.
504 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
DEPARTMENT
DnumberDname
Ename Bdate Address Dnumber
EMPLOYEE
P.K.
P.K.
F.K.
Pname Pnumber Plocation Dnum
PROJECT F.K.
F.K.
DEPT_LOCATIONS
Dnumber Dlocation
P.K.
P.K.
Pnumber Hours
WORKS_ON F.K. F.K.
P.K.
F.K.
Ssn
Dmgr_ssn
Ssn
Figure 15.1 A simplified COMPANY relational database schema.
In general, the easier it is to explain the semantics of the relation, the better the rela- tion schema design will be. To illustrate this, consider Figure 15.1, a simplified ver- sion of the COMPANY relational database schema in Figure 3.5, and Figure 15.2, which presents an example of populated relation states of this schema. The meaning of the EMPLOYEE relation schema is quite simple: Each tuple represents an employee, with values for the employee’s name (Ename), Social Security number (Ssn), birth date (Bdate), and address (Address), and the number of the department that the employee works for (Dnumber). The Dnumber attribute is a foreign key that represents an implicit relationship between EMPLOYEE and DEPARTMENT. The semantics of the DEPARTMENT and PROJECT schemas are also straightforward: Each DEPARTMENT tuple represents a department entity, and each PROJECT tuple represents a project entity. The attribute Dmgr_ssn of DEPARTMENT relates a depart- ment to the employee who is its manager, while Dnum of PROJECT relates a project to its controlling department; both are foreign key attributes. The ease with which the meaning of a relation’s attributes can be explained is an informal measure of how well the relation is designed.
15.1 Informal Design Guidelines for Relation Schemas 505
Ename
EMPLOYEE
Smith, John B.
Wong, Franklin T.
Zelaya, Alicia J.
Wallace, Jennifer S. Narayan, Ramesh K.
English, Joyce A.
Jabbar, Ahmad V.
Borg, James E.
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638 Voss, Houston, TX
5631 Rice, Houston, TX
980 Dallas, Houston, TX
450 Stone, Houston, TX
291Berry, Bellaire, TX
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Pnumber Hours
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Figure 15.2 Sample database state for the relational database schema in Figure 15.1.
506 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Ssn
EMP_PROJ
(b)
(a)
FD1
FD2
FD3
Pnumber Hours Ename Pname Plocation
Ename Ssn
EMP_DEPT Bdate Address Dnumber Dname Dmgr_ssn
Figure 15.3 Two relation schemas suffering from update anomalies. (a) EMP_DEPT and (b) EMP_PROJ.
The semantics of the other two relation schemas in Figure 15.1 are slightly more complex. Each tuple in DEPT_LOCATIONS gives a department number (Dnumber) and one of the locations of the department (Dlocation). Each tuple in WORKS_ON gives an employee Social Security number (Ssn), the project number of one of the projects that the employee works on (Pnumber), and the number of hours per week that the employee works on that project (Hours). However, both schemas have a well-defined and unambiguous interpretation. The schema DEPT_LOCATIONS rep- resents a multivalued attribute of DEPARTMENT, whereas WORKS_ON represents an M:N relationship between EMPLOYEE and PROJECT. Hence, all the relation schemas in Figure 15.1 may be considered as easy to explain and therefore good from the standpoint of having clear semantics. We can thus formulate the following informal design guideline.
Guideline 1 Design a relation schema so that it is easy to explain its meaning. Do not combine attributes from multiple entity types and relationship types into a single relation. Intuitively, if a relation schema corresponds to one entity type or one relationship type, it is straightforward to interpret and to explain its meaning. Otherwise, if the relation corresponds to a mixture of multiple entities and relationships, semantic ambiguities will result and the relation cannot be easily explained.
Examples of Violating Guideline 1. The relation schemas in Figures 15.3(a) and 15.3(b) also have clear semantics. (The reader should ignore the lines under the relations for now; they are used to illustrate functional dependency notation, dis- cussed in Section 15.2.) A tuple in the EMP_DEPT relation schema in Figure 15.3(a) represents a single employee but includes additional information—namely, the name (Dname) of the department for which the employee works and the Social Security number (Dmgr_ssn) of the department manager. For the EMP_PROJ rela- tion in Figure 15.3(b), each tuple relates an employee to a project but also includes
15.1 Informal Design Guidelines for Relation Schemas 507
the employee name (Ename), project name (Pname), and project location (Plocation). Although there is nothing wrong logically with these two relations, they violate Guideline 1 by mixing attributes from distinct real-world entities: EMP_DEPT mixes attributes of employees and departments, and EMP_PROJ mixes attributes of employees and projects and the WORKS_ON relationship. Hence, they fare poorly against the above measure of design quality. They may be used as views, but they cause problems when used as base relations, as we discuss in the following section.
15.1.2 Redundant Information in Tuples and Update Anomalies
One goal of schema design is to minimize the storage space used by the base rela- tions (and hence the corresponding files). Grouping attributes into relation schemas has a significant effect on storage space. For example, compare the space used by the two base relations EMPLOYEE and DEPARTMENT in Figure 15.2 with that for an EMP_DEPT base relation in Figure 15.4, which is the result of applying the NATURAL JOIN operation to EMPLOYEE and DEPARTMENT. In EMP_DEPT, the attribute values pertaining to a particular department (Dnumber, Dname, Dmgr_ssn) are repeated for every employee who works for that department. In contrast, each department’s information appears only once in the DEPARTMENT relation in Figure 15.2. Only the department number (Dnumber) is repeated in the EMPLOYEE relation for each employee who works in that department as a foreign key. Similar com- ments apply to the EMP_PROJ relation (see Figure 15.4), which augments the WORKS_ON relation with additional attributes from EMPLOYEE and PROJECT.
Storing natural joins of base relations leads to an additional problem referred to as update anomalies. These can be classified into insertion anomalies, deletion anom- alies, and modification anomalies.2
Insertion Anomalies. Insertion anomalies can be differentiated into two types, illustrated by the following examples based on the EMP_DEPT relation:
■ To insert a new employee tuple into EMP_DEPT, we must include either the attribute values for the department that the employee works for, or NULLs (if the employee does not work for a department as yet). For example, to insert a new tuple for an employee who works in department number 5, we must enter all the attribute values of department 5 correctly so that they are consistent with the corresponding values for department 5 in other tuples in EMP_DEPT. In the design of Figure 15.2, we do not have to worry about this consistency problem because we enter only the department number in the employee tuple; all other attribute values of department 5 are recorded only once in the database, as a single tuple in the DEPARTMENT relation.
■ It is difficult to insert a new department that has no employees as yet in the EMP_DEPT relation. The only way to do this is to place NULL values in the
2These anomalies were identified by Codd (1972a) to justify the need for normalization of relations, as we shall discuss in Section 15.3.
508 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Ename
EMP_DEPT
Smith, John B.
Wong, Franklin T.
Zelaya, Alicia J.
Wallace, Jennifer S.
Narayan, Ramesh K.
English, Joyce A.
Jabbar, Ahmad V.
Borg, James E.
999887777
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666884444
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3321 Castle, Spring, TX
731 Fondren, Houston, TX 5
638 Voss, Houston, TX
5631 Rice, Houston, TX
980 Dallas, Houston, TX
450 Stone, Houston, TX
291 Berry, Bellaire, TX
975 FireOak, Humble, TX
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English, Joyce A.
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Ename
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Pname Plocation
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Figure 15.4 Sample states for EMP_DEPT and EMP_PROJ resulting from applying NATURAL JOIN to the relations in Figure 15.2. These may be stored as base relations for performance reasons.
attributes for employee. This violates the entity integrity for EMP_DEPT because Ssn is its primary key. Moreover, when the first employee is assigned to that department, we do not need this tuple with NULL values any more. This problem does not occur in the design of Figure 15.2 because a depart- ment is entered in the DEPARTMENT relation whether or not any employees work for it, and whenever an employee is assigned to that department, a cor- responding tuple is inserted in EMPLOYEE.
15.1 Informal Design Guidelines for Relation Schemas 509
Deletion Anomalies. The problem of deletion anomalies is related to the second insertion anomaly situation just discussed. If we delete from EMP_DEPT an employee tuple that happens to represent the last employee working for a particular department, the information concerning that department is lost from the database. This problem does not occur in the database of Figure 15.2 because DEPARTMENT tuples are stored separately.
Modification Anomalies. In EMP_DEPT, if we change the value of one of the attributes of a particular department—say, the manager of department 5—we must update the tuples of all employees who work in that department; otherwise, the database will become inconsistent. If we fail to update some tuples, the same depart- ment will be shown to have two different values for manager in different employee tuples, which would be wrong.3
It is easy to see that these three anomalies are undesirable and cause difficulties to maintain consistency of data as well as require unnecessary updates that can be avoided; hence, we can state the next guideline as follows.
Guideline 2 Design the base relation schemas so that no insertion, deletion, or modification anomalies are present in the relations. If any anomalies are present,4 note them clearly and make sure that the programs that update the database will operate correctly.
The second guideline is consistent with and, in a way, a restatement of the first guideline. We can also see the need for a more formal approach to evaluating whether a design meets these guidelines. Sections 15.2 through 15.4 provide these needed formal concepts. It is important to note that these guidelines may some- times have to be violated in order to improve the performance of certain queries. If EMP_DEPT is used as a stored relation (known otherwise as a materialized view) in addition to the base relations of EMPLOYEE and DEPARTMENT, the anomalies in EMP_DEPT must be noted and accounted for (for example, by using triggers or stored procedures that would make automatic updates). This way, whenever the base relation is updated, we do not end up with inconsistencies. In general, it is advisable to use anomaly-free base relations and to specify views that include the joins for placing together the attributes frequently referenced in important queries.
15.1.3 NULL Values in Tuples In some schema designs we may group many attributes together into a “fat” rela- tion. If many of the attributes do not apply to all tuples in the relation, we end up with many NULLs in those tuples. This can waste space at the storage level and may
3This is not as serious as the other problems, because all tuples can be updated by a single SQL query. 4Other application considerations may dictate and make certain anomalies unavoidable. For example, the EMP_DEPT relation may correspond to a query or a report that is frequently required.
510 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
also lead to problems with understanding the meaning of the attributes and with specifying JOIN operations at the logical level.5 Another problem with NULLs is how to account for them when aggregate operations such as COUNT or SUM are applied. SELECT and JOIN operations involve comparisons; if NULL values are present, the results may become unpredictable.6 Moreover, NULLs can have multiple interpreta- tions, such as the following:
■ The attribute does not apply to this tuple. For example, Visa_status may not apply to U.S. students.
■ The attribute value for this tuple is unknown. For example, the Date_of_birth may be unknown for an employee.
■ The value is known but absent; that is, it has not been recorded yet. For exam- ple, the Home_Phone_Number for an employee may exist, but may not be available and recorded yet.
Having the same representation for all NULLs compromises the different meanings they may have. Therefore, we may state another guideline.
Guideline 3 As far as possible, avoid placing attributes in a base relation whose values may fre- quently be NULL. If NULLs are unavoidable, make sure that they apply in exceptional cases only and do not apply to a majority of tuples in the relation.
Using space efficiently and avoiding joins with NULL values are the two overriding criteria that determine whether to include the columns that may have NULLs in a relation or to have a separate relation for those columns (with the appropriate key columns). For example, if only 15 percent of employees have individual offices, there is little justification for including an attribute Office_number in the EMPLOYEE relation; rather, a relation EMP_OFFICES(Essn, Office_number) can be created to include tuples for only the employees with individual offices.
15.1.4 Generation of Spurious Tuples Consider the two relation schemas EMP_LOCS and EMP_PROJ1 in Figure 15.5(a), which can be used instead of the single EMP_PROJ relation in Figure 15.3(b). A tuple in EMP_LOCS means that the employee whose name is Ename works on some project whose location is Plocation. A tuple in EMP_PROJ1 refers to the fact that the employee whose Social Security number is Ssn works Hours per week on the project whose name, number, and location are Pname, Pnumber, and Plocation. Figure 15.5(b) shows relation states of EMP_LOCS and EMP_PROJ1 corresponding to the
5This is because inner and outer joins produce different results when NULLs are involved in joins. The users must thus be aware of the different meanings of the various types of joins. Although this is rea- sonable for sophisticated users, it may be difficult for others. 6In Section 5.5.1 we presented comparisons involving NULL values where the outcome (in three-valued logic) are TRUE, FALSE, and UNKNOWN.
15.1 Informal Design Guidelines for Relation Schemas 511
Ssn Pnumber Hours Pname Plocation
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Ename Smith, John B. Smith, John B. Narayan, Ramesh K. English, Joyce A. English, Joyce A. Wong, Franklin T. Wong, Franklin T. Wong, Franklin T. Zelaya, Alicia J. Jabbar, Ahmad V. Wallace, Jennifer S. Wallace, Jennifer S. Borg, James E.
Houston
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EMP_LOCS Figure 15.5 Particularly poor design for the EMP_PROJ relation in Figure 15.3(b). (a) The two relation schemas EMP_LOCS and EMP_PROJ1. (b) The result of projecting the exten- sion of EMP_PROJ from Figure 15.4 onto the relations EMP_LOCS and EMP_PROJ1.
EMP_PROJ relation in Figure 15.4, which are obtained by applying the appropriate PROJECT (π) operations to EMP_PROJ (ignore the dashed lines in Figure 15.5(b) for now).
Suppose that we used EMP_PROJ1 and EMP_LOCS as the base relations instead of EMP_PROJ. This produces a particularly bad schema design because we cannot recover the information that was originally in EMP_PROJ from EMP_PROJ1 and EMP_LOCS. If we attempt a NATURAL JOIN operation on EMP_PROJ1 and EMP_LOCS, the result produces many more tuples than the original set of tuples in EMP_PROJ. In Figure 15.6, the result of applying the join to only the tuples above the dashed lines in Figure 15.5(b) is shown (to reduce the size of the resulting rela- tion). Additional tuples that were not in EMP_PROJ are called spurious tuples
512 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Ssn 123456789
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Smith, John B.
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English, Joyce A.
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Plocation Ename
* *
*
Figure 15.6 Result of applying NATURAL JOIN to the tuples above the dashed lines in EMP_PROJ1 and EMP_LOCS of Figure 15.5. Generated spurious tuples are marked by asterisks.
because they represent spurious information that is not valid. The spurious tuples are marked by asterisks (*) in Figure 15.6.
Decomposing EMP_PROJ into EMP_LOCS and EMP_PROJ1 is undesirable because when we JOIN them back using NATURAL JOIN, we do not get the correct original information. This is because in this case Plocation is the attribute that relates EMP_LOCS and EMP_PROJ1, and Plocation is neither a primary key nor a foreign key in either EMP_LOCS or EMP_PROJ1. We can now informally state another design guideline.
Guideline 4 Design relation schemas so that they can be joined with equality conditions on attributes that are appropriately related (primary key, foreign key) pairs in a way that guarantees that no spurious tuples are generated. Avoid relations that contain
15.2 Functional Dependencies 513
matching attributes that are not (foreign key, primary key) combinations because joining on such attributes may produce spurious tuples.
This informal guideline obviously needs to be stated more formally. In Section 16.2 we discuss a formal condition called the nonadditive (or lossless) join property that guarantees that certain joins do not produce spurious tuples.
15.1.5 Summary and Discussion of Design Guidelines In Sections 15.1.1 through 15.1.4, we informally discussed situations that lead to problematic relation schemas and we proposed informal guidelines for a good rela- tional design. The problems we pointed out, which can be detected without addi- tional tools of analysis, are as follows:
■ Anomalies that cause redundant work to be done during insertion into and modification of a relation, and that may cause accidental loss of information during a deletion from a relation
■ Waste of storage space due to NULLs and the difficulty of performing selec- tions, aggregation operations, and joins due to NULL values
■ Generation of invalid and spurious data during joins on base relations with matched attributes that may not represent a proper (foreign key, primary key) relationship
In the rest of this chapter we present formal concepts and theory that may be used to define the goodness and badness of individual relation schemas more precisely. First we discuss functional dependency as a tool for analysis. Then we specify the three normal forms and Boyce-Codd normal form (BCNF) for relation schemas. The strategy for achieving a good design is to decompose a badly designed relation appropriately. We also briefly introduce additional normal forms that deal with additional dependencies. In Chapter 16, we discuss the properties of decomposition in detail, and provide algorithms that design relations bottom-up by using the func- tional dependencies as a starting point.
15.2 Functional Dependencies So far we have dealt with the informal measures of database design. We now intro- duce a formal tool for analysis of relational schemas that enables us to detect and describe some of the above-mentioned problems in precise terms. The single most important concept in relational schema design theory is that of a functional dependency. In this section we formally define the concept, and in Section 15.3 we see how it can be used to define normal forms for relation schemas.
15.2.1 Definition of Functional Dependency A functional dependency is a constraint between two sets of attributes from the database. Suppose that our relational database schema has n attributes A1, A2, ..., An; let us think of the whole database as being described by a single universal
514 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
relation schema R = {A1, A2, ... , An}. 7 We do not imply that we will actually store the
database as a single universal table; we use this concept only in developing the for- mal theory of data dependencies.8
Definition. A functional dependency, denoted by X → Y, between two sets of attributes X and Y that are subsets of R specifies a constraint on the possible tuples that can form a relation state r of R. The constraint is that, for any two tuples t1 and t2 in r that have t1[X] = t2[X], they must also have t1[Y] = t2[Y].
This means that the values of the Y component of a tuple in r depend on, or are determined by, the values of the X component; alternatively, the values of the X com- ponent of a tuple uniquely (or functionally) determine the values of the Y compo- nent. We also say that there is a functional dependency from X to Y, or that Y is functionally dependent on X. The abbreviation for functional dependency is FD or f.d. The set of attributes X is called the left-hand side of the FD, and Y is called the right-hand side.
Thus, X functionally determines Y in a relation schema R if, and only if, whenever two tuples of r(R) agree on their X-value, they must necessarily agree on their Y- value. Note the following:
■ If a constraint on R states that there cannot be more than one tuple with a given X-value in any relation instance r(R)—that is, X is a candidate key of R—this implies that X → Y for any subset of attributes Y of R (because the key constraint implies that no two tuples in any legal state r(R) will have the same value of X). If X is a candidate key of R, then X → R.
■ If X → Y in R, this does not say whether or not Y → X in R.
A functional dependency is a property of the semantics or meaning of the attrib- utes. The database designers will use their understanding of the semantics of the attributes of R—that is, how they relate to one another—to specify the functional dependencies that should hold on all relation states (extensions) r of R. Whenever the semantics of two sets of attributes in R indicate that a functional dependency should hold, we specify the dependency as a constraint. Relation extensions r(R) that satisfy the functional dependency constraints are called legal relation states (or legal extensions) of R. Hence, the main use of functional dependencies is to describe further a relation schema R by specifying constraints on its attributes that must hold at all times. Certain FDs can be specified without referring to a specific relation, but as a property of those attributes given their commonly understood meaning. For example, {State, Driver_license_number} → Ssn should hold for any adult in the United States and hence should hold whenever these attributes appear in a relation. It is also possible that certain functional dependencies may cease to
7This concept of a universal relation is important when we discuss the algorithms for relational database design in Chapter 16. 8This assumption implies that every attribute in the database should have a distinct name. In Chapter 3 we prefixed attribute names by relation names to achieve uniqueness whenever attributes in distinct relations had the same name.
15.2 Functional Dependencies 515
TEACH
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Data Structures
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Course Text Figure 15.7 A relation state of TEACH with a possible functional dependency TEXT → COURSE. However, TEACHER → COURSE is ruled out.
exist in the real world if the relationship changes. For example, the FD Zip_code → Area_code used to exist as a relationship between postal codes and telephone num- ber codes in the United States, but with the proliferation of telephone area codes it is no longer true.
Consider the relation schema EMP_PROJ in Figure 15.3(b); from the semantics of the attributes and the relation, we know that the following functional dependencies should hold:
a. Ssn → Ename b. Pnumber →{Pname, Plocation} c. {Ssn, Pnumber} → Hours
These functional dependencies specify that (a) the value of an employee’s Social Security number (Ssn) uniquely determines the employee name (Ename), (b) the value of a project’s number (Pnumber) uniquely determines the project name (Pname) and location (Plocation), and (c) a combination of Ssn and Pnumber values uniquely determines the number of hours the employee currently works on the project per week (Hours). Alternatively, we say that Ename is functionally determined by (or functionally dependent on) Ssn, or given a value of Ssn, we know the value of Ename, and so on.
A functional dependency is a property of the relation schema R, not of a particular legal relation state r of R. Therefore, an FD cannot be inferred automatically from a given relation extension r but must be defined explicitly by someone who knows the semantics of the attributes of R. For example, Figure 15.7 shows a particular state of the TEACH relation schema. Although at first glance we may think that Text → Course, we cannot confirm this unless we know that it is true for all possible legal states of TEACH. It is, however, sufficient to demonstrate a single counterexample to disprove a functional dependency. For example, because ‘Smith’ teaches both ‘Data Structures’ and ‘Data Management,’ we can conclude that Teacher does not function- ally determine Course.
Given a populated relation, one cannot determine which FDs hold and which do not unless the meaning of and the relationships among the attributes are known. All one can say is that a certain FD may exist if it holds in that particular extension. One cannot guarantee its existence until the meaning of the corresponding attributes is clearly understood. One can, however, emphatically state that a certain FD does not
516 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Figure 15.8 A relation R (A, B, C, D) with its extension.
A B C D
a1 b1 c1 d1
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a2 b2 c2 d3
a3 b3 c4 d3
hold if there are tuples that show the violation of such an FD. See the illustrative example relation in Figure 15.8. Here, the following FDs may hold because the four tuples in the current extension have no violation of these constraints: B → C; C → B; {A, B} → C; {A, B} → D; and {C, D} → B. However, the following do not hold because we already have violations of them in the given extension: A → B (tuples 1 and 2 violate this constraint); B → A (tuples 2 and 3 violate this con- straint); D → C (tuples 3 and 4 violate it).
Figure 15.3 introduces a diagrammatic notation for displaying FDs: Each FD is dis- played as a horizontal line. The left-hand-side attributes of the FD are connected by vertical lines to the line representing the FD, while the right-hand-side attributes are connected by the lines with arrows pointing toward the attributes.
We denote by F the set of functional dependencies that are specified on relation schema R. Typically, the schema designer specifies the functional dependencies that are semantically obvious; usually, however, numerous other functional dependencies hold in all legal relation instances among sets of attributes that can be derived from and satisfy the dependencies in F. Those other dependencies can be inferred or deduced from the FDs in F. We defer the details of inference rules and properties of functional dependencies to Chapter 16.
15.3 Normal Forms Based on Primary Keys Having introduced functional dependencies, we are now ready to use them to spec- ify some aspects of the semantics of relation schemas. We assume that a set of func- tional dependencies is given for each relation, and that each relation has a designated primary key; this information combined with the tests (conditions) for normal forms drives the normalization process for relational schema design. Most practical relational design projects take one of the following two approaches:
■ Perform a conceptual schema design using a conceptual model such as ER or EER and map the conceptual design into a set of relations
■ Design the relations based on external knowledge derived from an existing implementation of files or forms or reports
Following either of these approaches, it is then useful to evaluate the relations for goodness and decompose them further as needed to achieve higher normal forms, using the normalization theory presented in this chapter and the next. We focus in
15.3 Normal Forms Based on Primary Keys 517
this section on the first three normal forms for relation schemas and the intuition behind them, and discuss how they were developed historically. More general defi- nitions of these normal forms, which take into account all candidate keys of a rela- tion rather than just the primary key, are deferred to Section 15.4.
We start by informally discussing normal forms and the motivation behind their development, as well as reviewing some definitions from Chapter 3 that are needed here. Then we discuss the first normal form (1NF) in Section 15.3.4, and present the definitions of second normal form (2NF) and third normal form (3NF), which are based on primary keys, in Sections 15.3.5 and 15.3.6, respectively.
15.3.1 Normalization of Relations The normalization process, as first proposed by Codd (1972a), takes a relation schema through a series of tests to certify whether it satisfies a certain normal form. The process, which proceeds in a top-down fashion by evaluating each relation against the criteria for normal forms and decomposing relations as necessary, can thus be considered as relational design by analysis. Initially, Codd proposed three normal forms, which he called first, second, and third normal form. A stronger def- inition of 3NF—called Boyce-Codd normal form (BCNF)—was proposed later by Boyce and Codd. All these normal forms are based on a single analytical tool: the functional dependencies among the attributes of a relation. Later, a fourth normal form (4NF) and a fifth normal form (5NF) were proposed, based on the concepts of multivalued dependencies and join dependencies, respectively; these are briefly dis- cussed in Sections 15.6 and 15.7.
Normalization of data can be considered a process of analyzing the given relation schemas based on their FDs and primary keys to achieve the desirable properties of (1) minimizing redundancy and (2) minimizing the insertion, deletion, and update anomalies discussed in Section 15.1.2. It can be considered as a “filtering” or “purifi- cation” process to make the design have successively better quality. Unsatisfactory relation schemas that do not meet certain conditions—the normal form tests—are decomposed into smaller relation schemas that meet the tests and hence possess the desirable properties. Thus, the normalization procedure provides database design- ers with the following:
■ A formal framework for analyzing relation schemas based on their keys and on the functional dependencies among their attributes
■ A series of normal form tests that can be carried out on individual relation schemas so that the relational database can be normalized to any desired degree
Definition. The normal form of a relation refers to the highest normal form condition that it meets, and hence indicates the degree to which it has been nor- malized.
Normal forms, when considered in isolation from other factors, do not guarantee a good database design. It is generally not sufficient to check separately that each
518 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
relation schema in the database is, say, in BCNF or 3NF. Rather, the process of nor- malization through decomposition must also confirm the existence of additional properties that the relational schemas, taken together, should possess. These would include two properties:
■ The nonadditive join or lossless join property, which guarantees that the spurious tuple generation problem discussed in Section 15.1.4 does not occur with respect to the relation schemas created after decomposition.
■ The dependency preservation property, which ensures that each functional dependency is represented in some individual relation resulting after decomposition.
The nonadditive join property is extremely critical and must be achieved at any cost, whereas the dependency preservation property, although desirable, is some- times sacrificed, as we discuss in Section 16.1.2. We defer the presentation of the for- mal concepts and techniques that guarantee the above two properties to Chapter 16.
15.3.2 Practical Use of Normal Forms Most practical design projects acquire existing designs of databases from previous designs, designs in legacy models, or from existing files. Normalization is carried out in practice so that the resulting designs are of high quality and meet the desir- able properties stated previously. Although several higher normal forms have been defined, such as the 4NF and 5NF that we discuss in Sections 15.6 and 15.7, the practical utility of these normal forms becomes questionable when the constraints on which they are based are rare, and hard to understand or to detect by the data- base designers and users who must discover these constraints. Thus, database design as practiced in industry today pays particular attention to normalization only up to 3NF, BCNF, or at most 4NF.
Another point worth noting is that the database designers need not normalize to the highest possible normal form. Relations may be left in a lower normalization status, such as 2NF, for performance reasons, such as those discussed at the end of Section 15.1.2. Doing so incurs the corresponding penalties of dealing with the anomalies.
Definition. Denormalization is the process of storing the join of higher nor- mal form relations as a base relation, which is in a lower normal form.
15.3.3 Definitions of Keys and Attributes Participating in Keys
Before proceeding further, let’s look again at the definitions of keys of a relation schema from Chapter 3.
Definition. A superkey of a relation schema R = {A1, A2, ... , An} is a set of attributes S ⊆ R with the property that no two tuples t1 and t2 in any legal rela- tion state r of R will have t1[S] = t2[S]. A key K is a superkey with the additional property that removal of any attribute from K will cause K not to be a superkey any more.
15.3 Normal Forms Based on Primary Keys 519
The difference between a key and a superkey is that a key has to be minimal; that is, if we have a key K = {A1, A2, ..., Ak} of R, then K – {Ai} is not a key of R for any Ai, 1 ≤ i ≤ k. In Figure 15.1, {Ssn} is a key for EMPLOYEE, whereas {Ssn}, {Ssn, Ename}, {Ssn, Ename, Bdate}, and any set of attributes that includes Ssn are all superkeys.
If a relation schema has more than one key, each is called a candidate key. One of the candidate keys is arbitrarily designated to be the primary key, and the others are called secondary keys. In a practical relational database, each relation schema must have a primary key. If no candidate key is known for a relation, the entire relation can be treated as a default superkey. In Figure 15.1, {Ssn} is the only candidate key for EMPLOYEE, so it is also the primary key.
Definition. An attribute of relation schema R is called a prime attribute of R if it is a member of some candidate key of R. An attribute is called nonprime if it is not a prime attribute—that is, if it is not a member of any candidate key.
In Figure 15.1, both Ssn and Pnumber are prime attributes of WORKS_ON, whereas other attributes of WORKS_ON are nonprime.
We now present the first three normal forms: 1NF, 2NF, and 3NF. These were pro- posed by Codd (1972a) as a sequence to achieve the desirable state of 3NF relations by progressing through the intermediate states of 1NF and 2NF if needed. As we shall see, 2NF and 3NF attack different problems. However, for historical reasons, it is customary to follow them in that sequence; hence, by definition a 3NF relation already satisfies 2NF.
15.3.4 First Normal Form First normal form (1NF) is now considered to be part of the formal definition of a relation in the basic (flat) relational model; historically, it was defined to disallow multivalued attributes, composite attributes, and their combinations. It states that the domain of an attribute must include only atomic (simple, indivisible) values and that the value of any attribute in a tuple must be a single value from the domain of that attribute. Hence, 1NF disallows having a set of values, a tuple of values, or a combination of both as an attribute value for a single tuple. In other words, 1NF dis- allows relations within relations or relations as attribute values within tuples. The only attribute values permitted by 1NF are single atomic (or indivisible) values.
Consider the DEPARTMENT relation schema shown in Figure 15.1, whose primary key is Dnumber, and suppose that we extend it by including the Dlocations attribute as shown in Figure 15.9(a). We assume that each department can have a number of locations. The DEPARTMENT schema and a sample relation state are shown in Figure 15.9. As we can see, this is not in 1NF because Dlocations is not an atomic attribute, as illustrated by the first tuple in Figure 15.9(b). There are two ways we can look at the Dlocations attribute:
■ The domain of Dlocations contains atomic values, but some tuples can have a set of these values. In this case, Dlocations is not functionally dependent on the primary key Dnumber.
520 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Dname DEPARTMENT (a)
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■ The domain of Dlocations contains sets of values and hence is nonatomic. In this case, Dnumber → Dlocations because each set is considered a single mem- ber of the attribute domain.9
In either case, the DEPARTMENT relation in Figure 15.9 is not in 1NF; in fact, it does not even qualify as a relation according to our definition of relation in Section 3.1. There are three main techniques to achieve first normal form for such a relation:
1. Remove the attribute Dlocations that violates 1NF and place it in a separate relation DEPT_LOCATIONS along with the primary key Dnumber of DEPARTMENT. The primary key of this relation is the combination {Dnumber, Dlocation}, as shown in Figure 15.2. A distinct tuple in DEPT_LOCATIONS exists for each location of a department. This decomposes the non-1NF relation into two 1NF relations.
9In this case we can consider the domain of Dlocations to be the power set of the set of single loca- tions; that is, the domain is made up of all possible subsets of the set of single locations.
15.3 Normal Forms Based on Primary Keys 521
2. Expand the key so that there will be a separate tuple in the original DEPARTMENT relation for each location of a DEPARTMENT, as shown in Figure 15.9(c). In this case, the primary key becomes the combination {Dnumber, Dlocation}. This solution has the disadvantage of introducing redundancy in the relation.
3. If a maximum number of values is known for the attribute—for example, if it is known that at most three locations can exist for a department—replace the Dlocations attribute by three atomic attributes: Dlocation1, Dlocation2, and Dlocation3. This solution has the disadvantage of introducing NULL values if most departments have fewer than three locations. It further introduces spu- rious semantics about the ordering among the location values that is not originally intended. Querying on this attribute becomes more difficult; for example, consider how you would write the query: List the departments that have ‘Bellaire’ as one of their locations in this design.
Of the three solutions above, the first is generally considered best because it does not suffer from redundancy and it is completely general, having no limit placed on a maximum number of values. In fact, if we choose the second solution, it will be decomposed further during subsequent normalization steps into the first solution.
First normal form also disallows multivalued attributes that are themselves com- posite. These are called nested relations because each tuple can have a relation within it. Figure 15.10 shows how the EMP_PROJ relation could appear if nesting is allowed. Each tuple represents an employee entity, and a relation PROJS(Pnumber, Hours) within each tuple represents the employee’s projects and the hours per week that employee works on each project. The schema of this EMP_PROJ relation can be represented as follows:
EMP_PROJ(Ssn, Ename, {PROJS(Pnumber, Hours)})
The set braces { } identify the attribute PROJS as multivalued, and we list the com- ponent attributes that form PROJS between parentheses ( ). Interestingly, recent trends for supporting complex objects (see Chapter 11) and XML data (see Chapter 12) attempt to allow and formalize nested relations within relational database sys- tems, which were disallowed early on by 1NF.
Notice that Ssn is the primary key of the EMP_PROJ relation in Figures 15.10(a) and (b), while Pnumber is the partial key of the nested relation; that is, within each tuple, the nested relation must have unique values of Pnumber. To normalize this into 1NF, we remove the nested relation attributes into a new relation and propagate the pri- mary key into it; the primary key of the new relation will combine the partial key with the primary key of the original relation. Decomposition and primary key propagation yield the schemas EMP_PROJ1 and EMP_PROJ2, as shown in Figure 15.10(c).
This procedure can be applied recursively to a relation with multiple-level nesting to unnest the relation into a set of 1NF relations. This is useful in converting an unnormalized relation schema with many levels of nesting into 1NF relations. The
522 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
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existence of more than one multivalued attribute in one relation must be handled carefully. As an example, consider the following non-1NF relation:
PERSON (Ss#, {Car_lic#}, {Phone#})
This relation represents the fact that a person has multiple cars and multiple phones. If strategy 2 above is followed, it results in an all-key relation:
PERSON_IN_1NF (Ss#, Car_lic#, Phone#)
15.3 Normal Forms Based on Primary Keys 523
To avoid introducing any extraneous relationship between Car_lic# and Phone#, all possible combinations of values are represented for every Ss#, giving rise to redun- dancy. This leads to the problems handled by multivalued dependencies and 4NF, which we will discuss in Section 15.6. The right way to deal with the two multival- ued attributes in PERSON shown previously is to decompose it into two separate relations, using strategy 1 discussed above: P1(Ss#, Car_lic#) and P2(Ss#, Phone#).
15.3.5 Second Normal Form Second normal form (2NF) is based on the concept of full functional dependency. A functional dependency X → Y is a full functional dependency if removal of any attribute A from X means that the dependency does not hold any more; that is, for any attribute A ε X, (X – {A}) does not functionally determine Y. A functional dependency X → Y is a partial dependency if some attribute A ε X can be removed from X and the dependency still holds; that is, for some A ε X, (X – {A}) → Y. In Figure 15.3(b), {Ssn, Pnumber} → Hours is a full dependency (neither Ssn → Hours nor Pnumber → Hours holds). However, the dependency {Ssn, Pnumber} → Ename is partial because Ssn → Ename holds.
Definition. A relation schema R is in 2NF if every nonprime attribute A in R is fully functionally dependent on the primary key of R.
The test for 2NF involves testing for functional dependencies whose left-hand side attributes are part of the primary key. If the primary key contains a single attribute, the test need not be applied at all. The EMP_PROJ relation in Figure 15.3(b) is in 1NF but is not in 2NF. The nonprime attribute Ename violates 2NF because of FD2, as do the nonprime attributes Pname and Plocation because of FD3. The functional dependencies FD2 and FD3 make Ename, Pname, and Plocation partially dependent on the primary key {Ssn, Pnumber} of EMP_PROJ, thus violating the 2NF test.
If a relation schema is not in 2NF, it can be second normalized or 2NF normalized into a number of 2NF relations in which nonprime attributes are associated only with the part of the primary key on which they are fully functionally dependent. Therefore, the functional dependencies FD1, FD2, and FD3 in Figure 15.3(b) lead to the decomposition of EMP_PROJ into the three relation schemas EP1, EP2, and EP3 shown in Figure 15.11(a), each of which is in 2NF.
15.3.6 Third Normal Form Third normal form (3NF) is based on the concept of transitive dependency. A functional dependency X → Y in a relation schema R is a transitive dependency if there exists a set of attributes Z in R that is neither a candidate key nor a subset of any key of R,10 and both X → Z and Z → Y hold. The dependency Ssn → Dmgr_ssn is transitive through Dnumber in EMP_DEPT in Figure 15.3(a), because both the
10This is the general definition of transitive dependency. Because we are concerned only with primary keys in this section, we allow transitive dependencies where X is the primary key but Z may be (a subset of) a candidate key.
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Figure 15.11 Normalizing into 2NF and 3NF. (a) Normalizing EMP_PROJ into 2NF relations. (b) Normalizing EMP_DEPT into 3NF relations.
dependencies Ssn → Dnumber and Dnumber → Dmgr_ssn hold and Dnumber is nei- ther a key itself nor a subset of the key of EMP_DEPT. Intuitively, we can see that the dependency of Dmgr_ssn on Dnumber is undesirable in EMP_DEPT since Dnumber is not a key of EMP_DEPT.
Definition. According to Codd’s original definition, a relation schema R is in 3NF if it satisfies 2NF and no nonprime attribute of R is transitively dependent on the primary key.
The relation schema EMP_DEPT in Figure 15.3(a) is in 2NF, since no partial depen- dencies on a key exist. However, EMP_DEPT is not in 3NF because of the transitive dependency of Dmgr_ssn (and also Dname) on Ssn via Dnumber. We can normalize
15.4 General Definitions of Second and Third Normal Forms 525
Table 15.1 Summary of Normal Forms Based on Primary Keys and Corresponding Normalization
Normal Form Test Remedy (Normalization)
First (1NF) Relation should have no multivalued attributes or nested relations.
Form new relations for each multivalued attribute or nested relation.
Second (2NF) For relations where primary key con- tains multiple attributes, no nonkey attribute should be functionally dependent on a part of the primary key.
Decompose and set up a new relation for each partial key with its dependent attrib- ute(s). Make sure to keep a relation with the original primary key and any attributes that are fully functionally dependent on it.
Third (3NF) Relation should not have a nonkey attribute functionally determined by another nonkey attribute (or by a set of nonkey attributes). That is, there should be no transitive dependency of a non- key attribute on the primary key.
Decompose and set up a relation that includes the nonkey attribute(s) that func- tionally determine(s) other nonkey attrib- ute(s).
EMP_DEPT by decomposing it into the two 3NF relation schemas ED1 and ED2 shown in Figure 15.11(b). Intuitively, we see that ED1 and ED2 represent independ- ent entity facts about employees and departments. A NATURAL JOIN operation on ED1 and ED2 will recover the original relation EMP_DEPT without generating spu- rious tuples.
Intuitively, we can see that any functional dependency in which the left-hand side is part (a proper subset) of the primary key, or any functional dependency in which the left-hand side is a nonkey attribute, is a problematic FD. 2NF and 3NF normal- ization remove these problem FDs by decomposing the original relation into new relations. In terms of the normalization process, it is not necessary to remove the partial dependencies before the transitive dependencies, but historically, 3NF has been defined with the assumption that a relation is tested for 2NF first before it is tested for 3NF. Table 15.1 informally summarizes the three normal forms based on primary keys, the tests used in each case, and the corresponding remedy or normal- ization performed to achieve the normal form.
15.4 General Definitions of Second and Third Normal Forms
In general, we want to design our relation schemas so that they have neither partial nor transitive dependencies because these types of dependencies cause the update anomalies discussed in Section 15.1.2. The steps for normalization into 3NF rela- tions that we have discussed so far disallow partial and transitive dependencies on the primary key. The normalization procedure described so far is useful for analysis in practical situations for a given database where primary keys have already been defined. These definitions, however, do not take other candidate keys of a relation, if
526 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
any, into account. In this section we give the more general definitions of 2NF and 3NF that take all candidate keys of a relation into account. Notice that this does not affect the definition of 1NF since it is independent of keys and functional depen- dencies. As a general definition of prime attribute, an attribute that is part of any candidate key will be considered as prime. Partial and full functional dependencies and transitive dependencies will now be considered with respect to all candidate keys of a relation.
15.4.1 General Definition of Second Normal Form Definition. A relation schema R is in second normal form (2NF) if every non- prime attribute A in R is not partially dependent on any key of R.11
The test for 2NF involves testing for functional dependencies whose left-hand side attributes are part of the primary key. If the primary key contains a single attribute, the test need not be applied at all. Consider the relation schema LOTS shown in Figure 15.12(a), which describes parcels of land for sale in various counties of a state. Suppose that there are two candidate keys: Property_id# and {County_name, Lot#}; that is, lot numbers are unique only within each county, but Property_id# numbers are unique across counties for the entire state.
Based on the two candidate keys Property_id# and {County_name, Lot#}, the func- tional dependencies FD1 and FD2 in Figure 15.12(a) hold. We choose Property_id# as the primary key, so it is underlined in Figure 15.12(a), but no special consideration will be given to this key over the other candidate key. Suppose that the following two additional functional dependencies hold in LOTS:
FD3: County_name → Tax_rate FD4: Area → Price
In words, the dependency FD3 says that the tax rate is fixed for a given county (does not vary lot by lot within the same county), while FD4 says that the price of a lot is determined by its area regardless of which county it is in. (Assume that this is the price of the lot for tax purposes.)
The LOTS relation schema violates the general definition of 2NF because Tax_rate is partially dependent on the candidate key {County_name, Lot#}, due to FD3. To nor- malize LOTS into 2NF, we decompose it into the two relations LOTS1 and LOTS2, shown in Figure 15.12(b). We construct LOTS1 by removing the attribute Tax_rate that violates 2NF from LOTS and placing it with County_name (the left-hand side of FD3 that causes the partial dependency) into another relation LOTS2. Both LOTS1 and LOTS2 are in 2NF. Notice that FD4 does not violate 2NF and is carried over to LOTS1.
11This definition can be restated as follows: A relation schema R is in 2NF if every nonprime attribute A in R is fully functionally dependent on every key of R.
15.4 General Definitions of Second and Third Normal Forms 527
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Figure 15.12 Normalization into 2NF and 3NF. (a) The LOTS relation with its functional dependencies FD1 through FD4. (b) Decomposing into the 2NF relations LOTS1 and LOTS2. (c) Decomposing LOTS1 into the 3NF relations LOTS1A and LOTS1B. (d) Summary of the progressive normalization of LOTS.
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15.4.2 General Definition of Third Normal Form Definition. A relation schema R is in third normal form (3NF) if, whenever a nontrivial functional dependency X → A holds in R, either (a) X is a superkey of R, or (b) A is a prime attribute of R.
According to this definition, LOTS2 (Figure 15.12(b)) is in 3NF. However, FD4 in LOTS1 violates 3NF because Area is not a superkey and Price is not a prime attribute in LOTS1. To normalize LOTS1 into 3NF, we decompose it into the relation schemas LOTS1A and LOTS1B shown in Figure 15.12(c). We construct LOTS1A by removing the attribute Price that violates 3NF from LOTS1 and placing it with Area (the left- hand side of FD4 that causes the transitive dependency) into another relation LOTS1B. Both LOTS1A and LOTS1B are in 3NF.
Two points are worth noting about this example and the general definition of 3NF:
■ LOTS1 violates 3NF because Price is transitively dependent on each of the candidate keys of LOTS1 via the nonprime attribute Area.
■ This general definition can be applied directly to test whether a relation schema is in 3NF; it does not have to go through 2NF first. If we apply the above 3NF definition to LOTS with the dependencies FD1 through FD4, we find that both FD3 and FD4 violate 3NF. Therefore, we could decompose LOTS into LOTS1A, LOTS1B, and LOTS2 directly. Hence, the transitive and partial dependencies that violate 3NF can be removed in any order.
15.4.3 Interpreting the General Definition of Third Normal Form
A relation schema R violates the general definition of 3NF if a functional depen- dency X → A holds in R that does not meet either condition—meaning that it vio- lates both conditions (a) and (b) of 3NF. This can occur due to two types of problematic functional dependencies:
■ A nonprime attribute determines another nonprime attribute. Here we typ- ically have a transitive dependency that violates 3NF.
■ A proper subset of a key of R functionally determines a nonprime attribute. Here we have a partial dependency that violates 3NF (and also 2NF).
Therefore, we can state a general alternative definition of 3NF as follows:
Alternative Definition. A relation schema R is in 3NF if every nonprime attribute of R meets both of the following conditions:
■ It is fully functionally dependent on every key of R.
■ It is nontransitively dependent on every key of R.
15.5 Boyce-Codd Normal Form 529
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Figure 15.13 Boyce-Codd normal form. (a) BCNF normalization of LOTS1A with the func- tional dependency FD2 being lost in the decomposition. (b) A schematic relation with FDs; it is in 3NF, but not in BCNF.
15.5 Boyce-Codd Normal Form Boyce-Codd normal form (BCNF) was proposed as a simpler form of 3NF, but it was found to be stricter than 3NF. That is, every relation in BCNF is also in 3NF; however, a relation in 3NF is not necessarily in BCNF. Intuitively, we can see the need for a stronger normal form than 3NF by going back to the LOTS relation schema in Figure 15.12(a) with its four functional dependencies FD1 through FD4. Suppose that we have thousands of lots in the relation but the lots are from only two coun- ties: DeKalb and Fulton. Suppose also that lot sizes in DeKalb County are only 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0 acres, whereas lot sizes in Fulton County are restricted to 1.1, 1.2, ..., 1.9, and 2.0 acres. In such a situation we would have the additional func- tional dependency FD5: Area → County_name. If we add this to the other dependen- cies, the relation schema LOTS1A still is in 3NF because County_name is a prime attribute.
The area of a lot that determines the county, as specified by FD5, can be represented by 16 tuples in a separate relation R(Area, County_name), since there are only 16 pos- sible Area values (see Figure 15.13). This representation reduces the redundancy of repeating the same information in the thousands of LOTS1A tuples. BCNF is a stronger normal form that would disallow LOTS1A and suggest the need for decom- posing it.
Definition. A relation schema R is in BCNF if whenever a nontrivial functional dependency X → A holds in R, then X is a superkey of R.
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TEACH
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Figure 15.14 A relation TEACH that is in 3NF but not BCNF.
The formal definition of BCNF differs from the definition of 3NF in that condition (b) of 3NF, which allows A to be prime, is absent from BCNF. That makes BCNF a stronger normal form compared to 3NF. In our example, FD5 violates BCNF in LOTS1A because AREA is not a superkey of LOTS1A. Note that FD5 satisfies 3NF in LOTS1A because County_name is a prime attribute (condition b), but this condition does not exist in the definition of BCNF. We can decompose LOTS1A into two BCNF relations LOTS1AX and LOTS1AY, shown in Figure 15.13(a). This decomposition loses the functional dependency FD2 because its attributes no longer coexist in the same relation after decomposition.
In practice, most relation schemas that are in 3NF are also in BCNF. Only if X → A holds in a relation schema R with X not being a superkey and A being a prime attribute will R be in 3NF but not in BCNF. The relation schema R shown in Figure 15.13(b) illustrates the general case of such a relation. Ideally, relational database design should strive to achieve BCNF or 3NF for every relation schema. Achieving the normalization status of just 1NF or 2NF is not considered adequate, since they were developed historically as stepping stones to 3NF and BCNF.
As another example, consider Figure 15.14, which shows a relation TEACH with the following dependencies:
FD1: {Student, Course} → Instructor FD2:12 Instructor → Course
Note that {Student, Course} is a candidate key for this relation and that the depen- dencies shown follow the pattern in Figure 15.13(b), with Student as A, Course as B, and Instructor as C. Hence this relation is in 3NF but not BCNF. Decomposition of this relation schema into two schemas is not straightforward because it may be
12This dependency means that each instructor teaches one course is a constraint for this application.
15.6 Multivalued Dependency and Fourth Normal Form 531
decomposed into one of the three following possible pairs:
1. {Student, Instructor} and {Student, Course}.
2. {Course, Instructor} and {Course, Student}.
3. {Instructor, Course} and {Instructor, Student}.
All three decompositions lose the functional dependency FD1. The desirable decom- position of those just shown is 3 because it will not generate spurious tuples after a join.
A test to determine whether a decomposition is nonadditive (or lossless) is dis- cussed in Section 16.2.4 under Property NJB. In general, a relation not in BCNF should be decomposed so as to meet this property.
We make sure that we meet this property, because nonadditive decomposition is a must during normalization. We may have to possibly forgo the preservation of all functional dependencies in the decomposed relations, as is the case in this example. Algorithm 16.5 does that and could be used above to give decomposition 3 for TEACH, which yields two relations in BCNF as:
(Instructor, Course) and (Instructor, Student)
Note that if we designate (Student, Instructor) as a primary key of the relation TEACH, the FD Instructor → Course causes a partial (non-full-functional) depend- ency of Course on a part of this key. This FD may be removed as a part of second normalization yielding exactly the same two relations in the result. This is an example of a case where we may reach the same ultimate BCNF design via alternate paths of normalization.
15.6 Multivalued Dependency and Fourth Normal Form
So far we have discussed the concept of functional dependency, which is by far the most important type of dependency in relational database design theory, and nor- mal forms based on functional dependencies. However, in many cases relations have constraints that cannot be specified as functional dependencies. In this section, we discuss the concept of multivalued dependency (MVD) and define fourth normal form, which is based on this dependency. A more formal discussion of MVDs and their properties is deferred to Chapter 16. Multivalued dependencies are a conse- quence of first normal form (1NF) (see Section 15.3.4), which disallows an attribute in a tuple to have a set of values, and the accompanying process of converting an unnormalized relation into 1NF. If we have two or more multivalued independent attributes in the same relation schema, we get into a problem of having to repeat every value of one of the attributes with every value of the other attribute to keep the relation state consistent and to maintain the independence among the attributes involved. This constraint is specified by a multivalued dependency.
532 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
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Figure 15.15 Fourth and fifth normal forms. (a) The EMP relation with two MVDs: Ename →→ Pname and Ename →→ Dname. (b) Decomposing the EMP relation into two 4NF relations EMP_PROJECTS and
EMP_DEPENDENTS. (c) The relation SUPPLY with no MVDs is in 4NF but not in 5NF if it has the JD(R1, R2, R3). (d) Decomposing the relation SUPPLY into the 5NF relations R1, R2, R3.
For example, consider the relation EMP shown in Figure 15.15(a). A tuple in this EMP relation represents the fact that an employee whose name is Ename works on the project whose name is Pname and has a dependent whose name is Dname. An employee may work on several projects and may have several dependents, and the employee’s projects and dependents are independent of one another.13 To keep the relation state consistent, and to avoid any spurious relationship between the two independent attributes, we must have a separate tuple to represent every combina- tion of an employee’s dependent and an employee’s project. This constraint is spec-
13In an ER diagram, each would be represented as a multivalued attribute or as a weak entity type (see Chapter 7).
15.6 Multivalued Dependency and Fourth Normal Form 533
ified as a multivalued dependency on the EMP relation, which we define in this sec- tion. Informally, whenever two independent 1:N relationships A:B and A:C are mixed in the same relation, R(A, B, C), an MVD may arise.14
15.6.1 Formal Definition of Multivalued Dependency Definition. A multivalued dependency X →→ Y specified on relation schema R, where X and Y are both subsets of R, specifies the following constraint on any relation state r of R: If two tuples t1 and t2 exist in r such that t1[X] = t2[X], then two tuples t3 and t4 should also exist in r with the following properties,
15 where we use Z to denote (R – (X ∪ Y)):16
■ t3[X] = t4[X] = t1[X] = t2[X].
■ t3[Y] = t1[Y] and t4[Y] = t2[Y].
■ t3[Z] = t2[Z] and t4[Z] = t1[Z].
Whenever X →→ Y holds, we say that X multidetermines Y. Because of the symme- try in the definition, whenever X →→ Y holds in R, so does X →→ Z. Hence, X →→ Y implies X →→ Z, and therefore it is sometimes written as X →→ Y|Z.
An MVD X →→ Y in R is called a trivial MVD if (a) Y is a subset of X, or (b) X ∪ Y = R. For example, the relation EMP_PROJECTS in Figure 15.15(b) has the trivial MVD Ename →→ Pname. An MVD that satisfies neither (a) nor (b) is called a nontrivial MVD. A trivial MVD will hold in any relation state r of R; it is called triv- ial because it does not specify any significant or meaningful constraint on R.
If we have a nontrivial MVD in a relation, we may have to repeat values redundantly in the tuples. In the EMP relation of Figure 15.15(a), the values ‘X’ and ‘Y’ of Pname are repeated with each value of Dname (or, by symmetry, the values ‘John’ and ‘Anna’ of Dname are repeated with each value of Pname). This redundancy is clearly unde- sirable. However, the EMP schema is in BCNF because no functional dependencies hold in EMP. Therefore, we need to define a fourth normal form that is stronger than BCNF and disallows relation schemas such as EMP. Notice that relations con- taining nontrivial MVDs tend to be all-key relations—that is, their key is all their attributes taken together. Furthermore, it is rare that such all-key relations with a combinatorial occurrence of repeated values would be designed in practice. However, recognition of MVDs as a potential problematic dependency is essential in relational design.
We now present the definition of fourth normal form (4NF), which is violated when a relation has undesirable multivalued dependencies, and hence can be used to identify and decompose such relations.
14This MVD is denoted as A →→ B|C. 15The tuples t1, t2, t3, and t4 are not necessarily distinct. 16Z is shorthand for the attributes in R after the attributes in (X ∪ Y) are removed from R.
534 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Definition. A relation schema R is in 4NF with respect to a set of dependencies F (that includes functional dependencies and multivalued dependencies) if, for every nontrivial multivalued dependency X →→ Y in F+17 X is a superkey for R.
We can state the following points:
■ An all-key relation is always in BCNF since it has no FDs.
■ An all-key relation such as the EMP relation in Figure 15.15(a), which has no FDs but has the MVD Ename →→ Pname | Dname, is not in 4NF.
■ A relation that is not in 4NF due to a nontrivial MVD must be decomposed to convert it into a set of relations in 4NF.
■ The decomposition removes the redundancy caused by the MVD.
The process of normalizing a relation involving the nontrivial MVDs that is not in 4NF consists of decomposing it so that each MVD is represented by a separate rela- tion where it becomes a trivial MVD. Consider the EMP relation in Figure 15.15(a). EMP is not in 4NF because in the nontrivial MVDs Ename →→ Pname and Ename →→ Dname, and Ename is not a superkey of EMP. We decompose EMP into EMP_PROJECTS and EMP_DEPENDENTS, shown in Figure 15.15(b). Both EMP_PROJECTS and EMP_DEPENDENTS are in 4NF, because the MVDs Ename →→ Pname in EMP_PROJECTS and Ename →→ Dname in EMP_DEPENDENTS are trivial MVDs. No other nontrivial MVDs hold in either EMP_PROJECTS or EMP_DEPENDENTS. No FDs hold in these relation schemas either.
15.7 Join Dependencies and Fifth Normal Form
In our discussion so far, we have pointed out the problematic functional dependen- cies and showed how they were eliminated by a process of repeated binary decom- position to remove them during the process of normalization to achieve 1NF, 2NF, 3NF and BCNF. These binary decompositions must obey the NJB property from Section 16.2.4 that we referenced while discussing the decomposition to achieve BCNF. Achieving 4NF typically involves eliminating MVDs by repeated binary decompositions as well. However, in some cases there may be no nonadditive join decomposition of R into two relation schemas, but there may be a nonadditive join decomposition into more than two relation schemas. Moreover, there may be no functional dependency in R that violates any normal form up to BCNF, and there may be no nontrivial MVD present in R either that violates 4NF. We then resort to another dependency called the join dependency and, if it is present, carry out a multiway decomposition into fifth normal form (5NF). It is important to note that such a dependency is a very peculiar semantic constraint that is very difficult to detect in practice; therefore, normalization into 5NF is very rarely done in practice.
17F+ refers to the cover of functional dependencies F, or all dependencies that are implied by F. This is defined in Section 16.1.
15.8 Summary 535
Definition. A join dependency (JD), denoted by JD(R1, R2, ..., Rn), specified on relation schema R, specifies a constraint on the states r of R. The constraint states that every legal state r of R should have a nonadditive join decomposition into R1, R2, ..., Rn. Hence, for every such r we have
∗ (πR1(r), πR2(r), ..., πRn(r)) = r
Notice that an MVD is a special case of a JD where n = 2. That is, a JD denoted as JD(R1, R2) implies an MVD (R1 ∩ R2) →→ (R1 – R2) (or, by symmetry, (R1 ∩ R2) →→(R2 – R1)). A join dependency JD(R1, R2, ..., Rn), specified on relation schema R, is a trivial JD if one of the relation schemas Ri in JD(R1, R2, ..., Rn) is equal to R. Such a dependency is called trivial because it has the nonadditive join property for any relation state r of R and thus does not specify any constraint on R. We can now define fifth normal form, which is also called project-join normal form.
Definition. A relation schema R is in fifth normal form (5NF) (or project-join normal form (PJNF)) with respect to a set F of functional, multivalued, and join dependencies if, for every nontrivial join dependency JD(R1, R2, ..., Rn) in F+ (that is, implied by F),18 every Ri is a superkey of R.
For an example of a JD, consider once again the SUPPLY all-key relation in Figure 15.15(c). Suppose that the following additional constraint always holds: Whenever a supplier s supplies part p, and a project j uses part p, and the supplier s supplies at least one part to project j, then supplier s will also be supplying part p to project j. This constraint can be restated in other ways and specifies a join dependency JD(R1, R2, R3) among the three projections R1(Sname, Part_name), R2(Sname, Proj_name), and R3(Part_name, Proj_name) of SUPPLY. If this constraint holds, the tuples below the dashed line in Figure 15.15(c) must exist in any legal state of the SUPPLY rela- tion that also contains the tuples above the dashed line. Figure 15.15(d) shows how the SUPPLY relation with the join dependency is decomposed into three relations R1, R2, and R3 that are each in 5NF. Notice that applying a natural join to any two of these relations produces spurious tuples, but applying a natural join to all three together does not. The reader should verify this on the sample relation in Figure 15.15(c) and its projections in Figure 15.15(d). This is because only the JD exists, but no MVDs are specified. Notice, too, that the JD(R1, R2, R3) is specified on all legal relation states, not just on the one shown in Figure 15.15(c).
Discovering JDs in practical databases with hundreds of attributes is next to impos- sible. It can be done only with a great degree of intuition about the data on the part of the designer. Therefore, the current practice of database design pays scant atten- tion to them.
15.8 Summary In this chapter we discussed several pitfalls in relational database design using intu- itive arguments. We identified informally some of the measures for indicating
18Again, F+ refers to the cover of functional dependencies F, or all dependencies that are implied by F. This is defined in Section 16.1.
536 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
whether a relation schema is good or bad, and provided informal guidelines for a good design. These guidelines are based on doing a careful conceptual design in the ER and EER model, following the mapping procedure in Chapter 9 correctly to map entities and relationships into relations. Proper enforcement of these guidelines and lack of redundancy will avoid the insertion/deletion/update anomalies, and genera- tion of spurious data. We recommended limiting NULL values, which cause prob- lems during SELECT, JOIN, and aggregation operations. Then we presented some formal concepts that allow us to do relational design in a top-down fashion by ana- lyzing relations individually. We defined this process of design by analysis and decomposition by introducing the process of normalization.
We defined the concept of functional dependency, which is the basic tool for analyz- ing relational schemas, and discussed some of its properties. Functional dependen- cies specify semantic constraints among the attributes of a relation schema. Next we described the normalization process for achieving good designs by testing relations for undesirable types of problematic functional dependencies. We provided a treat- ment of successive normalization based on a predefined primary key in each rela- tion, and then relaxed this requirement and provided more general definitions of second normal form (2NF) and third normal form (3NF) that take all candidate keys of a relation into account. We presented examples to illustrate how by using the general definition of 3NF a given relation may be analyzed and decomposed to eventually yield a set of relations in 3NF.
We presented Boyce-Codd normal form (BCNF) and discussed how it is a stronger form of 3NF. We also illustrated how the decomposition of a non-BCNF relation must be done by considering the nonadditive decomposition requirement. Then we introduced the fourth normal form based on multivalued dependencies that typi- cally arise due to mixing independent multivalued attributes into a single relation. Finally, we introduced the fifth normal form, which is based on join dependency, and which identifies a peculiar constraint that causes a relation to be decomposed into several components so that they always yield the original relation back after a join. In practice, most commercial designs have followed the normal forms up to BCNF. Need for decomposing into 5NF rarely arises in practice, and join dependen- cies are difficult to detect for most practical situations, making 5NF more of theo- retical value.
Chapter 16 presents synthesis as well as decomposition algorithms for relational database design based on functional dependencies. Related to decomposition, we discuss the concepts of nonadditive (or lossless) join and dependency preservation, which are enforced by some of these algorithms. Other topics in Chapter 16 include a more detailed treatment of functional and multivalued dependencies, and other types of dependencies.
Review Questions 15.1. Discuss attribute semantics as an informal measure of goodness for a rela-
tion schema.
Exercises 537
15.2. Discuss insertion, deletion, and modification anomalies. Why are they con- sidered bad? Illustrate with examples.
15.3. Why should NULLs in a relation be avoided as much as possible? Discuss the problem of spurious tuples and how we may prevent it.
15.4. State the informal guidelines for relation schema design that we discussed. Illustrate how violation of these guidelines may be harmful.
15.5. What is a functional dependency? What are the possible sources of the infor- mation that defines the functional dependencies that hold among the attrib- utes of a relation schema?
15.6. Why can we not infer a functional dependency automatically from a partic- ular relation state?
15.7. What does the term unnormalized relation refer to? How did the normal forms develop historically from first normal form up to Boyce-Codd normal form?
15.8. Define first, second, and third normal forms when only primary keys are considered. How do the general definitions of 2NF and 3NF, which consider all keys of a relation, differ from those that consider only primary keys?
15.9. What undesirable dependencies are avoided when a relation is in 2NF?
15.10. What undesirable dependencies are avoided when a relation is in 3NF?
15.11. In what way do the generalized definitions of 2NF and 3NF extend the defi- nitions beyond primary keys?
15.12. Define Boyce-Codd normal form. How does it differ from 3NF? Why is it considered a stronger form of 3NF?
15.13. What is multivalued dependency? When does it arise?
15.14. Does a relation with two or more columns always have an MVD? Show with an example.
15.15. Define fourth normal form. When is it violated? When is it typically applicable?
15.16. Define join dependency and fifth normal form.
15.17. Why is 5NF also called project-join normal form (PJNF)?
15.18. Why do practical database designs typically aim for BCNF and not aim for higher normal forms?
Exercises 15.19. Suppose that we have the following requirements for a university database
that is used to keep track of students’ transcripts:
a. The university keeps track of each student’s name (Sname), student num- ber (Snum), Social Security number (Ssn), current address (Sc_addr) and
538 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
phone (Sc_phone), permanent address (Sp_addr) and phone (Sp_phone), birth date (Bdate), sex (Sex), class (Class) (‘freshman’, ‘sophomore’, ... , ‘graduate’), major department (Major_code), minor department (Minor_code) (if any), and degree program (Prog) (‘b.a.’, ‘b.s.’, ... , ‘ph.d.’). Both Ssn and student number have unique values for each student.
b. Each department is described by a name (Dname), department code (Dcode), office number (Doffice), office phone (Dphone), and college (Dcollege). Both name and code have unique values for each department.
c. Each course has a course name (Cname), description (Cdesc), course number (Cnum), number of semester hours (Credit), level (Level), and offering department (Cdept). The course number is unique for each course.
d. Each section has an instructor (Iname), semester (Semester), year (Year), course (Sec_course), and section number (Sec_num). The section number distinguishes different sections of the same course that are taught during the same semester/year; its values are 1, 2, 3, ..., up to the total number of sections taught during each semester.
e. A grade record refers to a student (Ssn), a particular section, and a grade (Grade).
Design a relational database schema for this database application. First show all the functional dependencies that should hold among the attributes. Then design relation schemas for the database that are each in 3NF or BCNF. Specify the key attributes of each relation. Note any unspecified require- ments, and make appropriate assumptions to render the specification complete.
15.20. What update anomalies occur in the EMP_PROJ and EMP_DEPT relations of Figures 15.3 and 15.4?
15.21. In what normal form is the LOTS relation schema in Figure 15.12(a) with respect to the restrictive interpretations of normal form that take only the primary key into account? Would it be in the same normal form if the gen- eral definitions of normal form were used?
15.22. Prove that any relation schema with two attributes is in BCNF.
15.23. Why do spurious tuples occur in the result of joining the EMP_PROJ1 and EMP_ LOCS relations in Figure 15.5 (result shown in Figure 15.6)?
15.24. Consider the universal relation R = {A, B, C, D, E, F, G, H, I, J} and the set of functional dependencies F = { {A, B}→{C}, {A}→{D, E}, {B}→{F}, {F}→{G, H}, {D}→{I, J} }. What is the key for R? Decompose R into 2NF and then 3NF relations.
15.25. Repeat Exercise 15.24 for the following different set of functional dependen- cies G = {{A, B}→{C}, {B, D}→{E, F}, {A, D}→{G, H}, {A}→{I}, {H}→{J} }.
Exercises 539
A B C TUPLE# 10 b1 c1 1 10 b2 c2 2 11 b4 c1 3 12 b3 c4 4 13 b1 c1 5 14 b3 c4 6
15.26. Consider the following relation:
a. Given the previous extension (state), which of the following dependencies may hold in the above relation? If the dependency cannot hold, explain why by specifying the tuples that cause the violation.
i. A → B, ii. B → C, iii. C → B, iv. B → A, v. C → A b. Does the above relation have a potential candidate key? If it does, what is
it? If it does not, why not?
15.27. Consider a relation R(A, B, C, D, E) with the following dependencies:
AB → C, CD → E, DE → B Is AB a candidate key of this relation? If not, is ABD? Explain your answer.
15.28. Consider the relation R, which has attributes that hold schedules of courses and sections at a university; R = {Course_no, Sec_no, Offering_dept, Credit_hours, Course_level, Instructor_ssn, Semester, Year, Days_hours, Room_no, No_of_students}. Suppose that the following functional dependencies hold on R:
{Course_no} → {Offering_dept, Credit_hours, Course_level} {Course_no, Sec_no, Semester, Year} → {Days_hours, Room_no,
No_of_students, Instructor_ssn} {Room_no, Days_hours, Semester, Year} → {Instructor_ssn, Course_no,
Sec_no}
Try to determine which sets of attributes form keys of R. How would you normalize this relation?
15.29. Consider the following relations for an order-processing application data- base at ABC, Inc.
ORDER (O#, Odate, Cust#, Total_amount) ORDER_ITEM(O#, I#, Qty_ordered, Total_price, Discount%)
Assume that each item has a different discount. The Total_price refers to one item, Odate is the date on which the order was placed, and the Total_amount is the amount of the order. If we apply a natural join on the relations ORDER_ITEM and ORDER in this database, what does the resulting relation schema look like? What will be its key? Show the FDs in this resulting rela- tion. Is it in 2NF? Is it in 3NF? Why or why not? (State assumptions, if you make any.)
540 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
15.30. Consider the following relation:
CAR_SALE(Car#, Date_sold, Salesperson#, Commission%, Discount_amt)
Assume that a car may be sold by multiple salespeople, and hence {Car#, Salesperson#} is the primary key. Additional dependencies are
Date_sold → Discount_amt and Salesperson# → Commission%
Based on the given primary key, is this relation in 1NF, 2NF, or 3NF? Why or why not? How would you successively normalize it completely?
15.31. Consider the following relation for published books:
BOOK (Book_title, Author_name, Book_type, List_price, Author_affil, Publisher)
Author_affil refers to the affiliation of author. Suppose the following depen- dencies exist:
Book_title → Publisher, Book_type Book_type → List_price Author_name → Author_affil
a. What normal form is the relation in? Explain your answer.
b. Apply normalization until you cannot decompose the relations further. State the reasons behind each decomposition.
15.32. This exercise asks you to convert business statements into dependencies. Consider the relation DISK_DRIVE (Serial_number, Manufacturer, Model, Batch, Capacity, Retailer). Each tuple in the relation DISK_DRIVE contains informa- tion about a disk drive with a unique Serial_number, made by a manufacturer, with a particular model number, released in a certain batch, which has a cer- tain storage capacity and is sold by a certain retailer. For example, the tuple Disk_drive (‘1978619’, ‘WesternDigital’, ‘A2235X’, ‘765234’, 500, ‘CompUSA’) specifies that WesternDigital made a disk drive with serial number 1978619 and model number A2235X, released in batch 765234; it is 500GB and sold by CompUSA.
Write each of the following dependencies as an FD:
a. The manufacturer and serial number uniquely identifies the drive.
b. A model number is registered by a manufacturer and therefore can’t be used by another manufacturer.
c. All disk drives in a particular batch are the same model.
d. All disk drives of a certain model of a particular manufacturer have exactly the same capacity.
15.33. Consider the following relation:
R (Doctor#, Patient#, Date, Diagnosis, Treat_code, Charge)
Exercises 541
In the above relation, a tuple describes a visit of a patient to a doctor along with a treatment code and daily charge. Assume that diagnosis is determined (uniquely) for each patient by a doctor. Assume that each treatment code has a fixed charge (regardless of patient). Is this relation in 2NF? Justify your answer and decompose if necessary. Then argue whether further normaliza- tion to 3NF is necessary, and if so, perform it.
15.34. Consider the following relation:
CAR_SALE (Car_id, Option_type, Option_listprice, Sale_date, Option_discountedprice)
This relation refers to options installed in cars (e.g., cruise control) that were sold at a dealership, and the list and discounted prices of the options.
If CarID → Sale_date and Option_type → Option_listprice and CarID, Option_type → Option_discountedprice, argue using the generalized defini- tion of the 3NF that this relation is not in 3NF. Then argue from your knowl- edge of 2NF, why it is not even in 2NF.
15.35. Consider the relation:
BOOK (Book_Name, Author, Edition, Year)
with the data:
a. Based on a common-sense understanding of the above data, what are the possible candidate keys of this relation?
b. Justify that this relation has the MVD { Book } →→ { Author } | { Edition, Year }. c. What would be the decomposition of this relation based on the above
MVD? Evaluate each resulting relation for the highest normal form it possesses.
15.36. Consider the following relation:
TRIP (Trip_id, Start_date, Cities_visited, Cards_used)
This relation refers to business trips made by company salespeople. Suppose the TRIP has a single Start_date, but involves many Cities and salespeople may use multiple credit cards on the trip. Make up a mock-up population of the table.
a. Discuss what FDs and/or MVDs exist in this relation.
b. Show how you will go about normalizing it.
Book_Name Author Edition Copyright_Year
DB_fundamentals Navathe 4 2004 DB_fundamentals Elmasri 4 2004 DB_fundamentals Elmasri 5 2007 DB_fundamentals Navathe 5 2007
542 Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
Laboratory Exercise Note: The following exercise use the DBD (Data Base Designer) system that is described in the laboratory manual. The relational schema R and set of functional dependencies F need to be coded as lists. As an example, R and F for this problem is coded as:
R = [a, b, c, d, e, f, g, h, i, j] F = [[[a, b],[c]],
[[a],[d, e]], [[b],[f]], [[f],[g, h]], [[d],[i, j]]]
Since DBD is implemented in Prolog, use of uppercase terms is reserved for vari- ables in the language and therefore lowercase constants are used to code the attrib- utes. For further details on using the DBD system, please refer to the laboratory manual.
15.37. Using the DBD system, verify your answers to the following exercises:
a. 15.24 (3NF only)
b. 15.25
c. 15.27
d. 15.28
Selected Bibliography Functional dependencies were originally introduced by Codd (1970). The original definitions of first, second, and third normal form were also defined in Codd (1972a), where a discussion on update anomalies can be found. Boyce-Codd nor- mal form was defined in Codd (1974). The alternative definition of third normal form is given in Ullman (1988), as is the definition of BCNF that we give here. Ullman (1988), Maier (1983), and Atzeni and De Antonellis (1993) contain many of the theorems and proofs concerning functional dependencies.
Additional references to relational design theory are given in Chapter 16.
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Relational Database Design Algorithms and Further
Dependencies
Chapter 15 presented a top-down relational designtechnique and related concepts used extensively in commercial database design projects today. The procedure involves designing an ER or EER conceptual schema, then mapping it to the relational model by a procedure such as the one described in Chapter 9. Primary keys are assigned to each relation based on known functional dependencies. In the subsequent process, which may be called relational design by analysis, initially designed relations from the above pro- cedure—or those inherited from previous files, forms, and other sources—are ana- lyzed to detect undesirable functional dependencies. These dependencies are removed by the successive normalization procedure that we described in Section 15.3 along with definitions of related normal forms, which are successively better states of design of individual relations. In Section 15.3 we assumed that primary keys were assigned to individual relations; in Section 15.4 a more general treatment of normalization was presented where all candidate keys are considered for each relation, and Section 15.5 discussed a further normal form called BCNF. Then in Sections 15.6 and 15.7 we discussed two more types of dependencies—multivalued dependencies and join dependencies—that can also cause redundancies and showed how they can be eliminated with further normalization.
In this chapter we use the theory of normal forms and functional, multivalued, and join dependencies developed in the last chapter and build upon it while maintain- ing three different thrusts. First, we discuss the concept of inferring new functional dependencies from a given set and discuss notions including cover, minimal cover, and equivalence. Conceptually, we need to capture the semantics of attibutes within
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544 Chapter 16 Relational Database Design Algorithms and Further Dependencies
a relation completely and succinctly, and the minimal cover allows us to do it. Second, we discuss the desirable properties of nonadditive (lossless) joins and preservation of functional dependencies. A general algorithm to test for nonadditiv- ity of joins among a set of relations is presented. Third, we present an approach to relational design by synthesis of functional dependencies. This is a bottom-up approach to design that presupposes that the known functional dependencies among sets of attributes in the Universe of Discourse (UoD) have been given as input. We present algorithms to achieve the desirable normal forms, namely 3NF and BCNF, and achieve one or both of the desirable properties of nonadditivity of joins and functional dependency preservation. Although the synthesis approach is theoretically appealing as a formal approach, it has not been used in practice for large database design projects because of the difficulty of providing all possible functional dependencies up front before the design can be attempted. Alternately, with the approach presented in Chapter 15, successive decompositions and ongoing refinements to design become more manageable and may evolve over time. The final goal of this chapter is to discuss further the multivalued dependency (MVD) concept we introduced in Chapter 15 and briefly point out other types of depen- dencies that have been identified.
In Section 16.1 we discuss the rules of inference for functional dependencies and use them to define the concepts of a cover, equivalence, and minimal cover among functional dependencies. In Section 16.2, first we describe the two desirable properties of decompositions, namely, the dependency preservation property and the nonadditive (or lossless) join property, which are both used by the design algo- rithms to achieve desirable decompositions. It is important to note that it is insufficient to test the relation schemas independently of one another for compliance with higher normal forms like 2NF, 3NF, and BCNF. The resulting relations must collectively satisfy these two additional properties to qualify as a good design. Section 16.3 is devoted to the development of relational design algorithms that start off with one giant relation schema called the universal relation, which is a hypo- thetical relation containing all the attributes. This relation is decomposed (or in other words, the given functional dependencies are synthesized) into relations that satisfy a certain normal form like 3NF or BCNF and also meet one or both of the desirable properties.
In Section 16.5 we discuss the multivalued dependency (MVD) concept further by applying the notions of inference, and equivalence to MVDs. Finally, in Section 16.6 we complete the discussion on dependencies among data by introducing inclusion dependencies and template dependencies. Inclusion dependencies can represent referential integrity constraints and class/subclass constraints across relations. Template dependencies are a way of representing any generalized constraint on attributes. We also describe some situations where a procedure or function is needed to state and verify a functional dependency among attributes. Then we briefly discuss domain-key normal form (DKNF), which is considered the most general normal form. Section 16.7 summarizes this chapter.
It is possible to skip some or all of Sections 16.3, 16.4, and 16.5 in an introductory database course.
16.1 Further Topics in Functional Dependencies: Inference Rules, Equivalence, and Minimal Cover 545
16.1 Further Topics in Functional Dependencies: Inference Rules, Equivalence, and Minimal Cover
We introduced the concept of functional dependencies (FDs) in Section 15.2, illus- trated it with some examples, and developed a notation to denote multiple FDs over a single relation. We identified and discussed problematic functional dependencies in Sections 15.3 and 15.4 and showed how they can be eliminated by a proper decomposition of a relation. This process was described as normalization and we showed how to achieve the first through third normal forms (1NF through 3NF) given primary keys in Section 15.3. In Sections 15.4 and 15.5 we provided general- ized tests for 2NF, 3NF, and BCNF given any number of candidate keys in a relation and showed how to achieve them. Now we return to the study of functional depen- dencies and show how new dependencies can be inferred from a given set and dis- cuss the concepts of closure, equivalence, and minimal cover that we will need when we later consider a synthesis approach to design of relations given a set of FDs.
16.1.1 Inference Rules for Functional Dependencies We denote by F the set of functional dependencies that are specified on relation schema R. Typically, the schema designer specifies the functional dependencies that are semantically obvious; usually, however, numerous other functional dependencies hold in all legal relation instances among sets of attributes that can be derived from and satisfy the dependencies in F. Those other dependencies can be inferred or deduced from the FDs in F.
In real life, it is impossible to specify all possible functional dependencies for a given situation. For example, if each department has one manager, so that Dept_no uniquely determines Mgr_ssn (Dept_no → Mgr_ssn), and a manager has a unique phone number called Mgr_phone (Mgr_ssn → Mgr_phone), then these two dependen- cies together imply that Dept_no → Mgr_phone. This is an inferred FD and need not be explicitly stated in addition to the two given FDs. Therefore, it is useful to define a concept called closure formally that includes all possible dependencies that can be inferred from the given set F.
Definition. Formally, the set of all dependencies that include F as well as all dependencies that can be inferred from F is called the closure of F; it is denoted by F+.
For example, suppose that we specify the following set F of obvious functional dependencies on the relation schema in Figure 15.3(a):
F = {Ssn → {Ename, Bdate, Address, Dnumber}, Dnumber → {Dname, Dmgr_ssn} }
Some of the additional functional dependencies that we can infer from F are the fol- lowing:
Ssn → {Dname, Dmgr_ssn} Ssn → Ssn Dnumber → Dname
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An FD X → Y is inferred from a set of dependencies F specified on R if X → Y holds in every legal relation state r of R; that is, whenever r satisfies all the depend- encies in F, X → Y also holds in r. The closure F+ of F is the set of all functional dependencies that can be inferred from F. To determine a systematic way to infer dependencies, we must discover a set of inference rules that can be used to infer new dependencies from a given set of dependencies. We consider some of these inference rules next. We use the notation F |=X → Y to denote that the functional dependency X → Y is inferred from the set of functional dependencies F.
In the following discussion, we use an abbreviated notation when discussing func- tional dependencies. We concatenate attribute variables and drop the commas for convenience. Hence, the FD {X,Y} → Z is abbreviated to XY → Z, and the FD {X, Y, Z} → {U, V} is abbreviated to XYZ → UV. The following six rules IR1 through IR6 are well-known inference rules for functional dependencies:
IR1 (reflexive rule)1: If X ⊇ Y, then X →Y. IR2 (augmentation rule)2: {X → Y} |=XZ → YZ. IR3 (transitive rule): {X → Y, Y → Z} |=X → Z. IR4 (decomposition, or projective, rule): {X → YZ} |=X → Y. IR5 (union, or additive, rule): {X → Y, X → Z} |=X → YZ. IR6 (pseudotransitive rule): {X → Y, WY → Z} |=WX → Z.
The reflexive rule (IR1) states that a set of attributes always determines itself or any of its subsets, which is obvious. Because IR1 generates dependencies that are always true, such dependencies are called trivial. Formally, a functional dependency X → Y is trivial if X ⊇ Y; otherwise, it is nontrivial. The augmentation rule (IR2) says that adding the same set of attributes to both the left- and right-hand sides of a depen- dency results in another valid dependency. According to IR3, functional dependen- cies are transitive. The decomposition rule (IR4) says that we can remove attributes from the right-hand side of a dependency; applying this rule repeatedly can decom- pose the FD X → {A1, A2, ..., An} into the set of dependencies {X → A1, X → A2, ..., X → An}. The union rule (IR5) allows us to do the opposite; we can combine a set of dependencies {X → A1, X → A2, ..., X → An} into the single FD X → {A1, A2, ..., An}. The pseudotransitive rule (IR6) allows us to replace a set of attributes Y on the left hand side of a dependency with another set X that functionally determines Y, and can be derived from IR2 and IR3 if we augment the first functional dependency X → Y with W (the augmentation rule) and then apply the transitive rule.
One cautionary note regarding the use of these rules. Although X → A and X → B implies X → AB by the union rule stated above, X → A and Y → B does imply that XY → AB. Also, XY → A does not necessarily imply either X → A or Y → A.
1The reflexive rule can also be stated as X → X; that is, any set of attributes functionally determines itself. 2The augmentation rule can also be stated as X → Y |=XZ → Y; that is, augmenting the left-hand side attributes of an FD produces another valid FD.
16.1 Further Topics in Functional Dependencies: Inference Rules, Equivalence, and Minimal Cover 547
Each of the preceding inference rules can be proved from the definition of func- tional dependency, either by direct proof or by contradiction. A proof by contradic- tion assumes that the rule does not hold and shows that this is not possible. We now prove that the first three rules IR1 through IR3 are valid. The second proof is by con- tradiction.
Proof of IR1. Suppose that X ⊇ Y and that two tuples t1 and t2 exist in some rela- tion instance r of R such that t1 [X] = t2 [X]. Then t1[Y] = t2[Y] because X ⊇ Y; hence, X → Y must hold in r. Proof of IR2 (by contradiction). Assume that X → Y holds in a relation instance r of R but that XZ → YZ does not hold. Then there must exist two tuples t1 and t2 in r such that (1) t1 [X] = t2 [X], (2) t1 [Y] = t2 [Y], (3) t1 [XZ] = t2 [XZ], and (4) t1 [YZ] ≠ t2 [YZ]. This is not possible because from (1) and (3) we deduce (5) t1 [Z] = t2 [Z], and from (2) and (5) we deduce (6) t1 [YZ] = t2 [YZ], contra- dicting (4).
Proof of IR3. Assume that (1) X → Y and (2) Y → Z both hold in a relation r. Then for any two tuples t1 and t2 in r such that t1 [X] = t2 [X], we must have (3) t1 [Y] = t2 [Y], from assumption (1); hence we must also have (4) t1 [Z] = t2 [Z] from (3) and assumption (2); thus X → Z must hold in r.
Using similar proof arguments, we can prove the inference rules IR4 to IR6 and any additional valid inference rules. However, a simpler way to prove that an inference rule for functional dependencies is valid is to prove it by using inference rules that have already been shown to be valid. For example, we can prove IR4 through IR6 by using IR1 through IR3 as follows.
Proof of IR4 (Using IR1 through IR3).
1. X → YZ (given). 2. YZ → Y (using IR1 and knowing that YZ ⊇ Y). 3. X → Y (using IR3 on 1 and 2).
Proof of IR5 (using IR1 through IR3).
1. X →Y (given). 2. X → Z (given). 3. X → XY (using IR2 on 1 by augmenting with X; notice that XX = X). 4. XY → YZ (using IR2 on 2 by augmenting with Y). 5. X → YZ (using IR3 on 3 and 4).
Proof of IR6 (using IR1 through IR3).
1. X → Y (given). 2. WY → Z (given). 3. WX → WY (using IR2 on 1 by augmenting with W). 4. WX → Z (using IR3 on 3 and 2).
It has been shown by Armstrong (1974) that inference rules IR1 through IR3 are sound and complete. By sound, we mean that given a set of functional dependencies
548 Chapter 16 Relational Database Design Algorithms and Further Dependencies
F specified on a relation schema R, any dependency that we can infer from F by using IR1 through IR3 holds in every relation state r of R that satisfies the dependen- cies in F. By complete, we mean that using IR1 through IR3 repeatedly to infer dependencies until no more dependencies can be inferred results in the complete set of all possible dependencies that can be inferred from F. In other words, the set of dependencies F+, which we called the closure of F, can be determined from F by using only inference rules IR1 through IR3. Inference rules IR1 through IR3 are known as Armstrong’s inference rules.3
Typically, database designers first specify the set of functional dependencies F that can easily be determined from the semantics of the attributes of R; then IR1, IR2, and IR3 are used to infer additional functional dependencies that will also hold on R. A systematic way to determine these additional functional dependencies is first to determine each set of attributes X that appears as a left-hand side of some func- tional dependency in F and then to determine the set of all attributes that are dependent on X.
Definition. For each such set of attributes X, we determine the set X+ of attrib- utes that are functionally determined by X based on F; X+ is called the closure of X under F. Algorithm 16.1 can be used to calculate X+.
Algorithm 16.1. Determining X+, the Closure of X under F
Input: A set F of FDs on a relation schema R, and a set of attributes X, which is a subset of R.
X+ := X; repeat
oldX+ := X+; for each functional dependency Y → Z in F do
if X+ ⊇ Y then X+ := X+ ∪ Z; until (X+ = oldX+);
Algorithm 16.1 starts by setting X+ to all the attributes in X. By IR1, we know that all these attributes are functionally dependent on X. Using inference rules IR3 and IR4, we add attributes to X+, using each functional dependency in F. We keep going through all the dependencies in F (the repeat loop) until no more attributes are added to X+ during a complete cycle (of the for loop) through the dependencies in F. For example, consider the relation schema EMP_PROJ in Figure 15.3(b); from the semantics of the attributes, we specify the following set F of functional dependen- cies that should hold on EMP_PROJ:
F = {Ssn → Ename, Pnumber → {Pname, Plocation}, {Ssn, Pnumber} → Hours}
3They are actually known as Armstrong’s axioms. In the strict mathematical sense, the axioms (given facts) are the functional dependencies in F, since we assume that they are correct, whereas IR1 through IR3 are the inference rules for inferring new functional dependencies (new facts).
16.1 Further Topics in Functional Dependencies: Inference Rules, Equivalence, and Minimal Cover 549
Using Algorithm 16.1, we calculate the following closure sets with respect to F:
{Ssn} + = {Ssn, Ename} {Pnumber} + = {Pnumber, Pname, Plocation} {Ssn, Pnumber} + = {Ssn, Pnumber, Ename, Pname, Plocation, Hours}
Intuitively, the set of attributes in the right-hand side in each line above represents all those attributes that are functionally dependent on the set of attributes in the left-hand side based on the given set F.
16.1.2 Equivalence of Sets of Functional Dependencies In this section we discuss the equivalence of two sets of functional dependencies. First, we give some preliminary definitions.
Definition. A set of functional dependencies F is said to cover another set of functional dependencies E if every FD in E is also in F+; that is, if every depen- dency in E can be inferred from F; alternatively, we can say that E is covered by F.
Definition. Two sets of functional dependencies E and F are equivalent if E+ = F+. Therefore, equivalence means that every FD in E can be inferred from F, and every FD in F can be inferred from E; that is, E is equivalent to F if both the conditions—E covers F and F covers E—hold.
We can determine whether F covers E by calculating X+ with respect to F for each FD X → Y in E, and then checking whether this X+ includes the attributes in Y. If this is the case for every FD in E, then F covers E. We determine whether E and F are equiv- alent by checking that E covers F and F covers E. It is left to the reader as an exercise to show that the following two sets of FDs are equivalent:
F = {A → C, AC → D, E → AD, E → H} and G = {A → CD, E → AH}.
16.1.3 Minimal Sets of Functional Dependencies Informally, a minimal cover of a set of functional dependencies E is a set of func- tional dependencies F that satisfies the property that every dependency in E is in the closure F+ of F. In addition, this property is lost if any dependency from the set F is removed; F must have no redundancies in it, and the dependencies in F are in a standard form. To satisfy these properties, we can formally define a set of functional dependencies F to be minimal if it satisfies the following conditions:
1. Every dependency in F has a single attribute for its right-hand side.
2. We cannot replace any dependency X → A in F with a dependency Y → A, where Y is a proper subset of X, and still have a set of dependencies that is equivalent to F.
3. We cannot remove any dependency from F and still have a set of dependen- cies that is equivalent to F.
We can think of a minimal set of dependencies as being a set of dependencies in a standard or canonical form and with no redundancies. Condition 1 just represents
550 Chapter 16 Relational Database Design Algorithms and Further Dependencies
every dependency in a canonical form with a single attribute on the right-hand side.4 Conditions 2 and 3 ensure that there are no redundancies in the dependencies either by having redundant attributes on the left-hand side of a dependency (Condition 2) or by having a dependency that can be inferred from the remaining FDs in F (Condition 3).
Definition. A minimal cover of a set of functional dependencies E is a minimal set of dependencies (in the standard canonical form and without redundancy) that is equivalent to E. We can always find at least one minimal cover F for any set of dependencies E using Algorithm 16.2.
If several sets of FDs qualify as minimal covers of E by the definition above, it is cus- tomary to use additional criteria for minimality. For example, we can choose the minimal set with the smallest number of dependencies or with the smallest total length (the total length of a set of dependencies is calculated by concatenating the dependencies and treating them as one long character string).
Algorithm 16.2. Finding a Minimal Cover F for a Set of Functional Dependencies E
Input: A set of functional dependencies E.
1. Set F := E.
2. Replace each functional dependency X → {A1, A2, ..., An} in F by the n func- tional dependencies X →A1, X →A2, ..., X → An.
3. For each functional dependency X → A in F for each attribute B that is an element of X
if { {F – {X → A} } ∪ { (X – {B} ) → A} } is equivalent to F then replace X → A with (X – {B} ) → A in F.
4. For each remaining functional dependency X → A in F if {F – {X → A} } is equivalent to F,
then remove X → A from F.
We illustrate the above algorithm with the following:
Let the given set of FDs be E : {B → A, D → A, AB → D}. We have to find the mini- mal cover of E.
■ All above dependencies are in canonical form (that is, they have only one attribute on the right-hand side), so we have completed step 1 of Algorithm 16.2 and can proceed to step 2. In step 2 we need to determine if AB → D has any redundant attribute on the left-hand side; that is, can it be replaced by B → D or A → D?
4This is a standard form to simplify the conditions and algorithms that ensure no redundancy exists in F. By using the inference rule IR4, we can convert a single dependency with multiple attributes on the right-hand side into a set of dependencies with single attributes on the right-hand side.
16.2 Properties of Relational Decompositions 551
■ Since B → A, by augmenting with B on both sides (IR2), we have BB → AB, or B → AB (i). However, AB → D as given (ii).
■ Hence by the transitive rule (IR3), we get from (i) and (ii), B → D. Thus AB → D may be replaced by B → D.
■ We now have a set equivalent to original E, say E �: {B → A, D → A, B → D}. No further reduction is possible in step 2 since all FDs have a single attribute on the left-hand side.
■ In step 3 we look for a redundant FD in E �. By using the transitive rule on B → D and D → A, we derive B → A. Hence B → A is redundant in E � and can be eliminated.
■ Therefore, the minimal cover of E is {B → D, D → A}.
In Section 16.3 we will see how relations can be synthesized from a given set of dependencies E by first finding the minimal cover F for E.
Next, we provide a simple algorithm to determine the key of a relation:
Algorithm 16.2(a). Finding a Key K for R Given a set F of Functional Dependencies
Input: A relation R and a set of functional dependencies F on the attributes of R.
1. Set K := R.
2. For each attribute A in K
{compute (K – A)+ with respect to F;
if (K – A)+ contains all the attributes in R, then set K := K – {A} };
In Algoritm 16.2(a), we start by setting K to all the attributes of R; we then remove one attribute at a time and check whether the remaining attributes still form a superkey. Notice, too, that Algorithm 16.2(a) determines only one key out of the possible candidate keys for R; the key returned depends on the order in which attributes are removed from R in step 2.
16.2 Properties of Relational Decompositions We now turn our attention to the process of decomposition that we used through- out Chapter 15 to decompose relations in order to get rid of unwanted dependen- cies and achieve higher normal forms. In Section 16.2.1 we give examples to show that looking at an individual relation to test whether it is in a higher normal form does not, on its own, guarantee a good design; rather, a set of relations that together form the relational database schema must possess certain additional properties to ensure a good design. In Sections 16.2.2 and 16.2.3 we discuss two of these proper- ties: the dependency preservation property and the nonadditive (or lossless) join property. Section 16.2.4 discusses binary decompositions and Section 16.2.5 dis- cusses successive nonadditive join decompositions.
552 Chapter 16 Relational Database Design Algorithms and Further Dependencies
16.2.1 Relation Decomposition and Insufficiency of Normal Forms
The relational database design algorithms that we present in Section 16.3 start from a single universal relation schema R = {A1, A2, ..., An} that includes all the attrib- utes of the database. We implicitly make the universal relation assumption, which states that every attribute name is unique. The set F of functional dependencies that should hold on the attributes of R is specified by the database designers and is made available to the design algorithms. Using the functional dependencies, the algo- rithms decompose the universal relation schema R into a set of relation schemas D = {R1, R2, ..., Rm} that will become the relational database schema; D is called a decomposition of R.
We must make sure that each attribute in R will appear in at least one relation schema Ri in the decomposition so that no attributes are lost; formally, we have
This is called the attribute preservation condition of a decomposition.
Another goal is to have each individual relation Ri in the decomposition D be in BCNF or 3NF. However, this condition is not sufficient to guarantee a good data- base design on its own. We must consider the decomposition of the universal rela- tion as a whole, in addition to looking at the individual relations. To illustrate this point, consider the EMP_LOCS(Ename, Plocation) relation in Figure 15.5, which is in 3NF and also in BCNF. In fact, any relation schema with only two attributes is auto- matically in BCNF.5 Although EMP_LOCS is in BCNF, it still gives rise to spurious tuples when joined with EMP_PROJ (Ssn, Pnumber, Hours, Pname, Plocation), which is not in BCNF (see the result of the natural join in Figure 15.6). Hence, EMP_LOCS represents a particularly bad relation schema because of its convoluted semantics by which Plocation gives the location of one of the projects on which an employee works. Joining EMP_LOCS with PROJECT(Pname, Pnumber, Plocation, Dnum) in Figure 15.2—which is in BCNF—using Plocation as a joining attribute also gives rise to spurious tuples. This underscores the need for other criteria that, together with the conditions of 3NF or BCNF, prevent such bad designs. In the next three subsections we discuss such additional conditions that should hold on a decomposition D as a whole.
16.2.2 Dependency Preservation Property of a Decomposition
It would be useful if each functional dependency X→Y specified in F either appeared directly in one of the relation schemas Ri in the decomposition D or could be inferred from the dependencies that appear in some Ri. Informally, this is the dependency preservation condition. We want to preserve the dependencies because
R Ri i
m
= =
1 ∪
5As an exercise, the reader should prove that this statement is true.
16.2 Properties of Relational Decompositions 553
each dependency in F represents a constraint on the database. If one of the depen- dencies is not represented in some individual relation Ri of the decomposition, we cannot enforce this constraint by dealing with an individual relation. We may have to join multiple relations so as to include all attributes involved in that dependency.
It is not necessary that the exact dependencies specified in F appear themselves in individual relations of the decomposition D. It is sufficient that the union of the dependencies that hold on the individual relations in D be equivalent to F. We now define these concepts more formally.
Definition. Given a set of dependencies F on R, the projection of F on Ri, denoted by πRi(F) where Ri is a subset of R, is the set of dependencies X → Y in F+ such that the attributes in X ∪ Y are all contained in Ri. Hence, the projec- tion of F on each relation schema Ri in the decomposition D is the set of func- tional dependencies in F+, the closure of F, such that all their left- and right-hand-side attributes are in Ri. We say that a decomposition D = {R1, R2, ..., Rm} of R is dependency-preserving with respect to F if the union of the projections of F on each Ri in D is equivalent to F; that is, ((πR1(F)) ∪ ... ∪ (πRm(F)))
+ = F+.
If a decomposition is not dependency-preserving, some dependency is lost in the decomposition. To check that a lost dependency holds, we must take the JOIN of two or more relations in the decomposition to get a relation that includes all left- and right-hand-side attributes of the lost dependency, and then check that the dependency holds on the result of the JOIN—an option that is not practical.
An example of a decomposition that does not preserve dependencies is shown in Figure 15.13(a), in which the functional dependency FD2 is lost when LOTS1A is decomposed into {LOTS1AX, LOTS1AY}. The decompositions in Figure 15.12, how- ever, are dependency-preserving. Similarly, for the example in Figure 15.14, no mat- ter what decomposition is chosen for the relation TEACH(Student, Course, Instructor) from the three provided in the text, one or both of the dependencies originally pres- ent are bound to be lost. We state a claim below related to this property without pro- viding any proof.
Claim 1. It is always possible to find a dependency-preserving decomposition D with respect to F such that each relation Ri in D is in 3NF.
In Section 16.3.1, we describe Algorithm 16.4, which creates a dependency- preserving decomposition D = {R1, R2, ..., Rm} of a universal relation R based on a set of functional dependencies F, such that each Ri in D is in 3NF.
16.2.3 Nonadditive (Lossless) Join Property of a Decomposition
Another property that a decomposition D should possess is the nonadditive join property, which ensures that no spurious tuples are generated when a NATURAL JOIN operation is applied to the relations resulting from the decomposition. We already illustrated this problem in Section 15.1.4 with the example in Figures 15.5
554 Chapter 16 Relational Database Design Algorithms and Further Dependencies
and 15.6. Because this is a property of a decomposition of relation schemas, the con- dition of no spurious tuples should hold on every legal relation state—that is, every relation state that satisfies the functional dependencies in F. Hence, the lossless join property is always defined with respect to a specific set F of dependencies.
Definition. Formally, a decomposition D = {R1, R2, ..., Rm} of R has the lossless (nonadditive) join property with respect to the set of dependencies F on R if, for every relation state r of R that satisfies F, the following holds, where * is the NATURAL JOIN of all the relations in D: *(πR1(r), ..., πRm(r)) = r.
The word loss in lossless refers to loss of information, not to loss of tuples. If a decom- position does not have the lossless join property, we may get additional spurious tuples after the PROJECT (π) and NATURAL JOIN (*) operations are applied; these additional tuples represent erroneous or invalid information. We prefer the term nonadditive join because it describes the situation more accurately. Although the term lossless join has been popular in the literature, we will henceforth use the term nonadditive join, which is self-explanatory and unambiguous. The nonadditive join property ensures that no spurious tuples result after the application of PROJECT and JOIN operations. We may, however, sometimes use the term lossy design to refer to a design that represents a loss of information (see example at the end of Algorithm 16.4).
The decomposition of EMP_PROJ(Ssn, Pnumber, Hours, Ename, Pname, Plocation) in Figure 15.3 into EMP_LOCS(Ename, Plocation) and EMP_PROJ1(Ssn, Pnumber, Hours, Pname, Plocation) in Figure 15.5 obviously does not have the nonadditive join prop- erty, as illustrated by Figure 15.6. We will use a general procedure for testing whether any decomposition D of a relation into n relations is nonadditive with respect to a set of given functional dependencies F in the relation; it is presented as Algorithm 16.3 below. It is possible to apply a simpler test to check if the decomposition is nonaddi- tive for binary decompositions; that test is described in Section 16.2.4.
Algorithm 16.3. Testing for Nonadditive Join Property
Input: A universal relation R, a decomposition D = {R1, R2, ..., Rm} of R, and a set F of functional dependencies.
Note: Explanatory comments are given at the end of some of the steps. They fol- low the format: (* comment *).
1. Create an initial matrix S with one row i for each relation Ri in D, and one column j for each attribute Aj in R.
2. Set S(i, j):= bij for all matrix entries. (* each bij is a distinct symbol associated with indices (i, j) *).
3. For each row i representing relation schema Ri {for each column j representing attribute Aj
{if (relation Ri includes attribute Aj) then set S(i, j):= aj ;};}; (* each aj is a distinct symbol associated with index ( j) *).
16.2 Properties of Relational Decompositions 555
4. Repeat the following loop until a complete loop execution results in no changes to S {for each functional dependency X → Y in F
{for all rows in S that have the same symbols in the columns corresponding to attributes in X
{make the symbols in each column that correspond to an attribute in Y be the same in all these rows as follows: If any of the rows has an a sym- bol for the column, set the other rows to that same a symbol in the col- umn. If no a symbol exists for the attribute in any of the rows, choose one of the b symbols that appears in one of the rows for the attribute and set the other rows to that same b symbol in the column ;} ; } ;};
5. If a row is made up entirely of a symbols, then the decomposition has the nonadditive join property; otherwise, it does not.
Given a relation R that is decomposed into a number of relations R1, R2, ..., Rm, Algorithm 16.3 begins the matrix S that we consider to be some relation state r of R. Row i in S represents a tuple ti (corresponding to relation Ri) that has a symbols in the columns that correspond to the attributes of Ri and b symbols in the remaining columns. The algorithm then transforms the rows of this matrix (during the loop in step 4) so that they represent tuples that satisfy all the functional dependencies in F. At the end of step 4, any two rows in S—which represent two tuples in r—that agree in their values for the left-hand-side attributes X of a functional dependency X → Y in F will also agree in their values for the right-hand-side attributes Y. It can be shown that after applying the loop of step 4, if any row in S ends up with all a sym- bols, then the decomposition D has the nonadditive join property with respect to F.
If, on the other hand, no row ends up being all a symbols, D does not satisfy the lossless join property. In this case, the relation state r represented by S at the end of the algorithm will be an example of a relation state r of R that satisfies the depend- encies in F but does not satisfy the nonadditive join condition. Thus, this relation serves as a counterexample that proves that D does not have the nonadditive join property with respect to F. Note that the a and b symbols have no special meaning at the end of the algorithm.
Figure 16.1(a) shows how we apply Algorithm 16.3 to the decomposition of the EMP_PROJ relation schema from Figure 15.3(b) into the two relation schemas EMP_PROJ1 and EMP_LOCS in Figure 15.5(a). The loop in step 4 of the algorithm cannot change any b symbols to a symbols; hence, the resulting matrix S does not have a row with all a symbols, and so the decomposition does not have the non- additive join property.
Figure 16.1(b) shows another decomposition of EMP_PROJ (into EMP, PROJECT, and WORKS_ON) that does have the nonadditive join property, and Figure 16.1(c) shows how we apply the algorithm to that decomposition. Once a row consists only of a symbols, we conclude that the decomposition has the nonadditive join prop- erty, and we can stop applying the functional dependencies (step 4 in the algorithm) to the matrix S.
556 Chapter 16 Relational Database Design Algorithms and Further Dependencies
Pnumber
PROJECT(b) Pname Plocation
Ssn R1 b11
a1
a2 b22
b13 a3
b14 a4
a5 a5
b16 a6
a1 b21
a2 b22
b13 a3
b14 a4
b15 a5
b16 b26
R2
R1 R2 R3
D = {R1, R2 }
(No changes to matrix after applying functional dependencies)
Ename Pnumber Pname HoursPlocation
Ssn
EMP
(a) R = {Ssn, Ename, Pnumber, Pname, Plocation, Hours} R1 = EMP_LOCS = {Ename, Plocation} R2 = EMP_PROJ1 = {Ssn, Pnumber, Hours, Pname, Plocation}
(c)
Ename Ssn
WORKS_ON Pnumber Hours
Ssn
a1 b32 a3 b34 b35 a6
a1 b21
a2 b22
b13 a3
b14 a4
b15 a5
b16 b26
R1 R2 R3 a1 a2b32 b34a3 a4 a5 a6
(Original matrix S at start of algorithm)
Ename Pnumber Pname HoursPlocation
Ssn
(Matrix S after applying the first two functional dependencies; last row is all “a” symbols so we stop)
Ename Pnumber Pname HoursPlocation
F = {Ssn Ename; Pnumber {Pname, Plocation}; {Ssn, Pnumber} Hours}
D = {R1, R2, R3}R = {Ssn, Ename, Pnumber, Pname, Plocation, Hours} R1 = EMP = {Ssn, Ename} R2 = PROJ = {Pnumber, Pname, Plocation} R3 = WORKS_ON = {Ssn, Pnumber, Hours}
F = {Ssn Ename; Pnumber {Pname, Plocation}; {Ssn, Pnumber} Hours}
b35
Figure 16.1 Nonadditive join test for n-ary decompositions. (a) Case 1: Decomposition of EMP_PROJ into EMP_PROJ1 and EMP_LOCS fails test. (b) A decomposition of EMP_PROJ that has the lossless join property. (c) Case 2: Decomposition of EMP_PROJ into EMP, PROJECT, and WORKS_ON satisfies test.
16.3 Algorithms for Relational Database Schema Design 557
16.2.4 Testing Binary Decompositions for the Nonadditive Join Property
Algorithm 16.3 allows us to test whether a particular decomposition D into n rela- tions obeys the nonadditive join property with respect to a set of functional dependencies F. There is a special case of a decomposition called a binary decom- position—decomposition of a relation R into two relations. We give an easier test to apply than Algorithm 16.3, but while it is very handy to use, it is limited to binary decompositions only.
Property NJB (Nonadditive Join Test for Binary Decompositions). A decomposition D = {R1, R2} of R has the lossless (nonadditive) join property with respect to a set of functional dependencies F on R if and only if either
■ The FD ((R1 ∩ R2) → (R1 – R2)) is in F +, or
■ The FD ((R1 ∩ R2) → (R2 – R1)) is in F +
You should verify that this property holds with respect to our informal successive normalization examples in Sections 15.3 and 15.4. In Section 15.5 we decomposed LOTS1A into two BCNF relations LOTS1AX and LOTS1AY, and decomposed the TEACH relation in Figure 15.14 into the two relations {Instructor, Course} and {Instructor, Student}. These are valid decompositions because they are nonadditive per the above test.
16.2.5 Successive Nonadditive Join Decompositions We saw the successive decomposition of relations during the process of second and third normalization in Sections 15.3 and 15.4. To verify that these decompositions are nonadditive, we need to ensure another property, as set forth in Claim 2.
Claim 2 (Preservation of Nonadditivity in Successive Decompositions). If a decomposition D = {R1, R2, ..., Rm} of R has the nonadditive (lossless) join property with respect to a set of functional dependencies F on R, and if a decomposition Di = {Q1, Q2, ..., Qk} of Ri has the nonadditive join property with respect to the projection of F on Ri, then the decomposition D2 = {R1, R2, ..., Ri−1, Q1, Q2, ..., Qk, Ri+1, ..., Rm} of R has the nonadditive join property with respect to F.
16.3 Algorithms for Relational Database Schema Design
We now give three algorithms for creating a relational decomposition from a uni- versal relation. Each algorithm has specific properties, as we discuss next.
558 Chapter 16 Relational Database Design Algorithms and Further Dependencies
16.3.1 Dependency-Preserving Decomposition into 3NF Schemas
Algorithm 16.4 creates a dependency-preserving decomposition D = {R1, R2, ..., Rm} of a universal relation R based on a set of functional dependencies F, such that each Ri in D is in 3NF. It guarantees only the dependency-preserving property; it does not guarantee the nonadditive join property. The first step of Algorithm 16.4 is to find a minimal cover G for F; Algorithm 16.2 can be used for this step. Note that multiple minimal covers may exist for a given set F (as we illustrate later in the example after Algorithm 16.4). In such cases the algorithms can potentially yield multiple alternative designs.
Algorithm 16.4. Relational Synthesis into 3NF with Dependency Preservation Input: A universal relation R and a set of functional dependencies F on the attributes of R.
1. Find a minimal cover G for F (use Algorithm 16.2);
2. For each left-hand-side X of a functional dependency that appears in G, cre- ate a relation schema in D with attributes {X ∪ {A1} ∪ {A2} ... ∪ {Ak} }, where X → A1, X → A2, ..., X → Ak are the only dependencies in G with X as the left-hand-side (X is the key of this relation);
3. Place any remaining attributes (that have not been placed in any relation) in a single relation schema to ensure the attribute preservation property.
Example of Algorithm 16.4. Consider the following universal relation:
U(Emp_ssn, Pno, Esal, Ephone, Dno, Pname, Plocation)
Emp_ssn, Esal, Ephone refer to the Social Security number, salary, and phone number of the employee. Pno, Pname, and Plocation refer to the number, name, and location of the project. Dno is department number.
The following dependencies are present:
FD1: Emp_ssn → {Esal, Ephone, Dno} FD2: Pno → { Pname, Plocation} FD3: Emp_ssn, Pno → {Esal, Ephone, Dno, Pname, Plocation}
By virtue of FD3, the attribute set {Emp_ssn, Pno} represents a key of the universal relation. Hence F, the set of given FDs includes {Emp_ssn → Esal, Ephone, Dno; Pno → Pname, Plocation; Emp_ssn, Pno → Esal, Ephone, Dno, Pname, Plocation}.
By applying the minimal cover Algorithm 16.2, in step 3 we see that Pno is a redun- dant attribute in Emp_ssn, Pno → Esal, Ephone, Dno. Moreover, Emp_ssn is redun- dant in Emp_ssn, Pno → Pname, Plocation. Hence the minimal cover consists of FD1 and FD2 only (FD3 being completely redundant) as follows (if we group attributes with the same left-hand side into one FD):
Minimal cover G: {Emp_ssn → Esal, Ephone, Dno; Pno → Pname, Plocation}
16.3 Algorithms for Relational Database Schema Design 559
6See Maier (1983) or Ullman (1982) for a proof.
By applying Algorithm 16.4 to the above Minimal cover G, we get a 3NF design con- sisting of two relations with keys Emp_ssn and Pno as follows:
R1 (Emp_ssn, Esal, Ephone, Dno) R2 (Pno, Pname, Plocation)
An observant reader would notice easily that these two relations have lost the original information contained in the key of the universal relation U (namely, that there are certain employees working on certain projects in a many-to-many relationship). Thus, while the algorithm does preserve the original dependencies, it makes no guar- antee of preserving all of the information. Hence, the resulting design is a lossy design.
Claim 3. Every relation schema created by Algorithm 16.4 is in 3NF. (We will not provide a formal proof here;6 the proof depends on G being a minimal set of dependencies.)
It is obvious that all the dependencies in G are preserved by the algorithm because each dependency appears in one of the relations Ri in the decomposition D. Since G is equivalent to F, all the dependencies in F are either preserved directly in the decomposition or are derivable using the inference rules from Section 16.1.1 from those in the resulting relations, thus ensuring the dependency preservation prop- erty. Algorithm 16.4 is called a relational synthesis algorithm, because each rela- tion schema Ri in the decomposition is synthesized (constructed) from the set of functional dependencies in G with the same left-hand-side X.
16.3.2 Nonadditive Join Decomposition into BCNF Schemas The next algorithm decomposes a universal relation schema R = {A1, A2, ..., An} into a decomposition D = {R1, R2, ..., Rm} such that each Ri is in BCNF and the decom- position D has the lossless join property with respect to F. Algorithm 16.5 utilizes Property NJB and Claim 2 (preservation of nonadditivity in successive decomposi- tions) to create a nonadditive join decomposition D = {R1, R2, ..., Rm} of a universal relation R based on a set of functional dependencies F, such that each Ri in D is in BCNF.
Algorithm 16.5. Relational Decomposition into BCNF with Nonadditive Join Property
Input: A universal relation R and a set of functional dependencies F on the attributes of R.
1. Set D := {R} ;
2. While there is a relation schema Q in D that is not in BCNF do {
choose a relation schema Q in D that is not in BCNF; find a functional dependency X → Y in Q that violates BCNF; replace Q in D by two relation schemas (Q – Y) and (X ∪ Y);
} ;
560 Chapter 16 Relational Database Design Algorithms and Further Dependencies
Each time through the loop in Algorithm 16.5, we decompose one relation schema Q that is not in BCNF into two relation schemas. According to Property NJB for binary decompositions and Claim 2, the decomposition D has the nonadditive join property. At the end of the algorithm, all relation schemas in D will be in BCNF. The reader can check that the normalization example in Figures 15.12 and 15.13 basi- cally follows this algorithm. The functional dependencies FD3, FD4, and later FD5 violate BCNF, so the LOTS relation is decomposed appropriately into BCNF rela- tions, and the decomposition then satisfies the nonadditive join property. Similarly, if we apply the algorithm to the TEACH relation schema from Figure 15.14, it is decomposed into TEACH1(Instructor, Student) and TEACH2(Instructor, Course) because the dependency FD2 Instructor → Course violates BCNF.
In step 2 of Algorithm 16.5, it is necessary to determine whether a relation schema Q is in BCNF or not. One method for doing this is to test, for each functional dependency X → Y in Q, whether X+ fails to include all the attributes in Q, thereby determining whether or not X is a (super)key in Q. Another technique is based on an observation that whenever a relation schema Q has a BCNF violation, there exists a pair of attributes A and B in Q such that {Q – {A, B} } → A; by computing the closure {Q – {A, B} }+ for each pair of attributes {A, B} of Q, and checking whether the closure includes A (or B), we can determine whether Q is in BCNF.
16.3.3 Dependency-Preserving and Nonadditive (Lossless) Join Decomposition into 3NF Schemas
So far, in Algorithm 16.4 we showed how to achieve a 3NF design with the potential for loss of information and in Algorithm 16.5 we showed how to achieve BCNF design with the potential loss of certain functional dependencies. By now we know that it is not possible to have all three of the following: (1) guaranteed nonlossy design, (2) guaranteed dependency preservation, and (3) all relations in BCNF. As we have said before, the first condition is a must and cannot be compromised. The second condition is desirable, but not a must, and may have to be relaxed if we insist on achieving BCNF. Now we give an alternative algorithm where we achieve conditions 1 and 2 and only guarantee 3NF. A simple modification to Algorithm 16.4, shown as Algorithm 16.6, yields a decomposition D of R that does the following:
■ Preserves dependencies
■ Has the nonadditive join property
■ Is such that each resulting relation schema in the decomposition is in 3NF
Because the Algorithm 16.6 achieves both the desirable properties, rather than only functional dependency preservation as guaranteed by Algorithm 16.4, it is preferred over Algorithm 16.4.
Algorithm 16.6. Relational Synthesis into 3NF with Dependency Preservation and Nonadditive Join Property
Input: A universal relation R and a set of functional dependencies F on the attributes of R.
16.3 Algorithms for Relational Database Schema Design 561
7Step 3 of Algorithm 16.4 is not needed in Algorithm 16.6 to preserve attributes because the key will include any unplaced attributes; these are the attributes that do not participate in any functional depen- dency. 8Note that there is an additional type of dependency: R is a projection of the join of two or more rela- tions in the schema. This type of redundancy is considered join dependency, as we discussed in Section 15.7. Hence, technically, it may continue to exist without disturbing the 3NF status for the schema.
1. Find a minimal cover G for F (use Algorithm 16.2).
2. For each left-hand-side X of a functional dependency that appears in G, cre- ate a relation schema in D with attributes {X ∪ {A1} ∪ {A2} ... ∪ {Ak} }, where X → A1, X → A2, ..., X → Ak are the only dependencies in G with X as left-hand-side (X is the key of this relation).
3. If none of the relation schemas in D contains a key of R, then create one more relation schema in D that contains attributes that form a key of R.7
(Algorithm 16.2(a) may be used to find a key.)
4. Eliminate redundant relations from the resulting set of relations in the rela- tional database schema. A relation R is considered redundant if R is a projec- tion of another relation S in the schema; alternately, R is subsumed by S.8
Step 3 of Algorithm 16.6 involves identifying a key K of R. Algorithm 16.2(a) can be used to identify a key K of R based on the set of given functional dependencies F. Notice that the set of functional dependencies used to determine a key in Algorithm 16.2(a) could be either F or G, since they are equivalent.
Example 1 of Algorithm 16.6. Let us revisit the example given earlier at the end of Algorithm 16.4. The minimal cover G holds as before. The second step produces relations R1 and R2 as before. However, now in step 3, we will generate a relation corresponding to the key {Emp_ssn, Pno}. Hence, the resulting design contains:
R1 (Emp_ssn , Esal, Ephone, Dno) R2 (Pno, Pname, Plocation) R3 (Emp_ssn, Pno)
This design achieves both the desirable properties of dependency preservation and nonadditive join.
Example 2 of Algorithm 16.6 (Case X ). Consider the relation schema LOTS1A shown in Figure 15.13(a). Assume that this relation is given as a universal relation with the following functional dependencies:
FD1: Property_id → Lot#, County, Area FD2: Lot#, County → Area, Property_id FD3: Area → County
These were called FD1, FD2, and FD5 in Figure 15.13(a). The meanings of the above attributes and the implication of the above functional dependencies were explained
562 Chapter 16 Relational Database Design Algorithms and Further Dependencies
in Section 15.4. For ease of reference, let us abbreviate the above attributes with the first letter for each and represent the functional dependencies as the set
F : { P → LCA, LC → AP, A → C }.
If we apply the minimal cover Algorithm 16.2 to F, (in step 2) we first represent the set F as
F : {P → L, P → C, P → A, LC → A, LC → P, A → C}.
In the set F, P → A can be inferred from P → LC and LC → A; hence P → A by tran- sitivity and is therefore redundant. Thus, one possible minimal cover is
Minimal cover GX: {P → LC, LC → AP, A → C }.
In step 2 of Algorithm 16.6 we produce design X (before removing redundant rela- tions) using the above minimal cover as
Design X: R1 (P, L, C), R2 (L, C, A, P), and R3 (A, C).
In step 4 of the algorithm, we find that R3 is subsumed by R2 (that is, R3 is always a projection of R2 and R1 is a projection of R2 as well. Hence both of those relations are redundant. Thus the 3NF schema that achieves both of the desirable properties is (after removing redundant relations)
Design X: R2 (L, C, A, P).
or, in other words it is identical to the relation LOTS1A (Lot#, County, Area, Property_id) that we had determined to be in 3NF in Section 15.4.2.
Example 2 of Algorithm 16.6 (Case Y ). Starting with LOTS1A as the universal relation and with the same given set of functional dependencies, the second step of the minimal cover Algorithm 16.2 produces, as before
F: {P → C, P → A, P → L, LC → A, LC → P, A → C}.
The FD LC → A may be considered redundant because LC → P and P → A implies LC → A by transitivity. Also, P → C may be considered to be redundant because P → A and A → C implies P → C by transitivity. This gives a different minimal cover as
Minimal cover GY: { P → LA, LC → P, A → C }.
The alternative design Y produced by the algorithm now is
Design Y: S1 (P, A, L), S2 (L, C, P), and S3 (A, C).
Note that this design has three 3NF relations, none of which can be considered as redundant by the condition in step 4. All FDs in the original set F are preserved. The reader will notice that out of the above three relations, relations S1 and S3 were pro- duced as the BCNF design by the procedure given in Section 15.5 (implying that S2 is redundant in the presence of S1 and S3). However, we cannot eliminate relation S2 from the set of three 3NF relations above since it is not a projection of either S1 or S3. Design Y therefore remains as one possible final result of applying Algorithm 16.6 to the given universal relation that provides relations in 3NF.
It is important to note that the theory of nonadditive join decompositions is based on the assumption that no NULL values are allowed for the join attributes. The next
16.4 About Nulls, Dangling Tuples, and Alternative Relational Designs 563
section discusses some of the problems that NULLs may cause in relational decom- positions and provides a general discussion of the algorithms for relational design by synthesis presented in this section.
16.4 About Nulls, Dangling Tuples, and Alternative Relational Designs
In this section we will discuss a few general issues related to problems that arise when relational design is not approached properly.
16.4.1 Problems with NULL Values and Dangling Tuples We must carefully consider the problems associated with NULLs when designing a relational database schema. There is no fully satisfactory relational design theory as yet that includes NULL values. One problem occurs when some tuples have NULL values for attributes that will be used to join individual relations in the decomposi- tion. To illustrate this, consider the database shown in Figure 16.2(a), where two relations EMPLOYEE and DEPARTMENT are shown. The last two employee tuples— ‘Berger’ and ‘Benitez’—represent newly hired employees who have not yet been assigned to a department (assume that this does not violate any integrity con- straints). Now suppose that we want to retrieve a list of (Ename, Dname) values for all the employees. If we apply the NATURAL JOIN operation on EMPLOYEE and DEPARTMENT (Figure 16.2(b)), the two aforementioned tuples will not appear in the result. The OUTER JOIN operation, discussed in Chapter 6, can deal with this problem. Recall that if we take the LEFT OUTER JOIN of EMPLOYEE with DEPARTMENT, tuples in EMPLOYEE that have NULL for the join attribute will still appear in the result, joined with an imaginary tuple in DEPARTMENT that has NULLs for all its attribute values. Figure 16.2(c) shows the result.
In general, whenever a relational database schema is designed in which two or more relations are interrelated via foreign keys, particular care must be devoted to watch- ing for potential NULL values in foreign keys. This can cause unexpected loss of information in queries that involve joins on that foreign key. Moreover, if NULLs occur in other attributes, such as Salary, their effect on built-in functions such as SUM and AVERAGE must be carefully evaluated.
A related problem is that of dangling tuples, which may occur if we carry a decom- position too far. Suppose that we decompose the EMPLOYEE relation in Figure 16.2(a) further into EMPLOYEE_1 and EMPLOYEE_2, shown in Figure 16.3(a) and 16.3(b).9 If we apply the NATURAL JOIN operation to EMPLOYEE_1 and EMPLOYEE_2, we get the original EMPLOYEE relation. However, we may use the alternative representation, shown in Figure 16.3(c), where we do not include a tuple
9This sometimes happens when we apply vertical fragmentation to a relation in the context of a distrib- uted database (see Chapter 25).
564 Chapter 16 Relational Database Design Algorithms and Further Dependencies
(b)
Ename
EMPLOYEE (a)
Ssn Bdate Address Dnum
Smith, John B.
Wong, Franklin T.
Zelaya, Alicia J.
Wallace, Jennifer S.
Narayan, Ramesh K.
English, Joyce A.
Jabbar, Ahmad V.
Borg, James E.
987987987
888665555
1969-03-29
1937-11-10
980 Dallas, Houston, TX
450 Stone, Houston, TX
123456789
333445555
999887777
987654321
666884444
453453453
1965-01-09
1955-12-08
1968-07-19
1941-06-20
1962-09-15
1972-07-31
731 Fondren, Houston, TX
638 Voss, Houston, TX
3321 Castle, Spring, TX
291 Berry, Bellaire, TX
975 Fire Oak, Humble, TX
5631 Rice, Houston, TX
5
5
4
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Berger, Anders C. 999775555 1965-04-26 6530 Braes, Bellaire, TX NULL
Benitez, Carlos M. 888664444 1963-01-09 7654 Beech, Houston, TX NULL
5
Dname
DEPARTMENT
Dnum Dmgr_ssn
Research
Administration Headquarters
5
4 1
333445555
987654321 888665555
Ename
Smith, John B.
Wong, Franklin T.
Zelaya, Alicia J.
Wallace, Jennifer S.
Narayan, Ramesh K.
English, Joyce A.
Jabbar, Ahmad V.
Borg, James E.
999887777
123456789
333445555
453453453
987654321
666884444
987987987
888665555 1937-11-10
Ssn
1968-07-19
1965-01-09
1955-12-08
1972-07-31
1969-03-29
1941-06-20
1962-09-15
Bdate
3321 Castle, Spring, TX
731 Fondren, Houston, TX 5
638 Voss, Houston, TX
5631 Rice, Houston, TX
980 Dallas, Houston, TX
450 Stone, Houston, TX
291 Berry, Bellaire, TX
975 Fire Oak, Humble, TX
Address
4
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Dnum Dname Dmgr_ssn
(c)
Ename
Smith, John B.
Wong, Franklin T.
Zelaya, Alicia J.
Wallace, Jennifer S.
Narayan, Ramesh K.
English, Joyce A.
Jabbar, Ahmad V.
Borg, James E.
999887777
123456789
333445555
453453453
987654321
666884444
987987987
888665555 1937-11-10
1968-07-19
1965-01-09
1955-12-08
1972-07-31
1969-03-29
1941-06-20
1962-09-15
Bdate
3321 Castle, Spring, TX
731 Fondren, Houston, TX 5
638 Voss, Houston, TX
5631 Rice, Houston, TX
980 Dallas, Houston, TX
450 Stone, Houston, TX
291 Berry, Bellaire, TX
975 Fire Oak, Humble, TX
Address
4
5
5
4
1
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Administration
Research
Research
Research
Administration
Headquarters
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Research
987654321
333445555
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888665555
Berger, Anders C.
Benitez, Carlos M.
999775555
888665555 1963-01-09
1965-04-26 6530 Braes, Bellaire, TX
7654 Beech, Houston, TX
NULL
NULL
NULL
NULL
NULL
NULL
987654321
333445555
Dnum Dname Dmgr_ssnSsn
Figure 16.2 Issues with NULL-value joins. (a) Some EMPLOYEE tuples have NULL for the join attrib- ute Dnum. (b) Result of applying NATURAL JOIN to the EMPLOYEE and DEPARTMENT relations. (c) Result of applying LEFT OUTER JOIN to EMPLOYEE and DEPARTMENT.
Ename
EMPLOYEE_1(a)
(b)
Ssn Bdate Address
Smith, John B.
Wong, Franklin T.
Zelaya, Alicia J.
Wallace, Jennifer S.
Narayan, Ramesh K.
English, Joyce A.
Jabbar, Ahmad V.
Borg, James E.
987987987
888665555
1969-03-29
1937-11-10
980 Dallas, Houston, TX
450 Stone, Houston, TX
123456789
333445555
999887777
987654321
666884444
453453453
1965-01-09
1955-12-08
1968-07-19
1941-06-20
1962-09-15
1972-07-31
731 Fondren, Houston, TX
638 Voss, Houston, TX
3321 Castle, Spring, TX
291 Berry, Bellaire, TX
975 Fire Oak, Humble, TX
5631 Rice, Houston, TX
Berger, Anders C.
Benitez, Carlos M.
999775555
888665555
1965-04-26
1963-01-09
6530 Braes, Bellaire, TX
7654 Beech, Houston, TX
EMPLOYEE_2
Ssn
123456789
333445555
999887777
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453453453
987987987
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16.4 About Nulls, Dangling Tuples, and Alternative Relational Designs 565
Figure 16.3 The dangling tuple problem. (a) The relation EMPLOYEE_1 (includes
all attributes of EMPLOYEE from Figure 16.2(a) except Dnum).
(b) The relation EMPLOYEE_2 (includes Dnum attribute with NULL values).
(c) The relation EMPLOYEE_3 (includes Dnum attribute but does not include tuples for which Dnum has NULL values).
in EMPLOYEE_3 if the employee has not been assigned a department (instead of including a tuple with NULL for Dnum as in EMPLOYEE_2). If we use EMPLOYEE_3 instead of EMPLOYEE_2 and apply a NATURAL JOIN on EMPLOYEE_1 and EMPLOYEE_3, the tuples for Berger and Benitez will not appear in the result; these are called dangling tuples in EMPLOYEE_1 because they are represented in only one of the two relations that represent employees, and hence are lost if we apply an (INNER) JOIN operation.
16.4.2 Discussion of Normalization Algorithms and Alternative Relational Designs
One of the problems with the normalization algorithms we described is that the database designer must first specify all the relevant functional dependencies among
566 Chapter 16 Relational Database Design Algorithms and Further Dependencies
the database attributes. This is not a simple task for a large database with hundreds of attributes. Failure to specify one or two important dependencies may result in an undesirable design. Another problem is that these algorithms are not deterministic in general. For example, the synthesis algorithms (Algorithms 16.4 and 16.6) require the specification of a minimal cover G for the set of functional dependencies F. Because there may be in general many minimal covers corresponding to F, as we illustrated in Example 2 of Algorithm 16.6 above, the algorithm can give different designs depending on the particular minimal cover used. Some of these designs may not be desirable. The decomposition algorithm to achieve BCNF (Algorithm 16.5) depends on the order in which the functional dependencies are supplied to the algo- rithm to check for BCNF violation. Again, it is possible that many different designs may arise corresponding to the same set of functional dependencies, depending on the order in which such dependencies are considered for violation of BCNF. Some of the designs may be preferred, whereas others may be undesirable.
It is not always possible to find a decomposition into relation schemas that pre- serves dependencies and allows each relation schema in the decomposition to be in BCNF (instead of 3NF as in Algorithm 16.6). We can check the 3NF relation schemas in the decomposition individually to see whether each satisfies BCNF. If some relation schema Ri is not in BCNF, we can choose to decompose it further or to leave it as it is in 3NF (with some possible update anomalies).
To illustrate the above points, let us revisit the LOTS1A relation in Figure 15.13(a). It is a relation in 3NF, which is not in BCNF as was shown in Section 15.5. We also showed that starting with the functional dependencies (FD1, FD2, and FD5 in Figure 15.13(a)), using the bottom-up approach to design and applying Algorithm 16.6, it is possible to either come up with the LOTS1A relation as the 3NF design (which was called design X previously), or an alternate design Y which consists of three relations S1, S2, S3 (design Y), each of which is a 3NF relation. Note that if we test design Y further for BCNF, each of S1, S2, and S3 turn out to be individually in BCNF. The design X, however, when tested for BCNF, fails the test. It yields the two relations S1 and S3 by applying Algorithm 16.5 (because of the violating functional dependency A → C). Thus, the bottom-up design procedure of applying Algorithm 16.6 to design 3NF relations to achieve both properties and then applying Algorithm 16.5 to achieve BCNF with the nonadditive join property (and sacrific- ing functional dependency preservation) yields S1, S2, S3 as the final BCNF design by one route (Y design route) and S1, S3 by the other route (X design route). This happens due to the multiple minimal covers for the original set of functional dependencies. Note that S2 is a redundant relation in the Y design; however, it does not violate the nonadditive join constraint. It is easy to see that S2 is a valid and meaningful relation that has the two candidate keys (L, C), and P placed side-by- side.
Table 16.1 summarizes the properties of the algorithms discussed in this chapter so far.
16.5 Futher Discussion of Multivalued Dependencies and 4NF 567
Table 16.1 Summary of the Algorithms Discussed in This Chapter
Algorithm Input Output Properties/Purpose Remarks
16.1 An attribute or a set of attributes X, and a set of FDs F
A set of attrbutes in the closure of X with respect to F
Determine all the attributes that can be functionally deter- mined from X
The closure of a key is the entire relation
16.2 A set of functional dependencies F
The minimal cover of functional dependencies
To determine the minimal cover of a set of dependencies F
Multiple minimal covers may exist— depends on the order of selecting function- al dependencies
16.2a Relation schema R with a set of func- tional dependencies F
Key K of R To find a key K (that is a subset of R)
The entire relation R is always a default superkey
16.3 A decomposition D of R and a set F of functional depen- dencies
Boolean result: yes or no for nonadditive join property
Testing for nonaddi- tive join decomposi- tion
See a simpler test NJB in Section 16.2.4 for binary decompo- sitions
16.4 A relation R and a set of functional dependencies F
A set of relations in 3NF
Dependency preser- vation
No guarantee of sat- isfying lossless join property
16.5 A relation R and a set of functional dependencies F
A set of relations in BCNF
Nonadditive join decomposition
No guarantee of dependency preser- vation
16.6 A relation R and a set of functional dependencies F
A set of relations in 3NF
Nonadditive join and dependency- preserving decompo- sition
May not achieve BCNF, but achieves all desirable proper- ties and 3NF
16.7 A relation R and a set of functional and multivalued depen- dencies
A set of relations in 4NF
Nonadditive join decomposition
No guarantee of dependency preser- vation
16.5 Further Discussion of Multivalued Dependencies and 4NF
We introduced and defined the concept of multivalued dependencies and used it to define the fourth normal form in Section 15.6. Now we revisit MVDs to make our treatment complete by stating the rules of inference on MVDs.
568 Chapter 16 Relational Database Design Algorithms and Further Dependencies
16.5.1 Inference Rules for Functional and Multivalued Dependencies
As with functional dependencies (FDs), inference rules for multivalued dependen- cies (MVDs) have been developed. It is better, though, to develop a unified frame- work that includes both FDs and MVDs so that both types of constraints can be considered together. The following inference rules IR1 through IR8 form a sound and complete set for inferring functional and multivalued dependencies from a given set of dependencies. Assume that all attributes are included in a universal rela- tion schema R = {A1, A2, ..., An} and that X, Y, Z, and W are subsets of R.
IR1 (reflexive rule for FDs): If X ⊇ Y, then X → Y. IR2 (augmentation rule for FDs): {X → Y} |= XZ → YZ. IR3 (transitive rule for FDs): {X → Y, Y → Z} |= X → Z. IR4 (complementation rule for MVDs): {X →→ Y} |= {X →→ (R – (X ∪ Y))}. IR5 (augmentation rule for MVDs): If X →→ Y and W ⊇ Z, then WX →→ YZ. IR6 (transitive rule for MVDs): {X →→ Y, Y →→ Z} |= X →→ (Z – Y). IR7 (replication rule for FD to MVD): {X → Y} |= X →→ Y. IR8 (coalescence rule for FDs and MVDs): If X →→ Y and there exists W with the properties that (a) W ∩ Y is empty, (b) W → Z, and (c) Y ⊇ Z, then X → Z.
IR1 through IR3 are Armstrong’s inference rules for FDs alone. IR4 through IR6 are inference rules pertaining to MVDs only. IR7 and IR8 relate FDs and MVDs. In par- ticular, IR7 says that a functional dependency is a special case of a multivalued dependency; that is, every FD is also an MVD because it satisfies the formal defini- tion of an MVD. However, this equivalence has a catch: An FD X → Y is an MVD X →→ Y with the additional implicit restriction that at most one value of Y is associ- ated with each value of X.10 Given a set F of functional and multivalued dependen- cies specified on R = {A1, A2, ..., An}, we can use IR1 through IR8 to infer the (complete) set of all dependencies (functional or multivalued) F+ that will hold in every relation state r of R that satisfies F. We again call F+ the closure of F.
16.5.2 Fourth Normal Form Revisited We restate the definition of fourth normal form (4NF) from Section 15.6:
Definition. A relation schema R is in 4NF with respect to a set of dependen- cies F (that includes functional dependencies and multivalued dependencies) if, for every nontrivial multivalued dependency X →→ Y in F+, X is a superkey for R.
10That is, the set of values of Y determined by a value of X is restricted to being a singleton set with only one value. Hence, in practice, we never view an FD as an MVD.
(a) EMP
Ename
Smith
Smith
Smith
Smith
Brown
Brown
Brown
Brown
Brown
Brown
Brown
Brown
Brown
Brown
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John
Anna
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John
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EMP_DEPENDENTS
Ename
Smith Smith
Brown Brown
Brown
Jim Joan
Bob
Anna John
Dname
16.5 Futher Discussion of Multivalued Dependencies and 4NF 569
Figure 16.4 Decomposing a relation state of EMP that is not in 4NF. (a) EMP relation with additional tuples. (b) Two corresponding 4NF relations EMP_PROJECTS and EMP_DEPENDENTS.
To illustrate the importance of 4NF, Figure 16.4(a) shows the EMP relation in Figure 15.15 with an additional employee, ‘Brown’, who has three dependents (‘Jim’, ‘Joan’, and ‘Bob’) and works on four different projects (‘W’, ‘X’, ‘Y’, and ‘Z’). There are 16 tuples in EMP in Figure 16.4(a). If we decompose EMP into EMP_PROJECTS and EMP_DEPENDENTS, as shown in Figure 16.4(b), we need to store a total of only 11 tuples in both relations. Not only would the decomposition save on storage, but the update anomalies associated with multivalued dependencies would also be avoided. For example, if ‘Brown’ starts working on a new additional project ‘P,’ we must insert three tuples in EMP—one for each dependent. If we forget to insert any one of those, the relation violates the MVD and becomes inconsistent in that it incorrectly implies a relationship between project and dependent.
If the relation has nontrivial MVDs, then insert, delete, and update operations on single tuples may cause additional tuples to be modified besides the one in question. If the update is handled incorrectly, the meaning of the relation may change. However, after normalization into 4NF, these update anomalies disappear. For
570 Chapter 16 Relational Database Design Algorithms and Further Dependencies
example, to add the information that ‘Brown’ will be assigned to project ‘P’, only a single tuple need be inserted in the 4NF relation EMP_PROJECTS.
The EMP relation in Figure 15.15(a) is not in 4NF because it represents two independent 1:N relationships—one between employees and the projects they work on and the other between employees and their dependents. We sometimes have a relationship among three entities that depends on all three participating entities, such as the SUPPLY relation shown in Figure 15.15(c). (Consider only the tuples in Figure 15.5(c) above the dashed line for now.) In this case a tuple represents a sup- plier supplying a specific part to a particular project, so there are no nontrivial MVDs. Hence, the SUPPLY all-key relation is already in 4NF and should not be decomposed.
16.5.3 Nonadditive Join Decomposition into 4NF Relations Whenever we decompose a relation schema R into R1 = (X ∪ Y) and R2 = (R – Y) based on an MVD X →→ Y that holds in R, the decomposition has the nonadditive join property. It can be shown that this is a necessary and sufficient condition for decomposing a schema into two schemas that have the nonadditive join property, as given by Property NJB� that is a further generalization of Property NJB given earlier. Property NJB dealt with FDs only, whereas NJB� deals with both FDs and MVDs (recall that an FD is also an MVD).
Property NJB�. The relation schemas R1 and R2 form a nonadditive join decomposition of R with respect to a set F of functional and multivalued dependencies if and only if
(R1 ∩ R2) →→ (R1 – R2) or, by symmetry, if and only if
(R1 ∩ R2) →→ (R2 – R1).
We can use a slight modification of Algorithm 16.5 to develop Algorithm 16.7, which creates a nonadditive join decomposition into relation schemas that are in 4NF (rather than in BCNF). As with Algorithm 16.5, Algorithm 16.7 does not neces- sarily produce a decomposition that preserves FDs.
Algorithm 16.7. Relational Decomposition into 4NF Relations with Nonadditive Join Property
Input: A universal relation R and a set of functional and multivalued depend- encies F.
1. Set D:= { R };
2. While there is a relation schema Q in D that is not in 4NF, do { choose a relation schema Q in D that is not in 4NF;
find a nontrivial MVD X →→ Y in Q that violates 4NF; replace Q in D by two relation schemas (Q – Y) and (X ∪ Y);
};
16.6 Other Dependencies and Normal Forms 571
16.6 Other Dependencies and Normal Forms We already introduced another type of dependency called join dependency (JD) in Section 15.7. It arises when a relation is decomposable into a set of projected rela- tions that can be joined back to yield the original relation. After defining JD, we defined the fifth normal form based on it in Section 15.7. In the present section we will introduce some other types of dependencies that have been identified.
16.6.1 Inclusion Dependencies Inclusion dependencies were defined in order to formalize two types of interrela- tional constraints:
■ The foreign key (or referential integrity) constraint cannot be specified as a functional or multivalued dependency because it relates attributes across relations.
■ The constraint between two relations that represent a class/subclass relation- ship (see Chapters 8 and 9) also has no formal definition in terms of the functional, multivalued, and join dependencies.
Definition. An inclusion dependency R.X < S.Y between two sets of attrib- utes—X of relation schema R, and Y of relation schema S—specifies the con- straint that, at any specific time when r is a relation state of R and s a relation state of S, we must have
πX(r(R)) ⊆ πY(s(S))
The ⊆ (subset) relationship does not necessarily have to be a proper subset. Obviously, the sets of attributes on which the inclusion dependency is specified—X of R and Y of S—must have the same number of attributes. In addition, the domains for each pair of corresponding attributes should be compatible. For exam- ple, if X = {A1, A2, ..., An} and Y = {B1, B2, ..., Bn}, one possible correspondence is to have dom(Ai) compatible with dom(Bi) for 1 ≤ i ≤ n. In this case, we say that Ai corresponds to Bi.
For example, we can specify the following inclusion dependencies on the relational schema in Figure 15.1:
DEPARTMENT.Dmgr_ssn < EMPLOYEE.Ssn
WORKS_ON.Ssn < EMPLOYEE.Ssn
EMPLOYEE.Dnumber < DEPARTMENT.Dnumber
PROJECT.Dnum < DEPARTMENT.Dnumber
WORKS_ON.Pnumber < PROJECT.Pnumber
DEPT_LOCATIONS.Dnumber < DEPARTMENT.Dnumber
All the preceding inclusion dependencies represent referential integrity constraints. We can also use inclusion dependencies to represent class/subclass
572 Chapter 16 Relational Database Design Algorithms and Further Dependencies
relationships. For example, in the relational schema of Figure 9.6, we can specify the following inclusion dependencies:
EMPLOYEE.Ssn < PERSON.Ssn ALUMNUS.Ssn < PERSON.Ssn STUDENT.Ssn < PERSON.Ssn
As with other types of dependencies, there are inclusion dependency inference rules (IDIRs). The following are three examples:
IDIR1 (reflexivity): R.X < R.X.
IDIR2 (attribute correspondence): If R.X < S.Y, where X = {A1, A2, ..., An} and Y = {B1, B2, ..., Bn} and Ai corresponds to Bi, then R.Ai < S.Bi for 1 ≤ i ≤ n. IDIR3 (transitivity): If R.X < S.Y and S.Y < T.Z, then R.X < T.Z.
The preceding inference rules were shown to be sound and complete for inclusion dependencies. So far, no normal forms have been developed based on inclusion dependencies.
16.6.2 Template Dependencies Template dependencies provide a technique for representing constraints in relations that typically have no easy and formal definitions. No matter how many types of dependencies we develop, some peculiar constraint may come up based on the semantics of attributes within relations that cannot be represented by any of them. The idea behind template dependencies is to specify a template—or example—that defines each constraint or dependency.
There are two types of templates: tuple-generating templates and constraint- generating templates. A template consists of a number of hypothesis tuples that are meant to show an example of the tuples that may appear in one or more relations. The other part of the template is the template conclusion. For tuple-generating templates, the conclusion is a set of tuples that must also exist in the relations if the hypothesis tuples are there. For constraint-generating templates, the template con- clusion is a condition that must hold on the hypothesis tuples. Using constraint- generating templates, we are able to define semantic constraints—those that are beyond the scope of the relational model in terms of its data definition language and notation.
Figure 16.5 shows how we may define functional, multivalued, and inclusion dependencies by templates. Figure 16.6 shows how we may specify the constraint that an employee’s salary cannot be higher than the salary of his or her direct supervisor on the relation schema EMPLOYEE in Figure 3.5.
16.6 Other Dependencies and Normal Forms 573
(a)
X = {A, B}
Y = {C, D}
Hypothesis
Conclusion
a1 b1 c1
c1 = c2 and d1 = d2
a1 b1 c2
(b) R = {A, B, C, D}
R = {A, B, C, D}
X = {A, B} Y = {C}
X = {C, D}
Y = {E, F}
Hypothesis
Conclusion
(c)
Hypothesis
S = {E, F, G}
Conclusion
a1 b1 c1 d1
a1 b1 c1 d1
c1 d1 g
a1 b1 c2 d2
a1 b1 c1
d1 a1 b1
c2 d2
R = {A, B, C, D}
d1 d2
Figure 16.5 Templates for some common type of dependencies. (a) Template for functional dependency X → Y. (b) Template for the multivalued dependency X →→ Y. (c) Template for the inclusion dependency R.X < S.Y.
EMPLOYEE = {Name, Ssn, . . . , Salary, Supervisor_ssn}
Hypothesis
Conclusion
a b c d
e f g
c < f
d
Figure 16.6 Templates for the constraint that an employee’s salary must be less than the supervisor’s salary.
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16.6.3 Functional Dependencies Based on Arithmetic Functions and Procedures
Sometimes some attributes in a relation may be related via some arithmetic func- tion or a more complicated functional relationship. As long as a unique value of Y is associated with every X, we can still consider that the FD X → Y exists. For example, in the relation
ORDER_LINE (Order#, Item#, Quantity, Unit_price, Extended_price, Discounted_price)
each tuple represents an item from an order with a particular quantity, and the price per unit for that item. In this relation, (Quantity, Unit_price ) → Extended_price by the formula
Extended_price = Unit_price * Quantity.
Hence, there is a unique value for Extended_price for every pair (Quantity, Unit_price ), and thus it conforms to the definition of functional dependency.
Moreover, there may be a procedure that takes into account the quantity discounts, the type of item, and so on and computes a discounted price for the total quantity ordered for that item. Therefore, we can say
(Item#, Quantity, Unit_price ) → Discounted_price, or (Item#, Quantity, Extended_price) → Discounted_price.
To check the above FD, a more complex procedure COMPUTE_TOTAL_PRICE may have to be called into play. Although the above kinds of FDs are technically present in most relations, they are not given particular attention during normalization.
16.6.4 Domain-Key Normal Form There is no hard-and-fast rule about defining normal forms only up to 5NF. Historically, the process of normalization and the process of discovering undesir- able dependencies were carried through 5NF, but it has been possible to define stricter normal forms that take into account additional types of dependencies and constraints. The idea behind domain-key normal form (DKNF) is to specify (theo- retically, at least) the ultimate normal form that takes into account all possible types of dependencies and constraints. A relation schema is said to be in DKNF if all con- straints and dependencies that should hold on the valid relation states can be enforced simply by enforcing the domain constraints and key constraints on the relation. For a relation in DKNF, it becomes very straightforward to enforce all data- base constraints by simply checking that each attribute value in a tuple is of the appropriate domain and that every key constraint is enforced.
However, because of the difficulty of including complex constraints in a DKNF rela- tion, its practical utility is limited, since it may be quite difficult to specify general integrity constraints. For example, consider a relation CAR(Make, Vin#) (where Vin# is the vehicle identification number) and another relation MANUFACTURE(Vin#,
16.7 Summary 575
Country) (where Country is the country of manufacture). A general constraint may be of the following form: If the Make is either ‘Toyota’ or ‘Lexus,’ then the first character of the Vin# is a ‘J’ if the country of manufacture is ‘Japan’; if the Make is ‘Honda’ or ‘Acura,’ the second character of the Vin# is a ‘J’ if the country of manufacture is ‘Japan.’ There is no simplified way to represent such constraints short of writing a proce- dure (or general assertions) to test them. The procedure COMPUTE_TOTAL_PRICE above is an example of such procedures needed to enforce an appropriate integrity constraint.
16.7 Summary In this chapter we presented a further set of topics related to dependencies, a discus- sion of decomposition, and several algorithms related to them as well as to normal- ization. In Section 16.1 we presented inference rules for functional dependencies (FDs), the notion of closure of an attribute, closure of a set of functional dependen- cies, equivalence among sets of functional dependencies, and algorithms for finding the closure of an attribute (Algorithm 16.1) and the minimal cover of a set of FDs (Algorithm 16.2). We then discussed two important properties of decompositions: the nonadditive join property and the dependency-preserving property. An algo- rithm to test for nonadditive decomposition (Algorithm 16.3), and a simpler test for checking the losslessness of binary decompositions (Property NJB) were described. We then discussed relational design by synthesis, based on a set of given functional dependencies. The relational synthesis algorithms (such as Algorithms 16.4 and 16.6) create 3NF relations from a universal relation schema based on a given set of func- tional dependencies that has been specified by the database designer. The relational decomposition algorithms (such as Algorithms 16.5 and 16.7) create BCNF (or 4NF) relations by successive nonadditive decomposition of unnormalized relations into two component relations at a time. We saw that it is possible to synthesize 3NF rela- tion schemas that meet both of the above properties; however, in the case of BCNF, it is possible to aim only for the nonadditiveness of joins—dependency preservation cannot be necessarily guaranteed. If the designer has to aim for one of these two, the nonadditive join condition is an absolute must. In Section 16.4 we showed how cer- tain difficulties arise in a collection of relations due to null values that may exist in relations in spite of the relations being individually in 3NF or BCNF. Sometimes when decomposition is improperly carried too far, certain “dangling tuples” may result that do not participate in results of joins and hence may become invisible. We also showed how it is possible to have alternative designs that meet a given desired normal form.
Then we revisited multivalued dependencies (MVDs) in Section 16.5, which arise from an improper combination of two or more independent multivalued attributes in the same relation, and that result in a combinational expansion of the tuples used to define fourth normal form (4NF). We discussed inference rules applicable to MVDs and discussed the importance of 4NF. Finally, in Section 16.6 we discussed inclusion dependencies, which are used to specify referential integrity and class/subclass constraints, and template dependencies, which can be used to specify
576 Chapter 16 Relational Database Design Algorithms and Further Dependencies
arbitrary types of constraints. We pointed out the need for arithmetic functions or more complex procedures to enforce certain functional dependency constraints. We concluded with a brief discussion of the domain-key normal form (DKNF).
Review Questions 16.1. What is the role of Armstrong’s inference rules (inference rules IR1 through
IR3) in the development of the theory of relational design?
16.2. What is meant by the completeness and soundness of Armstrong’s inference rules?
16.3. What is meant by the closure of a set of functional dependencies? Illustrate with an example.
16.4. When are two sets of functional dependencies equivalent? How can we determine their equivalence?
16.5. What is a minimal set of functional dependencies? Does every set of depen- dencies have a minimal equivalent set? Is it always unique?
16.6. What is meant by the attribute preservation condition on a decomposition?
16.7. Why are normal forms alone insufficient as a condition for a good schema design?
16.8. What is the dependency preservation property for a decomposition? Why is it important?
16.9. Why can we not guarantee that BCNF relation schemas will be produced by dependency-preserving decompositions of non-BCNF relation schemas? Give a counterexample to illustrate this point.
16.10. What is the lossless (or nonadditive) join property of a decomposition? Why is it important?
16.11. Between the properties of dependency preservation and losslessness, which one must definitely be satisfied? Why?
16.12. Discuss the NULL value and dangling tuple problems.
16.13. Illustrate how the process of creating first normal form relations may lead to multivalued dependencies. How should the first normalization be done properly so that MVDs are avoided?
16.14. What types of constraints are inclusion dependencies meant to represent?
16.15. How do template dependencies differ from the other types of dependencies we discussed?
16.16. Why is the domain-key normal form (DKNF) known as the ultimate normal form?
Exercises 577
Exercises 16.17. Show that the relation schemas produced by Algorithm 16.4 are in 3NF.
16.18. Show that, if the matrix S resulting from Algorithm 16.3 does not have a row that is all a symbols, projecting S on the decomposition and joining it back will always produce at least one spurious tuple.
16.19. Show that the relation schemas produced by Algorithm 16.5 are in BCNF.
16.20. Show that the relation schemas produced by Algorithm 16.6 are in 3NF.
16.21. Specify a template dependency for join dependencies.
16.22. Specify all the inclusion dependencies for the relational schema in Figure 3.5.
16.23. Prove that a functional dependency satisfies the formal definition of multi- valued dependency.
16.24. Consider the example of normalizing the LOTS relation in Sections 15.4 and 15.5. Determine whether the decomposition of LOTS into {LOTS1AX, LOTS1AY, LOTS1B, LOTS2} has the lossless join property, by applying Algorithm 16.3 and also by using the test under Property NJB.
16.25. Show how the MVDs Ename →→ Pname and Ename →→ Dname in Figure 15.15(a) may arise during normalization into 1NF of a relation, where the attributes Pname and Dname are multivalued.
16.26. Apply Algorithm 16.2(a) to the relation in Exercise 15.24 to determine a key for R. Create a minimal set of dependencies G that is equivalent to F, and apply the synthesis algorithm (Algorithm 16.6) to decompose R into 3NF relations.
16.27. Repeat Exercise 16.26 for the functional dependencies in Exercise 15.25.
16.28. Apply the decomposition algorithm (Algorithm 16.5) to the relation R and the set of dependencies F in Exercise 15.24. Repeat for the dependencies G in Exercise 15.25.
16.29. Apply Algorithm 16.2(a) to the relations in Exercises 15.27 and 15.28 to determine a key for R. Apply the synthesis algorithm (Algorithm 16.6) to decompose R into 3NF relations and the decomposition algorithm (Algorithm 16.5) to decompose R into BCNF relations.
16.30. Write programs that implement Algorithms 16.5 and 16.6.
16.31. Consider the following decompositions for the relation schema R of Exercise 15.24. Determine whether each decomposition has (1) the dependency preservation property, and (2) the lossless join property, with respect to F. Also determine which normal form each relation in the decomposition is in.
a. D1 = {R1, R2, R3, R4, R5} ; R1 = {A, B, C} , R2 = {A, D, E} , R3 = {B, F} , R4 = {F, G, H} , R5 = {D, I, J}
578 Chapter 16 Relational Database Design Algorithms and Further Dependencies
b. D2 = {R1, R2, R3} ; R1 = {A, B, C, D, E} , R2 = {B, F, G, H} , R3 = {D, I, J}
c. D3 = {R1, R2, R3, R4, R5} ; R1 = {A, B, C, D} , R2 = {D, E} , R3 = {B, F} , R4 = {F, G, H} , R5 = {D, I, J}
16.32. Consider the relation REFRIG(Model#, Year, Price, Manuf_plant, Color), which is abbreviated as REFRIG(M, Y, P, MP, C), and the following set F of func- tional dependencies: F = {M → MP, {M, Y} → P, MP → C} a. Evaluate each of the following as a candidate key for REFRIG, giving rea-
sons why it can or cannot be a key: {M}, {M, Y}, {M, C}.
b. Based on the above key determination, state whether the relation REFRIG is in 3NF and in BCNF, giving proper reasons.
c. Consider the decomposition of REFRIG into D = {R1(M, Y, P), R2(M, MP, C)}. Is this decomposition lossless? Show why. (You may consult the test under Property NJB in Section 16.2.4.)
Laboratory Exercises Note: These exercises use the DBD (Data Base Designer) system that is described in the laboratory manual. The relational schema R and set of functional dependencies F need to be coded as lists. As an example, R and F for problem 15.24 are coded as:
R = [a, b, c, d, e, f, g, h, i, j] F = [[[a, b],[c]],
[[a],[d, e]], [[b],[f]], [[f],[g, h]], [[d],[i, j]]]
Since DBD is implemented in Prolog, use of uppercase terms is reserved for vari- ables in the language and therefore lowercase constants are used to code the attrib- utes. For further details on using the DBD system, please refer to the laboratory manual.
16.33. Using the DBD system, verify your answers to the following exercises:
a. 16.24
b. 16.26
c. 16.27
d. 16.28
e. 16.29
f. 16.31 (a) and (b)
g. 16.32 (a) and (c)
Selected Bibliography 579
Selected Bibliography The books by Maier (1983) and Atzeni and De Antonellis (1993) include a compre- hensive discussion of relational dependency theory. The decomposition algorithm (Algorithm 16.5) is due to Bernstein (1976). Algorithm 16.6 is based on the normal- ization algorithm presented in Biskup et al. (1979). Tsou and Fischer (1982) give a polynomial-time algorithm for BCNF decomposition.
The theory of dependency preservation and lossless joins is given in Ullman (1988), where proofs of some of the algorithms discussed here appear. The lossless join property is analyzed in Aho et al. (1979). Algorithms to determine the keys of a rela- tion from functional dependencies are given in Osborn (1977); testing for BCNF is discussed in Osborn (1979). Testing for 3NF is discussed in Tsou and Fischer (1982). Algorithms for designing BCNF relations are given in Wang (1990) and Hernandez and Chan (1991).
Multivalued dependencies and fourth normal form are defined in Zaniolo (1976) and Nicolas (1978). Many of the advanced normal forms are due to Fagin: the fourth normal form in Fagin (1977), PJNF in Fagin (1979), and DKNF in Fagin (1981). The set of sound and complete rules for functional and multivalued dependencies was given by Beeri et al. (1977). Join dependencies are discussed by Rissanen (1977) and Aho et al. (1979). Inference rules for join dependencies are given by Sciore (1982). Inclusion dependencies are discussed by Casanova et al. (1981) and analyzed further in Cosmadakis et al. (1990). Their use in optimizing relational schemas is discussed in Casanova et al. (1989). Template dependencies are discussed by Sadri and Ullman (1982). Other dependencies are discussed in Nicolas (1978), Furtado (1978), and Mendelzon and Maier (1979). Abiteboul et al. (1995) provides a theoretical treatment of many of the ideas presented in this chapter and Chapter 15.
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part 7 File Structures, Indexing,
and Hashing
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583
Disk Storage, Basic File Structures, and Hashing
Databases are stored physically as files of records,which are typically stored on magnetic disks. This chapter and the next deal with the organization of databases in storage and the tech- niques for accessing them efficiently using various algorithms, some of which require auxiliary data structures called indexes. These structures are often referred to as physical database file structures, and are at the physical level of the three- schema architecture described in Chapter 2. We start in Section 17.1 by introducing the concepts of computer storage hierarchies and how they are used in database sys- tems. Section 17.2 is devoted to a description of magnetic disk storage devices and their characteristics, and we also briefly describe magnetic tape storage devices. After discussing different storage technologies, we turn our attention to the meth- ods for physically organizing data on disks. Section 17.3 covers the technique of double buffering, which is used to speed retrieval of multiple disk blocks. In Section 17.4 we discuss various ways of formatting and storing file records on disk. Section 17.5 discusses the various types of operations that are typically applied to file records. We present three primary methods for organizing file records on disk: unordered records, in Section 17.6; ordered records, in Section 17.7; and hashed records, in Section 17.8.
Section 17.9 briefly introduces files of mixed records and other primary methods for organizing records, such as B-trees. These are particularly relevant for storage of object-oriented databases, which we discussed in Chapter 11. Section 17.10 describes RAID (Redundant Arrays of Inexpensive (or Independent) Disks)—a data storage system architecture that is commonly used in large organizations for better reliability and performance. Finally, in Section 17.11 we describe three devel- opments in the storage systems area: storage area networks (SAN), network-
17chapter 17
584 Chapter 17 Disk Storage, Basic File Structures, and Hashing
attached storage (NAS), and iSCSI (Internet SCSI—Small Computer System Interface), the latest technology, which makes storage area networks more afford- able without the use of the Fiber Channel infrastructure and hence is getting very wide acceptance in industry. Section 17.12 summarizes the chapter. In Chapter 18 we discuss techniques for creating auxiliary data structures, called indexes, which speed up the search for and retrieval of records. These techniques involve storage of auxiliary data, called index files, in addition to the file records themselves.
Chapters 17 and 18 may be browsed through or even omitted by readers who have already studied file organizations and indexing in a separate course. The material covered here, in particular Sections 17.1 through 17.8, is necessary for understand- ing Chapters 19 and 20, which deal with query processing and optimization, and database tuning for improving performance of queries.
17.1 Introduction The collection of data that makes up a computerized database must be stored phys- ically on some computer storage medium. The DBMS software can then retrieve, update, and process this data as needed. Computer storage media form a storage hierarchy that includes two main categories:
■ Primary storage. This category includes storage media that can be operated on directly by the computer’s central processing unit (CPU), such as the com- puter’s main memory and smaller but faster cache memories. Primary stor- age usually provides fast access to data but is of limited storage capacity. Although main memory capacities have been growing rapidly in recent years, they are still more expensive and have less storage capacity than sec- ondary and tertiary storage devices.
■ Secondary and tertiary storage. This category includes magnetic disks, optical disks (CD-ROMs, DVDs, and other similar storage media), and tapes. Hard-disk drives are classified as secondary storage, whereas remov- able media such as optical disks and tapes are considered tertiary storage. These devices usually have a larger capacity, cost less, and provide slower access to data than do primary storage devices. Data in secondary or tertiary storage cannot be processed directly by the CPU; first it must be copied into primary storage and then processed by the CPU.
We first give an overview of the various storage devices used for primary and sec- ondary storage in Section 17.1.1 and then discuss how databases are typically han- dled in the storage hierarchy in Section 17.1.2.
17.1.1 Memory Hierarchies and Storage Devices In a modern computer system, data resides and is transported throughout a hierar- chy of storage media. The highest-speed memory is the most expensive and is there- fore available with the least capacity. The lowest-speed memory is offline tape storage, which is essentially available in indefinite storage capacity.
17.1 Introduction 585
At the primary storage level, the memory hierarchy includes at the most expensive end, cache memory, which is a static RAM (Random Access Memory). Cache mem- ory is typically used by the CPU to speed up execution of program instructions using techniques such as prefetching and pipelining. The next level of primary stor- age is DRAM (Dynamic RAM), which provides the main work area for the CPU for keeping program instructions and data. It is popularly called main memory. The advantage of DRAM is its low cost, which continues to decrease; the drawback is its volatility1 and lower speed compared with static RAM. At the secondary and tertiary storage level, the hierarchy includes magnetic disks, as well as mass storage in the form of CD-ROM (Compact Disk–Read-Only Memory) and DVD (Digital Video Disk or Digital Versatile Disk) devices, and finally tapes at the least expensive end of the hierarchy. The storage capacity is measured in kilobytes (Kbyte or 1000 bytes), megabytes (MB or 1 million bytes), gigabytes (GB or 1 billion bytes), and even ter- abytes (1000 GB). The word petabyte (1000 terabytes or 10**15 bytes) is now becoming relevant in the context of very large repositories of data in physics, astronomy, earth sciences, and other scientific applications.
Programs reside and execute in DRAM. Generally, large permanent databases reside on secondary storage, (magnetic disks), and portions of the database are read into and written from buffers in main memory as needed. Nowadays, personal comput- ers and workstations have large main memories of hundreds of megabytes of RAM and DRAM, so it is becoming possible to load a large part of the database into main memory. Eight to 16 GB of main memory on a single server is becoming common- place. In some cases, entire databases can be kept in main memory (with a backup copy on magnetic disk), leading to main memory databases; these are particularly useful in real-time applications that require extremely fast response times. An example is telephone switching applications, which store databases that contain routing and line information in main memory.
Between DRAM and magnetic disk storage, another form of memory, flash mem- ory, is becoming common, particularly because it is nonvolatile. Flash memories are high-density, high-performance memories using EEPROM (Electrically Erasable Programmable Read-Only Memory) technology. The advantage of flash memory is the fast access speed; the disadvantage is that an entire block must be erased and written over simultaneously. Flash memory cards are appearing as the data storage medium in appliances with capacities ranging from a few megabytes to a few giga- bytes. These are appearing in cameras, MP3 players, cell phones, PDAs, and so on. USB (Universal Serial Bus) flash drives have become the most portable medium for carrying data between personal computers; they have a flash memory storage device integrated with a USB interface.
CD-ROM (Compact Disk – Read Only Memory) disks store data optically and are read by a laser. CD-ROMs contain prerecorded data that cannot be overwritten. WORM (Write-Once-Read-Many) disks are a form of optical storage used for
1Volatile memory typically loses its contents in case of a power outage, whereas nonvolatile memory does not.
586 Chapter 17 Disk Storage, Basic File Structures, and Hashing
archiving data; they allow data to be written once and read any number of times without the possibility of erasing. They hold about half a gigabyte of data per disk and last much longer than magnetic disks.2 Optical jukebox memories use an array of CD-ROM platters, which are loaded onto drives on demand. Although optical jukeboxes have capacities in the hundreds of gigabytes, their retrieval times are in the hundreds of milliseconds, quite a bit slower than magnetic disks. This type of storage is continuing to decline because of the rapid decrease in cost and increase in capacities of magnetic disks. The DVD is another standard for optical disks allowing 4.5 to 15 GB of storage per disk. Most personal computer disk drives now read CD- ROM and DVD disks. Typically, drives are CD-R (Compact Disk Recordable) that can create CD-ROMs and audio CDs (Compact Disks), as well as record on DVDs.
Finally, magnetic tapes are used for archiving and backup storage of data. Tape jukeboxes—which contain a bank of tapes that are catalogued and can be automat- ically loaded onto tape drives—are becoming popular as tertiary storage to hold terabytes of data. For example, NASA’s EOS (Earth Observation Satellite) system stores archived databases in this fashion.
Many large organizations are already finding it normal to have terabyte-sized data- bases. The term very large database can no longer be precisely defined because disk storage capacities are on the rise and costs are declining. Very soon the term may be reserved for databases containing tens of terabytes.
17.1.2 Storage of Databases Databases typically store large amounts of data that must persist over long periods of time, and hence is often referred to as persistent data. Parts of this data are accessed and processed repeatedly during this period. This contrasts with the notion of transient data that persist for only a limited time during program execution. Most databases are stored permanently (or persistently) on magnetic disk secondary storage, for the following reasons:
■ Generally, databases are too large to fit entirely in main memory.
■ The circumstances that cause permanent loss of stored data arise less fre- quently for disk secondary storage than for primary storage. Hence, we refer to disk—and other secondary storage devices—as nonvolatile storage, whereas main memory is often called volatile storage.
■ The cost of storage per unit of data is an order of magnitude less for disk sec- ondary storage than for primary storage.
Some of the newer technologies—such as optical disks, DVDs, and tape juke- boxes—are likely to provide viable alternatives to the use of magnetic disks. In the future, databases may therefore reside at different levels of the memory hierarchy from those described in Section 17.1.1. However, it is anticipated that magnetic
2Their rotational speeds are lower (around 400 rpm), giving higher latency delays and low transfer rates (around 100 to 200 KB/second).
17.2 Secondary Storage Devices 587
disks will continue to be the primary medium of choice for large databases for years to come. Hence, it is important to study and understand the properties and charac- teristics of magnetic disks and the way data files can be organized on disk in order to design effective databases with acceptable performance.
Magnetic tapes are frequently used as a storage medium for backing up databases because storage on tape costs even less than storage on disk. However, access to data on tape is quite slow. Data stored on tapes is offline; that is, some intervention by an operator—or an automatic loading device—to load a tape is needed before the data becomes available. In contrast, disks are online devices that can be accessed directly at any time.
The techniques used to store large amounts of structured data on disk are impor- tant for database designers, the DBA, and implementers of a DBMS. Database designers and the DBA must know the advantages and disadvantages of each stor- age technique when they design, implement, and operate a database on a specific DBMS. Usually, the DBMS has several options available for organizing the data. The process of physical database design involves choosing the particular data organiza- tion techniques that best suit the given application requirements from among the options. DBMS system implementers must study data organization techniques so that they can implement them efficiently and thus provide the DBA and users of the DBMS with sufficient options.
Typical database applications need only a small portion of the database at a time for processing. Whenever a certain portion of the data is needed, it must be located on disk, copied to main memory for processing, and then rewritten to the disk if the data is changed. The data stored on disk is organized as files of records. Each record is a collection of data values that can be interpreted as facts about entities, their attributes, and their relationships. Records should be stored on disk in a manner that makes it possible to locate them efficiently when they are needed.
There are several primary file organizations, which determine how the file records are physically placed on the disk, and hence how the records can be accessed. A heap file (or unordered file) places the records on disk in no particular order by appending new records at the end of the file, whereas a sorted file (or sequential file) keeps the records ordered by the value of a particular field (called the sort key). A hashed file uses a hash function applied to a particular field (called the hash key) to determine a record’s placement on disk. Other primary file organizations, such as B-trees, use tree structures. We discuss primary file organizations in Sections 17.6 through 17.9. A secondary organization or auxiliary access structure allows efficient access to file records based on alternate fields than those that have been used for the primary file organization. Most of these exist as indexes and will be discussed in Chapter 18.
17.2 Secondary Storage Devices In this section we describe some characteristics of magnetic disk and magnetic tape storage devices. Readers who have already studied these devices may simply browse through this section.
588 Chapter 17 Disk Storage, Basic File Structures, and Hashing
Actuator movement
Track
ArmActuator Read/write
head Spindle Disk rotation
Cylinder of tracks (imaginary)
(a)
(b)
Figure 17.1 (a) A single-sided disk with read/write hardware. (b) A disk pack with read/write hardware.
17.2.1 Hardware Description of Disk Devices Magnetic disks are used for storing large amounts of data. The most basic unit of data on the disk is a single bit of information. By magnetizing an area on disk in cer- tain ways, one can make it represent a bit value of either 0 (zero) or 1 (one). To code information, bits are grouped into bytes (or characters). Byte sizes are typically 4 to 8 bits, depending on the computer and the device. We assume that one character is stored in a single byte, and we use the terms byte and character interchangeably. The capacity of a disk is the number of bytes it can store, which is usually very large. Small floppy disks used with microcomputers typically hold from 400 KB to 1.5 MB; they are rapidly going out of circulation. Hard disks for personal computers typically hold from several hundred MB up to tens of GB; and large disk packs used with servers and mainframes have capacities of hundreds of GB. Disk capacities continue to grow as technology improves.
Whatever their capacity, all disks are made of magnetic material shaped as a thin circular disk, as shown in Figure 17.1(a), and protected by a plastic or acrylic cover.
17.2 Secondary Storage Devices 589
Track(a) Sector (arc of track)
(b)
Three sectors
Two sectors One sector
Figure 17.2 Different sector organ- izations on disk. (a) Sectors subtending a fixed angle. (b) Sectors maintaining a uniform recording density.
A disk is single-sided if it stores information on one of its surfaces only and double- sided if both surfaces are used. To increase storage capacity, disks are assembled into a disk pack, as shown in Figure 17.1(b), which may include many disks and there- fore many surfaces. Information is stored on a disk surface in concentric circles of small width,3 each having a distinct diameter. Each circle is called a track. In disk packs, tracks with the same diameter on the various surfaces are called a cylinder because of the shape they would form if connected in space. The concept of a cylin- der is important because data stored on one cylinder can be retrieved much faster than if it were distributed among different cylinders.
The number of tracks on a disk ranges from a few hundred to a few thousand, and the capacity of each track typically ranges from tens of Kbytes to 150 Kbytes. Because a track usually contains a large amount of information, it is divided into smaller blocks or sectors. The division of a track into sectors is hard-coded on the disk surface and cannot be changed. One type of sector organization, as shown in Figure 17.2(a), calls a portion of a track that subtends a fixed angle at the center a sector. Several other sector organizations are possible, one of which is to have the sectors subtend smaller angles at the center as one moves away, thus maintaining a uniform density of recording, as shown in Figure 17.2(b). A technique called ZBR (Zone Bit Recording) allows a range of cylinders to have the same number of sectors per arc. For example, cylinders 0–99 may have one sector per track, 100–199 may have two per track, and so on. Not all disks have their tracks divided into sectors.
The division of a track into equal-sized disk blocks (or pages) is set by the operat- ing system during disk formatting (or initialization). Block size is fixed during ini- tialization and cannot be changed dynamically. Typical disk block sizes range from 512 to 8192 bytes. A disk with hard-coded sectors often has the sectors subdivided into blocks during initialization. Blocks are separated by fixed-size interblock gaps, which include specially coded control information written during disk initializa- tion. This information is used to determine which block on the track follows each
3In some disks, the circles are now connected into a kind of continuous spiral.
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Table 17.1 Specifications of Typical High-End Cheetah Disks from Seagate
Description Cheetah 15K.6 Cheetah NS 10K Model Number ST3450856SS/FC ST3400755FC Height 25.4 mm 26.11 mm Width 101.6 mm 101.85 mm Length 146.05 mm 147 mm Weight 0.709 kg 0.771 kg
Capacity Formatted Capacity 450 Gbytes 400 Gbytes
Configuration Number of disks (physical) 4 4 Number of heads (physical) 8 8
Performance
Transfer Rates Internal Transfer Rate (min) 1051 Mb/sec Internal Transfer Rate (max) 2225 Mb/sec 1211 Mb/sec Mean Time Between Failure (MTBF) 1.4 M hours
Seek Times Avg. Seek Time (Read) 3.4 ms (typical) 3.9 ms (typical) Avg. Seek Time (Write) 3.9 ms (typical) 4.2 ms (typical) Track-to-track, Seek, Read 0.2 ms (typical) 0.35 ms (typical) Track-to-track, Seek, Write 0.4 ms (typical) 0.35 ms (typical) Average Latency 2 ms 2.98 msec
Courtesy Seagate Technology
interblock gap. Table 17.1 illustrates the specifications of typical disks used on large servers in industry. The 10K and 15K prefixes on disk names refer to the rotational speeds in rpm (revolutions per minute).
There is continuous improvement in the storage capacity and transfer rates associ- ated with disks; they are also progressively getting cheaper—currently costing only a fraction of a dollar per megabyte of disk storage. Costs are going down so rapidly that costs as low 0.025 cent/MB—which translates to $0.25/GB and $250/TB—are already here.
A disk is a random access addressable device. Transfer of data between main memory and disk takes place in units of disk blocks. The hardware address of a block—a combination of a cylinder number, track number (surface number within the cylin- der on which the track is located), and block number (within the track) is supplied to the disk I/O (input/output) hardware. In many modern disk drives, a single num- ber called LBA (Logical Block Address), which is a number between 0 and n (assum- ing the total capacity of the disk is n + 1 blocks), is mapped automatically to the right block by the disk drive controller. The address of a buffer—a contiguous
reserved area in main storage that holds one disk block—is also provided. For a read command, the disk block is copied into the buffer; whereas for a write com- mand, the contents of the buffer are copied into the disk block. Sometimes several contiguous blocks, called a cluster, may be transferred as a unit. In this case, the buffer size is adjusted to match the number of bytes in the cluster.
The actual hardware mechanism that reads or writes a block is the disk read/write head, which is part of a system called a disk drive. A disk or disk pack is mounted in the disk drive, which includes a motor that rotates the disks. A read/write head includes an electronic component attached to a mechanical arm. Disk packs with multiple surfaces are controlled by several read/write heads—one for each surface, as shown in Figure 17.1(b). All arms are connected to an actuator attached to another electrical motor, which moves the read/write heads in unison and positions them precisely over the cylinder of tracks specified in a block address.
Disk drives for hard disks rotate the disk pack continuously at a constant speed (typically ranging between 5,400 and 15,000 rpm). Once the read/write head is positioned on the right track and the block specified in the block address moves under the read/write head, the electronic component of the read/write head is acti- vated to transfer the data. Some disk units have fixed read/write heads, with as many heads as there are tracks. These are called fixed-head disks, whereas disk units with an actuator are called movable-head disks. For fixed-head disks, a track or cylinder is selected by electronically switching to the appropriate read/write head rather than by actual mechanical movement; consequently, it is much faster. However, the cost of the additional read/write heads is quite high, so fixed-head disks are not com- monly used.
A disk controller, typically embedded in the disk drive, controls the disk drive and interfaces it to the computer system. One of the standard interfaces used today for disk drives on PCs and workstations is called SCSI (Small Computer System Interface). The controller accepts high-level I/O commands and takes appropriate action to position the arm and causes the read/write action to take place. To transfer a disk block, given its address, the disk controller must first mechanically position the read/write head on the correct track. The time required to do this is called the seek time. Typical seek times are 5 to 10 msec on desktops and 3 to 8 msecs on servers. Following that, there is another delay—called the rotational delay or latency—while the beginning of the desired block rotates into position under the read/write head. It depends on the rpm of the disk. For example, at 15,000 rpm, the time per rotation is 4 msec and the average rotational delay is the time per half rev- olution, or 2 msec. At 10,000 rpm the average rotational delay increases to 3 msec. Finally, some additional time is needed to transfer the data; this is called the block transfer time. Hence, the total time needed to locate and transfer an arbitrary block, given its address, is the sum of the seek time, rotational delay, and block transfer time. The seek time and rotational delay are usually much larger than the block transfer time. To make the transfer of multiple blocks more efficient, it is common to transfer several consecutive blocks on the same track or cylinder. This eliminates the seek time and rotational delay for all but the first block and can result
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in a substantial saving of time when numerous contiguous blocks are transferred. Usually, the disk manufacturer provides a bulk transfer rate for calculating the time required to transfer consecutive blocks. Appendix B contains a discussion of these and other disk parameters.
The time needed to locate and transfer a disk block is in the order of milliseconds, usually ranging from 9 to 60 msec. For contiguous blocks, locating the first block takes from 9 to 60 msec, but transferring subsequent blocks may take only 0.4 to 2 msec each. Many search techniques take advantage of consecutive retrieval of blocks when searching for data on disk. In any case, a transfer time in the order of millisec- onds is considered quite high compared with the time required to process data in main memory by current CPUs. Hence, locating data on disk is a major bottleneck in database applications. The file structures we discuss here and in Chapter 18 attempt to minimize the number of block transfers needed to locate and transfer the required data from disk to main memory. Placing “related information” on contiguous blocks is the basic goal of any storage organization on disk.
17.2.2 Magnetic Tape Storage Devices Disks are random access secondary storage devices because an arbitrary disk block may be accessed at random once we specify its address. Magnetic tapes are sequen- tial access devices; to access the nth block on tape, first we must scan the preceding n – 1 blocks. Data is stored on reels of high-capacity magnetic tape, somewhat sim- ilar to audiotapes or videotapes. A tape drive is required to read the data from or write the data to a tape reel. Usually, each group of bits that forms a byte is stored across the tape, and the bytes themselves are stored consecutively on the tape.
A read/write head is used to read or write data on tape. Data records on tape are also stored in blocks—although the blocks may be substantially larger than those for disks, and interblock gaps are also quite large. With typical tape densities of 1600 to 6250 bytes per inch, a typical interblock gap4 of 0.6 inch corresponds to 960 to 3750 bytes of wasted storage space. It is customary to group many records together in one block for better space utilization.
The main characteristic of a tape is its requirement that we access the data blocks in sequential order. To get to a block in the middle of a reel of tape, the tape is mounted and then scanned until the required block gets under the read/write head. For this reason, tape access can be slow and tapes are not used to store online data, except for some specialized applications. However, tapes serve a very important function—backing up the database. One reason for backup is to keep copies of disk files in case the data is lost due to a disk crash, which can happen if the disk read/write head touches the disk surface because of mechanical malfunction. For this reason, disk files are copied periodically to tape. For many online critical appli- cations, such as airline reservation systems, to avoid any downtime, mirrored sys- tems are used to keep three sets of identical disks—two in online operation and one
4Called interrecord gaps in tape terminology.
17.3 Buffering of Blocks 593
as backup. Here, offline disks become a backup device. The three are rotated so that they can be switched in case there is a failure on one of the live disk drives. Tapes can also be used to store excessively large database files. Database files that are seldom used or are outdated but required for historical record keeping can be archived on tape. Originally, half-inch reel tape drives were used for data storage employing the so-called 9 track tapes. Later, smaller 8-mm magnetic tapes (similar to those used in camcorders) that can store up to 50 GB, as well as 4-mm helical scan data cartridges and writable CDs and DVDs, became popular media for backing up data files from PCs and workstations. They are also used for storing images and system libraries.
Backing up enterprise databases so that no transaction information is lost is a major undertaking. Currently, tape libraries with slots for several hundred cartridges are used with Digital and Superdigital Linear Tapes (DLTs and SDLTs) having capacities in hundreds of gigabytes that record data on linear tracks. Robotic arms are used to write on multiple cartridges in parallel using multiple tape drives with automatic labeling software to identify the backup cartridges. An example of a giant library is the SL8500 model of Sun Storage Technology that can store up to 70 petabytes (petabyte = 1000 TB) of data using up to 448 drives with a maximum throughput rate of 193.2 TB/hour. We defer the discussion of disk storage technology called RAID, and of storage area networks, network-attached storage, and iSCSI storage systems to the end of the chapter.
17.3 Buffering of Blocks When several blocks need to be transferred from disk to main memory and all the block addresses are known, several buffers can be reserved in main memory to speed up the transfer. While one buffer is being read or written, the CPU can process data in the other buffer because an independent disk I/O processor (con- troller) exists that, once started, can proceed to transfer a data block between mem- ory and disk independent of and in parallel to CPU processing.
Figure 17.3 illustrates how two processes can proceed in parallel. Processes A and B are running concurrently in an interleaved fashion, whereas processes C and D are running concurrently in a parallel fashion. When a single CPU controls multiple processes, parallel execution is not possible. However, the processes can still run concurrently in an interleaved way. Buffering is most useful when processes can run concurrently in a parallel fashion, either because a separate disk I/O processor is available or because multiple CPU processors exist.
Figure 17.4 illustrates how reading and processing can proceed in parallel when the time required to process a disk block in memory is less than the time required to read the next block and fill a buffer. The CPU can start processing a block once its transfer to main memory is completed; at the same time, the disk I/O processor can be reading and transferring the next block into a different buffer. This technique is called double buffering and can also be used to read a continuous stream of blocks from disk to memory. Double buffering permits continuous reading or writing of data on consecutive disk blocks, which eliminates the seek time and rotational delay
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Interleaved concurrency of operations A and B
Parallel execution of operations C and D
t1
A A
B B
t2 t3 t4 Time
Figure 17.3 Interleaved concurrency versus parallel execution.
i + 1 Process B
i + 2 Fill A
Time
i Process A
i + 1 Fill B
Disk Block: I/O:
Disk Block: PROCESSING:
i Fill A
i + 2 Process A
i + 3 Fill A
i + 4 Process A
i + 3 Process B
i + 4 Fill A
Figure 17.4 Use of two buffers, A and B, for reading from disk.
for all but the first block transfer. Moreover, data is kept ready for processing, thus reducing the waiting time in the programs.
17.4 Placing File Records on Disk In this section, we define the concepts of records, record types, and files. Then we discuss techniques for placing file records on disk.
17.4.1 Records and Record Types Data is usually stored in the form of records. Each record consists of a collection of related data values or items, where each value is formed of one or more bytes and corresponds to a particular field of the record. Records usually describe entities and their attributes. For example, an EMPLOYEE record represents an employee entity, and each field value in the record specifies some attribute of that employee, such as Name, Birth_date, Salary, or Supervisor. A collection of field names and their corre-
17.4 Placing File Records on Disk 595
sponding data types constitutes a record type or record format definition. A data type, associated with each field, specifies the types of values a field can take.
The data type of a field is usually one of the standard data types used in program- ming. These include numeric (integer, long integer, or floating point), string of characters (fixed-length or varying), Boolean (having 0 and 1 or TRUE and FALSE values only), and sometimes specially coded date and time data types. The number of bytes required for each data type is fixed for a given computer system. An integer may require 4 bytes, a long integer 8 bytes, a real number 4 bytes, a Boolean 1 byte, a date 10 bytes (assuming a format of YYYY-MM-DD), and a fixed-length string of k characters k bytes. Variable-length strings may require as many bytes as there are characters in each field value. For example, an EMPLOYEE record type may be defined—using the C programming language notation—as the following structure:
struct employee{ char name[30]; char ssn[9]; int salary; int job_code; char department[20];
} ;
In some database applications, the need may arise for storing data items that consist of large unstructured objects, which represent images, digitized video or audio streams, or free text. These are referred to as BLOBs (binary large objects). A BLOB data item is typically stored separately from its record in a pool of disk blocks, and a pointer to the BLOB is included in the record.
17.4.2 Files, Fixed-Length Records, and Variable-Length Records
A file is a sequence of records. In many cases, all records in a file are of the same record type. If every record in the file has exactly the same size (in bytes), the file is said to be made up of fixed-length records. If different records in the file have dif- ferent sizes, the file is said to be made up of variable-length records. A file may have variable-length records for several reasons:
■ The file records are of the same record type, but one or more of the fields are of varying size (variable-length fields). For example, the Name field of EMPLOYEE can be a variable-length field.
■ The file records are of the same record type, but one or more of the fields may have multiple values for individual records; such a field is called a repeating field and a group of values for the field is often called a repeating group.
■ The file records are of the same record type, but one or more of the fields are optional; that is, they may have values for some but not all of the file records (optional fields).
Name = Smith, John Ssn = 123456789 DEPARTMENT = Computer
Smith, John
Name
1
(a)
(b)
(c)
1 12 21 25 29
Name Ssn Salary Job_code Department Hire_date
31 40 44 48 68
Ssn Salary Job_code Department
Separator Characters123456789 XXXX XXXX Computer
Separator Characters
Separates field name from field value
Separates fields
Terminates record
=
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Figure 17.5 Three record storage formats. (a) A fixed-length record with six fields and size of 71 bytes. (b) A record with two variable-length fields and three fixed-length fields. (c) A variable-field record with three types of separator characters.
■ The file contains records of different record types and hence of varying size (mixed file). This would occur if related records of different types were clustered (placed together) on disk blocks; for example, the GRADE_REPORT records of a particular student may be placed following that STUDENT’s record.
The fixed-length EMPLOYEE records in Figure 17.5(a) have a record size of 71 bytes. Every record has the same fields, and field lengths are fixed, so the system can iden- tify the starting byte position of each field relative to the starting position of the record. This facilitates locating field values by programs that access such files. Notice that it is possible to represent a file that logically should have variable-length records as a fixed-length records file. For example, in the case of optional fields, we could have every field included in every file record but store a special NULL value if no value exists for that field. For a repeating field, we could allocate as many spaces in each record as the maximum possible number of occurrences of the field. In either case, space is wasted when certain records do not have values for all the physical spaces provided in each record. Now we consider other options for formatting records of a file of variable-length records.
17.4 Placing File Records on Disk 597
For variable-length fields, each record has a value for each field, but we do not know the exact length of some field values. To determine the bytes within a particular record that represent each field, we can use special separator characters (such as ? or % or $)—which do not appear in any field value—to terminate variable-length fields, as shown in Figure 17.5(b), or we can store the length in bytes of the field in the record, preceding the field value.
A file of records with optional fields can be formatted in different ways. If the total number of fields for the record type is large, but the number of fields that actually appear in a typical record is small, we can include in each record a sequence of <field-name, field-value> pairs rather than just the field values. Three types of sep- arator characters are used in Figure 17.5(c), although we could use the same separa- tor character for the first two purposes—separating the field name from the field value and separating one field from the next field. A more practical option is to assign a short field type code—say, an integer number—to each field and include in each record a sequence of <field-type, field-value> pairs rather than <field-name, field-value> pairs.
A repeating field needs one separator character to separate the repeating values of the field and another separator character to indicate termination of the field. Finally, for a file that includes records of different types, each record is preceded by a record type indicator. Understandably, programs that process files of variable- length records—which are usually part of the file system and hence hidden from the typical programmers—need to be more complex than those for fixed-length records, where the starting position and size of each field are known and fixed.5
17.4.3 Record Blocking and Spanned versus Unspanned Records
The records of a file must be allocated to disk blocks because a block is the unit of data transfer between disk and memory. When the block size is larger than the record size, each block will contain numerous records, although some files may have unusually large records that cannot fit in one block. Suppose that the block size is B bytes. For a file of fixed-length records of size R bytes, with B ≥ R, we can fit bfr = ⎣B/R⎦ records per block, where the ⎣(x)⎦ (floor function) rounds down the number x to an integer. The value bfr is called the blocking factor for the file. In general, R may not divide B exactly, so we have some unused space in each block equal to
B − (bfr * R) bytes To utilize this unused space, we can store part of a record on one block and the rest on another. A pointer at the end of the first block points to the block containing the remainder of the record in case it is not the next consecutive block on disk. This organization is called spanned because records can span more than one block. Whenever a record is larger than a block, we must use a spanned organization. If records are not allowed to cross block boundaries, the organization is called unspanned. This is used with fixed-length records having B > R because it makes
5Other schemes are also possible for representing variable-length records.
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Record 1Block i Record 2 Record 3 Record 4 P
Record 4 (rest)Block i + 1 Record 5 Record 6 Record 7 P
Record 1Block i
(b)
(a) Record 2 Record 3
Record 4Block i + 1 Record 5 Record 6
Figure 17.6 Types of record organization. (a) Unspanned. (b) Spanned.
each record start at a known location in the block, simplifying record processing. For variable-length records, either a spanned or an unspanned organization can be used. If the average record is large, it is advantageous to use spanning to reduce the lost space in each block. Figure 17.6 illustrates spanned versus unspanned organization.
For variable-length records using spanned organization, each block may store a dif- ferent number of records. In this case, the blocking factor bfr represents the average number of records per block for the file. We can use bfr to calculate the number of blocks b needed for a file of r records:
b = ⎡(r/bfr)⎤ blocks where the ⎡(x)⎤ (ceiling function) rounds the value x up to the next integer.
17.4.4 Allocating File Blocks on Disk There are several standard techniques for allocating the blocks of a file on disk. In contiguous allocation, the file blocks are allocated to consecutive disk blocks. This makes reading the whole file very fast using double buffering, but it makes expand- ing the file difficult. In linked allocation, each file block contains a pointer to the next file block. This makes it easy to expand the file but makes it slow to read the whole file. A combination of the two allocates clusters of consecutive disk blocks, and the clusters are linked. Clusters are sometimes called file segments or extents. Another possibility is to use indexed allocation, where one or more index blocks contain pointers to the actual file blocks. It is also common to use combinations of these techniques.
17.4.5 File Headers A file header or file descriptor contains information about a file that is needed by the system programs that access the file records. The header includes information to determine the disk addresses of the file blocks as well as to record format descrip- tions, which may include field lengths and the order of fields within a record for fixed-length unspanned records and field type codes, separator characters, and record type codes for variable-length records.
To search for a record on disk, one or more blocks are copied into main memory buffers. Programs then search for the desired record or records within the buffers, using the information in the file header. If the address of the block that contains the desired record is not known, the search programs must do a linear search through
17.5 Operations on Files 599
the file blocks. Each file block is copied into a buffer and searched until the record is located or all the file blocks have been searched unsuccessfully. This can be very time-consuming for a large file. The goal of a good file organization is to locate the block that contains a desired record with a minimal number of block transfers.
17.5 Operations on Files Operations on files are usually grouped into retrieval operations and update oper- ations. The former do not change any data in the file, but only locate certain records so that their field values can be examined and processed. The latter change the file by insertion or deletion of records or by modification of field values. In either case, we may have to select one or more records for retrieval, deletion, or modification based on a selection condition (or filtering condition), which specifies criteria that the desired record or records must satisfy.
Consider an EMPLOYEE file with fields Name, Ssn, Salary, Job_code, and Department. A simple selection condition may involve an equality comparison on some field value—for example, (Ssn = ‘123456789’) or (Department = ‘Research’). More com- plex conditions can involve other types of comparison operators, such as > or ≥; an example is (Salary ≥ 30000). The general case is to have an arbitrary Boolean expres- sion on the fields of the file as the selection condition.
Search operations on files are generally based on simple selection conditions. A complex condition must be decomposed by the DBMS (or the programmer) to extract a simple condition that can be used to locate the records on disk. Each located record is then checked to determine whether it satisfies the full selection condition. For example, we may extract the simple condition (Department = ‘Research’) from the complex condition ((Salary ≥ 30000) AND (Department = ‘Research’)); each record satisfying (Department = ‘Research’) is located and then tested to see if it also satisfies (Salary ≥ 30000).
When several file records satisfy a search condition, the first record—with respect to the physical sequence of file records—is initially located and designated the current record. Subsequent search operations commence from this record and locate the next record in the file that satisfies the condition.
Actual operations for locating and accessing file records vary from system to system. Below, we present a set of representative operations. Typically, high-level programs, such as DBMS software programs, access records by using these commands, so we sometimes refer to program variables in the following descriptions:
■ Open. Prepares the file for reading or writing. Allocates appropriate buffers (typically at least two) to hold file blocks from disk, and retrieves the file header. Sets the file pointer to the beginning of the file.
■ Reset. Sets the file pointer of an open file to the beginning of the file. ■ Find (or Locate). Searches for the first record that satisfies a search condi-
tion. Transfers the block containing that record into a main memory buffer (if it is not already there). The file pointer points to the record in the buffer
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and it becomes the current record. Sometimes, different verbs are used to indicate whether the located record is to be retrieved or updated.
■ Read (or Get). Copies the current record from the buffer to a program vari- able in the user program. This command may also advance the current record pointer to the next record in the file, which may necessitate reading the next file block from disk.
■ FindNext. Searches for the next record in the file that satisfies the search condition. Transfers the block containing that record into a main memory buffer (if it is not already there). The record is located in the buffer and becomes the current record. Various forms of FindNext (for example, Find Next record within a current parent record, Find Next record of a given type, or Find Next record where a complex condition is met) are available in legacy DBMSs based on the hierarchical and network models.
■ Delete. Deletes the current record and (eventually) updates the file on disk to reflect the deletion.
■ Modify. Modifies some field values for the current record and (eventually) updates the file on disk to reflect the modification.
■ Insert. Inserts a new record in the file by locating the block where the record is to be inserted, transferring that block into a main memory buffer (if it is not already there), writing the record into the buffer, and (eventually) writ- ing the buffer to disk to reflect the insertion.
■ Close. Completes the file access by releasing the buffers and performing any other needed cleanup operations.
The preceding (except for Open and Close) are called record-at-a-time operations because each operation applies to a single record. It is possible to streamline the operations Find, FindNext, and Read into a single operation, Scan, whose descrip- tion is as follows:
■ Scan. If the file has just been opened or reset, Scan returns the first record; otherwise it returns the next record. If a condition is specified with the oper- ation, the returned record is the first or next record satisfying the condition.
In database systems, additional set-at-a-time higher-level operations may be applied to a file. Examples of these are as follows:
■ FindAll. Locates all the records in the file that satisfy a search condition. ■ Find (or Locate) n. Searches for the first record that satisfies a search condi-
tion and then continues to locate the next n – 1 records satisfying the same condition. Transfers the blocks containing the n records to the main memory buffer (if not already there).
■ FindOrdered. Retrieves all the records in the file in some specified order. ■ Reorganize. Starts the reorganization process. As we shall see, some file
organizations require periodic reorganization. An example is to reorder the file records by sorting them on a specified field.
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At this point, it is worthwhile to note the difference between the terms file organiza- tion and access method. A file organization refers to the organization of the data of a file into records, blocks, and access structures; this includes the way records and blocks are placed on the storage medium and interlinked. An access method, on the other hand, provides a group of operations—such as those listed earlier—that can be applied to a file. In general, it is possible to apply several access methods to a file organization. Some access methods, though, can be applied only to files organized in certain ways. For example, we cannot apply an indexed access method to a file without an index (see Chapter 18).
Usually, we expect to use some search conditions more than others. Some files may be static, meaning that update operations are rarely performed; other, more dynamic files may change frequently, so update operations are constantly applied to them. A successful file organization should perform as efficiently as possible the operations we expect to apply frequently to the file. For example, consider the EMPLOYEE file, as shown in Figure 17.5(a), which stores the records for current employees in a company. We expect to insert records (when employees are hired), delete records (when employees leave the company), and modify records (for exam- ple, when an employee’s salary or job is changed). Deleting or modifying a record requires a selection condition to identify a particular record or set of records. Retrieving one or more records also requires a selection condition.
If users expect mainly to apply a search condition based on Ssn, the designer must choose a file organization that facilitates locating a record given its Ssn value. This may involve physically ordering the records by Ssn value or defining an index on Ssn (see Chapter 18). Suppose that a second application uses the file to generate employees’ paychecks and requires that paychecks are grouped by department. For this application, it is best to order employee records by department and then by name within each department. The clustering of records into blocks and the organ- ization of blocks on cylinders would now be different than before. However, this arrangement conflicts with ordering the records by Ssn values. If both applications are important, the designer should choose an organization that allows both opera- tions to be done efficiently. Unfortunately, in many cases a single organization does not allow all needed operations on a file to be implemented efficiently. This requires that a compromise must be chosen that takes into account the expected importance and mix of retrieval and update operations.
In the following sections and in Chapter 18, we discuss methods for organizing records of a file on disk. Several general techniques, such as ordering, hashing, and indexing, are used to create access methods. Additionally, various general tech- niques for handling insertions and deletions work with many file organizations.
17.6 Files of Unordered Records (Heap Files) In this simplest and most basic type of organization, records are placed in the file in the order in which they are inserted, so new records are inserted at the end of the
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file. Such an organization is called a heap or pile file.6 This organization is often used with additional access paths, such as the secondary indexes discussed in Chapter 18. It is also used to collect and store data records for future use.
Inserting a new record is very efficient. The last disk block of the file is copied into a buffer, the new record is added, and the block is then rewritten back to disk. The address of the last file block is kept in the file header. However, searching for a record using any search condition involves a linear search through the file block by block—an expensive procedure. If only one record satisfies the search condition, then, on the average, a program will read into memory and search half the file blocks before it finds the record. For a file of b blocks, this requires searching (b/2) blocks, on average. If no records or several records satisfy the search condition, the program must read and search all b blocks in the file.
To delete a record, a program must first find its block, copy the block into a buffer, delete the record from the buffer, and finally rewrite the block back to the disk. This leaves unused space in the disk block. Deleting a large number of records in this way results in wasted storage space. Another technique used for record deletion is to have an extra byte or bit, called a deletion marker, stored with each record. A record is deleted by setting the deletion marker to a certain value. A different value for the marker indicates a valid (not deleted) record. Search programs consider only valid records in a block when conducting their search. Both of these deletion techniques require periodic reorganization of the file to reclaim the unused space of deleted records. During reorganization, the file blocks are accessed consecutively, and records are packed by removing deleted records. After such a reorganization, the blocks are filled to capacity once more. Another possibility is to use the space of deleted records when inserting new records, although this requires extra bookkeep- ing to keep track of empty locations.
We can use either spanned or unspanned organization for an unordered file, and it may be used with either fixed-length or variable-length records. Modifying a vari- able-length record may require deleting the old record and inserting a modified record because the modified record may not fit in its old space on disk.
To read all records in order of the values of some field, we create a sorted copy of the file. Sorting is an expensive operation for a large disk file, and special techniques for external sorting are used (see Chapter 19).
For a file of unordered fixed-length records using unspanned blocks and contiguous allocation, it is straightforward to access any record by its position in the file. If the file records are numbered 0, 1, 2, ..., r − 1 and the records in each block are num- bered 0, 1, ..., bfr − 1, where bfr is the blocking factor, then the ith record of the file is located in block ⎣(i/bfr)⎦ and is the (i mod bfr)th record in that block. Such a file is often called a relative or direct file because records can easily be accessed directly by their relative positions. Accessing a record by its position does not help locate a record based on a search condition; however, it facilitates the construction of access paths on the file, such as the indexes discussed in Chapter 18.
6Sometimes this organization is called a sequential file.
17.7 Files of Ordered Records (Sorted Files) 603
17.7 Files of Ordered Records (Sorted Files) We can physically order the records of a file on disk based on the values of one of their fields—called the ordering field. This leads to an ordered or sequential file.7
If the ordering field is also a key field of the file—a field guaranteed to have a unique value in each record—then the field is called the ordering key for the file. Figure 17.7 shows an ordered file with Name as the ordering key field (assuming that employees have distinct names).
Ordered records have some advantages over unordered files. First, reading the records in order of the ordering key values becomes extremely efficient because no sorting is required. Second, finding the next record from the current one in order of the order- ing key usually requires no additional block accesses because the next record is in the same block as the current one (unless the current record is the last one in the block). Third, using a search condition based on the value of an ordering key field results in faster access when the binary search technique is used, which constitutes an improve- ment over linear searches, although it is not often used for disk files. Ordered files are blocked and stored on contiguous cylinders to minimize the seek time.
A binary search for disk files can be done on the blocks rather than on the records. Suppose that the file has b blocks numbered 1, 2, ..., b; the records are ordered by ascending value of their ordering key field; and we are searching for a record whose ordering key field value is K. Assuming that disk addresses of the file blocks are avail- able in the file header, the binary search can be described by Algorithm 17.1. A binary search usually accesses log2(b) blocks, whether the record is found or not—an improvement over linear searches, where, on the average, (b/2) blocks are accessed when the record is found and b blocks are accessed when the record is not found.
Algorithm 17.1. Binary Search on an Ordering Key of a Disk File l ← 1; u ← b; (* b is the number of file blocks *) while (u ≥ l ) do
begin i ← (l + u) div 2; read block i of the file into the buffer; if K < (ordering key field value of the first record in block i )
then u ← i – 1 else if K > (ordering key field value of the last record in block i )
then l ← i + 1 else if the record with ordering key field value = K is in the buffer
then goto found else goto notfound; end;
goto notfound;
A search criterion involving the conditions >, <, ≥, and ≤ on the ordering field is quite efficient, since the physical ordering of records means that all records
7The term sequential file has also been used to refer to unordered files, although it is more appropriate for ordered files.
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Name
Aaron, Ed
Abbott, Diane
Block 1
Acosta, Marc
Ssn Birth_date
...
Job Salary Sex
...
Adams, John
Adams, Robin
Block 2
Akers, Jan
...
Alexander, Ed
Alfred, Bob
Block 3
Allen, Sam
...
Allen, Troy
Anders, Keith
Block 4
Anderson, Rob
...
Anderson, Zach
Angeli, Joe
Block 5
Archer, Sue
...
Arnold, Mack
Arnold, Steven
Block 6
Atkins, Timothy
Wong, James
Wood, Donald
Block n–1
Woods, Manny
...
Wright, Pam
Wyatt, Charles
Block n
Zimmer, Byron
...
Figure 17.7 Some blocks of an ordered (sequential) file of EMPLOYEE records with Name as the ordering key field.
satisfying the condition are contiguous in the file. For example, referring to Figure 17.7, if the search criterion is (Name < ‘G’)—where < means alphabetically before— the records satisfying the search criterion are those from the beginning of the file up to the first record that has a Name value starting with the letter ‘G’.
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Ordering does not provide any advantages for random or ordered access of the records based on values of the other nonordering fields of the file. In these cases, we do a linear search for random access. To access the records in order based on a nonordering field, it is necessary to create another sorted copy—in a different order—of the file.
Inserting and deleting records are expensive operations for an ordered file because the records must remain physically ordered. To insert a record, we must find its cor- rect position in the file, based on its ordering field value, and then make space in the file to insert the record in that position. For a large file this can be very time- consuming because, on the average, half the records of the file must be moved to make space for the new record. This means that half the file blocks must be read and rewritten after records are moved among them. For record deletion, the problem is less severe if deletion markers and periodic reorganization are used.
One option for making insertion more efficient is to keep some unused space in each block for new records. However, once this space is used up, the original problem resurfaces. Another frequently used method is to create a temporary unordered file called an overflow or transaction file. With this technique, the actual ordered file is called the main or master file. New records are inserted at the end of the overflow file rather than in their correct position in the main file. Periodically, the overflow file is sorted and merged with the master file during file reorganization. Insertion becomes very efficient, but at the cost of increased complexity in the search algorithm. The overflow file must be searched using a linear search if, after the binary search, the record is not found in the main file. For applications that do not require the most up- to-date information, overflow records can be ignored during a search.
Modifying a field value of a record depends on two factors: the search condition to locate the record and the field to be modified. If the search condition involves the ordering key field, we can locate the record using a binary search; otherwise we must do a linear search. A nonordering field can be modified by changing the record and rewriting it in the same physical location on disk—assuming fixed-length records. Modifying the ordering field means that the record can change its position in the file. This requires deletion of the old record followed by insertion of the modified record.
Reading the file records in order of the ordering field is quite efficient if we ignore the records in overflow, since the blocks can be read consecutively using double buffering. To include the records in overflow, we must merge them in their correct positions; in this case, first we can reorganize the file, and then read its blocks sequentially. To reorganize the file, first we sort the records in the overflow file, and then merge them with the master file. The records marked for deletion are removed during the reorganization.
Table 17.2 summarizes the average access time in block accesses to find a specific record in a file with b blocks.
Ordered files are rarely used in database applications unless an additional access path, called a primary index, is used; this results in an indexed-sequential file. This
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Table 17.2 Average Access Times for a File of b Blocks under Basic File Organizations
Average Blocks to Access Type of Organization Access/Search Method a Specific Record
Heap (unordered) Sequential scan (linear search) b/2 Ordered Sequential scan b/2 Ordered Binary search log2 b
further improves the random access time on the ordering key field. (We discuss indexes in Chapter 18.) If the ordering attribute is not a key, the file is called a clustered file.
17.8 Hashing Techniques Another type of primary file organization is based on hashing, which provides very fast access to records under certain search conditions. This organization is usually called a hash file.8 The search condition must be an equality condition on a single field, called the hash field. In most cases, the hash field is also a key field of the file, in which case it is called the hash key. The idea behind hashing is to provide a func- tion h, called a hash function or randomizing function, which is applied to the hash field value of a record and yields the address of the disk block in which the record is stored. A search for the record within the block can be carried out in a main memory buffer. For most records, we need only a single-block access to retrieve that record.
Hashing is also used as an internal search structure within a program whenever a group of records is accessed exclusively by using the value of one field. We describe the use of hashing for internal files in Section 17.8.1; then we show how it is modi- fied to store external files on disk in Section 17.8.2. In Section 17.8.3 we discuss techniques for extending hashing to dynamically growing files.
17.8.1 Internal Hashing For internal files, hashing is typically implemented as a hash table through the use of an array of records. Suppose that the array index range is from 0 to M – 1, as shown in Figure 17.8(a); then we have M slots whose addresses correspond to the array indexes. We choose a hash function that transforms the hash field value into an integer between 0 and M − 1. One common hash function is the h(K) = K mod M function, which returns the remainder of an integer hash field value K after divi- sion by M; this value is then used for the record address.
8A hash file has also been called a direct file.
17.8 Hashing Techniques 607
Noninteger hash field values can be transformed into integers before the mod func- tion is applied. For character strings, the numeric (ASCII) codes associated with characters can be used in the transformation—for example, by multiplying those code values. For a hash field whose data type is a string of 20 characters, Algorithm 17.2(a) can be used to calculate the hash address. We assume that the code function returns the numeric code of a character and that we are given a hash field value K of type K: array [1..20] of char (in Pascal) or char K[20] (in C).
(a)
–1
–1 –1
M + 2
M
0
1 2
3
M – 2
M – 1
Data fields Overflow pointer
Address space
Overflow space
M + 1
M + 5 –1
M + 4
–1
M + 0 – 2 M + 0 – 1
null pointer = –1 overflow pointer refers to position of next record in linked list
M – 2
M M + 1 M + 2
M – 1
Name Ssn Job Salary
(b) 0
1 2
3
4
...
Figure 17.8 Internal hashing data structures. (a) Array of M positions for use in internal hashing. (b) Collision resolution by chaining records.
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Algorithm 17.2. Two simple hashing algorithms: (a) Applying the mod hash function to a character string K. (b) Collision resolution by open addressing.
(a) temp ← 1; for i ← 1 to 20 do temp ← temp * code(K[i ] ) mod M ; hash_address ← temp mod M;
(b) i ← hash_address(K); a ← i; if location i is occupied
then begin i ← (i + 1) mod M; while (i ≠ a) and location i is occupied
do i ← (i + 1) mod M; if (i = a) then all positions are full else new_hash_address ← i; end;
Other hashing functions can be used. One technique, called folding, involves apply- ing an arithmetic function such as addition or a logical function such as exclusive or to different portions of the hash field value to calculate the hash address (for exam- ple, with an address space from 0 to 999 to store 1,000 keys, a 6-digit key 235469 may be folded and stored at the address: (235+964) mod 1000 = 199). Another tech- nique involves picking some digits of the hash field value—for instance, the third, fifth, and eighth digits—to form the hash address (for example, storing 1,000 employees with Social Security numbers of 10 digits into a hash file with 1,000 posi- tions would give the Social Security number 301-67-8923 a hash value of 172 by this hash function).9 The problem with most hashing functions is that they do not guar- antee that distinct values will hash to distinct addresses, because the hash field space—the number of possible values a hash field can take—is usually much larger than the address space—the number of available addresses for records. The hashing function maps the hash field space to the address space.
A collision occurs when the hash field value of a record that is being inserted hashes to an address that already contains a different record. In this situation, we must insert the new record in some other position, since its hash address is occupied. The process of finding another position is called collision resolution. There are numer- ous methods for collision resolution, including the following:
■ Open addressing. Proceeding from the occupied position specified by the hash address, the program checks the subsequent positions in order until an unused (empty) position is found. Algorithm 17.2(b) may be used for this purpose.
■ Chaining. For this method, various overflow locations are kept, usually by extending the array with a number of overflow positions. Additionally, a pointer field is added to each record location. A collision is resolved by plac- ing the new record in an unused overflow location and setting the pointer of the occupied hash address location to the address of that overflow location.
9A detailed discussion of hashing functions is outside the scope of our presentation.
17.8 Hashing Techniques 609
A linked list of overflow records for each hash address is thus maintained, as shown in Figure 17.8(b).
■ Multiple hashing. The program applies a second hash function if the first results in a collision. If another collision results, the program uses open addressing or applies a third hash function and then uses open addressing if necessary.
Each collision resolution method requires its own algorithms for insertion, retrieval, and deletion of records. The algorithms for chaining are the simplest. Deletion algorithms for open addressing are rather tricky. Data structures textbooks discuss internal hashing algorithms in more detail.
The goal of a good hashing function is to distribute the records uniformly over the address space so as to minimize collisions while not leaving many unused locations. Simulation and analysis studies have shown that it is usually best to keep a hash table between 70 and 90 percent full so that the number of collisions remains low and we do not waste too much space. Hence, if we expect to have r records to store in the table, we should choose M locations for the address space such that (r/M) is between 0.7 and 0.9. It may also be useful to choose a prime number for M, since it has been demonstrated that this distributes the hash addresses better over the address space when the mod hashing function is used. Other hash functions may require M to be a power of 2.
17.8.2 External Hashing for Disk Files Hashing for disk files is called external hashing. To suit the characteristics of disk storage, the target address space is made of buckets, each of which holds multiple records. A bucket is either one disk block or a cluster of contiguous disk blocks. The hashing function maps a key into a relative bucket number, rather than assigning an absolute block address to the bucket. A table maintained in the file header converts the bucket number into the corresponding disk block address, as illustrated in Figure 17.9.
The collision problem is less severe with buckets, because as many records as will fit in a bucket can hash to the same bucket without causing problems. However, we must make provisions for the case where a bucket is filled to capacity and a new record being inserted hashes to that bucket. We can use a variation of chaining in which a pointer is maintained in each bucket to a linked list of overflow records for the bucket, as shown in Figure 17.10. The pointers in the linked list should be record pointers, which include both a block address and a relative record position within the block.
Hashing provides the fastest possible access for retrieving an arbitrary record given the value of its hash field. Although most good hash functions do not maintain records in order of hash field values, some functions—called order preserving— do. A simple example of an order preserving hash function is to take the leftmost three digits of an invoice number field that yields a bucket address as the hash address and keep the records sorted by invoice number within each bucket. Another
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0 1 2
M – 2 M – 1
Bucket Number Block address on disk
Figure 17.9 Matching bucket numbers to disk block addresses.
example is to use an integer hash key directly as an index to a relative file, if the hash key values fill up a particular interval; for example, if employee numbers in a com- pany are assigned as 1, 2, 3, ... up to the total number of employees, we can use the identity hash function that maintains order. Unfortunately, this only works if keys are generated in order by some application.
The hashing scheme described so far is called static hashing because a fixed number of buckets M is allocated. This can be a serious drawback for dynamic files. Suppose that we allocate M buckets for the address space and let m be the maximum number of records that can fit in one bucket; then at most (m * M) records will fit in the allo- cated space. If the number of records turns out to be substantially fewer than (m * M), we are left with a lot of unused space. On the other hand, if the number of records increases to substantially more than (m * M), numerous collisions will result and retrieval will be slowed down because of the long lists of overflow records. In either case, we may have to change the number of blocks M allocated and then use a new hashing function (based on the new value of M) to redistribute the records. These reorganizations can be quite time-consuming for large files. Newer dynamic file organizations based on hashing allow the number of buckets to vary dynamically with only localized reorganization (see Section 17.8.3).
When using external hashing, searching for a record given a value of some field other than the hash field is as expensive as in the case of an unordered file. Record deletion can be implemented by removing the record from its bucket. If the bucket has an overflow chain, we can move one of the overflow records into the bucket to replace the deleted record. If the record to be deleted is already in overflow, we sim- ply remove it from the linked list. Notice that removing an overflow record implies that we should keep track of empty positions in overflow. This is done easily by maintaining a linked list of unused overflow locations.
17.8 Hashing Techniques 611
Bucket 0
Main buckets
Overflow buckets
340
460
Record pointer
NULL
NULL
NULL
Bucket 1 321
761 91
Record pointer
981
182
Record pointer
(Pointers are to records within the overflow blocks)
Record pointer
Record pointer
652 Record pointer
Record pointer
Record pointer
Bucket 2 22
72
522 Record pointer
Bucket 9 399
89
Record pointer
NULL
...
Figure 17.10 Handling overflow for buckets by chaining.
Modifying a specific record’s field value depends on two factors: the search condi- tion to locate that specific record and the field to be modified. If the search condi- tion is an equality comparison on the hash field, we can locate the record efficiently by using the hashing function; otherwise, we must do a linear search. A nonhash field can be modified by changing the record and rewriting it in the same bucket. Modifying the hash field means that the record can move to another bucket, which requires deletion of the old record followed by insertion of the modified record.
17.8.3 Hashing Techniques That Allow Dynamic File Expansion A major drawback of the static hashing scheme just discussed is that the hash address space is fixed. Hence, it is difficult to expand or shrink the file dynamically. The schemes described in this section attempt to remedy this situation. The first scheme—extendible hashing—stores an access structure in addition to the file, and
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hence is somewhat similar to indexing (see Chapter 18). The main difference is that the access structure is based on the values that result after application of the hash function to the search field. In indexing, the access structure is based on the values of the search field itself. The second technique, called linear hashing, does not require additional access structures. Another scheme, called dynamic hashing, uses an access structure based on binary tree data structures..
These hashing schemes take advantage of the fact that the result of applying a hash- ing function is a nonnegative integer and hence can be represented as a binary num- ber. The access structure is built on the binary representation of the hashing function result, which is a string of bits. We call this the hash value of a record. Records are distributed among buckets based on the values of the leading bits in their hash values.
Extendible Hashing. In extendible hashing, a type of directory—an array of 2d bucket addresses—is maintained, where d is called the global depth of the direc- tory. The integer value corresponding to the first (high-order) d bits of a hash value is used as an index to the array to determine a directory entry, and the address in that entry determines the bucket in which the corresponding records are stored. However, there does not have to be a distinct bucket for each of the 2d directory locations. Several directory locations with the same first d� bits for their hash values may contain the same bucket address if all the records that hash to these locations fit in a single bucket. A local depth d�—stored with each bucket—specifies the number of bits on which the bucket contents are based. Figure 17.11 shows a directory with global depth d = 3.
The value of d can be increased or decreased by one at a time, thus doubling or halv- ing the number of entries in the directory array. Doubling is needed if a bucket, whose local depth d� is equal to the global depth d, overflows. Halving occurs if d > d� for all the buckets after some deletions occur. Most record retrievals require two block accesses—one to the directory and the other to the bucket.
To illustrate bucket splitting, suppose that a new inserted record causes overflow in the bucket whose hash values start with 01—the third bucket in Figure 17.11. The records will be distributed between two buckets: the first contains all records whose hash values start with 010, and the second all those whose hash values start with 011. Now the two directory locations for 010 and 011 point to the two new distinct buckets. Before the split, they pointed to the same bucket. The local depth d� of the two new buckets is 3, which is one more than the local depth of the old bucket.
If a bucket that overflows and is split used to have a local depth d� equal to the global depth d of the directory, then the size of the directory must now be doubled so that we can use an extra bit to distinguish the two new buckets. For example, if the bucket for records whose hash values start with 111 in Figure 17.11 overflows, the two new buckets need a directory with global depth d = 4, because the two buckets are now labeled 1110 and 1111, and hence their local depths are both 4. The direc- tory size is hence doubled, and each of the other original locations in the directory
17.8 Hashing Techniques 613
Global depth d = 3
000
001
010
011
100
101
110
111
d´ = 3 Bucket for records whose hash values start with 000
Directory Data file buckets Local depth of each bucket
d´ = 3 Bucket for records whose hash values start with 001
d´ = 2 Bucket for records whose hash values start with 01
d´ = 2 Bucket for records whose hash values start with 10
d´ = 3 Bucket for records whose hash values start with 110
d´ = 3 Bucket for records whose hash values start with 111
Figure 17.11 Structure of the extendible hashing scheme.
is also split into two locations, both of which have the same pointer value as did the original location.
The main advantage of extendible hashing that makes it attractive is that the per- formance of the file does not degrade as the file grows, as opposed to static external hashing where collisions increase and the corresponding chaining effectively
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increases the average number of accesses per key. Additionally, no space is allocated in extendible hashing for future growth, but additional buckets can be allocated dynamically as needed. The space overhead for the directory table is negligible. The maximum directory size is 2k, where k is the number of bits in the hash value. Another advantage is that splitting causes minor reorganization in most cases, since only the records in one bucket are redistributed to the two new buckets. The only time reorganization is more expensive is when the directory has to be doubled (or halved). A disadvantage is that the directory must be searched before accessing the buckets themselves, resulting in two block accesses instead of one in static hashing. This performance penalty is considered minor and thus the scheme is considered quite desirable for dynamic files.
Dynamic Hashing. A precursor to extendible hashing was dynamic hashing, in which the addresses of the buckets were either the n high-order bits or n − 1 high- order bits, depending on the total number of keys belonging to the respective bucket. The eventual storage of records in buckets for dynamic hashing is somewhat similar to extendible hashing. The major difference is in the organization of the directory. Whereas extendible hashing uses the notion of global depth (high-order d bits) for the flat directory and then combines adjacent collapsible buckets into a bucket of local depth d − 1, dynamic hashing maintains a tree-structured directory with two types of nodes:
■ Internal nodes that have two pointers—the left pointer corresponding to the 0 bit (in the hashed address) and a right pointer corresponding to the 1 bit.
■ Leaf nodes—these hold a pointer to the actual bucket with records.
An example of the dynamic hashing appears in Figure 17.12. Four buckets are shown (“000”, “001”, “110”, and “111”) with high-order 3-bit addresses (corre- sponding to the global depth of 3), and two buckets (“01” and “10” ) are shown with high-order 2-bit addresses (corresponding to the local depth of 2). The latter two are the result of collapsing the “010” and “011” into “01” and collapsing “100” and “101” into “10”. Note that the directory nodes are used implicitly to determine the “global” and “local” depths of buckets in dynamic hashing. The search for a record given the hashed address involves traversing the directory tree, which leads to the bucket holding that record. It is left to the reader to develop algorithms for inser- tion, deletion, and searching of records for the dynamic hashing scheme.
Linear Hashing. The idea behind linear hashing is to allow a hash file to expand and shrink its number of buckets dynamically without needing a directory. Suppose that the file starts with M buckets numbered 0, 1, ..., M − 1 and uses the mod hash function h(K) = K mod M; this hash function is called the initial hash function hi. Overflow because of collisions is still needed and can be handled by maintaining individual overflow chains for each bucket. However, when a collision leads to an overflow record in any file bucket, the first bucket in the file—bucket 0—is split into two buckets: the original bucket 0 and a new bucket M at the end of the file. The records originally in bucket 0 are distributed between the two buckets based on a different hashing function hi+1(K) = K mod 2M. A key property of the two hash
17.8 Hashing Techniques 615
Data File Buckets
Bucket for records whose hash values start with 000
Bucket for records whose hash values start with 001
Bucket for records whose hash values start with 01
Bucket for records whose hash values start with 10
Bucket for records whose hash values start with 110
Bucket for records whose hash values start with 111
Directory 0
1
0
1
0
1
0
1
0
1
internal directory node
leaf directory node
Figure 17.12 Structure of the dynamic hashing scheme.
functions hi and hi+1 is that any records that hashed to bucket 0 based on hi will hash to either bucket 0 or bucket M based on hi+1; this is necessary for linear hashing to work.
As further collisions lead to overflow records, additional buckets are split in the linear order 1, 2, 3, .... If enough overflows occur, all the original file buckets 0, 1, ..., M − 1 will have been split, so the file now has 2M instead of M buckets, and all buck- ets use the hash function hi+1. Hence, the records in overflow are eventually redis- tributed into regular buckets, using the function hi+1 via a delayed split of their buckets. There is no directory; only a value n—which is initially set to 0 and is incre- mented by 1 whenever a split occurs—is needed to determine which buckets have been split. To retrieve a record with hash key value K, first apply the function hi to K; if hi(K) < n, then apply the function hi+1 on K because the bucket is already split. Initially, n = 0, indicating that the function hi applies to all buckets; n grows linearly as buckets are split.
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When n = M after being incremented, this signifies that all the original buckets have been split and the hash function hi+1 applies to all records in the file. At this point, n is reset to 0 (zero), and any new collisions that cause overflow lead to the use of a new hashing function hi+2(K) = K mod 4M. In general, a sequence of hashing func- tions hi+j(K) = K mod (2
jM) is used, where j = 0, 1, 2, ...; a new hashing function hi+j+1 is needed whenever all the buckets 0, 1, ..., (2
jM) − 1 have been split and n is reset to 0. The search for a record with hash key value K is given by Algorithm 17.3.
Splitting can be controlled by monitoring the file load factor instead of by splitting whenever an overflow occurs. In general, the file load factor l can be defined as l = r/(bfr * N), where r is the current number of file records, bfr is the maximum num- ber of records that can fit in a bucket, and N is the current number of file buckets. Buckets that have been split can also be recombined if the load factor of the file falls below a certain threshold. Blocks are combined linearly, and N is decremented appropriately. The file load can be used to trigger both splits and combinations; in this manner the file load can be kept within a desired range. Splits can be triggered when the load exceeds a certain threshold—say, 0.9—and combinations can be trig- gered when the load falls below another threshold—say, 0.7. The main advantages of linear hashing are that it maintains the load factor fairly constantly while the file grows and shrinks, and it does not require a directory.10
Algorithm 17.3. The Search Procedure for Linear Hashing if n = 0
then m ← hj (K) (* m is the hash value of record with hash key K *) else begin
m ← hj (K); if m < n then m ← hj+1 (K) end;
search the bucket whose hash value is m (and its overflow, if any);
17.9 Other Primary File Organizations
17.9.1 Files of Mixed Records The file organizations we have studied so far assume that all records of a particular file are of the same record type. The records could be of EMPLOYEEs, PROJECTs, STUDENTs, or DEPARTMENTs, but each file contains records of only one type. In most database applications, we encounter situations in which numerous types of entities are interrelated in various ways, as we saw in Chapter 7. Relationships among records in various files can be represented by connecting fields.11 For exam- ple, a STUDENT record can have a connecting field Major_dept whose value gives the
10For details of insertion and deletion into Linear hashed files, refer to Litwin (1980) and Salzberg (1988). 11The concept of foreign keys in the relational data model (Chapter 3) and references among objects in object-oriented models (Chapter 11) are examples of connecting fields.
17.10 Parallelizing Disk Access Using RAID Technology 617
name of the DEPARTMENT in which the student is majoring. This Major_dept field refers to a DEPARTMENT entity, which should be represented by a record of its own in the DEPARTMENT file. If we want to retrieve field values from two related records, we must retrieve one of the records first. Then we can use its connecting field value to retrieve the related record in the other file. Hence, relationships are implemented by logical field references among the records in distinct files.
File organizations in object DBMSs, as well as legacy systems such as hierarchical and network DBMSs, often implement relationships among records as physical relationships realized by physical contiguity (or clustering) of related records or by physical pointers. These file organizations typically assign an area of the disk to hold records of more than one type so that records of different types can be physically clustered on disk. If a particular relationship is expected to be used fre- quently, implementing the relationship physically can increase the system’s effi- ciency at retrieving related records. For example, if the query to retrieve a DEPARTMENT record and all records for STUDENTs majoring in that department is frequent, it would be desirable to place each DEPARTMENT record and its cluster of STUDENT records contiguously on disk in a mixed file. The concept of physical clustering of object types is used in object DBMSs to store related objects together in a mixed file.
To distinguish the records in a mixed file, each record has—in addition to its field values—a record type field, which specifies the type of record. This is typically the first field in each record and is used by the system software to determine the type of record it is about to process. Using the catalog information, the DBMS can deter- mine the fields of that record type and their sizes, in order to interpret the data val- ues in the record.
17.9.2 B-Trees and Other Data Structures as Primary Organization
Other data structures can be used for primary file organizations. For example, if both the record size and the number of records in a file are small, some DBMSs offer the option of a B-tree data structure as the primary file organization. We will describe B- trees in Section 18.3.1, when we discuss the use of the B-tree data structure for index- ing. In general, any data structure that can be adapted to the characteristics of disk devices can be used as a primary file organization for record placement on disk. Recently, column-based storage of data has been proposed as a primary method for storage of relations in relational databases. We will briefly introduce it in Chapter 18 as a possible alternative storage scheme for relational databases.
17.10 Parallelizing Disk Access Using RAID Technology
With the exponential growth in the performance and capacity of semiconductor devices and memories, faster microprocessors with larger and larger primary mem- ories are continually becoming available. To match this growth, it is natural to
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(a) Disk 0
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Figure 17.13 Striping of data across multiple disks. (a) Bit-level striping across four disks. (b) Block-level striping across four disks.
expect that secondary storage technology must also take steps to keep up with processor technology in performance and reliability.
A major advance in secondary storage technology is represented by the develop- ment of RAID, which originally stood for Redundant Arrays of Inexpensive Disks. More recently, the I in RAID is said to stand for Independent. The RAID idea received a very positive industry endorsement and has been developed into an elab- orate set of alternative RAID architectures (RAID levels 0 through 6). We highlight the main features of the technology in this section.
The main goal of RAID is to even out the widely different rates of performance improvement of disks against those in memory and microprocessors.12 While RAM capacities have quadrupled every two to three years, disk access times are improving at less than 10 percent per year, and disk transfer rates are improving at roughly 20 percent per year. Disk capacities are indeed improving at more than 50 percent per year, but the speed and access time improvements are of a much smaller magnitude.
A second qualitative disparity exists between the ability of special microprocessors that cater to new applications involving video, audio, image, and spatial data pro- cessing (see Chapters 26 and 30 for details of these applications), with correspond- ing lack of fast access to large, shared data sets.
The natural solution is a large array of small independent disks acting as a single higher-performance logical disk. A concept called data striping is used, which uti- lizes parallelism to improve disk performance. Data striping distributes data trans- parently over multiple disks to make them appear as a single large, fast disk. Figure 17.13 shows a file distributed or striped over four disks. Striping improves overall I/O performance by allowing multiple I/Os to be serviced in parallel, thus providing high overall transfer rates. Data striping also accomplishes load balancing among disks. Moreover, by storing redundant information on disks using parity or some other error-correction code, reliability can be improved. In Sections 17.10.1 and
12This was predicted by Gordon Bell to be about 40 percent every year between 1974 and 1984 and is now supposed to exceed 50 percent per year.
17.10 Parallelizing Disk Access Using RAID Technology 619
17.10.2, we discuss how RAID achieves the two important objectives of improved reliability and higher performance. Section 17.10.3 discusses RAID organizations and levels.
17.10.1 Improving Reliability with RAID For an array of n disks, the likelihood of failure is n times as much as that for one disk. Hence, if the MTBF (Mean Time Between Failures) of a disk drive is assumed to be 200,000 hours or about 22.8 years (for the disk drive in Table 17.1 called Cheetah NS, it is 1.4 million hours), the MTBF for a bank of 100 disk drives becomes only 2,000 hours or 83.3 days (for 1,000 Cheetah NS disks it would be 1,400 hours or 58.33 days). Keeping a single copy of data in such an array of disks will cause a signif- icant loss of reliability. An obvious solution is to employ redundancy of data so that disk failures can be tolerated. The disadvantages are many: additional I/O operations for write, extra computation to maintain redundancy and to do recovery from errors, and additional disk capacity to store redundant information.
One technique for introducing redundancy is called mirroring or shadowing. Data is written redundantly to two identical physical disks that are treated as one logical disk. When data is read, it can be retrieved from the disk with shorter queuing, seek, and rotational delays. If a disk fails, the other disk is used until the first is repaired. Suppose the mean time to repair is 24 hours, then the mean time to data loss of a mirrored disk system using 100 disks with MTBF of 200,000 hours each is (200,000)2/(2 * 24) = 8.33 * 10
8 hours, which is 95,028 years.13 Disk mirroring also doubles the rate at which read requests are handled, since a read can go to either disk. The transfer rate of each read, however, remains the same as that for a single disk.
Another solution to the problem of reliability is to store extra information that is not normally needed but that can be used to reconstruct the lost information in case of disk failure. The incorporation of redundancy must consider two problems: selecting a technique for computing the redundant information, and selecting a method of distributing the redundant information across the disk array. The first problem is addressed by using error-correcting codes involving parity bits, or specialized codes such as Hamming codes. Under the parity scheme, a redundant disk may be consid- ered as having the sum of all the data in the other disks. When a disk fails, the miss- ing information can be constructed by a process similar to subtraction.
For the second problem, the two major approaches are either to store the redundant information on a small number of disks or to distribute it uniformly across all disks. The latter results in better load balancing. The different levels of RAID choose a combination of these options to implement redundancy and improve reliability.
17.10.2 Improving Performance with RAID The disk arrays employ the technique of data striping to achieve higher transfer rates. Note that data can be read or written only one block at a time, so a typical transfer contains 512 to 8192 bytes. Disk striping may be applied at a finer granularity by
13The formulas for MTBF calculations appear in Chen et al. (1994).
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breaking up a byte of data into bits and spreading the bits to different disks. Thus, bit-level data striping consists of splitting a byte of data and writing bit j to the jth disk. With 8-bit bytes, eight physical disks may be considered as one logical disk with an eightfold increase in the data transfer rate. Each disk participates in each I/O request and the total amount of data read per request is eight times as much. Bit-level striping can be generalized to a number of disks that is either a multiple or a factor of eight. Thus, in a four-disk array, bit n goes to the disk which is (n mod 4). Figure 17.13(a) shows bit-level striping of data.
The granularity of data interleaving can be higher than a bit; for example, blocks of a file can be striped across disks, giving rise to block-level striping. Figure 17.13(b) shows block-level data striping assuming the data file contains four blocks. With block-level striping, multiple independent requests that access single blocks (small requests) can be serviced in parallel by separate disks, thus decreasing the queuing time of I/O requests. Requests that access multiple blocks (large requests) can be parallelized, thus reducing their response time. In general, the more the number of disks in an array, the larger the potential performance benefit. However, assuming independent failures, the disk array of 100 disks collectively has 1/100th the reliabil- ity of a single disk. Thus, redundancy via error-correcting codes and disk mirroring is necessary to provide reliability along with high performance.
17.10.3 RAID Organizations and Levels Different RAID organizations were defined based on different combinations of the two factors of granularity of data interleaving (striping) and pattern used to com- pute redundant information. In the initial proposal, levels 1 through 5 of RAID were proposed, and two additional levels—0 and 6—were added later.
RAID level 0 uses data striping, has no redundant data, and hence has the best write performance since updates do not have to be duplicated. It splits data evenly across two or more disks. However, its read performance is not as good as RAID level 1, which uses mirrored disks. In the latter, performance improvement is possible by scheduling a read request to the disk with shortest expected seek and rotational delay. RAID level 2 uses memory-style redundancy by using Hamming codes, which contain parity bits for distinct overlapping subsets of components. Thus, in one particular version of this level, three redundant disks suffice for four original disks, whereas with mirroring—as in level 1—four would be required. Level 2 includes both error detection and correction, although detection is generally not required because broken disks identify themselves.
RAID level 3 uses a single parity disk relying on the disk controller to figure out which disk has failed. Levels 4 and 5 use block-level data striping, with level 5 dis- tributing data and parity information across all disks. Figure 17.14(b) shows an illustration of RAID level 5, where parity is shown with subscript p. If one disk fails, the missing data is calculated based on the parity available from the remaining disks. Finally, RAID level 6 applies the so-called P + Q redundancy scheme using Reed-Soloman codes to protect against up to two disk failures by using just two redundant disks.
17.11 New Storage Systems 621
Disk 0 Disk 1
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A3 Bp C2 D2
Ap B3 C3 D3
(a)
(b)
File A
File B
File C
File D
File A
File B
File C
File D
Figure 17.14 Some popular levels of RAID. (a) RAID level 1: Mirroring of data on two disks. (b) RAID level 5: Striping of data with distributed parity across four disks.
Rebuilding in case of disk failure is easiest for RAID level 1. Other levels require the reconstruction of a failed disk by reading multiple disks. Level 1 is used for critical applications such as storing logs of transactions. Levels 3 and 5 are preferred for large volume storage, with level 3 providing higher transfer rates. Most popular use of RAID technology currently uses level 0 (with striping), level 1 (with mirroring), and level 5 with an extra drive for parity. A combination of multiple RAID levels are also used – for example, 0+1 combines striping and mirroring using a minimum of four disks. Other nonstandard RAID levels include: RAID 1.5, RAID 7, RAID-DP, RAID S or Parity RAID, Matrix RAID, RAID-K, RAID-Z, RAIDn, Linux MD RAID 10, IBM ServeRAID 1E, and unRAID. A discussion of these nonstandard levels is beyond the scope of this book. Designers of a RAID setup for a given application mix have to confront many design decisions such as the level of RAID, the number of disks, the choice of parity schemes, and grouping of disks for block-level striping. Detailed performance studies on small reads and writes (referring to I/O requests for one striping unit) and large reads and writes (referring to I/O requests for one stripe unit from each disk in an error-correction group) have been performed.
17.11 New Storage Systems In this section, we describe three recent developments in storage systems that are becoming an integral part of most enterprise’s information system architectures.
17.11.1 Storage Area Networks With the rapid growth of electronic commerce, Enterprise Resource Planning (ERP) systems that integrate application data across organizations, and data ware- houses that keep historical aggregate information (see Chapter 29), the demand for storage has gone up substantially. For today’s Internet-driven organizations, it has
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become necessary to move from a static fixed data center-oriented operation to a more flexible and dynamic infrastructure for their information processing require- ments. The total cost of managing all data is growing so rapidly that in many instances the cost of managing server-attached storage exceeds the cost of the server itself. Furthermore, the procurement cost of storage is only a small fraction—typi- cally, only 10 to 15 percent of the overall cost of storage management. Many users of RAID systems cannot use the capacity effectively because it has to be attached in a fixed manner to one or more servers. Therefore, most large organizations have moved to a concept called storage area networks (SANs). In a SAN, online storage peripherals are configured as nodes on a high-speed network and can be attached and detached from servers in a very flexible manner. Several companies have emerged as SAN providers and supply their own proprietary topologies. They allow storage systems to be placed at longer distances from the servers and provide differ- ent performance and connectivity options. Existing storage management applica- tions can be ported into SAN configurations using Fiber Channel networks that encapsulate the legacy SCSI protocol. As a result, the SAN-attached devices appear as SCSI devices.
Current architectural alternatives for SAN include the following: point-to-point connections between servers and storage systems via fiber channel; use of a fiber channel switch to connect multiple RAID systems, tape libraries, and so on to servers; and the use of fiber channel hubs and switches to connect servers and stor- age systems in different configurations. Organizations can slowly move up from simpler topologies to more complex ones by adding servers and storage devices as needed. We do not provide further details here because they vary among SAN ven- dors. The main advantages claimed include:
■ Flexible many-to-many connectivity among servers and storage devices using fiber channel hubs and switches
■ Up to 10 km separation between a server and a storage system using appro- priate fiber optic cables
■ Better isolation capabilities allowing nondisruptive addition of new periph- erals and servers
SANs are growing very rapidly, but are still faced with many problems, such as com- bining storage options from multiple vendors and dealing with evolving standards of storage management software and hardware. Most major companies are evaluat- ing SANs as a viable option for database storage.
17.11.2 Network-Attached Storage With the phenomenal growth in digital data, particularly generated from multime- dia and other enterprise applications, the need for high-performance storage solu- tions at low cost has become extremely important. Network-attached storage (NAS) devices are among the storage devices being used for this purpose. These devices are, in fact, servers that do not provide any of the common server services, but simply allow the addition of storage for file sharing. NAS devices allow vast
17.11 New Storage Systems 623
amounts of hard-disk storage space to be added to a network and can make that space available to multiple servers without shutting them down for maintenance and upgrades. NAS devices can reside anywhere on a local area network (LAN) and may be combined in different configurations. A single hardware device, often called the NAS box or NAS head, acts as the interface between the NAS system and net- work clients. These NAS devices require no monitor, keyboard, or mouse. One or more disk or tape drives can be attached to many NAS systems to increase total capacity. Clients connect to the NAS head rather than to the individual storage devices. An NAS can store any data that appears in the form of files, such as e-mail boxes, Web content, remote system backups, and so on. In that sense, NAS devices are being deployed as a replacement for traditional file servers.
NAS systems strive for reliable operation and easy administration. They include built-in features such as secure authentication, or the automatic sending of e-mail alerts in case of error on the device. The NAS devices (or appliances, as some ven- dors refer to them) are being offered with a high degree of scalability, reliability, flexibility, and performance. Such devices typically support RAID levels 0, 1, and 5. Traditional storage area networks (SANs) differ from NAS in several ways. Specifically, SANs often utilize Fiber Channel rather than Ethernet, and a SAN often incorporates multiple network devices or endpoints on a self-contained or private LAN, whereas NAS relies on individual devices connected directly to the existing public LAN. Whereas Windows, UNIX, and NetWare file servers each demand spe- cific protocol support on the client side, NAS systems claim greater operating sys- tem independence of clients.
17.11.3 iSCSI Storage Systems A new protocol called iSCSI (Internet SCSI) has been proposed recently. It allows clients (called initiators) to send SCSI commands to SCSI storage devices on remote channels. The main advantage of iSCSI is that it does not require the special cabling needed by Fiber Channel and it can run over longer distances using existing network infrastructure. By carrying SCSI commands over IP networks, iSCSI facilitates data transfers over intranets and manages storage over long distances. It can transfer data over local area networks (LANs), wide area networks (WANs), or the Internet.
iSCSI works as follows. When a DBMS needs to access data, the operating system generates the appropriate SCSI commands and data request, which then go through encapsulation and, if necessary, encryption procedures. A packet header is added before the resulting IP packets are transmitted over an Ethernet connection. When a packet is received, it is decrypted (if it was encrypted before transmission) and dis- assembled, separating the SCSI commands and request. The SCSI commands go via the SCSI controller to the SCSI storage device. Because iSCSI is bidirectional, the protocol can also be used to return data in response to the original request. Cisco and IBM have marketed switches and routers based on this technology.
iSCSI storage has mainly impacted small- and medium-sized businesses because of its combination of simplicity, low cost, and the functionality of iSCSI devices. It allows them not to learn the ins and outs of Fiber Channel (FC) technology and
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instead benefit from their familiarity with the IP protocol and Ethernet hardware. iSCSI implementations in the data centers of very large enterprise businesses are slow in development due to their prior investment in Fiber Channel-based SANs.
iSCSI is one of two main approaches to storage data transmission over IP networks. The other method, Fiber Channel over IP (FCIP), translates Fiber Channel control codes and data into IP packets for transmission between geographically distant Fiber Channel storage area networks. This protocol, known also as Fiber Channel tunneling or storage tunneling, can only be used in conjunction with Fiber Channel technology, whereas iSCSI can run over existing Ethernet networks.
The latest idea to enter the enterprise IP storage race is Fiber Channel over Ethernet (FCoE), which can be thought of as iSCSI without the IP. It uses many ele- ments of SCSI and FC (just like iSCSI), but it does not include TCP/IP components. This promises excellent performance, especially on 10 Gigabit Ethernet (10GbE), and is relatively easy for vendors to add to their products.
17.12 Summary We began this chapter by discussing the characteristics of memory hierarchies and then concentrated on secondary storage devices. In particular, we focused on mag- netic disks because they are used most often to store online database files.
Data on disk is stored in blocks; accessing a disk block is expensive because of the seek time, rotational delay, and block transfer time. To reduce the average block access time, double buffering can be used when accessing consecutive disk blocks. (Other disk parameters are discussed in Appendix B.) We presented different ways of storing file records on disk. File records are grouped into disk blocks and can be fixed length or variable length, spanned or unspanned, and of the same record type or mixed types. We discussed the file header, which describes the record formats and keeps track of the disk addresses of the file blocks. Information in the file header is used by system software accessing the file records.
Then we presented a set of typical commands for accessing individual file records and discussed the concept of the current record of a file. We discussed how complex record search conditions are transformed into simple search conditions that are used to locate records in the file.
Three primary file organizations were then discussed: unordered, ordered, and hashed. Unordered files require a linear search to locate records, but record inser- tion is very simple. We discussed the deletion problem and the use of deletion markers.
Ordered files shorten the time required to read records in order of the ordering field. The time required to search for an arbitrary record, given the value of its ordering key field, is also reduced if a binary search is used. However, maintaining the records in order makes insertion very expensive; thus the technique of using an unordered overflow file to reduce the cost of record insertion was discussed. Overflow records are merged with the master file periodically during file reorganization.
Review Questions 625
Hashing provides very fast access to an arbitrary record of a file, given the value of its hash key. The most suitable method for external hashing is the bucket technique, with one or more contiguous blocks corresponding to each bucket. Collisions caus- ing bucket overflow are handled by chaining. Access on any nonhash field is slow, and so is ordered access of the records on any field. We discussed three hashing tech- niques for files that grow and shrink in the number of records dynamically: extendible, dynamic, and linear hashing. The first two use the higher-order bits of the hash address to organize a directory. Linear hashing is geared to keep the load factor of the file within a given range and adds new buckets linearly.
We briefly discussed other possibilities for primary file organizations, such as B- trees, and files of mixed records, which implement relationships among records of different types physically as part of the storage structure. We reviewed the recent advances in disk technology represented by RAID (Redundant Arrays of Inexpensive (or Independent) Disks), which has become a standard technique in large enterprises to provide better reliability and fault tolerance features in storage. Finally, we reviewed three currently popular options in enterprise storage systems: storage area networks (SANs), network-attached storage (NAS), and iSCSI storage systems.
Review Questions 17.1. What is the difference between primary and secondary storage?
17.2. Why are disks, not tapes, used to store online database files?
17.3. Define the following terms: disk, disk pack, track, block, cylinder, sector, interblock gap, read/write head.
17.4. Discuss the process of disk initialization.
17.5. Discuss the mechanism used to read data from or write data to the disk.
17.6. What are the components of a disk block address?
17.7. Why is accessing a disk block expensive? Discuss the time components involved in accessing a disk block.
17.8. How does double buffering improve block access time?
17.9. What are the reasons for having variable-length records? What types of sep- arator characters are needed for each?
17.10. Discuss the techniques for allocating file blocks on disk.
17.11. What is the difference between a file organization and an access method?
17.12. What is the difference between static and dynamic files?
17.13. What are the typical record-at-a-time operations for accessing a file? Which of these depend on the current file record?
17.14. Discuss the techniques for record deletion.
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17.15. Discuss the advantages and disadvantages of using (a) an unordered file, (b) an ordered file, and (c) a static hash file with buckets and chaining. Which operations can be performed efficiently on each of these organizations, and which operations are expensive?
17.16. Discuss the techniques for allowing a hash file to expand and shrink dynam- ically. What are the advantages and disadvantages of each?
17.17. What is the difference between the directories of extendible and dynamic hashing?
17.18. What are mixed files used for? What are other types of primary file organiza- tions?
17.19. Describe the mismatch between processor and disk technologies.
17.20. What are the main goals of the RAID technology? How does it achieve them?
17.21. How does disk mirroring help improve reliability? Give a quantitative example.
17.22. What characterizes the levels in RAID organization?
17.23. What are the highlights of the popular RAID levels 0, 1, and 5?
17.24. What are storage area networks? What flexibility and advantages do they offer?
17.25. Describe the main features of network-attached storage as an enterprise storage solution.
17.26. How have new iSCSI systems improved the applicability of storage area net- works?
Exercises 17.27. Consider a disk with the following characteristics (these are not parameters
of any particular disk unit): block size B = 512 bytes; interblock gap size G = 128 bytes; number of blocks per track = 20; number of tracks per surface = 400. A disk pack consists of 15 double-sided disks.
a. What is the total capacity of a track, and what is its useful capacity (excluding interblock gaps)?
b. How many cylinders are there?
c. What are the total capacity and the useful capacity of a cylinder?
d. What are the total capacity and the useful capacity of a disk pack?
e. Suppose that the disk drive rotates the disk pack at a speed of 2400 rpm (revolutions per minute); what are the transfer rate (tr) in bytes/msec and the block transfer time (btt) in msec? What is the average rotational delay (rd) in msec? What is the bulk transfer rate? (See Appendix B.)
f. Suppose that the average seek time is 30 msec. How much time does it take (on the average) in msec to locate and transfer a single block, given its block address?
Exercises 627
g. Calculate the average time it would take to transfer 20 random blocks, and compare this with the time it would take to transfer 20 consecutive blocks using double buffering to save seek time and rotational delay.
17.28. A file has r = 20,000 STUDENT records of fixed length. Each record has the following fields: Name (30 bytes), Ssn (9 bytes), Address (40 bytes), PHONE (10 bytes), Birth_date (8 bytes), Sex (1 byte), Major_dept_code (4 bytes), Minor_dept_code (4 bytes), Class_code (4 bytes, integer), and Degree_program (3 bytes). An additional byte is used as a deletion marker. The file is stored on the disk whose parameters are given in Exercise 17.27.
a. Calculate the record size R in bytes.
b. Calculate the blocking factor bfr and the number of file blocks b, assum- ing an unspanned organization.
c. Calculate the average time it takes to find a record by doing a linear search on the file if (i) the file blocks are stored contiguously, and double buffer- ing is used; (ii) the file blocks are not stored contiguously.
d. Assume that the file is ordered by Ssn; by doing a binary search, calculate the time it takes to search for a record given its Ssn value.
17.29. Suppose that only 80 percent of the STUDENT records from Exercise 17.28 have a value for Phone, 85 percent for Major_dept_code, 15 percent for Minor_dept_code, and 90 percent for Degree_program; and suppose that we use a variable-length record file. Each record has a 1-byte field type for each field in the record, plus the 1-byte deletion marker and a 1-byte end-of- record marker. Suppose that we use a spanned record organization, where each block has a 5-byte pointer to the next block (this space is not used for record storage).
a. Calculate the average record length R in bytes.
b. Calculate the number of blocks needed for the file.
17.30. Suppose that a disk unit has the following parameters: seek time s = 20 msec; rotational delay rd = 10 msec; block transfer time btt = 1 msec; block size B = 2400 bytes; interblock gap size G = 600 bytes. An EMPLOYEE file has the fol- lowing fields: Ssn, 9 bytes; Last_name, 20 bytes; First_name, 20 bytes; Middle_init, 1 byte; Birth_date, 10 bytes; Address, 35 bytes; Phone, 12 bytes; Supervisor_ssn, 9 bytes; Department, 4 bytes; Job_code, 4 bytes; deletion marker, 1 byte. The EMPLOYEE file has r = 30,000 records, fixed-length for- mat, and unspanned blocking. Write appropriate formulas and calculate the following values for the above EMPLOYEE file:
a. The record size R (including the deletion marker), the blocking factor bfr, and the number of disk blocks b.
b. Calculate the wasted space in each disk block because of the unspanned organization.
c. Calculate the transfer rate tr and the bulk transfer rate btr for this disk unit (see Appendix B for definitions of tr and btr).
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d. Calculate the average number of block accesses needed to search for an arbitrary record in the file, using linear search.
e. Calculate in msec the average time needed to search for an arbitrary record in the file, using linear search, if the file blocks are stored on con- secutive disk blocks and double buffering is used.
f. Calculate in msec the average time needed to search for an arbitrary record in the file, using linear search, if the file blocks are not stored on consecutive disk blocks.
g. Assume that the records are ordered via some key field. Calculate the average number of block accesses and the average time needed to search for an arbitrary record in the file, using binary search.
17.31. A PARTS file with Part# as the hash key includes records with the following Part# values: 2369, 3760, 4692, 4871, 5659, 1821, 1074, 7115, 1620, 2428, 3943, 4750, 6975, 4981, and 9208. The file uses eight buckets, numbered 0 to 7. Each bucket is one disk block and holds two records. Load these records into the file in the given order, using the hash function h(K) = K mod 8. Calculate the average number of block accesses for a random retrieval on Part#.
17.32. Load the records of Exercise 17.31 into expandable hash files based on extendible hashing. Show the structure of the directory at each step, and the global and local depths. Use the hash function h(K) = K mod 128.
17.33. Load the records of Exercise 17.31 into an expandable hash file, using linear hashing. Start with a single disk block, using the hash function h0 = K mod 20, and show how the file grows and how the hash functions change as the records are inserted. Assume that blocks are split whenever an overflow occurs, and show the value of n at each stage.
17.34. Compare the file commands listed in Section 17.5 to those available on a file access method you are familiar with.
17.35. Suppose that we have an unordered file of fixed-length records that uses an unspanned record organization. Outline algorithms for insertion, deletion, and modification of a file record. State any assumptions you make.
17.36. Suppose that we have an ordered file of fixed-length records and an unordered overflow file to handle insertion. Both files use unspanned records. Outline algorithms for insertion, deletion, and modification of a file record and for reorganizing the file. State any assumptions you make.
17.37. Can you think of techniques other than an unordered overflow file that can be used to make insertions in an ordered file more efficient?
17.38. Suppose that we have a hash file of fixed-length records, and suppose that overflow is handled by chaining. Outline algorithms for insertion, deletion, and modification of a file record. State any assumptions you make.
Exercises 629
17.39. Can you think of techniques other than chaining to handle bucket overflow in external hashing?
17.40. Write pseudocode for the insertion algorithms for linear hashing and for extendible hashing.
17.41. Write program code to access individual fields of records under each of the following circumstances. For each case, state the assumptions you make con- cerning pointers, separator characters, and so on. Determine the type of information needed in the file header in order for your code to be general in each case.
a. Fixed-length records with unspanned blocking
b. Fixed-length records with spanned blocking
c. Variable-length records with variable-length fields and spanned blocking
d. Variable-length records with repeating groups and spanned blocking
e. Variable-length records with optional fields and spanned blocking
f. Variable-length records that allow all three cases in parts c, d, and e
17.42. Suppose that a file initially contains r = 120,000 records of R = 200 bytes each in an unsorted (heap) file. The block size B = 2400 bytes, the average seek time s = 16 ms, the average rotational latency rd = 8.3 ms, and the block transfer time btt = 0.8 ms. Assume that 1 record is deleted for every 2 records added until the total number of active records is 240,000.
a. How many block transfers are needed to reorganize the file?
b. How long does it take to find a record right before reorganization?
c. How long does it take to find a record right after reorganization?
17.43. Suppose we have a sequential (ordered) file of 100,000 records where each record is 240 bytes. Assume that B = 2400 bytes, s = 16 ms, rd = 8.3 ms, and btt = 0.8 ms. Suppose we want to make X independent random record reads from the file. We could make X random block reads or we could perform one exhaustive read of the entire file looking for those X records. The question is to decide when it would be more efficient to perform one exhaustive read of the entire file than to perform X individual random reads. That is, what is the value for X when an exhaustive read of the file is more efficient than ran- dom X reads? Develop this as a function of X.
17.44. Suppose that a static hash file initially has 600 buckets in the primary area and that records are inserted that create an overflow area of 600 buckets. If we reorganize the hash file, we can assume that most of the overflow is elim- inated. If the cost of reorganizing the file is the cost of the bucket transfers (reading and writing all of the buckets) and the only periodic file operation is the fetch operation, then how many times would we have to perform a fetch (successfully) to make the reorganization cost effective? That is, the reorganization cost and subsequent search cost are less than the search cost before reorganization. Support your answer. Assume s = 16 ms, rd = 8.3 ms, and btt = 1 ms.
630 Chapter 17 Disk Storage, Basic File Structures, and Hashing
17.45. Suppose we want to create a linear hash file with a file load factor of 0.7 and a blocking factor of 20 records per bucket, which is to contain 112,000 records initially.
a. How many buckets should we allocate in the primary area?
b. What should be the number of bits used for bucket addresses?
Selected Bibliography Wiederhold (1987) has a detailed discussion and analysis of secondary storage devices and file organizations as a part of database design. Optical disks are described in Berg and Roth (1989) and analyzed in Ford and Christodoulakis (1991). Flash memory is discussed by Dipert and Levy (1993). Ruemmler and Wilkes (1994) present a survey of the magnetic-disk technology. Most textbooks on databases include discussions of the material presented here. Most data structures textbooks, including Knuth (1998), discuss static hashing in more detail; Knuth has a complete discussion of hash functions and collision resolution techniques, as well as of their performance comparison. Knuth also offers a detailed discussion of tech- niques for sorting external files. Textbooks on file structures include Claybrook (1992), Smith and Barnes (1987), and Salzberg (1988); they discuss additional file organizations including tree-structured files, and have detailed algorithms for oper- ations on files. Salzberg et al. (1990) describe a distributed external sorting algo- rithm. File organizations with a high degree of fault tolerance are described by Bitton and Gray (1988) and by Gray et al. (1990). Disk striping was proposed in Salem and Garcia Molina (1986). The first paper on redundant arrays of inexpen- sive disks (RAID) is by Patterson et al. (1988). Chen and Patterson (1990) and the excellent survey of RAID by Chen et al. (1994) are additional references. Grochowski and Hoyt (1996) discuss future trends in disk drives. Various formulas for the RAID architecture appear in Chen et al. (1994).
Morris (1968) is an early paper on hashing. Extendible hashing is described in Fagin et al. (1979). Linear hashing is described by Litwin (1980). Algorithms for insertion and deletion for linear hashing are discussed with illustrations in Salzberg (1988). Dynamic hashing, which we briefly introduced, was proposed by Larson (1978). There are many proposed variations for extendible and linear hashing; for exam- ples, see Cesarini and Soda (1991), Du and Tong (1991), and Hachem and Berra (1992).
Details of disk storage devices can be found at manufacturer sites (for example, http://www.seagate.com, http://www.ibm.com, http://www.emc.com, http://www .hp.com, http://www.storagetek.com,. IBM has a storage technology research center at IBM Almaden (http://www.almaden.ibm.com/).
631
Indexing Structures for Files
In this chapter we assume that a file already exists withsome primary organization such as the unordered, ordered, or hashed organizations that were described in Chapter 17. We will describe additional auxiliary access structures called indexes, which are used to speed up the retrieval of records in response to certain search conditions. The index structures are additional files on disk that provide secondary access paths, which provide alternative ways to access the records without affecting the physical place- ment of records in the primary data file on disk. They enable efficient access to records based on the indexing fields that are used to construct the index. Basically, any field of the file can be used to create an index, and multiple indexes on different fields—as well as indexes on multiple fields—can be constructed on the same file. A variety of indexes are possible; each of them uses a particular data structure to speed up the search. To find a record or records in the data file based on a search condition on an indexing field, the index is searched, which leads to pointers to one or more disk blocks in the data file where the required records are located. The most preva- lent types of indexes are based on ordered files (single-level indexes) and tree data structures (multilevel indexes, B+-trees). Indexes can also be constructed based on hashing or other search data structures. We also discuss indexes that are vectors of bits called bitmap indexes.
We describe different types of single-level ordered indexes—primary, secondary, and clustering—in Section 18.1. By viewing a single-level index as an ordered file, one can develop additional indexes for it, giving rise to the concept of multilevel indexes. A popular indexing scheme called ISAM (Indexed Sequential Access Method) is based on this idea. We discuss multilevel tree-structured indexes in Section 18.2. In Section 18.3 we describe B-trees and B+-trees, which are data struc- tures that are commonly used in DBMSs to implement dynamically changing mul- tilevel indexes. B+-trees have become a commonly accepted default structure for
18chapter 18
632 Chapter 18 Indexing Structures for Files
generating indexes on demand in most relational DBMSs. Section 18.4 is devoted to alternative ways to access data based on a combination of multiple keys. In Section 18.5 we discuss hash indexes and introduce the concept of logical indexes, which give an additional level of indirection from physical indexes, allowing for the physi- cal index to be flexible and extensible in its organization. In Section 18.6 we discuss multikey indexing and bitmap indexes used for searching on one or more keys. Section 18.7 summarizes the chapter.
18.1 Types of Single-Level Ordered Indexes The idea behind an ordered index is similar to that behind the index used in a text- book, which lists important terms at the end of the book in alphabetical order along with a list of page numbers where the term appears in the book. We can search the book index for a certain term in the textbook to find a list of addresses—page num- bers in this case—and use these addresses to locate the specified pages first and then search for the term on each specified page. The alternative, if no other guidance is given, would be to sift slowly through the whole textbook word by word to find the term we are interested in; this corresponds to doing a linear search, which scans the whole file. Of course, most books do have additional information, such as chapter and section titles, which help us find a term without having to search through the whole book. However, the index is the only exact indication of the pages where each term occurs in the book.
For a file with a given record structure consisting of several fields (or attributes), an index access structure is usually defined on a single field of a file, called an indexing field (or indexing attribute).1 The index typically stores each value of the index field along with a list of pointers to all disk blocks that contain records with that field value. The values in the index are ordered so that we can do a binary search on the index. If both the data file and the index file are ordered, and since the index file is typically much smaller than the data file, searching the index using a binary search is a better option. Tree-structured multilevel indexes (see Section 18.2) implement an extension of the binary search idea that reduces the search space by 2- way partitioning at each search step, thereby creating a more efficient approach that divides the search space in the file n-ways at each stage.
There are several types of ordered indexes. A primary index is specified on the ordering key field of an ordered file of records. Recall from Section 17.7 that an ordering key field is used to physically order the file records on disk, and every record has a unique value for that field. If the ordering field is not a key field—that is, if numerous records in the file can have the same value for the ordering field— another type of index, called a clustering index, can be used. The data file is called a clustered file in this latter case. Notice that a file can have at most one physical ordering field, so it can have at most one primary index or one clustering index, but not both. A third type of index, called a secondary index, can be specified on any
1We use the terms field and attribute interchangeably in this chapter.
18.1 Types of Single-Level Ordered Indexes 633
nonordering field of a file. A data file can have several secondary indexes in addition to its primary access method. We discuss these types of single-level indexes in the next three subsections.
18.1.1 Primary Indexes A primary index is an ordered file whose records are of fixed length with two fields, and it acts like an access structure to efficiently search for and access the data records in a data file. The first field is of the same data type as the ordering key field—called the primary key—of the data file, and the second field is a pointer to a disk block (a block address). There is one index entry (or index record) in the index file for each block in the data file. Each index entry has the value of the pri- mary key field for the first record in a block and a pointer to that block as its two field values. We will refer to the two field values of index entry i as <K(i), P(i)>.
To create a primary index on the ordered file shown in Figure 17.7, we use the Name field as primary key, because that is the ordering key field of the file (assuming that each value of Name is unique). Each entry in the index has a Name value and a pointer. The first three index entries are as follows:
<K(1) = (Aaron, Ed), P(1) = address of block 1>
<K(2) = (Adams, John), P(2) = address of block 2>
<K(3) = (Alexander, Ed), P(3) = address of block 3>
Figure 18.1 illustrates this primary index. The total number of entries in the index is the same as the number of disk blocks in the ordered data file. The first record in each block of the data file is called the anchor record of the block, or simply the block anchor.2
Indexes can also be characterized as dense or sparse. A dense index has an index entry for every search key value (and hence every record) in the data file. A sparse (or nondense) index, on the other hand, has index entries for only some of the search values. A sparse index has fewer entries than the number of records in the file. Thus, a primary index is a nondense (sparse) index, since it includes an entry for each disk block of the data file and the keys of its anchor record rather than for every search value (or every record).
The index file for a primary index occupies a much smaller space than does the data file, for two reasons. First, there are fewer index entries than there are records in the data file. Second, each index entry is typically smaller in size than a data record because it has only two fields; consequently, more index entries than data records can fit in one block. Therefore, a binary search on the index file requires fewer block accesses than a binary search on the data file. Referring to Table 17.2, note that the binary search for an ordered data file required log2b block accesses. But if the pri- mary index file contains only bi blocks, then to locate a record with a search key
2We can use a scheme similar to the one described here, with the last record in each block (rather than the first) as the block anchor. This slightly improves the efficiency of the search algorithm.
634 Chapter 18 Indexing Structures for Files
Index file (<K(i), P(i)> entries)
Block anchor primary key
value Block
pointer
(Primary key field)
Name
Aaron, Ed Abbot, Diane
...
...
...
...
...
Acosta, Marc
Adams, John
Adams, Robin
Akers, Jan
Alexander, Ed
Alfred, Bob
Allen, Sam
Allen, Troy
Anders, Keith
Anderson, Rob
Anderson, Zach
Angel, Joe
Archer, Sue
Arnold, Mack
Arnold, Steven
Atkins, Timothy
Wong, James
Wood, Donald
Woods, Manny
Wright, Pam
Wyatt, Charles
Zimmer, Byron
Aaron, Ed
Adams, John
Alexander, Ed
Allen, Troy
Anderson, Zach
Arnold, Mack
Wong, James
Wright, Pam
...
...
...
. . .
. . .
Ssn Birth_date Job Salary Sex
Figure 18.1 Primary index on the ordering key field of the file shown in Figure 17.7.
18.1 Types of Single-Level Ordered Indexes 635
value requires a binary search of that index and access to the block containing that record: a total of log2bi + 1 accesses.
A record whose primary key value is K lies in the block whose address is P(i), where K(i) ≤ K < K(i + 1). The ith block in the data file contains all such records because of the physical ordering of the file records on the primary key field. To retrieve a record, given the value K of its primary key field, we do a binary search on the index file to find the appropriate index entry i, and then retrieve the data file block whose address is P(i).3 Example 1 illustrates the saving in block accesses that is attainable when a primary index is used to search for a record.
Example 1. Suppose that we have an ordered file with r = 30,000 records stored on a disk with block size B = 1024 bytes. File records are of fixed size and are unspanned, with record length R = 100 bytes. The blocking factor for the file would be bfr = ⎣(B/R)⎦ = ⎣(1024/100)⎦ = 10 records per block. The number of blocks needed for the file is b = ⎡(r/bfr)⎤ = ⎡(30000/10)⎤ = 3000 blocks. A binary search on the data file would need approximately ⎡log2b⎤= ⎡(log23000)⎤ = 12 block accesses.
Now suppose that the ordering key field of the file is V = 9 bytes long, a block pointer is P = 6 bytes long, and we have constructed a primary index for the file. The size of each index entry is Ri = (9 + 6) = 15 bytes, so the blocking factor for the index is bfri = ⎣(B/Ri)⎦ = ⎣(1024/15)⎦ = 68 entries per block. The total number of index entries ri is equal to the number of blocks in the data file, which is 3000. The num- ber of index blocks is hence bi = ⎡(ri/bfri)⎤ = ⎡(3000/68)⎤ = 45 blocks. To perform a binary search on the index file would need ⎡(log2bi)⎤ = ⎡(log245)⎤ = 6 block accesses. To search for a record using the index, we need one additional block access to the data file for a total of 6 + 1 = 7 block accesses—an improvement over binary search on the data file, which required 12 disk block accesses.
A major problem with a primary index—as with any ordered file—is insertion and deletion of records. With a primary index, the problem is compounded because if we attempt to insert a record in its correct position in the data file, we must not only move records to make space for the new record but also change some index entries, since moving records will change the anchor records of some blocks. Using an unordered overflow file, as discussed in Section 17.7, can reduce this problem. Another possibility is to use a linked list of overflow records for each block in the data file. This is similar to the method of dealing with overflow records described with hashing in Section 17.8.2. Records within each block and its overflow linked list can be sorted to improve retrieval time. Record deletion is handled using dele- tion markers.
18.1.2 Clustering Indexes If file records are physically ordered on a nonkey field—which does not have a dis- tinct value for each record—that field is called the clustering field and the data file
3Notice that the above formula would not be correct if the data file were ordered on a nonkey field; in that case the same index value in the block anchor could be repeated in the last records of the previous block.
636 Chapter 18 Indexing Structures for Files
is called a clustered file. We can create a different type of index, called a clustering index, to speed up retrieval of all the records that have the same value for the clus- tering field. This differs from a primary index, which requires that the ordering field of the data file have a distinct value for each record.
A clustering index is also an ordered file with two fields; the first field is of the same type as the clustering field of the data file, and the second field is a disk block pointer. There is one entry in the clustering index for each distinct value of the clus- tering field, and it contains the value and a pointer to the first block in the data file that has a record with that value for its clustering field. Figure 18.2 shows an exam- ple. Notice that record insertion and deletion still cause problems because the data records are physically ordered. To alleviate the problem of insertion, it is common to reserve a whole block (or a cluster of contiguous blocks) for each value of the clus- tering field; all records with that value are placed in the block (or block cluster). This makes insertion and deletion relatively straightforward. Figure 18.3 shows this scheme.
A clustering index is another example of a nondense index because it has an entry for every distinct value of the indexing field, which is a nonkey by definition and hence has duplicate values rather than a unique value for every record in the file. There is some similarity between Figures 18.1, 18.2, and 18.3 and Figures 17.11 and 17.12. An index is somewhat similar to dynamic hashing (described in Section 17.8.3) and to the directory structures used for extendible hashing. Both are searched to find a pointer to the data block containing the desired record. A main difference is that an index search uses the values of the search field itself, whereas a hash directory search uses the binary hash value that is calculated by applying the hash function to the search field.
18.1.3 Secondary Indexes A secondary index provides a secondary means of accessing a data file for which some primary access already exists. The data file records could be ordered, unordered, or hashed. The secondary index may be created on a field that is a can- didate key and has a unique value in every record, or on a nonkey field with dupli- cate values. The index is again an ordered file with two fields. The first field is of the same data type as some nonordering field of the data file that is an indexing field. The second field is either a block pointer or a record pointer. Many secondary indexes (and hence, indexing fields) can be created for the same file—each repre- sents an additional means of accessing that file based on some specific field.
First we consider a secondary index access structure on a key (unique) field that has a distinct value for every record. Such a field is sometimes called a secondary key; in the relational model, this would correspond to any UNIQUE key attribute or to the primary key attribute of a table. In this case there is one index entry for each record in the data file, which contains the value of the field for the record and a pointer either to the block in which the record is stored or to the record itself. Hence, such an index is dense.
18.1 Types of Single-Level Ordered Indexes 637
Data file (Clustering
field) Dept_number
1
1
1 2
Name Ssn Birth_date SalaryJob
2
3
3 3
3
3
4 4
5
5
5 5
6 6
6 6
6
8
8 8
1
2
3
4
5
6 8
Index file (<K(i), P(i)> entries)
Clustering field value
Block pointer
Figure 18.2 A clustering index on the Dept_number ordering nonkey field of an EMPLOYEE file.
Again we refer to the two field values of index entry i as <K(i), P(i)>. The entries are ordered by value of K(i), so we can perform a binary search. Because the records of the data file are not physically ordered by values of the secondary key field, we cannot use block anchors. That is why an index entry is created for each record in the data
638 Chapter 18 Indexing Structures for Files
Data file
Block pointer NULL pointer
(Clustering field)
Dept_number 1
1 2 3 4 5 6 8
1 1
Name Ssn Birth_date SalaryJob
Block pointer
2 2
Block pointer
3
3 3 3
Block pointer
3
Block pointer
4 4
Block pointer
5 5 5 5
Block pointer
6 6 6 6
Block pointer
6
Block pointer
8
8 8
NULL pointer
NULL pointer
NULL pointer
NULL pointer
NULL pointer
NULL pointer
Index file (<K(i), P(i)> entries)
Clustering field value
Block pointer
Figure 18.3 Clustering index with a separate block cluster for each group of records that share the same value for the clustering field.
18.1 Types of Single-Level Ordered Indexes 639
Data file
Indexing field (secondary key field)
6 15
3
17
9 5
13 8
21
11
16 2
24
10
20 1
4
23
18 14
12
7
19 22
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15 16
17
18
19
20
21
22 23
24
Index file (<K(i), P(i)> entries)
Index field value
Block pointer
Figure 18.4 A dense secondary index (with block pointers) on a nonordering key field of a file.
file, rather than for each block, as in the case of a primary index. Figure 18.4 illustrates a secondary index in which the pointers P(i) in the index entries are block pointers, not record pointers. Once the appropriate disk block is transferred to a main memory buffer, a search for the desired record within the block can be carried out.
640 Chapter 18 Indexing Structures for Files
A secondary index usually needs more storage space and longer search time than does a primary index, because of its larger number of entries. However, the improvement in search time for an arbitrary record is much greater for a secondary index than for a primary index, since we would have to do a linear search on the data file if the secondary index did not exist. For a primary index, we could still use a binary search on the main file, even if the index did not exist. Example 2 illustrates the improvement in number of blocks accessed.
Example 2. Consider the file of Example 1 with r = 30,000 fixed-length records of size R = 100 bytes stored on a disk with block size B = 1024 bytes. The file has b = 3000 blocks, as calculated in Example 1. Suppose we want to search for a record with a specific value for the secondary key—a nonordering key field of the file that is V = 9 bytes long. Without the secondary index, to do a linear search on the file would require b/2 = 3000/2 = 1500 block accesses on the average. Suppose that we con- struct a secondary index on that nonordering key field of the file. As in Example 1, a block pointer is P = 6 bytes long, so each index entry is Ri = (9 + 6) = 15 bytes, and the blocking factor for the index is bfri = ⎣(B/Ri)⎦ = ⎣(1024/15)⎦ = 68 entries per block. In a dense secondary index such as this, the total number of index entries ri is equal to the number of records in the data file, which is 30,000. The number of blocks needed for the index is hence bi = ⎡(ri /bfri)⎤ = ⎡(3000/68)⎤ = 442 blocks.
A binary search on this secondary index needs ⎡(log2bi)⎤ = ⎡(log2442)⎤ = 9 block accesses. To search for a record using the index, we need an additional block access to the data file for a total of 9 + 1 = 10 block accesses—a vast improvement over the 1500 block accesses needed on the average for a linear search, but slightly worse than the 7 block accesses required for the primary index. This difference arose because the primary index was nondense and hence shorter, with only 45 blocks in length.
We can also create a secondary index on a nonkey, nonordering field of a file. In this case, numerous records in the data file can have the same value for the indexing field. There are several options for implementing such an index:
■ Option 1 is to include duplicate index entries with the same K(i) value—one for each record. This would be a dense index.
■ Option 2 is to have variable-length records for the index entries, with a repeating field for the pointer. We keep a list of pointers <P(i, 1), ..., P(i, k)> in the index entry for K(i)—one pointer to each block that contains a record whose indexing field value equals K(i). In either option 1 or option 2, the binary search algorithm on the index must be modified appropriately to account for a variable number of index entries per index key value.
■ Option 3, which is more commonly used, is to keep the index entries them- selves at a fixed length and have a single entry for each index field value, but to create an extra level of indirection to handle the multiple pointers. In this nondense scheme, the pointer P(i) in index entry <K(i), P(i)> points to a disk block, which contains a set of record pointers; each record pointer in that disk block points to one of the data file records with value K(i) for the index- ing field. If some value K(i) occurs in too many records, so that their record pointers cannot fit in a single disk block, a cluster or linked list of blocks is
18.1 Types of Single-Level Ordered Indexes 641
Data file
(Indexing field)
Dept_number
3
5
1 6
Name Ssn Birth_date SalaryJob
2
3
4
8
6 8
4
1
6
5
2
5
5
1
6
3
6
3
8
3
1
2
3
4
5
6 8
Blocks of record
pointers
Index file
(<K(i), P(i)> entries)
Field value
Block pointer
Figure 18.5 A secondary index (with record pointers) on a non- key field implemented using one level of indirec- tion so that index entries are of fixed length and have unique field values.
used. This technique is illustrated in Figure 18.5. Retrieval via the index requires one or more additional block accesses because of the extra level, but the algorithms for searching the index and (more importantly) for inserting of new records in the data file are straightforward. In addition, retrievals on complex selection conditions may be handled by referring to the record pointers, without having to retrieve many unnecessary records from the data file (see Exercise 18.23).
642 Chapter 18 Indexing Structures for Files
Notice that a secondary index provides a logical ordering on the records by the indexing field. If we access the records in order of the entries in the secondary index, we get them in order of the indexing field. The primary and clustering indexes assume that the field used for physical ordering of records in the file is the same as the indexing field.
18.1.4 Summary To conclude this section, we summarize the discussion of index types in two tables. Table 18.1 shows the index field characteristics of each type of ordered single-level index discussed—primary, clustering, and secondary. Table 18.2 summarizes the properties of each type of index by comparing the number of index entries and specifying which indexes are dense and which use block anchors of the data file.
Table 18.1 Types of Indexes Based on the Properties of the Indexing Field
Index Field Used for Physical Ordering of the File
Index Field Not Used for Physical Ordering of the File
Indexing field is key Primary index Secondary index (Key)
Indexing field is nonkey Clustering index Secondary index (NonKey)
Table 18.2 Properties of Index Types
Type of Index Number of (First-level) Index Entries
Dense or Nondense (Sparse)
Block Anchoring on the Data File
Primary Number of blocks in data file
Nondense Yes
Clustering Number of distinct index field values
Nondense Yes/noa
Secondary (key) Number of records in data file
Dense No
Secondary (nonkey) Number of recordsb or number of distinct index field valuesc
Dense or Nondense No
aYes if every distinct value of the ordering field starts a new block; no otherwise. bFor option 1. cFor options 2 and 3.
18.2 Multilevel Indexes 643
18.2 Multilevel Indexes The indexing schemes we have described thus far involve an ordered index file. A binary search is applied to the index to locate pointers to a disk block or to a record (or records) in the file having a specific index field value. A binary search requires approximately (log2bi) block accesses for an index with bi blocks because each step of the algorithm reduces the part of the index file that we continue to search by a factor of 2. This is why we take the log function to the base 2. The idea behind a multilevel index is to reduce the part of the index that we continue to search by bfri, the blocking factor for the index, which is larger than 2. Hence, the search space is reduced much faster. The value bfri is called the fan-out of the multilevel index, and we will refer to it by the symbol fo. Whereas we divide the record search space into two halves at each step during a binary search, we divide it n-ways (where n = the fan-out) at each search step using the multilevel index. Searching a multilevel index requires approximately (logfobi) block accesses, which is a substantially smaller number than for a binary search if the fan-out is larger than 2. In most cases, the fan-out is much larger than 2.
A multilevel index considers the index file, which we will now refer to as the first (or base) level of a multilevel index, as an ordered file with a distinct value for each K(i). Therefore, by considering the first-level index file as a sorted data file, we can create a primary index for the first level; this index to the first level is called the second level of the multilevel index. Because the second level is a primary index, we can use block anchors so that the second level has one entry for each block of the first level. The blocking factor bfri for the second level—and for all subsequent levels—is the same as that for the first-level index because all index entries are the same size; each has one field value and one block address. If the first level has r1 entries, and the blocking factor—which is also the fan-out—for the index is bfri = fo, then the first level needs ⎡(r1/fo)⎤ blocks, which is therefore the number of entries r2 needed at the second level of the index.
We can repeat this process for the second level. The third level, which is a primary index for the second level, has an entry for each second-level block, so the number of third-level entries is r3 = ⎡(r2/fo)⎤. Notice that we require a second level only if the first level needs more than one block of disk storage, and, similarly, we require a third level only if the second level needs more than one block. We can repeat the preceding process until all the entries of some index level t fit in a single block. This block at the tth level is called the top index level.4 Each level reduces the number of entries at the previous level by a factor of fo—the index fan-out—so we can use the formula 1 ≤ (r1/((fo)
t)) to calculate t. Hence, a multilevel index with r1 first-level entries will have approximately t levels, where t = ⎡(logfo(r1))⎤. When searching the
4The numbering scheme for index levels used here is the reverse of the way levels are commonly defined for tree data structures. In tree data structures, t is referred to as level 0 (zero), t – 1 is level 1, and so on.
644 Chapter 18 Indexing Structures for Files
index, a single disk block is retrieved at each level. Hence, t disk blocks are accessed for an index search, where t is the number of index levels.
The multilevel scheme described here can be used on any type of index—whether it is primary, clustering, or secondary—as long as the first-level index has distinct val- ues for K(i) and fixed-length entries. Figure 18.6 shows a multilevel index built over a primary index. Example 3 illustrates the improvement in number of blocks accessed when a multilevel index is used to search for a record.
Example 3. Suppose that the dense secondary index of Example 2 is converted into a multilevel index. We calculated the index blocking factor bfri = 68 index entries per block, which is also the fan-out fo for the multilevel index; the number of first- level blocks b1 = 442 blocks was also calculated. The number of second-level blocks will be b2 = ⎡(b1/fo)⎤ = ⎡(442/68)⎤ = 7 blocks, and the number of third-level blocks will be b3 = ⎡(b2/fo)⎤ = ⎡(7/68)⎤ = 1 block. Hence, the third level is the top level of the index, and t = 3. To access a record by searching the multilevel index, we must access one block at each level plus one block from the data file, so we need t + 1 = 3 + 1 = 4 block accesses. Compare this to Example 2, where 10 block accesses were needed when a single-level index and binary search were used.
Notice that we could also have a multilevel primary index, which would be non- dense. Exercise 18.18(c) illustrates this case, where we must access the data block from the file before we can determine whether the record being searched for is in the file. For a dense index, this can be determined by accessing the first index level (without having to access a data block), since there is an index entry for every record in the file.
A common file organization used in business data processing is an ordered file with a multilevel primary index on its ordering key field. Such an organization is called an indexed sequential file and was used in a large number of early IBM systems. IBM’s ISAM organization incorporates a two-level index that is closely related to the organization of the disk in terms of cylinders and tracks (see Section 17.2.1). The first level is a cylinder index, which has the key value of an anchor record for each cylinder of a disk pack occupied by the file and a pointer to the track index for the cylinder. The track index has the key value of an anchor record for each track in the cylinder and a pointer to the track. The track can then be searched sequentially for the desired record or block. Insertion is handled by some form of overflow file that is merged periodically with the data file. The index is recreated during file reor- ganization.
Algorithm 18.1 outlines the search procedure for a record in a data file that uses a nondense multilevel primary index with t levels. We refer to entry i at level j of the index as <Kj(i), Pj(i)>, and we search for a record whose primary key value is K. We assume that any overflow records are ignored. If the record is in the file, there must be some entry at level 1 with K1(i) K < K1(i + 1) and the record will be in the block of the data file whose address is P1(i). Exercise 18.23 discusses modifying the search algorithm for other types of indexes.
18.2 Multilevel Indexes 645
Data file
Primary key field
Second (top) level
Two-level index
2 5
8 12
15 21
24 29
35 36
39 41
44 46
51 52
55 58
63 66
71 78
80 82
85 89
2
35
55
85
First (base) level
2
8
15
24
35
39
44
51
55
63
71
80
85
Figure 18.6 A two-level primary index resembling ISAM (Indexed Sequential Access Method) organization.
646 Chapter 18 Indexing Structures for Files
Algorithm 18.1. Searching a Nondense Multilevel Primary Index with t Levels
(* We assume the index entry to be a block anchor that is the first key per block. *) p ← address of top-level block of index; for j ← t step – 1 to 1 do
begin read the index block (at jth index level) whose address is p; search block p for entry i such that Kj (i) ≤ K < Kj(i + 1)
(* if Kj(i) is the last entry in the block, it is sufficient to satisfy Kj(i) ≤ K *); p ← Pj(i ) (* picks appropriate pointer at jth index level *)
end; read the data file block whose address is p; search block p for record with key = K;
As we have seen, a multilevel index reduces the number of blocks accessed when searching for a record, given its indexing field value. We are still faced with the prob- lems of dealing with index insertions and deletions, because all index levels are physically ordered files. To retain the benefits of using multilevel indexing while reducing index insertion and deletion problems, designers adopted a multilevel index called a dynamic multilevel index that leaves some space in each of its blocks for inserting new entries and uses appropriate insertion/deletion algorithms for cre- ating and deleting new index blocks when the data file grows and shrinks. It is often implemented by using data structures called B-trees and B+-trees, which we describe in the next section.
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees
B-trees and B+-trees are special cases of the well-known search data structure known as a tree. We briefly introduce the terminology used in discussing tree data structures. A tree is formed of nodes. Each node in the tree, except for a special node called the root, has one parent node and zero or more child nodes. The root node has no parent. A node that does not have any child nodes is called a leaf node; a nonleaf node is called an internal node. The level of a node is always one more than the level of its parent, with the level of the root node being zero.5 A subtree of a node consists of that node and all its descendant nodes—its child nodes, the child nodes of its child nodes, and so on. A precise recursive definition of a subtree is that it consists of a node n and the subtrees of all the child nodes of n. Figure 18.7 illus- trates a tree data structure. In this figure the root node is A, and its child nodes are B, C, and D. Nodes E, J, C, G, H, and K are leaf nodes. Since the leaf nodes are at dif- ferent levels of the tree, this tree is called unbalanced.
5This standard definition of the level of a tree node, which we use throughout Section 18.3, is different from the one we gave for multilevel indexes in Section 18.2.
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 647
A
CB
Subtree for node B
(Nodes E, J, C, G, H, and K are leaf nodes of the tree)
Root node (level 0)
Nodes at level 1D
Nodes at level 2DF I
Nodes at level 3I
HG
J
E
K
Figure 18.7 A tree data structure that shows an unbalanced tree.
In Section 18.3.1, we introduce search trees and then discuss B-trees, which can be used as dynamic multilevel indexes to guide the search for records in a data file. B- tree nodes are kept between 50 and 100 percent full, and pointers to the data blocks are stored in both internal nodes and leaf nodes of the B-tree structure. In Section 18.3.2 we discuss B+-trees, a variation of B-trees in which pointers to the data blocks of a file are stored only in leaf nodes, which can lead to fewer levels and higher- capacity indexes. In the DBMSs prevalent in the market today, the common struc- ture used for indexing is B+-trees.
18.3.1 Search Trees and B-Trees A search tree is a special type of tree that is used to guide the search for a record, given the value of one of the record’s fields. The multilevel indexes discussed in Section 18.2 can be thought of as a variation of a search tree; each node in the mul- tilevel index can have as many as fo pointers and fo key values, where fo is the index fan-out. The index field values in each node guide us to the next node, until we reach the data file block that contains the required records. By following a pointer, we restrict our search at each level to a subtree of the search tree and ignore all nodes not in this subtree.
Search Trees. A search tree is slightly different from a multilevel index. A search tree of order p is a tree such that each node contains at most p − 1 search values and p pointers in the order <P1, K1, P2, K2, ..., Pq−1, Kq−1, Pq>, where q ≤ p. Each Pi is a pointer to a child node (or a NULL pointer), and each Ki is a search value from some
648 Chapter 18 Indexing Structures for Files
P1
P1
K1 Ki–1
Kq–1 < X
X
Ki–1 < X < Ki
X
X < K1
X
Pi Ki Kq–1 Pq. . . . . .Figure 18.8 A node in a search tree with pointers to subtrees below it.
5
3
Tree node pointer
Null tree pointer
6 9
7 8 121
Figure 18.9 A search tree of order p = 3.
ordered set of values. All search values are assumed to be unique.6 Figure 18.8 illus- trates a node in a search tree. Two constraints must hold at all times on the search tree:
1. Within each node, K1 < K2 < ... < Kq−1.
2. For all values X in the subtree pointed at by Pi, we have Ki−1 < X < Ki for 1 < i < q; X < Ki for i = 1; and Ki−1 < X for i = q (see Figure 18.8).
Whenever we search for a value X, we follow the appropriate pointer Pi according to the formulas in condition 2 above. Figure 18.9 illustrates a search tree of order p = 3 and integer search values. Notice that some of the pointers Pi in a node may be NULL pointers.
We can use a search tree as a mechanism to search for records stored in a disk file. The values in the tree can be the values of one of the fields of the file, called the search field (which is the same as the index field if a multilevel index guides the search). Each key value in the tree is associated with a pointer to the record in the data file having that value. Alternatively, the pointer could be to the disk block con- taining that record. The search tree itself can be stored on disk by assigning each tree node to a disk block. When a new record is inserted in the file, we must update the search tree by inserting an entry in the tree containing the search field value of the new record and a pointer to the new record.
6This restriction can be relaxed. If the index is on a nonkey field, duplicate search values may exist and the node structure and the navigation rules for the tree may be modified.
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 649
Algorithms are necessary for inserting and deleting search values into and from the search tree while maintaining the preceding two constraints. In general, these algo- rithms do not guarantee that a search tree is balanced, meaning that all of its leaf nodes are at the same level.7 The tree in Figure 18.7 is not balanced because it has leaf nodes at levels 1, 2, and 3. The goals for balancing a search tree are as follows:
■ To guarantee that nodes are evenly distributed, so that the depth of the tree is minimized for the given set of keys and that the tree does not get skewed with some nodes being at very deep levels
■ To make the search speed uniform, so that the average time to find any ran- dom key is roughly the same
While minimizing the number of levels in the tree is one goal, another implicit goal is to make sure that the index tree does not need too much restructuring as records are inserted into and deleted from the main file. Thus we want the nodes to be as full as possible and do not want any nodes to be empty if there are too many deletions. Record deletion may leave some nodes in the tree nearly empty, thus wasting storage space and increasing the number of levels. The B-tree addresses both of these prob- lems by specifying additional constraints on the search tree.
B-Trees. The B-tree has additional constraints that ensure that the tree is always balanced and that the space wasted by deletion, if any, never becomes excessive. The algorithms for insertion and deletion, though, become more complex in order to maintain these constraints. Nonetheless, most insertions and deletions are simple processes; they become complicated only under special circumstances—namely, whenever we attempt an insertion into a node that is already full or a deletion from a node that makes it less than half full. More formally, a B-tree of order p, when used as an access structure on a key field to search for records in a data file, can be defined as follows:
1. Each internal node in the B-tree (Figure 18.10(a)) is of the form
<P1, <K1, Pr1>, P2, <K2, Pr2>, ..., <Kq–1, Prq–1>, Pq>
where q ≤ p. Each Pi is a tree pointer—a pointer to another node in the B- tree. Each Pri is a data pointer
8—a pointer to the record whose search key field value is equal to Ki (or to the data file block containing that record).
2. Within each node, K1 < K2 < ... < Kq−1.
3. For all search key field values X in the subtree pointed at by Pi (the ith sub- tree, see Figure 18.10(a)), we have:
Ki–1 < X < Ki for 1 < i < q; X < Ki for i = 1; and Ki–1 < X for i = q.
4. Each node has at most p tree pointers.
7The definition of balanced is different for binary trees. Balanced binary trees are known as AVL trees. 8A data pointer is either a block address or a record address; the latter is essentially a block address and a record offset within the block.
650 Chapter 18 Indexing Structures for Files
X Tree pointer
Tree pointer
Tree pointer
(a)
(b)
P2
X
Data pointer
Data pointer
Data pointer
5 o 8 Tree node pointero
6 o 7 o 9 o 12 o
Data pointer
Null tree pointer
1 o 3 o
Data pointer
P1 Pr1K1 Ki–1 Prq–1Kq–1
X
Kq–1 < XKi–1 < X < KiX < K1
Pi Pq. . . . . .Pri–1 Ki Pri
Tree pointer
o
Figure 18.10 B-tree structures. (a) A node in a B-tree with q – 1 search values. (b) A B-tree of order p = 3.The values were inserted in the order 8, 5, 1, 7, 3, 12, 9, 6.
5. Each node, except the root and leaf nodes, has at least ⎡(p/2)⎤ tree pointers. The root node has at least two tree pointers unless it is the only node in the tree.
6. A node with q tree pointers, q ≤ p, has q – 1 search key field values (and hence has q – 1 data pointers).
7. All leaf nodes are at the same level. Leaf nodes have the same structure as internal nodes except that all of their tree pointers Pi are NULL.
Figure 18.10(b) illustrates a B-tree of order p = 3. Notice that all search values K in the B-tree are unique because we assumed that the tree is used as an access structure on a key field. If we use a B-tree on a nonkey field, we must change the definition of the file pointers Pri to point to a block—or a cluster of blocks—that contain the pointers to the file records. This extra level of indirection is similar to option 3, dis- cussed in Section 18.1.3, for secondary indexes.
A B-tree starts with a single root node (which is also a leaf node) at level 0 (zero). Once the root node is full with p – 1 search key values and we attempt to insert another entry in the tree, the root node splits into two nodes at level 1. Only the middle value is kept in the root node, and the rest of the values are split evenly
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 651
between the other two nodes. When a nonroot node is full and a new entry is inserted into it, that node is split into two nodes at the same level, and the middle entry is moved to the parent node along with two pointers to the new split nodes. If the parent node is full, it is also split. Splitting can propagate all the way to the root node, creating a new level if the root is split. We do not discuss algorithms for B- trees in detail in this book,9 but we outline search and insertion procedures for B+-trees in the next section.
If deletion of a value causes a node to be less than half full, it is combined with its neighboring nodes, and this can also propagate all the way to the root. Hence, dele- tion can reduce the number of tree levels. It has been shown by analysis and simula- tion that, after numerous random insertions and deletions on a B-tree, the nodes are approximately 69 percent full when the number of values in the tree stabilizes. This is also true of B+-trees. If this happens, node splitting and combining will occur only rarely, so insertion and deletion become quite efficient. If the number of values grows, the tree will expand without a problem—although splitting of nodes may occur, so some insertions will take more time. Each B-tree node can have at most p tree pointers, p – 1 data pointers, and p – 1 search key field values (see Figure 18.10(a)).
In general, a B-tree node may contain additional information needed by the algo- rithms that manipulate the tree, such as the number of entries q in the node and a pointer to the parent node. Next, we illustrate how to calculate the number of blocks and levels for a B-tree.
Example 4. Suppose that the search field is a nonordering key field, and we con- struct a B-tree on this field with p = 23. Assume that each node of the B-tree is 69 percent full. Each node, on the average, will have p * 0.69 = 23 * 0.69 or approxi- mately 16 pointers and, hence, 15 search key field values. The average fan-out fo = 16. We can start at the root and see how many values and pointers can exist, on the average, at each subsequent level:
Root: 1 node 15 key entries 16 pointers Level 1: 16 nodes 240 key entries 256 pointers Level 2: 256 nodes 3840 key entries 4096 pointers Level 3: 4096 nodes 61,440 key entries
At each level, we calculated the number of key entries by multiplying the total num- ber of pointers at the previous level by 15, the average number of entries in each node. Hence, for the given block size, pointer size, and search key field size, a two- level B-tree holds 3840 + 240 + 15 = 4095 entries on the average; a three-level B-tree holds 65,535 entries on the average.
B-trees are sometimes used as primary file organizations. In this case, whole records are stored within the B-tree nodes rather than just the <search key, record pointer> entries. This works well for files with a relatively small number of records and a small
9For details on insertion and deletion algorithms for B-trees, consult Ramakrishnan and Gehrke [2003].
652 Chapter 18 Indexing Structures for Files
record size. Otherwise, the fan-out and the number of levels become too great to permit efficient access.
In summary, B-trees provide a multilevel access structure that is a balanced tree structure in which each node is at least half full. Each node in a B-tree of order p can have at most p − 1 search values.
18.3.2 B+-Trees Most implementations of a dynamic multilevel index use a variation of the B-tree data structure called a B+-tree. In a B-tree, every value of the search field appears once at some level in the tree, along with a data pointer. In a B+-tree, data pointers are stored only at the leaf nodes of the tree; hence, the structure of leaf nodes differs from the structure of internal nodes. The leaf nodes have an entry for every value of the search field, along with a data pointer to the record (or to the block that contains this record) if the search field is a key field. For a nonkey search field, the pointer points to a block containing pointers to the data file records, creating an extra level of indirection.
The leaf nodes of the B+-tree are usually linked to provide ordered access on the search field to the records. These leaf nodes are similar to the first (base) level of an index. Internal nodes of the B+-tree correspond to the other levels of a multilevel index. Some search field values from the leaf nodes are repeated in the internal nodes of the B+-tree to guide the search. The structure of the internal nodes of a B+- tree of order p (Figure 18.11(a)) is as follows:
1. Each internal node is of the form
<P1, K1, P2, K2, ..., Pq – 1, Kq –1, Pq>
where q ≤ p and each Pi is a tree pointer. 2. Within each internal node, K1 < K2 < ... < Kq−1.
3. For all search field values X in the subtree pointed at by Pi, we have Ki−1 < X ≤ Ki for 1 < i < q; X ≤ Ki for i = 1; and Ki−1 < X for i = q (see Figure 18.11(a)).10
4. Each internal node has at most p tree pointers.
5. Each internal node, except the root, has at least ⎡(p/2)⎤ tree pointers. The root node has at least two tree pointers if it is an internal node.
6. An internal node with q pointers, q ≤ p, has q − 1 search field values.
The structure of the leaf nodes of a B+-tree of order p (Figure 18.11(b)) is as follows:
1. Each leaf node is of the form
<<K1, Pr1>, <K2, Pr2>, ..., <Kq–1, Prq–1>, Pnext>
where q ≤ p, each Pri is a data pointer, and Pnext points to the next leaf node of the B+-tree.
10Our definition follows Knuth (1998). One can define a B+-tree differently by exchanging the < and symbols (K
i−1 X < Ki; Kq−1 ≤ X), but the principles remain the same.
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 653
(b) Pointer to next leaf node in tree
Data pointer
Data pointer
Data pointer
Data pointer
Pr1K1 Pr2K2 Pri Prq–1 PnextKi Kq–1. . . . . .
(a) P1 K1 Ki–1
Kq–1 < X
XX
X < K1
X
Pi Ki Kq–1 Pq. . . . . .
Tree pointer
Tree pointer
Tree pointer
Ki–1 < X < Ki
Figure 18.11 The nodes of a B+-tree. (a) Internal node of a B+-tree with q – 1 search values. (b) Leaf node of a B+-tree with q – 1 search values and q – 1 data pointers.
2. Within each leaf node, K1 ≤ K2 ... , Kq−1, q ≤ p. 3. Each Pri is a data pointer that points to the record whose search field value is
Ki or to a file block containing the record (or to a block of record pointers that point to records whose search field value is Ki if the search field is not a key).
4. Each leaf node has at least ⎡(p/2)⎤ values. 5. All leaf nodes are at the same level.
The pointers in internal nodes are tree pointers to blocks that are tree nodes, whereas the pointers in leaf nodes are data pointers to the data file records or blocks—except for the Pnext pointer, which is a tree pointer to the next leaf node. By starting at the leftmost leaf node, it is possible to traverse leaf nodes as a linked list, using the Pnext pointers. This provides ordered access to the data records on the indexing field. A Pprevious pointer can also be included. For a B
+-tree on a nonkey field, an extra level of indirection is needed similar to the one shown in Figure 18.5, so the Pr pointers are block pointers to blocks that contain a set of record pointers to the actual records in the data file, as discussed in option 3 of Section 18.1.3.
Because entries in the internal nodes of a B+-tree include search values and tree pointers without any data pointers, more entries can be packed into an internal node of a B+-tree than for a similar B-tree. Thus, for the same block (node) size, the order p will be larger for the B+-tree than for the B-tree, as we illustrate in Example 5. This can lead to fewer B+-tree levels, improving search time. Because the structures for internal and for leaf nodes of a B+-tree are different, the order p can be different. We
654 Chapter 18 Indexing Structures for Files
will use p to denote the order for internal nodes and pleaf to denote the order for leaf nodes, which we define as being the maximum number of data pointers in a leaf node.
Example 5. To calculate the order p of a B+-tree, suppose that the search key field is V = 9 bytes long, the block size is B = 512 bytes, a record pointer is Pr = 7 bytes, and a block pointer is P = 6 bytes. An internal node of the B+-tree can have up to p tree pointers and p – 1 search field values; these must fit into a single block. Hence, we have:
(p * P) + ((p – 1) * V) ≤ B (P * 6) + ((P − 1) * 9) ≤ 512 (15 * p) ≤ 521
We can choose p to be the largest value satisfying the above inequality, which gives p = 34. This is larger than the value of 23 for the B-tree (it is left to the reader to compute the order of the B-tree assuming same size pointers), resulting in a larger fan-out and more entries in each internal node of a B+-tree than in the correspon- ding B-tree. The leaf nodes of the B+-tree will have the same number of values and pointers, except that the pointers are data pointers and a next pointer. Hence, the order pleaf for the leaf nodes can be calculated as follows:
(pleaf * (Pr + V)) + P ≤ B (pleaf * (7 + 9)) + 6 ≤ 512 (16 * pleaf) ≤ 506
It follows that each leaf node can hold up to pleaf = 31 key value/data pointer combi- nations, assuming that the data pointers are record pointers.
As with the B-tree, we may need additional information—to implement the inser- tion and deletion algorithms—in each node. This information can include the type of node (internal or leaf ), the number of current entries q in the node, and pointers to the parent and sibling nodes. Hence, before we do the above calculations for p and pleaf, we should reduce the block size by the amount of space needed for all such information. The next example illustrates how we can calculate the number of entries in a B+-tree.
Example 6. Suppose that we construct a B+-tree on the field in Example 5. To cal- culate the approximate number of entries in the B+-tree, we assume that each node is 69 percent full. On the average, each internal node will have 34 * 0.69 or approxi- mately 23 pointers, and hence 22 values. Each leaf node, on the average, will hold 0.69 * pleaf = 0.69 * 31 or approximately 21 data record pointers. A B
+-tree will have the following average number of entries at each level:
Root: 1 node 22 key entries 23 pointers Level 1: 23 nodes 506 key entries 529 pointers Level 2: 529 nodes 11,638 key entries 12,167 pointers Leaf level: 12,167 nodes 255,507 data record pointers
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 655
For the block size, pointer size, and search field size given above, a three-level B+- tree holds up to 255,507 record pointers, with the average 69 percent occupancy of nodes. Compare this to the 65,535 entries for the corresponding B-tree in Example 4. This is the main reason that B+-trees are preferred to B-trees as indexes to data- base files.
Search, Insertion, and Deletion with B+-Trees. Algorithm 18.2 outlines the procedure using the B+-tree as the access structure to search for a record. Algorithm 18.3 illustrates the procedure for inserting a record in a file with a B+-tree access structure. These algorithms assume the existence of a key search field, and they must be modified appropriately for the case of a B+-tree on a nonkey field. We illus- trate insertion and deletion with an example.
Algorithm 18.2. Searching for a Record with Search Key Field Value K, Using a B+-tree
n ← block containing root node of B+-tree; read block n; while (n is not a leaf node of the B+-tree) do
begin q ← number of tree pointers in node n; if K ≤ n.K1 (*n.Ki refers to the ith search field value in node n*)
then n ← n.P1 (*n.Pi refers to the ith tree pointer in node n*) else if K > n.Kq−1
then n ← n.Pq else begin
search node n for an entry i such that n.Ki−1 < K ≤n.Ki; n ← n.Pi end;
read block n end;
search block n for entry (Ki, Pri) with K = Ki; (* search leaf node *) if found
then read data file block with address Pri and retrieve record else the record with search field value K is not in the data file;
Algorithm 18.3. Inserting a Record with Search Key Field Value K in a B+-tree of Order p
n ← block containing root node of B+-tree; read block n; set stack S to empty; while (n is not a leaf node of the B+-tree) do
begin push address of n on stack S;
(*stack S holds parent nodes that are needed in case of split*) q ← number of tree pointers in node n; if K ≤n.K1 (*n.Ki refers to the ith search field value in node n*)
656 Chapter 18 Indexing Structures for Files
then n ← n.P1 (*n.Pi refers to the ith tree pointer in node n*) else if K > n.Kq−1
then n ← n.Pq else begin
search node n for an entry i such that n.Ki−1 < K n.Ki; n ← n.Pi end;
read block n end;
search block n for entry (Ki,Pri) with K = Ki; (*search leaf node n*) if found
then record already in file; cannot insert else (*insert entry in B+-tree to point to record*)
begin create entry (K, Pr) where Pr points to the new record; if leaf node n is not full
then insert entry (K, Pr) in correct position in leaf node n else begin (*leaf node n is full with pleaf record pointers; is split*)
copy n to temp (*temp is an oversize leaf node to hold extra entries*);
insert entry (K, Pr) in temp in correct position; (*temp now holds pleaf + 1 entries of the form (Ki, Pri)*) new ← a new empty leaf node for the tree; new.Pnext ← n.Pnext ; j ← ⎡(pleaf + 1)/2 ⎤ ; n ← first j entries in temp (up to entry (Kj, Prj)); n.Pnext ← new; new ← remaining entries in temp; K ← Kj ; (*now we must move (K, new) and insert in parent internal node;
however, if parent is full, split may propagate*) finished ← false; repeat if stack S is empty
then (*no parent node; new root node is created for the tree*) begin root ← a new empty internal node for the tree; root ← <n, K, new>; finished ← true; end
else begin n ← pop stack S; if internal node n is not full
then begin (*parent node not full; no split*) insert (K, new) in correct position in internal node n; finished ← true end
else begin (*internal node n is full with p tree pointers; overflow condition; node is split*)
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 657
copy n to temp (*temp is an oversize internal node*); insert (K, new) in temp in correct position; (*temp now has p + 1 tree pointers*) new ← a new empty internal node for the tree; j ← ⎣((p + 1)/2⎦ ; n ← entries up to tree pointer Pj in temp; (*n contains <P1, K1, P2, K2, ..., Pj−1, Kj−1, Pj >*) new ← entries from tree pointer Pj+1 in temp; (*new contains < Pj+1, Kj+1, ..., Kp−1, Pp, Kp, Pp+1 >*) K ← Kj (*now we must move (K, new) and insert in parent
internal node*) end
end until finished end;
end;
Figure 18.12 illustrates insertion of records in a B+-tree of order p = 3 and pleaf = 2. First, we observe that the root is the only node in the tree, so it is also a leaf node. As soon as more than one level is created, the tree is divided into internal nodes and leaf nodes. Notice that every key value must exist at the leaf level, because all data pointers are at the leaf level. However, only some values exist in internal nodes to guide the search. Notice also that every value appearing in an internal node also appears as the rightmost value in the leaf level of the subtree pointed at by the tree pointer to the left of the value.
When a leaf node is full and a new entry is inserted there, the node overflows and must be split. The first j = ⎡((pleaf + 1)/2)⎤ entries in the original node are kept there, and the remaining entries are moved to a new leaf node. The jth search value is replicated in the parent internal node, and an extra pointer to the new node is cre- ated in the parent. These must be inserted in the parent node in their correct sequence. If the parent internal node is full, the new value will cause it to overflow also, so it must be split. The entries in the internal node up to Pj—the jth tree pointer after inserting the new value and pointer, where j = ⎣((p + 1)/2)⎦—are kept, while the jth search value is moved to the parent, not replicated. A new internal node will hold the entries from Pj+1 to the end of the entries in the node (see Algorithm 18.3). This splitting can propagate all the way up to create a new root node and hence a new level for the B+-tree.
Figure 18.13 illustrates deletion from a B+-tree. When an entry is deleted, it is always removed from the leaf level. If it happens to occur in an internal node, it must also be removed from there. In the latter case, the value to its left in the leaf node must replace it in the internal node because that value is now the rightmost entry in the subtree. Deletion may cause underflow by reducing the number of entries in the leaf node to below the minimum required. In this case, we try to find a sibling leaf node—a leaf node directly to the left or to the right of the node with underflow—
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5 0 8 Insert 1: overflow (new level)
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Insertion sequence: 8, 5, 1, 7, 3, 12, 9, 6
0
Figure 18.12 An example of insertion in a B+-tree with p = 3 and pleaf = 2.
18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees 659
7
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Deletion sequence: 5, 12, 9
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o 8 o 9 o 12 o7 o
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Delete 12: underflow (redistribute)
Delete 9: underflow (merge with left, redistribute)6
1 7
1 o 6 o 8 o7 o
Figure 18.13 An example of deletion from a B+-tree.
and redistribute the entries among the node and its sibling so that both are at least half full; otherwise, the node is merged with its siblings and the number of leaf nodes is reduced. A common method is to try to redistribute entries with the left sibling; if this is not possible, an attempt to redistribute with the right sibling is
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made. If this is also not possible, the three nodes are merged into two leaf nodes. In such a case, underflow may propagate to internal nodes because one fewer tree pointer and search value are needed. This can propagate and reduce the tree levels.
Notice that implementing the insertion and deletion algorithms may require parent and sibling pointers for each node, or the use of a stack as in Algorithm 18.3. Each node should also include the number of entries in it and its type (leaf or internal). Another alternative is to implement insertion and deletion as recursive procedures.11
Variations of B-Trees and B+-Trees. To conclude this section, we briefly men- tion some variations of B-trees and B+-trees. In some cases, constraint 5 on the B- tree (or for the internal nodes of the B+–tree, except the root node), which requires each node to be at least half full, can be changed to require each node to be at least two-thirds full. In this case the B-tree has been called a B*-tree. In general, some systems allow the user to choose a fill factor between 0.5 and 1.0, where the latter means that the B-tree (index) nodes are to be completely full. It is also possible to specify two fill factors for a B+-tree: one for the leaf level and one for the internal nodes of the tree. When the index is first constructed, each node is filled up to approximately the fill factors specified. Some investigators have suggested relaxing the requirement that a node be half full, and instead allow a node to become com- pletely empty before merging, to simplify the deletion algorithm. Simulation studies show that this does not waste too much additional space under randomly distrib- uted insertions and deletions.
18.4 Indexes on Multiple Keys In our discussion so far, we have assumed that the primary or secondary keys on which files were accessed were single attributes (fields). In many retrieval and update requests, multiple attributes are involved. If a certain combination of attrib- utes is used frequently, it is advantageous to set up an access structure to provide efficient access by a key value that is a combination of those attributes.
For example, consider an EMPLOYEE file containing attributes Dno (department number), Age, Street, City, Zip_code, Salary and Skill_code, with the key of Ssn (Social Security number). Consider the query: List the employees in department number 4 whose age is 59. Note that both Dno and Age are nonkey attributes, which means that a search value for either of these will point to multiple records. The following alter- native search strategies may be considered:
1. Assuming Dno has an index, but Age does not, access the records having Dno = 4 using the index, and then select from among them those records that satisfy Age = 59.
11For more details on insertion and deletion algorithms for B+ trees, consult Ramakrishnan and Gehrke [2003].
18.4 Indexes on Multiple Keys 661
2. Alternately, if Age is indexed but Dno is not, access the records having Age = 59 using the index, and then select from among them those records that sat- isfy Dno = 4.
3. If indexes have been created on both Dno and Age, both indexes may be used; each gives a set of records or a set of pointers (to blocks or records). An inter- section of these sets of records or pointers yields those records or pointers that satisfy both conditions.
All of these alternatives eventually give the correct result. However, if the set of records that meet each condition (Dno = 4 or Age = 59) individually are large, yet only a few records satisfy the combined condition, then none of the above is an effi- cient technique for the given search request. A number of possibilities exist that would treat the combination < Dno, Age> or < Age, Dno> as a search key made up of multiple attributes. We briefly outline these techniques in the following sections. We will refer to keys containing multiple attributes as composite keys.
18.4.1 Ordered Index on Multiple Attributes All the discussion in this chapter so far still applies if we create an index on a search key field that is a combination of <Dno, Age>. The search key is a pair of values <4, 59> in the above example. In general, if an index is created on attributes <A1, A2, ..., An>, the search key values are tuples with n values: <v1, v2, ..., vn>.
A lexicographic ordering of these tuple values establishes an order on this compos- ite search key. For our example, all of the department keys for department number 3 precede those for department number 4. Thus <3, n> precedes <4, m> for any val- ues of m and n. The ascending key order for keys with Dno = 4 would be <4, 18>, <4, 19>, <4, 20>, and so on. Lexicographic ordering works similarly to ordering of character strings. An index on a composite key of n attributes works similarly to any index discussed in this chapter so far.
18.4.2 Partitioned Hashing Partitioned hashing is an extension of static external hashing (Section 17.8.2) that allows access on multiple keys. It is suitable only for equality comparisons; range queries are not supported. In partitioned hashing, for a key consisting of n compo- nents, the hash function is designed to produce a result with n separate hash addresses. The bucket address is a concatenation of these n addresses. It is then pos- sible to search for the required composite search key by looking up the appropriate buckets that match the parts of the address in which we are interested.
For example, consider the composite search key <Dno, Age>. If Dno and Age are hashed into a 3-bit and 5-bit address respectively, we get an 8-bit bucket address. Suppose that Dno = 4 has a hash address ‘100’ and Age = 59 has hash address ‘10101’. Then to search for the combined search value, Dno = 4 and Age = 59, one goes to bucket address 100 10101; just to search for all employees with Age = 59, all buckets (eight of them) will be searched whose addresses are ‘000 10101’, ‘001 10101’, ... and
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Linear Scale for Age
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Figure 18.14 Example of a grid array on Dno and Age attributes.
so on. An advantage of partitioned hashing is that it can be easily extended to any number of attributes. The bucket addresses can be designed so that high-order bits in the addresses correspond to more frequently accessed attributes. Additionally, no separate access structure needs to be maintained for the individual attributes. The main drawback of partitioned hashing is that it cannot handle range queries on any of the component attributes.
18.4.3 Grid Files Another alternative is to organize the EMPLOYEE file as a grid file. If we want to access a file on two keys, say Dno and Age as in our example, we can construct a grid array with one linear scale (or dimension) for each of the search attributes. Figure 18.14 shows a grid array for the EMPLOYEE file with one linear scale for Dno and another for the Age attribute. The scales are made in a way as to achieve a uniform distribution of that attribute. Thus, in our example, we show that the linear scale for Dno has Dno = 1, 2 combined as one value 0 on the scale, while Dno = 5 corresponds to the value 2 on that scale. Similarly, Age is divided into its scale of 0 to 5 by group- ing ages so as to distribute the employees uniformly by age. The grid array shown for this file has a total of 36 cells. Each cell points to some bucket address where the records corresponding to that cell are stored. Figure 18.14 also shows the assign- ment of cells to buckets (only partially).
Thus our request for Dno = 4 and Age = 59 maps into the cell (1, 5) corresponding to the grid array. The records for this combination will be found in the correspond- ing bucket. This method is particularly useful for range queries that would map into a set of cells corresponding to a group of values along the linear scales. If a range query corresponds to a match on the some of the grid cells, it can be processed by accessing exactly the buckets for those grid cells. For example, a query for Dno ≤ 5
18.5 Other Types of Indexes 663
and Age > 40 refers to the data in the top bucket shown in Figure 18.14. The grid file concept can be applied to any number of search keys. For example, for n search keys, the grid array would have n dimensions. The grid array thus allows a partitioning of the file along the dimensions of the search key attributes and provides an access by combinations of values along those dimensions. Grid files perform well in terms of reduction in time for multiple key access. However, they represent a space overhead in terms of the grid array structure. Moreover, with dynamic files, a frequent reor- ganization of the file adds to the maintenance cost.12
18.5 Other Types of Indexes
18.5.1 Hash Indexes It is also possible to create access structures similar to indexes that are based on hashing. The hash index is a secondary structure to access the file by using hashing on a search key other than the one used for the primary data file organization. The index entries are of the type <K, Pr> or <K, P>, where Pr is a pointer to the record containing the key, or P is a pointer to the block containing the record for that key. The index file with these index entries can be organized as a dynamically expand- able hash file, using one of the techniques described in Section 17.8.3; searching for an entry uses the hash search algorithm on K. Once an entry is found, the pointer Pr (or P) is used to locate the corresponding record in the data file. Figure 18.15 illus- trates a hash index on the Emp_id field for a file that has been stored as a sequential file ordered by Name. The Emp_id is hashed to a bucket number by using a hashing function: the sum of the digits of Emp_id modulo 10. For example, to find Emp_id 51024, the hash function results in bucket number 2; that bucket is accessed first. It contains the index entry < 51024, Pr >; the pointer Pr leads us to the actual record in the file. In a practical application, there may be thousands of buckets; the bucket number, which may be several bits long, would be subjected to the directory schemes discussed about dynamic hashing in Section 17.8.3. Other search struc- tures can also be used as indexes.
18.5.2 Bitmap Indexes The bitmap index is another popular data structure that facilitates querying on multiple keys. Bitmap indexing is used for relations that contain a large number of rows. It creates an index for one or more columns, and each value or value range in those columns is indexed. Typically, a bitmap index is created for those columns that contain a fairly small number of unique values. To build a bitmap index on a set of records in a relation, the records must be numbered from 0 to n with an id (a record id or a row id) that can be mapped to a physical address made of a block number and a record offset within the block.
12Insertion/deletion algorithms for grid files may be found in Nievergelt et al. (1984).
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Bucket 0 Emp_id . . . . . . . . . .
12676 Marcus M . .
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13646 Hanson M . .
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21124 Dunhill M . .
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Figure 18.15 Hash-based indexing.
A bitmap index is built on one particular value of a particular field (the column in a relation) and is just an array of bits. Consider a bitmap index for the column C and a value V for that column. For a relation with n rows, it contains n bits. The ith bit is set to 1 if the row i has the value V for column C; otherwise it is set to a 0. If C con- tains the valueset <v1, v2, ..., vm> with m distinct values, then m bitmap indexes would be created for that column. Figure 18.16 shows the relation EMPLOYEE with columns Emp_id, Lname, Sex, Zipcode, and Salary_grade (with just 8 rows for illustra- tion) and a bitmap index for the Sex and Zipcode columns. As an example, if the bitmap for Sex = F, the bits for Row_ids 1, 3, 4, and 7 are set to 1, and the rest of the bits are set to 0, the bitmap indexes could have the following query applications:
■ For the query C1 = V1 , the corresponding bitmap for value V1 returns the Row_ids containing the rows that qualify.
18.5 Other Types of Indexes 665
EMPLOYEE
Row_id Emp_id Lname Sex Zipcode Salary_grade 0 51024 Bass M 94040 .. 1 23402 Clarke F 30022 .. 2 62104 England M 19046 .. 3 34723 Ferragamo F 30022 .. 4 81165 Gucci F 19046 .. 5 13646 Hanson M 19046 .. 6 12676 Marcus M 30022 .. 7 41301 Zara F 94040 ..
Bitmap index for Sex
M F 10100110 01011001
Bitmap index for Zipcode
Zipcode 19046 Zipcode 30022 Zipcode 94040 00101100 01010010 10000001
Figure 18.16 Bitmap indexes for Sex and Zipcode
■ For the query C1= V1 and C2 = V2 (a multikey search request), the two cor- responding bitmaps are retrieved and intersected (logically AND-ed) to yield the set of Row_ids that qualify. In general, k bitvectors can be intersected to deal with k equality conditions. Complex AND-OR conditions can also be supported using bitmap indexing.
■ To retrieve a count of rows that qualify for the condition C1 = V1, the “1” entries in the corresponding bitvector are counted.
■ Queries with negation, such as C1 ¬ = V1, can be handled by applying the Boolean complement operation on the corresponding bitmap.
Consider the example in Figure 18.16. To find employees with Sex = F and Zipcode = 30022, we intersect the bitmaps “01011001” and “01010010” yielding Row_ids 1 and 3. Employees who do not live in Zipcode = 94040 are obtained by complementing the bitvector “10000001” and yields Row_ids 1 through 6. In gen- eral, if we assume uniform distribution of values for a given column, and if one col- umn has 5 distinct values and another has 10 distinct values, the join condition on these two can be considered to have a selectivity of 1/50 (=1/5 * 1/10). Hence, only about 2 percent of the records would actually have to be retrieved. If a column has only a few values, like the Sex column in Figure 18.16, retrieval of the Sex = M con- dition on average would retrieve 50 percent of the rows; in such cases, it is better to do a complete scan rather than use bitmap indexing.
In general, bitmap indexes are efficient in terms of the storage space that they need. If we consider a file of 1 million rows (records) with record size of 100 bytes per row, each bitmap index would take up only one bit per row and hence would use 1 mil- lion bits or 125 Kbytes. Suppose this relation is for 1 million residents of a state, and they are spread over 200 ZIP Codes; the 200 bitmaps over Zipcodes contribute 200 bits (or 25 bytes) worth of space per row; hence, the 200 bitmaps occupy only 25 percent as much space as the data file. They allow an exact retrieval of all residents who live in a given ZIP Code by yielding their Row_ids.
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When records are deleted, renumbering rows and shifting bits in bitmaps becomes expensive. Another bitmap, called the existence bitmap, can be used to avoid this expense. This bitmap has a 0 bit for the rows that have been deleted but are still present and a 1 bit for rows that actually exist. Whenever a row is inserted in the relation, an entry must be made in all the bitmaps of all the columns that have a bitmap index; rows typically are appended to the relation or may replace deleted rows. This process represents an indexing overhead.
Large bitvectors are handled by treating them as a series of 32-bit or 64-bit vectors, and corresponding AND, OR, and NOT operators are used from the instruction set to deal with 32- or 64-bit input vectors in a single instruction. This makes bitvector operations computationally very efficient.
Bitmaps for B+-Tree Leaf Nodes. Bitmaps can be used on the leaf nodes of B+-tree indexes as well as to point to the set of records that contain each specific value of the indexed field in the leaf node. When the B+-tree is built on a nonkey search field, the leaf record must contain a list of record pointers alongside each value of the indexed attribute. For values that occur very frequently, that is, in a large percentage of the relation, a bitmap index may be stored instead of the point- ers. As an example, for a relation with n rows, suppose a value occurs in 10 percent of the file records. A bitvector would have n bits, having the “1” bit for those Row_ids that contain that search value, which is n/8 or 0.125n bytes in size. If the record pointer takes up 4 bytes (32 bits), then the n/10 record pointers would take up 4 * n/10 or 0.4n bytes. Since 0.4n is more than 3 times larger than 0.125n, it is better to store the bitmap index rather than the record pointers. Hence for search values that occur more frequently than a certain ratio (in this case that would be 1/32), it is beneficial to use bitmaps as a compressed storage mechanism for representing the record pointers in B+-trees that index a nonkey field.
18.5.3 Function-Based Indexing In this section we discuss a new type of indexing, called function-based indexing, that has been introduced in the Oracle relational DBMS as well as in some other commercial products.13
The idea behind function-based indexing is to create an index such that the value that results from applying some function on a field or a collection of fields becomes the key to the index. The following examples show how to create and use function- based indexes.
Example 1. The following statement creates a function-based index on the EMPLOYEE table based on an uppercase representation of the Lname column, which can be entered in many ways but is always queried by its uppercase representation.
CREATE INDEX upper_ix ON Employee (UPPER(Lname));
13Rafi Ahmed contributed most of this section.
18.5 Other Types of Indexes 667
This statement will create an index based on the function UPPER(Lname), which returns the last name in uppercase letters; for example, UPPER('Smith') will return ‘SMITH’.
Function-based indexes ensure that Oracle Database system will use the index rather than perform a full table scan, even when a function is used in the search predicate of a query. For example, the following query will use the index:
SELECT First_name, Lname FROM Employee WHERE UPPER(Lname)= "SMITH".
Without the function-based index, an Oracle Database might perform a full table scan, since a B+-tree index is searched only by using the column value directly; the use of any function on a column prevents such an index from being used.
Example 2. In this example, the EMPLOYEE table is supposed to contain two fields—salary and commission_pct (commission percentage)—and an index is being created on the sum of salary and commission based on the commission_pct.
CREATE INDEX income_ix ON Employee(Salary + (Salary*Commission_pct));
The following query uses the income_ix index even though the fields salary and commission_pct are occurring in the reverse order in the query when compared to the index definition.
SELECT First_name, Lname FROM Employee WHERE ((Salary*Commission_pct) + Salary ) > 15000;
Example 3. This is a more advanced example of using function-based indexing to define conditional uniqueness. The following statement creates a unique function- based index on the ORDERS table that prevents a customer from taking advantage of a promotion id (“blowout sale”) more than once. It creates a composite index on the Customer_id and Promotion_id fields together, and it allows only one entry in the index for a given Customer_id with the Promotion_id of “2” by declaring it as a unique index.
CREATE UNIQUE INDEX promo_ix ON Orders (CASE WHEN Promotion_id = 2 THEN Customer_id ELSE NULL END, CASE WHEN Promotion_id = 2 THEN Promotion_id ELSE NULL END);
Note that by using the CASE statement, the objective is to remove from the index any rows where Promotion_id is not equal to 2. Oracle Database does not store in the B+- tree index any rows where all the keys are NULL. Therefore, in this example, we map both Customer_id and Promotion_id to NULL unless Promotion_id is equal to 2. The result is that the index constraint is violated only if Promotion_id is equal to 2, for two (attempted insertions of ) rows with the same Customer_id value.
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18.6 Some General Issues Concerning Indexing
18.6.1 Logical versus Physical Indexes In the earlier discussion, we have assumed that the index entries <K, Pr> (or <K, P>) always include a physical pointer Pr (or P) that specifies the physical record address on disk as a block number and offset. This is sometimes called a physical index, and it has the disadvantage that the pointer must be changed if the record is moved to another disk location. For example, suppose that a primary file organiza- tion is based on linear hashing or extendible hashing; then, each time a bucket is split, some records are allocated to new buckets and hence have new physical addresses. If there was a secondary index on the file, the pointers to those records would have to be found and updated, which is a difficult task.
To remedy this situation, we can use a structure called a logical index, whose index entries are of the form <K, Kp>. Each entry has one value K for the secondary index- ing field matched with the value Kp of the field used for the primary file organiza- tion. By searching the secondary index on the value of K, a program can locate the corresponding value of Kp and use this to access the record through the primary file organization. Logical indexes thus introduce an additional level of indirection between the access structure and the data. They are used when physical record addresses are expected to change frequently. The cost of this indirection is the extra search based on the primary file organization.
18.6.2 Discussion In many systems, an index is not an integral part of the data file but can be created and discarded dynamically. That is why it is often called an access structure. Whenever we expect to access a file frequently based on some search condition involving a particular field, we can request the DBMS to create an index on that field. Usually, a secondary index is created to avoid physical ordering of the records in the data file on disk.
The main advantage of secondary indexes is that—theoretically, at least—they can be created in conjunction with virtually any primary record organization. Hence, a secondary index could be used to complement other primary access methods such as ordering or hashing, or it could even be used with mixed files. To create a B+-tree secondary index on some field of a file, we must go through all records in the file to create the entries at the leaf level of the tree. These entries are then sorted and filled according to the specified fill factor; simultaneously, the other index levels are cre- ated. It is more expensive and much harder to create primary indexes and clustering indexes dynamically, because the records of the data file must be physically sorted on disk in order of the indexing field. However, some systems allow users to create these indexes dynamically on their files by sorting the file during index creation.
It is common to use an index to enforce a key constraint on an attribute. While searching the index to insert a new record, it is straightforward to check at the same
18.6 Some General Issues Concerning Indexing 669
time whether another record in the file—and hence in the index tree—has the same key attribute value as the new record. If so, the insertion can be rejected.
If an index is created on a nonkey field, duplicates occur; handling of these dupli- cates is an issue the DBMS product vendors have to deal with and affects data stor- age as well as index creation and management. Data records for the duplicate key may be contained in the same block or may span multiple blocks where many dupli- cates are possible. Some systems add a row id to the record so that records with duplicate keys have their own unique identifiers. In such cases, the B+-tree index may regard a <key, Row_id> combination as the de facto key for the index, turning the index into a unique index with no duplicates. The deletion of a key K from such an index would involve deleting all occurrences of that key K—hence the deletion algorithm has to account for this.
In actual DBMS products, deletion from B+-tree indexes is also handled in various ways to improve performance and response times. Deleted records may be marked as deleted and the corresponding index entries may also not be removed until a garbage collection process reclaims the space in the data file; the index is rebuilt online after garbage collection.
A file that has a secondary index on every one of its fields is often called a fully inverted file. Because all indexes are secondary, new records are inserted at the end of the file; therefore, the data file itself is an unordered (heap) file. The indexes are usually implemented as B+-trees, so they are updated dynamically to reflect inser- tion or deletion of records. Some commercial DBMSs, such as Software AG’s Adabas, use this method extensively.
We referred to the popular IBM file organization called ISAM in Section 18.2. Another IBM method, the virtual storage access method (VSAM), is somewhat sim- ilar to the B+–tree access structure and is still being used in many commercial systems.
18.6.3 Column-Based Storage of Relations There has been a recent trend to consider a column-based storage of relations as an alternative to the traditional way of storing relations row by row. Commercial rela- tional DBMSs have offered B+-tree indexing on primary as well as secondary keys as an efficient mechanism to support access to data by various search criteria and the ability to write a row or a set of rows to disk at a time to produce write-optimized systems. For data warehouses (to be discussed in Chapter 29), which are read-only databases, the column-based storage offers particular advantages for read-only queries. Typically, the column-store RDBMSs consider storing each column of data individually and afford performance advantages in the following areas:
■ Vertically partitioning the table column by column, so that a two-column table can be constructed for every attribute and thus only the needed columns can be accessed
■ Use of column-wise indexes (similar to the bitmap indexes discussed in Section 18.5.2) and join indexes on multiple tables to answer queries with- out having to access the data tables
670 Chapter 18 Indexing Structures for Files
■ Use of materialized views (see Chapter 5) to support queries on multiple columns
Column-wise storage of data affords additional freedom in the creation of indexes, such as the bitmap indexes discussed earlier. The same column may be present in multiple projections of a table and indexes may be created on each projection. To store the values in the same column, strategies for data compression, null-value sup- pression, dictionary encoding techniques (where distinct values in the column are assigned shorter codes), and run-length encoding techniques have been devised. MonetDB/X100, C-Store, and Vertica are examples of such systems. Further discus- sion on column-store DBMSs can be found in the references mentioned in this chapter’s Selected Bibliography.
18.7 Summary In this chapter we presented file organizations that involve additional access struc- tures, called indexes, to improve the efficiency of retrieval of records from a data file. These access structures may be used in conjunction with the primary file organiza- tions discussed in Chapter 17, which are used to organize the file records themselves on disk.
Three types of ordered single-level indexes were introduced: primary, clustering, and secondary. Each index is specified on a field of the file. Primary and clustering indexes are constructed on the physical ordering field of a file, whereas secondary indexes are specified on nonordering fields as additional access structures to improve performance of queries and transactions. The field for a primary index must also be a key of the file, whereas it is a nonkey field for a clustering index. A single-level index is an ordered file and is searched using a binary search. We showed how multilevel indexes can be constructed to improve the efficiency of searching an index.
Next we showed how multilevel indexes can be implemented as B-trees and B+- trees, which are dynamic structures that allow an index to expand and shrink dynamically. The nodes (blocks) of these index structures are kept between half full and completely full by the insertion and deletion algorithms. Nodes eventually sta- bilize at an average occupancy of 69 percent full, allowing space for insertions with- out requiring reorganization of the index for the majority of insertions. B+-trees can generally hold more entries in their internal nodes than can B-trees, so they may have fewer levels or hold more entries than does a corresponding B-tree.
We gave an overview of multiple key access methods, and showed how an index can be constructed based on hash data structures. We discussed the hash index in some detail—it is a secondary structure to access the file by using hashing on a search key other than that used for the primary organization. Bitmap indexing is another important type of indexing used for querying by multiple keys and is particularly applicable on fields with a small number of unique values. Bitmaps can also be used at the leaf nodes of B+ tree indexes as well. We also discussed function-based index- ing, which is being provided by relational vendors to allow special indexes on a function of one or more attributes.
Review Questions 671
We introduced the concept of a logical index and compared it with the physical indexes we described before. They allow an additional level of indirection in index- ing in order to permit greater freedom for movement of actual record locations on disk. We also reviewed some general issues related to indexing, and commented on column-based storage of relations, which has particular advantages for read-only databases. Finally, we discussed how combinations of the above organizations can be used. For example, secondary indexes are often used with mixed files, as well as with unordered and ordered files.
Review Questions 18.1. Define the following terms: indexing field, primary key field, clustering field,
secondary key field, block anchor, dense index, and nondense (sparse) index.
18.2. What are the differences among primary, secondary, and clustering indexes? How do these differences affect the ways in which these indexes are imple- mented? Which of the indexes are dense, and which are not?
18.3. Why can we have at most one primary or clustering index on a file, but sev- eral secondary indexes?
18.4. How does multilevel indexing improve the efficiency of searching an index file?
18.5. What is the order p of a B-tree? Describe the structure of B-tree nodes.
18.6. What is the order p of a B+-tree? Describe the structure of both internal and leaf nodes of a B+-tree.
18.7. How does a B-tree differ from a B+-tree? Why is a B+-tree usually preferred as an access structure to a data file?
18.8. Explain what alternative choices exist for accessing a file based on multiple search keys.
18.9. What is partitioned hashing? How does it work? What are its limitations?
18.10. What is a grid file? What are its advantages and disadvantages?
18.11. Show an example of constructing a grid array on two attributes on some file.
18.12. What is a fully inverted file? What is an indexed sequential file?
18.13. How can hashing be used to construct an index?
18.14. What is bitmap indexing? Create a relation with two columns and sixteen tuples and show an example of a bitmap index on one or both.
18.15. What is the concept of function-based indexing? What additional purpose does it serve?
18.16. What is the difference between a logical index and a physical index?
18.17. What is column-based storage of a relational database?
672 Chapter 18 Indexing Structures for Files
Exercises 18.18. Consider a disk with block size B = 512 bytes. A block pointer is P = 6 bytes
long, and a record pointer is PR = 7 bytes long. A file has r = 30,000 EMPLOYEE records of fixed length. Each record has the following fields: Name (30 bytes), Ssn (9 bytes), Department_code (9 bytes), Address (40 bytes), Phone (10 bytes), Birth_date (8 bytes), Sex (1 byte), Job_code (4 bytes), and Salary (4 bytes, real number). An additional byte is used as a deletion marker.
a. Calculate the record size R in bytes.
b. Calculate the blocking factor bfr and the number of file blocks b, assum- ing an unspanned organization.
c. Suppose that the file is ordered by the key field Ssn and we want to con- struct a primary index on Ssn. Calculate (i) the index blocking factor bfri (which is also the index fan-out fo); (ii) the number of first-level index entries and the number of first-level index blocks; (iii) the number of lev- els needed if we make it into a multilevel index; (iv) the total number of blocks required by the multilevel index; and (v) the number of block accesses needed to search for and retrieve a record from the file—given its Ssn value—using the primary index.
d. Suppose that the file is not ordered by the key field Ssn and we want to construct a secondary index on Ssn. Repeat the previous exercise (part c) for the secondary index and compare with the primary index.
e. Suppose that the file is not ordered by the nonkey field Department_code and we want to construct a secondary index on Department_code, using option 3 of Section 18.1.3, with an extra level of indirection that stores record pointers. Assume there are 1,000 distinct values of Department_code and that the EMPLOYEE records are evenly distributed among these values. Calculate (i) the index blocking factor bfri (which is also the index fan-out fo); (ii) the number of blocks needed by the level of indirection that stores record pointers; (iii) the number of first-level index entries and the number of first-level index blocks; (iv) the number of levels needed if we make it into a multilevel index; (v) the total number of blocks required by the multilevel index and the blocks used in the extra level of indirection; and (vi) the approximate number of block accesses needed to search for and retrieve all records in the file that have a specific Department_code value, using the index.
f. Suppose that the file is ordered by the nonkey field Department_code and we want to construct a clustering index on Department_code that uses block anchors (every new value of Department_code starts at the beginning of a new block). Assume there are 1,000 distinct values of Department_code and that the EMPLOYEE records are evenly distributed among these values. Calculate (i) the index blocking factor bfri (which is also the index fan-out fo); (ii) the number of first-level index entries and the number of first-level index blocks; (iii) the number of levels needed if we make it into a multilevel index; (iv) the total number of blocks
Exercises 673
required by the multilevel index; and (v) the number of block accesses needed to search for and retrieve all records in the file that have a specific Department_code value, using the clustering index (assume that multiple blocks in a cluster are contiguous).
g. Suppose that the file is not ordered by the key field Ssn and we want to construct a B+-tree access structure (index) on Ssn. Calculate (i) the orders p and pleaf of the B
+-tree; (ii) the number of leaf-level blocks needed if blocks are approximately 69 percent full (rounded up for con- venience); (iii) the number of levels needed if internal nodes are also 69 percent full (rounded up for convenience); (iv) the total number of blocks required by the B+-tree; and (v) the number of block accesses needed to search for and retrieve a record from the file—given its Ssn value—using the B+-tree.
h. Repeat part g, but for a B-tree rather than for a B+-tree. Compare your results for the B-tree and for the B+-tree.
18.19. A PARTS file with Part# as the key field includes records with the following Part# values: 23, 65, 37, 60, 46, 92, 48, 71, 56, 59, 18, 21, 10, 74, 78, 15, 16, 20, 24, 28, 39, 43, 47, 50, 69, 75, 8, 49, 33, 38. Suppose that the search field values are inserted in the given order in a B+-tree of order p = 4 and pleaf = 3; show how the tree will expand and what the final tree will look like.
18.20. Repeat Exercise 18.19, but use a B-tree of order p = 4 instead of a B+-tree.
18.21. Suppose that the following search field values are deleted, in the given order, from the B+-tree of Exercise 18.19; show how the tree will shrink and show the final tree. The deleted values are 65, 75, 43, 18, 20, 92, 59, 37.
18.22. Repeat Exercise 18.21, but for the B-tree of Exercise 18.20.
18.23. Algorithm 18.1 outlines the procedure for searching a nondense multilevel primary index to retrieve a file record. Adapt the algorithm for each of the following cases:
a. A multilevel secondary index on a nonkey nonordering field of a file. Assume that option 3 of Section 18.1.3 is used, where an extra level of indirection stores pointers to the individual records with the corres- ponding index field value.
b. A multilevel secondary index on a nonordering key field of a file.
c. A multilevel clustering index on a nonkey ordering field of a file.
18.24. Suppose that several secondary indexes exist on nonkey fields of a file, implemented using option 3 of Section 18.1.3; for example, we could have secondary indexes on the fields Department_code, Job_code, and Salary of the EMPLOYEE file of Exercise 18.18. Describe an efficient way to search for and retrieve records satisfying a complex selection condition on these fields, such as (Department_code = 5 AND Job_code = 12 AND Salary = 50,000), using the record pointers in the indirection level.
674 Chapter 18 Indexing Structures for Files
18.25. Adapt Algorithms 18.2 and 18.3, which outline search and insertion proce- dures for a B+-tree, to a B-tree.
18.26. It is possible to modify the B+-tree insertion algorithm to delay the case where a new level is produced by checking for a possible redistribution of val- ues among the leaf nodes. Figure 18.17 (next page) illustrates how this could be done for our example in Figure 18.12; rather than splitting the leftmost leaf node when 12 is inserted, we do a left redistribution by moving 7 to the leaf node to its left (if there is space in this node). Figure 18.17 shows how the tree would look when redistribution is considered. It is also possible to consider right redistribution. Try to modify the B+-tree insertion algorithm to take redistribution into account.
18.27. Outline an algorithm for deletion from a B+-tree.
18.28. Repeat Exercise 18.27 for a B-tree.
Selected Bibliography Bayer and McCreight (1972) introduced B-trees and associated algorithms. Comer (1979) provides an excellent survey of B-trees and their history, and variations of B- trees. Knuth (1998) provides detailed analysis of many search techniques, including B-trees and some of their variations. Nievergelt (1974) discusses the use of binary search trees for file organization. Textbooks on file structures including Claybrook (1992), Smith and Barnes (1987), and Salzberg (1988), the algorithms and data structures textbook by Wirth (1985), as well as the database textbook by Ramakrihnan and Gehrke (2003) discuss indexing in detail and may be consulted for search, insertion, and deletion algorithms for B-trees and B+-trees. Larson (1981) analyzes index-sequential files, and Held and Stonebraker (1978) compare static multilevel indexes with B-tree dynamic indexes. Lehman and Yao (1981) and Srinivasan and Carey (1991) did further analysis of concurrent access to B-trees. The books by Wiederhold (1987), Smith and Barnes (1987), and Salzberg (1988), among others, discuss many of the search techniques described in this chapter. Grid files are introduced in Nievergelt et al. (1984). Partial-match retrieval, which uses partitioned hashing, is discussed in Burkhard (1976, 1979).
New techniques and applications of indexes and B+-trees are discussed in Lanka and Mays (1991), Zobel et al. (1992), and Faloutsos and Jagadish (1992). Mohan and Narang (1992) discuss index creation. The performance of various B–tree and B+-tree algorithms is assessed in Baeza-Yates and Larson (1989) and Johnson and Shasha (1993). Buffer management for indexes is discussed in Chan et al. (1992). Column-based storage of databases was proposed by Stonebraker et al. (2005) in the C-Store database system; MonetDB/X100 by Boncz et al. (2008) is another imple- mentation of the idea. Abadi et al. (2008) discuss the advantages of column stores over row-stored databases for read-only database applications.
Selected Bibliography 675
1 0 3 0 5 0 7 0 8 0
3 5
Insert 12: overflow (left redistribution)
Insert 9: overflow (new level)
Insert 6: overflow (split)
1 0 3 0 5 0 7 0 8 0 12 0
1 0 3 0 5 0 7 0 8 0 9 0 12 0
12 0
3 7
3 9
7
1 0 3 0 5 0 6 0 7 0 8 0 9 0
3 6 9
7
Figure 18.17 B+-tree insertion with left redistribution.
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Query Processing and Optimization, and Database Tuning
part 8
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679
Algorithms for Query Processing and Optimization
In this chapter we discuss the techniques used inter-nally by a DBMS to process, optimize, and execute high-level queries. A query expressed in a high-level query language such as SQL must first be scanned, parsed, and validated.1 The scanner identifies the query tokens—such as SQL keywords, attribute names, and relation names—that appear in the text of the query, whereas the parser checks the query syntax to determine whether it is formulated according to the syntax rules (rules of grammar) of the query language. The query must also be validated by checking that all attribute and relation names are valid and semantically meaningful names in the schema of the particular database being queried. An internal representation of the query is then created, usually as a tree data structure called a query tree. It is also possible to rep- resent the query using a graph data structure called a query graph. The DBMS must then devise an execution strategy or query plan for retrieving the results of the query from the database files. A query typically has many possible execution strate- gies, and the process of choosing a suitable one for processing a query is known as query optimization.
Figure 19.1 shows the different steps of processing a high-level query. The query optimizer module has the task of producing a good execution plan, and the code generator generates the code to execute that plan. The runtime database processor has the task of running (executing) the query code, whether in compiled or inter- preted mode, to produce the query result. If a runtime error results, an error mes- sage is generated by the runtime database processor.
19chapter 19
1We will not discuss the parsing and syntax-checking phase of query processing here; this material is discussed in compiler textbooks.
680 Chapter 19 Algorithms for Query Processing and Optimization
The term optimization is actually a misnomer because in some cases the chosen exe- cution plan is not the optimal (or absolute best) strategy—it is just a reasonably effi- cient strategy for executing the query. Finding the optimal strategy is usually too time-consuming—except for the simplest of queries. In addition, trying to find the optimal query execution strategy may require detailed information on how the files are implemented and even on the contents of the files—information that may not be fully available in the DBMS catalog. Hence, planning of a good execution strategy may be a more accurate description than query optimization.
For lower-level navigational database languages in legacy systems—such as the net- work DML or the hierarchical DL/1 (see Section 2.6)—the programmer must choose the query execution strategy while writing a database program. If a DBMS provides only a navigational language, there is limited need or opportunity for exten- sive query optimization by the DBMS; instead, the programmer is given the capabil- ity to choose the query execution strategy. On the other hand, a high-level query language—such as SQL for relational DBMSs (RDBMSs) or OQL (see Chapter 11) for object DBMSs (ODBMSs)—is more declarative in nature because it specifies what the intended results of the query are, rather than identifying the details of how the result should be obtained. Query optimization is thus necessary for queries that are specified in a high-level query language.
We will concentrate on describing query optimization in the context of an RDBMS because many of the techniques we describe have also been adapted for other types
Query in a high-level language
Scanning, parsing, and validating
Immediate form of query
Query optimizer
Execution plan
Query code generator
Code to execute the query
Runtime database processor
Code can be:
Executed directly (interpreted mode)
Stored and executed later whenever needed (compiled mode)
Result of query
Figure 19.1 Typical steps when processing a high-level query.
19.1 Translating SQL Queries into Relational Algebra 681
2There are some query optimization problems and techniques that are pertinent only to ODBMSs. However, we do not discuss them here because we give only an introduction to query optimization.
of database management systems, such as ODBMSs.2 A relational DBMS must sys- tematically evaluate alternative query execution strategies and choose a reasonably efficient or near-optimal strategy. Each DBMS typically has a number of general database access algorithms that implement relational algebra operations such as SELECT or JOIN (see Chapter 6) or combinations of these operations. Only execu- tion strategies that can be implemented by the DBMS access algorithms and that apply to the particular query, as well as to the particular physical database design, can be considered by the query optimization module.
This chapter starts with a general discussion of how SQL queries are typically trans- lated into relational algebra queries and then optimized in Section 19.1. Then we discuss algorithms for implementing relational algebra operations in Sections 19.2 through 19.6. Following this, we give an overview of query optimization strategies. There are two main techniques that are employed during query optimization. The first technique is based on heuristic rules for ordering the operations in a query execution strategy. A heuristic is a rule that works well in most cases but is not guar- anteed to work well in every case. The rules typically reorder the operations in a query tree. The second technique involves systematically estimating the cost of dif- ferent execution strategies and choosing the execution plan with the lowest cost esti- mate. These techniques are usually combined in a query optimizer. We discuss heuristic optimization in Section 19.7 and cost estimation in Section 19.8. Then we provide a brief overview of the factors considered during query optimization in the Oracle commercial RDBMS in Section 19.9. Section 19.10 introduces the topic of semantic query optimization, in which known constraints are used as an aid to devising efficient query execution strategies.
The topics covered in this chapter require that the reader be familiar with the mate- rial presented in several earlier chapters. In particular, the chapters on SQL (Chapters 4 and 5), relational algebra (Chapter 6), and file structures and indexing (Chapters 17 and 18) are a prerequisite to this chapter. Also, it is important to note that the topic of query processing and optimization is vast, and we can only give an introduction to the basic principles and techniques in this chapter.
19.1 Translating SQL Queries into Relational Algebra
In practice, SQL is the query language that is used in most commercial RDBMSs. An SQL query is first translated into an equivalent extended relational algebra expres- sion—represented as a query tree data structure—that is then optimized. Typically, SQL queries are decomposed into query blocks, which form the basic units that can be translated into the algebraic operators and optimized. A query block contains a single SELECT-FROM-WHERE expression, as well as GROUP BY and HAVING clauses if these are part of the block. Hence, nested queries within a query are identified as
682 Chapter 19 Algorithms for Query Processing and Optimization
separate query blocks. Because SQL includes aggregate operators—such as MAX, MIN, SUM, and COUNT—these operators must also be included in the extended algebra, as we discussed in Section 6.4.
Consider the following SQL query on the EMPLOYEE relation in Figure 3.5:
SELECT Lname, Fname FROM EMPLOYEE WHERE Salary > ( SELECT MAX (Salary)
FROM EMPLOYEE WHERE Dno=5 );
This query retrieves the names of employees (from any department in the com- pany) who earn a salary that is greater than the highest salary in department 5. The query includes a nested subquery and hence would be decomposed into two blocks. The inner block is:
( SELECT MAX (Salary) FROM EMPLOYEE WHERE Dno=5 )
This retrieves the highest salary in department 5. The outer query block is:
SELECT Lname, Fname FROM EMPLOYEE WHERE Salary > c
where c represents the result returned from the inner block. The inner block could be translated into the following extended relational algebra expression:
ℑMAX Salary(σDno=5(EMPLOYEE))
and the outer block into the expression:
πLname,Fname(σSalary>c(EMPLOYEE))
The query optimizer would then choose an execution plan for each query block. Notice that in the above example, the inner block needs to be evaluated only once to produce the maximum salary of employees in department 5, which is then used—as the constant c—by the outer block. We called this a nested query (without correlation with the outer query) in Section 5.1.2. It is much harder to optimize the more com- plex correlated nested queries (see Section 5.1.3), where a tuple variable from the outer query block appears in the WHERE-clause of the inner query block.
19.2 Algorithms for External Sorting Sorting is one of the primary algorithms used in query processing. For example, whenever an SQL query specifies an ORDER BY-clause, the query result must be sorted. Sorting is also a key component in sort-merge algorithms used for JOIN and other operations (such as UNION and INTERSECTION), and in duplicate elimination algorithms for the PROJECT operation (when an SQL query specifies the DISTINCT
19.2 Algorithms for External Sorting 683
option in the SELECT clause). We will discuss one of these algorithms in this sec- tion. Note that sorting of a particular file may be avoided if an appropriate index— such as a primary or clustering index (see Chapter 18)—exists on the desired file attribute to allow ordered access to the records of the file.
External sorting refers to sorting algorithms that are suitable for large files of records stored on disk that do not fit entirely in main memory, such as most data- base files.3 The typical external sorting algorithm uses a sort-merge strategy, which starts by sorting small subfiles—called runs—of the main file and then merges the sorted runs, creating larger sorted subfiles that are merged in turn. The sort-merge algorithm, like other database algorithms, requires buffer space in main memory, where the actual sorting and merging of the runs is performed. The basic algorithm, outlined in Figure 19.2, consists of two phases: the sorting phase and the merging phase. The buffer space in main memory is part of the DBMS cache—an area in the computer’s main memory that is controlled by the DBMS. The buffer space is divided into individual buffers, where each buffer is the same size in bytes as the size of one disk block. Thus, one buffer can hold the contents of exactly one disk block.
In the sorting phase, runs (portions or pieces) of the file that can fit in the available buffer space are read into main memory, sorted using an internal sorting algorithm, and written back to disk as temporary sorted subfiles (or runs). The size of each run and the number of initial runs (nR) are dictated by the number of file blocks (b) and the available buffer space (nB). For example, if the number of available main memory buffers nB = 5 disk blocks and the size of the file b = 1024 disk blocks, then nR= ⎡(b/nB)⎤ or 205 initial runs each of size 5 blocks (except the last run which will have only 4 blocks). Hence, after the sorting phase, 205 sorted runs (or 205 sorted subfiles of the original file) are stored as temporary subfiles on disk.
In the merging phase, the sorted runs are merged during one or more merge passes. Each merge pass can have one or more merge steps. The degree of merging (dM) is the number of sorted subfiles that can be merged in each merge step. During each merge step, one buffer block is needed to hold one disk block from each of the sorted subfiles being merged, and one additional buffer is needed for containing one disk block of the merge result, which will produce a larger sorted file that is the result of merging several smaller sorted subfiles. Hence, dM is the smaller of (nB − 1) and nR, and the number of merge passes is ⎡(logdM(nR))⎤. In our example where nB = 5, dM = 4 (four-way merging), so the 205 initial sorted runs would be merged 4 at a time in each step into 52 larger sorted subfiles at the end of the first merge pass. These 52 sorted files are then merged 4 at a time into 13 sorted files, which are then merged into 4 sorted files, and then finally into 1 fully sorted file, which means that four passes are needed.
3Internal sorting algorithms are suitable for sorting data structures, such as tables and lists, that can fit entirely in main memory. These algorithms are described in detail in data structures and algorithms books, and include techniques such as quick sort, heap sort, bubble sort, and many others. We do not discuss these here.
684 Chapter 19 Algorithms for Query Processing and Optimization
set i ← 1; j ← b; {size of the file in blocks} k ← nB; {size of buffer in blocks} m ← ⎡( j/k)⎤;
{Sorting Phase} while (i ≤ m) do {
read next k blocks of the file into the buffer or if there are less than k blocks remaining, then read in the remaining blocks;
sort the records in the buffer and write as a temporary subfile; i ← i + 1;
}
{Merging Phase: merge subfiles until only 1 remains} set i ← 1;
p ← ⎡logk–1m⎤ {p is the number of passes for the merging phase} j ← m;
while (i ≤ p) do {
n ← 1; q ← ( j/(k–1)⎤ ; {number of subfiles to write in this pass} while (n ≤ q) do {
read next k–1 subfiles or remaining subfiles (from previous pass) one block at a time;
merge and write as new subfile one block at a time; n ← n + 1;
} j ← q; i ← i + 1;
}
Figure 19.2 Outline of the sort-merge algorithm for external sorting.
The performance of the sort-merge algorithm can be measured in the number of disk block reads and writes (between the disk and main memory) before the sorting of the whole file is completed. The following formula approximates this cost:
(2 * b) + (2 * b * (logdM nR))
The first term (2 * b) represents the number of block accesses for the sorting phase, since each file block is accessed twice: once for reading into a main memory buffer and once for writing the sorted records back to disk into one of the sorted subfiles. The second term represents the number of block accesses for the merging phase. During each merge pass, a number of disk blocks approximately equal to the origi- nal file blocks b is read and written. Since the number of merge passes is (logdM nR), we get the total merge cost of (2 * b * (logdM nR)).
19.3 Algorithms for SELECT and JOIN Operations 685
The minimum number of main memory buffers needed is nB = 3, which gives a dM of 2 and an nR of ⎡(b/3)⎤. The minimum dM of 2 gives the worst-case performance of the algorithm, which is:
(2 * b) + (2 * (b * (log2 nR))).
The following sections discuss the various algorithms for the operations of the rela- tional algebra (see Chapter 6).
19.3 Algorithms for SELECT and JOIN Operations
19.3.1 Implementing the SELECT Operation There are many algorithms for executing a SELECT operation, which is basically a search operation to locate the records in a disk file that satisfy a certain condition. Some of the search algorithms depend on the file having specific access paths, and they may apply only to certain types of selection conditions. We discuss some of the algorithms for implementing SELECT in this section. We will use the following operations, specified on the relational database in Figure 3.5, to illustrate our dis- cussion:
OP1: σSsn = ‘123456789’ (EMPLOYEE)
OP2: σDnumber > 5 (DEPARTMENT)
OP3: σDno = 5 (EMPLOYEE)
OP4: σDno = 5 AND Salary > 30000 AND Sex = ‘F’ (EMPLOYEE)
OP5: σEssn=‘123456789’ AND Pno =10(WORKS_ON)
Search Methods for Simple Selection. A number of search algorithms are pos- sible for selecting records from a file. These are also known as file scans, because they scan the records of a file to search for and retrieve records that satisfy a selec- tion condition.4 If the search algorithm involves the use of an index, the index search is called an index scan. The following search methods (S1 through S6) are examples of some of the search algorithms that can be used to implement a select operation:
■ S1—Linear search (brute force algorithm). Retrieve every record in the file, and test whether its attribute values satisfy the selection condition. Since the records are grouped into disk blocks, each disk block is read into a main memory buffer, and then a search through the records within the disk block is conducted in main memory.
4A selection operation is sometimes called a filter, since it filters out the records in the file that do not satisfy the selection condition.
686 Chapter 19 Algorithms for Query Processing and Optimization
■ S2—Binary search. If the selection condition involves an equality compari- son on a key attribute on which the file is ordered, binary search—which is more efficient than linear search—can be used. An example is OP1 if Ssn is the ordering attribute for the EMPLOYEE file.5
■ S3a—Using a primary index. If the selection condition involves an equality comparison on a key attribute with a primary index—for example, Ssn = ‘123456789’ in OP1—use the primary index to retrieve the record. Note that this condition retrieves a single record (at most).
■ S3b—Using a hash key. If the selection condition involves an equality com- parison on a key attribute with a hash key—for example, Ssn = ‘123456789’ in OP1—use the hash key to retrieve the record. Note that this condition retrieves a single record (at most).
■ S4—Using a primary index to retrieve multiple records. If the comparison condition is >, >=, <, or <= on a key field with a primary index—for exam- ple, Dnumber > 5 in OP2—use the index to find the record satisfying the cor- responding equality condition (Dnumber = 5), then retrieve all subsequent records in the (ordered) file. For the condition Dnumber < 5, retrieve all the preceding records.
■ S5—Using a clustering index to retrieve multiple records. If the selection condition involves an equality comparison on a nonkey attribute with a clustering index—for example, Dno = 5 in OP3—use the index to retrieve all the records satisfying the condition.
■ S6—Using a secondary (B+-tree) index on an equality comparison. This search method can be used to retrieve a single record if the indexing field is a key (has unique values) or to retrieve multiple records if the indexing field is not a key. This can also be used for comparisons involving >, >=, <, or <=.
In Section 19.8, we discuss how to develop formulas that estimate the access cost of these search methods in terms of the number of block accesses and access time. Method S1 (linear search) applies to any file, but all the other methods depend on having the appropriate access path on the attribute used in the selection condition. Method S2 (binary search) requires the file to be sorted on the search attribute. The methods that use an index (S3a, S4, S5, and S6) are generally referred to as index searches, and they require the appropriate index to exist on the search attribute. Methods S4 and S6 can be used to retrieve records in a certain range—for example, 30000 <= Salary <= 35000. Queries involving such conditions are called range queries.
Search Methods for Complex Selection. If a condition of a SELECT operation is a conjunctive condition—that is, if it is made up of several simple conditions
5Generally, binary search is not used in database searches because ordered files are not used unless they also have a corresponding primary index.
19.3 Algorithms for SELECT and JOIN Operations 687
connected with the AND logical connective such as OP4 above—the DBMS can use the following additional methods to implement the operation:
■ S7—Conjunctive selection using an individual index. If an attribute involved in any single simple condition in the conjunctive select condition has an access path that permits the use of one of the methods S2 to S6, use that condition to retrieve the records and then check whether each retrieved record satisfies the remaining simple conditions in the conjunctive select condition.
■ S8—Conjunctive selection using a composite index. If two or more attrib- utes are involved in equality conditions in the conjunctive select condition and a composite index (or hash structure) exists on the combined fields— for example, if an index has been created on the composite key (Essn, Pno) of the WORKS_ON file for OP5—we can use the index directly.
■ S9—Conjunctive selection by intersection of record pointers.6 If second- ary indexes (or other access paths) are available on more than one of the fields involved in simple conditions in the conjunctive select condition, and if the indexes include record pointers (rather than block pointers), then each index can be used to retrieve the set of record pointers that satisfy the indi- vidual condition. The intersection of these sets of record pointers gives the record pointers that satisfy the conjunctive select condition, which are then used to retrieve those records directly. If only some of the conditions have secondary indexes, each retrieved record is further tested to determine whether it satisfies the remaining conditions.7 In general, method S9 assumes that each of the indexes is on a nonkey field of the file, because if one of the conditions is an equality condition on a key field, only one record will satisfy the whole condition.
Whenever a single condition specifies the selection—such as OP1, OP2, or OP3— the DBMS can only check whether or not an access path exists on the attribute involved in that condition. If an access path (such as index or hash key or sorted file) exists, the method corresponding to that access path is used; otherwise, the brute force, linear search approach of method S1 can be used. Query optimization for a SELECT operation is needed mostly for conjunctive select conditions whenever more than one of the attributes involved in the conditions have an access path. The optimizer should choose the access path that retrieves the fewest records in the most efficient way by estimating the different costs (see Section 19.8) and choosing the method with the least estimated cost.
Selectivity of a Condition. When the optimizer is choosing between multiple simple conditions in a conjunctive select condition, it typically considers the
6A record pointer uniquely identifies a record and provides the address of the record on disk; hence, it is also called the record identifier or record id. 7The technique can have many variations—for example, if the indexes are logical indexes that store pri- mary key values instead of record pointers.
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selectivity of each condition. The selectivity (sl) is defined as the ratio of the num- ber of records (tuples) that satisfy the condition to the total number of records (tuples) in the file (relation), and thus is a number between zero and one. Zero selec- tivity means none of the records in the file satisfies the selection condition, and a selectivity of one means that all the records in the file satisfy the condition. In gen- eral, the selectivity will not be either of these two extremes, but will be a fraction that estimates the percentage of file records that will be retrieved.
Although exact selectivities of all conditions may not be available, estimates of selectivities are often kept in the DBMS catalog and are used by the optimizer. For example, for an equality condition on a key attribute of relation r(R), s = 1/|r(R)|, where |r(R)| is the number of tuples in relation r(R). For an equality condition on a nonkey attribute with i distinct values, s can be estimated by (|r(R)|/i)/|r(R)| or 1/i, assuming that the records are evenly or uniformly distributed among the distinct values.8 Under this assumption, |r(R)|/i records will satisfy an equality condition on this attribute. In general, the number of records satisfying a selection condition with selectivity sl is estimated to be |r(R)| * sl. The smaller this estimate is, the higher the desirability of using that condition first to retrieve records. In certain cases, the actual distribution of records among the various distinct values of the attribute is kept by the DBMS in the form of a histogram, in order to get more accurate esti- mates of the number of records that satisfy a particular condition.
Disjunctive Selection Conditions. Compared to a conjunctive selection condi- tion, a disjunctive condition (where simple conditions are connected by the OR logical connective rather than by AND) is much harder to process and optimize. For example, consider OP4�:
OP4�: σDno=5 OR Salary > 30000 OR Sex=‘F’ (EMPLOYEE)
With such a condition, little optimization can be done, because the records satisfy- ing the disjunctive condition are the union of the records satisfying the individual conditions. Hence, if any one of the conditions does not have an access path, we are compelled to use the brute force, linear search approach. Only if an access path exists on every simple condition in the disjunction can we optimize the selection by retrieving the records satisfying each condition—or their record ids—and then applying the union operation to eliminate duplicates.
A DBMS will have available many of the methods discussed above, and typically many additional methods. The query optimizer must choose the appropriate one for executing each SELECT operation in a query. This optimization uses formulas that estimate the costs for each available access method, as we will discuss in Section 19.8. The optimizer chooses the access method with the lowest estimated cost.
8In more sophisticated optimizers, histograms representing the distribution of the records among the dif- ferent attribute values can be kept in the catalog.
19.3 Algorithms for SELECT and JOIN Operations 689
19.3.2 Implementing the JOIN Operation The JOIN operation is one of the most time-consuming operations in query pro- cessing. Many of the join operations encountered in queries are of the EQUIJOIN and NATURAL JOIN varieties, so we consider just these two here since we are only giving an overview of query processing and optimization. For the remainder of this chapter, the term join refers to an EQUIJOIN (or NATURAL JOIN).
There are many possible ways to implement a two-way join, which is a join on two files. Joins involving more than two files are called multiway joins. The number of possible ways to execute multiway joins grows very rapidly. In this section we dis- cuss techniques for implementing only two-way joins. To illustrate our discussion, we refer to the relational schema in Figure 3.5 once more—specifically, to the EMPLOYEE, DEPARTMENT, and PROJECT relations. The algorithms we discuss next are for a join operation of the form:
R A=B S
where A and B are the join attributes, which should be domain-compatible attrib- utes of R and S, respectively. The methods we discuss can be extended to more gen- eral forms of join. We illustrate four of the most common techniques for performing such a join, using the following sample operations:
OP6: EMPLOYEE Dno=Dnumber DEPARTMENT OP7: DEPARTMENT Mgr_ssn=Ssn EMPLOYEE
Methods for Implementing Joins.
■ J1—Nested-loop join (or nested-block join). This is the default (brute force) algorithm, as it does not require any special access paths on either file in the join. For each record t in R (outer loop), retrieve every record s from S (inner loop) and test whether the two records satisfy the join condition t[A] = s[B].9
■ J2—Single-loop join (using an access structure to retrieve the matching records). If an index (or hash key) exists for one of the two join attributes— say, attribute B of file S—retrieve each record t in R (loop over file R), and then use the access structure (such as an index or a hash key) to retrieve directly all matching records s from S that satisfy s[B] = t[A].
■ J3—Sort-merge join. If the records of R and S are physically sorted (ordered) by value of the join attributes A and B, respectively, we can implement the join in the most efficient way possible. Both files are scanned concurrently in order of the join attributes, matching the records that have the same values for A and B. If the files are not sorted, they may be sorted first by using external sorting (see Section 19.2). In this method, pairs of file blocks are copied into memory buffers in order and the records of each file are scanned only once each for
9For disk files, it is obvious that the loops will be over disk blocks, so this technique has also been called nested-block join.
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matching with the other file—unless both A and B are nonkey attributes, in which case the method needs to be modified slightly. A sketch of the sort- merge join algorithm is given in Figure 19.3(a). We use R(i) to refer to the ith record in file R. A variation of the sort-merge join can be used when secondary indexes exist on both join attributes. The indexes provide the ability to access (scan) the records in order of the join attributes, but the records themselves are physically scattered all over the file blocks, so this method may be quite ineffi- cient, as every record access may involve accessing a different disk block.
■ J4—Partition-hash join. The records of files R and S are partitioned into smaller files. The partitioning of each file is done using the same hashing function h on the join attribute A of R (for partitioning file R) and B of S (for partitioning file S). First, a single pass through the file with fewer records (say, R) hashes its records to the various partitions of R; this is called the partitioning phase, since the records of R are partitioned into the hash buck- ets. In the simplest case, we assume that the smaller file can fit entirely in main memory after it is partitioned, so that the partitioned subfiles of R are all kept in main memory. The collection of records with the same value of h(A) are placed in the same partition, which is a hash bucket in a hash table in main memory. In the second phase, called the probing phase, a single pass through the other file (S) then hashes each of its records using the same hash function h(B) to probe the appropriate bucket, and that record is combined with all matching records from R in that bucket. This simplified description of partition-hash join assumes that the smaller of the two files fits entirely into memory buckets after the first phase. We will discuss the general case of partition-hash join that does not require this assumption below. In practice, techniques J1 to J4 are implemented by accessing whole disk blocks of a file, rather than individual records. Depending on the available number of buffers in memory, the number of blocks read in from the file can be adjusted.
How Buffer Space and Choice of Outer-Loop File Affect Performance of Nested-Loop Join. The buffer space available has an important effect on some of the join algorithms. First, let us consider the nested-loop approach (J1). Looking again at the operation OP6 above, assume that the number of buffers available in main memory for implementing the join is nB = 7 blocks (buffers). Recall that we assume that each memory buffer is the same size as one disk block. For illustration, assume that the DEPARTMENT file consists of rD = 50 records stored in bD = 10 disk blocks and that the EMPLOYEE file consists of rE = 6000 records stored in bE = 2000 disk blocks. It is advantageous to read as many blocks as possible at a time into memory from the file whose records are used for the outer loop (that is, nB − 2 blocks). The algorithm can then read one block at a time for the inner-loop file and use its records to probe (that is, search) the outer-loop blocks that are currently in main memory for matching records. This reduces the total number of block accesses. An extra buffer in main memory is needed to contain the resulting records after they are joined, and the contents of this result buffer can be appended to the result file—the disk file that will contain the join result—whenever it is filled. This result buffer block then is reused to hold additional join result records.
(a) sort the tuples in R on attribute A; (* assume R has n tuples (records) *) sort the tuples in S on attribute B; (* assume S has m tuples (records) *) set i ← 1, j ← 1; while (i ≤ n) and ( j ≤ m) do { if R( i ) [A] > S( j ) [B]
then set j ← j + 1 elseif R( i ) [A] < S( j ) [B]
then set i ← i + 1 else { (* R( i ) [A] = S( j ) [B], so we output a matched tuple *)
output the combined tuple <R( i ) , S( j ) > to T;
(* output other tuples that match R(i), if any *) set I ← j + 1; while (l ≤ m) and (R( i ) [A] = S( l ) [B]) do { output the combined tuple <R( i ) , S( l ) > to T;
set l ← l + 1 }
(* output other tuples that match S(j), if any *) set k ← i + 1; while (k ≤ n) and (R( k ) [A] = S( j ) [B]) do { output the combined tuple <R( k ) , S( j ) > to T;
set k ← k + 1 } set i ← k, j ← l
} }
(b) create a tuple t[<attribute list>] in T � for each tuple t in R; (* T � contains the projection results before duplicate elimination *)
if <attribute list> includes a key of R then T ← T �
else { sort the tuples in T �; set i ← 1, j ← 2; while i n do { output the tuple T �[ i ] to T;
while T �[ i ] = T �[ j ] and j ≤ n do j ← j + 1; (* eliminate duplicates *) i ← j; j ← i + 1
} } (* T contains the projection result after duplicate elimination *) (continues)
19.3 Algorithms for SELECT and JOIN Operations 691
Figure 19.3 Implementing JOIN, PROJECT, UNION, INTERSECTION, and SET DIFFERENCE by using sort-merge, where R has n tuples and S has m tuples. (a) Implementing the opera- tion T ← R
A=B S. (b) Implementing the operation T ← π<attribute list>(R).
(c) sort the tuples in R and S using the same unique sort attributes; set i ← 1, j ← 1; while (i ≤ n) and (j ≤ m) do { if R( i ) > S( j )
then { output S( j ) to T; set j ← j + 1
} elseif R( i ) < S( j )
then { output R( i ) to T; set i ← i + 1
} else set j ← j + 1 (* R(i ) = S ( j ) , so we skip one of the duplicate tuples *)
} if (i ≤ n) then add tuples R( i ) to R(n) to T; if (j ≤ m) then add tuples S( j ) to S(m) to T;
(d) sort the tuples in R and S using the same unique sort attributes; set i ← 1, j ← 1; while ( i ≤ n) and ( j ≤ m) do { if R( i ) > S( j )
then set j ← j + 1 elseif R( i ) < S( j )
then set i ← i + 1 else { output R( j ) to T; (* R( i ) =S( j ) , so we output the tuple *)
set i ← i + 1, j ← j + 1 }
}
(e) sort the tuples in R and S using the same unique sort attributes; set i ← 1, j ← 1; while (i n) and ( j ≤ m) do { if R( i ) > S(j)
then set j ← j + 1 elseif R(i) < S( j )
then { output R( i ) to T; (* R( i ) has no matching S( j ) , so output R( i ) *) set i ← i + 1
} else set i ← i + 1, j ← j + 1
} if (i ≤ n) then add tuples R( i ) to R( n ) to T;
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Figure 19.3 (continued) Implementing JOIN, PROJECT, UNION, INTERSECTION, and SET DIFFERENCE by using sort-merge, where R has n tuples and S has m tuples. (c) Implementing the operation T ← R ∪ S. (d) Implementing the operation T ← R ∩ S. (e) Implementing the operation T ← R – S.
19.3 Algorithms for SELECT and JOIN Operations 693
In the nested-loop join, it makes a difference which file is chosen for the outer loop and which for the inner loop. If EMPLOYEE is used for the outer loop, each block of EMPLOYEE is read once, and the entire DEPARTMENT file (each of its blocks) is read once for each time we read in (nB – 2) blocks of the EMPLOYEE file. We get the follow- ing formulas for the number of disk blocks that are read from disk to main memory:
Total number of blocks accessed (read) for outer-loop file = bE Number of times (nB − 2) blocks of outer file are loaded into main memory
= ⎡bE/(nB – 2)⎤ Total number of blocks accessed (read) for inner-loop file = bD * ⎡bE/(nB – 2)⎤
Hence, we get the following total number of block read accesses:
bE + ( ⎡bE/(nB – 2)⎤ * bD) = 2000 + ( ⎡(2000/5)⎤ * 10) = 6000 block accesses
On the other hand, if we use the DEPARTMENT records in the outer loop, by symme- try we get the following total number of block accesses:
bD + ( ⎡bD/(nB – 2)⎤ * bE) = 10 + ( ⎡(10/5)⎤ * 2000) = 4010 block accesses
The join algorithm uses a buffer to hold the joined records of the result file. Once the buffer is filled, it is written to disk and its contents are appended to the result file, and then refilled with join result records.10
If the result file of the join operation has bRES disk blocks, each block is written once to disk, so an additional bRES block accesses (writes) should be added to the preced- ing formulas in order to estimate the total cost of the join operation. The same holds for the formulas developed later for other join algorithms. As this example shows, it is advantageous to use the file with fewer blocks as the outer-loop file in the nested-loop join.
How the Join Selection Factor Affects Join Performance. Another factor that affects the performance of a join, particularly the single-loop method J2, is the frac- tion of records in one file that will be joined with records in the other file. We call this the join selection factor11 of a file with respect to an equijoin condition with another file. This factor depends on the particular equijoin condition between the two files. To illustrate this, consider the operation OP7, which joins each DEPARTMENT record with the EMPLOYEE record for the manager of that depart- ment. Here, each DEPARTMENT record (there are 50 such records in our example) will be joined with a single EMPLOYEE record, but many EMPLOYEE records (the 5,950 of them that do not manage a department) will not be joined with any record from DEPARTMENT.
Suppose that secondary indexes exist on both the attributes Ssn of EMPLOYEE and Mgr_ssn of DEPARTMENT, with the number of index levels xSsn = 4 and xMgr_ssn= 2,
10If we reserve two buffers for the result file, double buffering can be used to speed the algorithm (see Section 17.3). 11This is different from the join selectivity, which we will discuss in Section 19.8.
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respectively. We have two options for implementing method J2. The first retrieves each EMPLOYEE record and then uses the index on Mgr_ssn of DEPARTMENT to find a matching DEPARTMENT record. In this case, no matching record will be found for employees who do not manage a department. The number of block accesses for this case is approximately:
bE + (rE * (xMgr_ssn + 1)) = 2000 + (6000 * 3) = 20,000 block accesses
The second option retrieves each DEPARTMENT record and then uses the index on Ssn of EMPLOYEE to find a matching manager EMPLOYEE record. In this case, every DEPARTMENT record will have one matching EMPLOYEE record. The number of block accesses for this case is approximately:
bD + (rD * (xSsn + 1)) = 10 + (50 * 5) = 260 block accesses
The second option is more efficient because the join selection factor of DEPARTMENT with respect to the join condition Ssn = Mgr_ssn is 1 (every record in DEPARTMENT will be joined), whereas the join selection factor of EMPLOYEE with respect to the same join condition is (50/6000), or 0.008 (only 0.8 percent of the records in EMPLOYEE will be joined). For method J2, either the smaller file or the file that has a match for every record (that is, the file with the high join selection fac- tor) should be used in the (single) join loop. It is also possible to create an index specifically for performing the join operation if one does not already exist.
The sort-merge join J3 is quite efficient if both files are already sorted by their join attribute. Only a single pass is made through each file. Hence, the number of blocks accessed is equal to the sum of the numbers of blocks in both files. For this method, both OP6 and OP7 would need bE + bD = 2000 + 10 = 2010 block accesses. However, both files are required to be ordered by the join attributes; if one or both are not, a sorted copy of each file must be created specifically for performing the join opera- tion. If we roughly estimate the cost of sorting an external file by (b log2b) block accesses, and if both files need to be sorted, the total cost of a sort-merge join can be estimated by (bE + bD + bE log2bE + bD log2bD).
12
General Case for Partition-Hash Join. The hash-join method J4 is also quite efficient. In this case only a single pass is made through each file, whether or not the files are ordered. If the hash table for the smaller of the two files can be kept entirely in main memory after hashing (partitioning) on its join attribute, the implementa- tion is straightforward. If, however, the partitions of both files must be stored on disk, the method becomes more complex, and a number of variations to improve the efficiency have been proposed. We discuss two techniques: the general case of partition-hash join and a variation called hybrid hash-join algorithm, which has been shown to be quite efficient.
In the general case of partition-hash join, each file is first partitioned into M parti- tions using the same partitioning hash function on the join attributes. Then, each
12We can use the more accurate formulas from Section 19.2 if we know the number of available buffers for sorting.
19.3 Algorithms for SELECT and JOIN Operations 695
pair of corresponding partitions is joined. For example, suppose we are joining rela- tions R and S on the join attributes R.A and S.B:
R A=B S
In the partitioning phase, R is partitioned into the M partitions R1, R2, ..., RM, and S into the M partitions S1, S2, ..., SM. The property of each pair of corresponding partitions Ri, Si with respect to the join operation is that records in Ri only need to be joined with records in Si, and vice versa. This property is ensured by using the same hash function to partition both files on their join attributes—attribute A for R and attribute B for S. The minimum number of in-memory buffers needed for the partitioning phase is M + 1. Each of the files R and S are partitioned separately. During partitioning of a file, M in-memory buffers are allocated to store the records that hash to each partition, and one additional buffer is needed to hold one block at a time of the input file being partitioned. Whenever the in-memory buffer for a par- tition gets filled, its contents are appended to a disk subfile that stores the partition. The partitioning phase has two iterations. After the first iteration, the first file R is partitioned into the subfiles R1, R2, ..., RM, where all the records that hashed to the same buffer are in the same partition. After the second iteration, the second file S is similarly partitioned.
In the second phase, called the joining or probing phase, M iterations are needed. During iteration i, two corresponding partitions Ri and Si are joined. The minimum number of buffers needed for iteration i is the number of blocks in the smaller of the two partitions, say Ri, plus two additional buffers. If we use a nested-loop join during iteration i, the records from the smaller of the two partitions Ri are copied into memory buffers; then all blocks from the other partition Si are read—one at a time—and each record is used to probe (that is, search) partition Ri for matching record(s). Any matching records are joined and written into the result file. To improve the efficiency of in-memory probing, it is common to use an in-memory hash table for storing the records in partition Ri by using a different hash function from the partitioning hash function.13
We can approximate the cost of this partition hash-join as 3 * (bR + bS) + bRES for our example, since each record is read once and written back to disk once during the partitioning phase. During the joining (probing) phase, each record is read a second time to perform the join. The main difficulty of this algorithm is to ensure that the partitioning hash function is uniform—that is, the partition sizes are nearly equal in size. If the partitioning function is skewed (nonuniform), then some partitions may be too large to fit in the available memory space for the second joining phase.
Notice that if the available in-memory buffer space nB > (bR + 2), where bR is the number of blocks for the smaller of the two files being joined, say R, then there is no reason to do partitioning since in this case the join can be performed entirely in memory using some variation of the nested-loop join based on hashing and probing.
13If the hash function used for partitioning is used again, all records in a partition will hash to the same bucket again.
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For illustration, assume we are performing the join operation OP6, repeated below:
OP6: EMPLOYEE Dno=Dnumber DEPARTMENT
In this example, the smaller file is the DEPARTMENT file; hence, if the number of available memory buffers nB > (bD + 2), the whole DEPARTMENT file can be read into main memory and organized into a hash table on the join attribute. Each EMPLOYEE block is then read into a buffer, and each EMPLOYEE record in the buffer is hashed on its join attribute and is used to probe the corresponding in-memory bucket in the DEPARTMENT hash table. If a matching record is found, the records are joined, and the result record(s) are written to the result buffer and eventually to the result file on disk. The cost in terms of block accesses is hence (bD + bE), plus bRES—the cost of writing the result file.
Hybrid Hash-Join. The hybrid hash-join algorithm is a variation of partition hash-join, where the joining phase for one of the partitions is included in the partitioning phase. To illustrate this, let us assume that the size of a memory buffer is one disk block; that nB such buffers are available; and that the partitioning hash function used is h(K) = K mod M, so that M partitions are being created, where M < nB. For illustration, assume we are performing the join operation OP6. In the first pass of the partitioning phase, when the hybrid hash-join algorithm is partitioning the smaller of the two files (DEPARTMENT in OP6), the algorithm divides the buffer space among the M partitions such that all the blocks of the first partition of DEPARTMENT completely reside in main memory. For each of the other partitions, only a single in-memory buffer—whose size is one disk block—is allocated; the remainder of the partition is written to disk as in the regular partition-hash join. Hence, at the end of the first pass of the partitioning phase, the first partition of DEPARTMENT resides wholly in main memory, whereas each of the other partitions of DEPARTMENT resides in a disk subfile.
For the second pass of the partitioning phase, the records of the second file being joined—the larger file, EMPLOYEE in OP6—are being partitioned. If a record hashes to the first partition, it is joined with the matching record in DEPARTMENT and the joined records are written to the result buffer (and eventually to disk). If an EMPLOYEE record hashes to a partition other than the first, it is partitioned nor- mally and stored to disk. Hence, at the end of the second pass of the partitioning phase, all records that hash to the first partition have been joined. At this point, there are M − 1 pairs of partitions on disk. Therefore, during the second joining or probing phase, M − 1 iterations are needed instead of M. The goal is to join as many records during the partitioning phase so as to save the cost of storing those records on disk and then rereading them a second time during the joining phase.
19.4 Algorithms for PROJECT and Set Operations
A PROJECT operation π<attribute list>(R) is straightforward to implement if <attribute list> includes a key of relation R, because in this case the result of the operation will
19.4 Algorithms for PROJECT and Set Operations 697
have the same number of tuples as R, but with only the values for the attributes in <attribute list> in each tuple. If <attribute list> does not include a key of R, duplicate tuples must be eliminated. This can be done by sorting the result of the operation and then eliminating duplicate tuples, which appear consecutively after sorting. A sketch of the algorithm is given in Figure 19.3(b). Hashing can also be used to eliminate duplicates: as each record is hashed and inserted into a bucket of the hash file in memory, it is checked against those records already in the bucket; if it is a duplicate, it is not inserted in the bucket. It is useful to recall here that in SQL queries, the default is not to eliminate duplicates from the query result; duplicates are eliminated from the query result only if the keyword DISTINCT is included.
Set operations—UNION, INTERSECTION, SET DIFFERENCE, and CARTESIAN PRODUCT—are sometimes expensive to implement. In particular, the CARTESIAN PRODUCT operation R × S is quite expensive because its result includes a record for each combination of records from R and S. Also, each record in the result includes all attributes of R and S. If R has n records and j attributes, and S has m records and k attributes, the result relation for R × S will have n * m records and each record will have j + k attributes. Hence, it is important to avoid the CARTESIAN PRODUCT operation and to substitute other operations such as join during query optimization (see Section 19.7).
The other three set operations—UNION, INTERSECTION, and SET DIFFERENCE14—apply only to type-compatible (or union-compatible) relations, which have the same number of attributes and the same attribute domains. The cus- tomary way to implement these operations is to use variations of the sort-merge technique: the two relations are sorted on the same attributes, and, after sorting, a single scan through each relation is sufficient to produce the result. For example, we can implement the UNION operation, R ∪ S, by scanning and merging both sorted files concurrently, and whenever the same tuple exists in both relations, only one is kept in the merged result. For the INTERSECTION operation, R ∩ S, we keep in the merged result only those tuples that appear in both sorted relations. Figure 19.3(c) to (e) sketches the implementation of these operations by sorting and merging. Some of the details are not included in these algorithms.
Hashing can also be used to implement UNION, INTERSECTION, and SET DIFFER- ENCE. One table is first scanned and then partitioned into an in-memory hash table with buckets, and the records in the other table are then scanned one at a time and used to probe the appropriate partition. For example, to implement R ∪ S, first hash (partition) the records of R; then, hash (probe) the records of S, but do not insert duplicate records in the buckets. To implement R ∩ S, first partition the records of R to the hash file. Then, while hashing each record of S, probe to check if an identi- cal record from R is found in the bucket, and if so add the record to the result file. To implement R – S, first hash the records of R to the hash file buckets. While hashing (probing) each record of S, if an identical record is found in the bucket, remove that record from the bucket.
14SET DIFFERENCE is called EXCEPT in SQL.
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In SQL, there are two variations of these set operations. The operations UNION, INTERSECTION, and EXCEPT (the SQL keyword for the SET DIFFERENCE opera- tion) apply to traditional sets, where no duplicate records exist in the result. The operations UNION ALL, INTERSECTION ALL, and EXCEPT ALL apply to multisets (or bags), and duplicates are fully considered. Variations of the above algorithms can be used for the multiset operations in SQL. We leave these as an exercise for the reader.
19.5 Implementing Aggregate Operations and OUTER JOINs
19.5.1 Implementing Aggregate Operations The aggregate operators (MIN, MAX, COUNT, AVERAGE, SUM), when applied to an entire table, can be computed by a table scan or by using an appropriate index, if available. For example, consider the following SQL query:
SELECT MAX(Salary) FROM EMPLOYEE;
If an (ascending) B+-tree index on Salary exists for the EMPLOYEE relation, then the optimizer can decide on using the Salary index to search for the largest Salary value in the index by following the rightmost pointer in each index node from the root to the rightmost leaf. That node would include the largest Salary value as its last entry. In most cases, this would be more efficient than a full table scan of EMPLOYEE, since no actual records need to be retrieved. The MIN function can be handled in a similar manner, except that the leftmost pointer in the index is followed from the root to leftmost leaf. That node would include the smallest Salary value as its first entry.
The index could also be used for the AVERAGE and SUM aggregate functions, but only if it is a dense index—that is, if there is an index entry for every record in the main file. In this case, the associated computation would be applied to the values in the index. For a nondense index, the actual number of records associated with each index value must be used for a correct computation. This can be done if the number of records associated with each value in the index is stored in each index entry. For the COUNT aggregate function, the number of values can be also computed from the index in a similar manner. If a COUNT(*) function is applied to a whole relation, the number of records currently in each relation are typically stored in the catalog, and so the result can be retrieved directly from the catalog.
When a GROUP BY clause is used in a query, the aggregate operator must be applied separately to each group of tuples as partitioned by the grouping attribute. Hence, the table must first be partitioned into subsets of tuples, where each partition (group) has the same value for the grouping attributes. In this case, the computa- tion is more complex. Consider the following query:
SELECT Dno, AVG(Salary) FROM EMPLOYEE GROUP BY Dno;
19.5 Implementing Aggregate Operations and OUTER JOINs 699
The usual technique for such queries is to first use either sorting or hashing on the grouping attributes to partition the file into the appropriate groups. Then the algo- rithm computes the aggregate function for the tuples in each group, which have the same grouping attribute(s) value. In the sample query, the set of EMPLOYEE tuples for each department number would be grouped together in a partition and the aver- age salary computed for each group.
Notice that if a clustering index (see Chapter 18) exists on the grouping attribute(s), then the records are already partitioned (grouped) into the appropriate subsets. In this case, it is only necessary to apply the computation to each group.
19.5.2 Implementing OUTER JOINs In Section 6.4, the outer join operation was discussed, with its three variations: left outer join, right outer join, and full outer join. We also discussed in Chapter 5 how these operations can be specified in SQL. The following is an example of a left outer join operation in SQL:
SELECT Lname, Fname, Dname FROM (EMPLOYEE LEFT OUTER JOIN DEPARTMENT ON Dno=Dnumber);
The result of this query is a table of employee names and their associated depart- ments. It is similar to a regular (inner) join result, with the exception that if an EMPLOYEE tuple (a tuple in the left relation) does not have an associated department, the employee’s name will still appear in the resulting table, but the department name would be NULL for such tuples in the query result.
Outer join can be computed by modifying one of the join algorithms, such as nested-loop join or single-loop join. For example, to compute a left outer join, we use the left relation as the outer loop or single-loop because every tuple in the left relation must appear in the result. If there are matching tuples in the other relation, the joined tuples are produced and saved in the result. However, if no matching tuple is found, the tuple is still included in the result but is padded with NULL value(s). The sort-merge and hash-join algorithms can also be extended to compute outer joins.
Theoretically, outer join can also be computed by executing a combination of rela- tional algebra operators. For example, the left outer join operation shown above is equivalent to the following sequence of relational operations:
1. Compute the (inner) JOIN of the EMPLOYEE and DEPARTMENT tables.
TEMP1 ← πLname, Fname, Dname (EMPLOYEE Dno=Dnumber DEPARTMENT)
2. Find the EMPLOYEE tuples that do not appear in the (inner) JOIN result.
TEMP2 ← πLname, Fname (EMPLOYEE) – πLname, Fname (TEMP1)
3. Pad each tuple in TEMP2 with a NULL Dname field.
TEMP2 ← TEMP2 × NULL
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4. Apply the UNION operation to TEMP1, TEMP2 to produce the LEFT OUTER JOIN result.
RESULT ← TEMP1 ∪ TEMP2
The cost of the outer join as computed above would be the sum of the costs of the associated steps (inner join, projections, set difference, and union). However, note that step 3 can be done as the temporary relation is being constructed in step 2; that is, we can simply pad each resulting tuple with a NULL. In addition, in step 4, we know that the two operands of the union are disjoint (no common tuples), so there is no need for duplicate elimination.
19.6 Combining Operations Using Pipelining A query specified in SQL will typically be translated into a relational algebra expres- sion that is a sequence of relational operations. If we execute a single operation at a time, we must generate temporary files on disk to hold the results of these tempo- rary operations, creating excessive overhead. Generating and storing large tempo- rary files on disk is time-consuming and can be unnecessary in many cases, since these files will immediately be used as input to the next operation. To reduce the number of temporary files, it is common to generate query execution code that cor- responds to algorithms for combinations of operations in a query.
For example, rather than being implemented separately, a JOIN can be combined with two SELECT operations on the input files and a final PROJECT operation on the resulting file; all this is implemented by one algorithm with two input files and a single output file. Rather than creating four temporary files, we apply the algorithm directly and get just one result file. In Section 19.7.2, we discuss how heuristic rela- tional algebra optimization can group operations together for execution. This is called pipelining or stream-based processing.
It is common to create the query execution code dynamically to implement multiple operations. The generated code for producing the query combines several algo- rithms that correspond to individual operations. As the result tuples from one oper- ation are produced, they are provided as input for subsequent operations. For example, if a join operation follows two select operations on base relations, the tuples resulting from each select are provided as input for the join algorithm in a stream or pipeline as they are produced.
19.7 Using Heuristics in Query Optimization In this section we discuss optimization techniques that apply heuristic rules to modify the internal representation of a query—which is usually in the form of a query tree or a query graph data structure—to improve its expected performance. The scanner and parser of an SQL query first generate a data structure that corre- sponds to an initial query representation, which is then optimized according to heuristic rules. This leads to an optimized query representation, which corresponds to the query execution strategy. Following that, a query execution plan is generated
19.7 Using Heuristics in Query Optimization 701
to execute groups of operations based on the access paths available on the files involved in the query.
One of the main heuristic rules is to apply SELECT and PROJECT operations before applying the JOIN or other binary operations, because the size of the file resulting from a binary operation—such as JOIN—is usually a multiplicative function of the sizes of the input files. The SELECT and PROJECT operations reduce the size of a file and hence should be applied before a join or other binary operation.
In Section 19.7.1 we reiterate the query tree and query graph notations that we introduced earlier in the context of relational algebra and calculus in Sections 6.3.5 and 6.6.5, respectively. These can be used as the basis for the data structures that are used for internal representation of queries. A query tree is used to represent a relational algebra or extended relational algebra expression, whereas a query graph is used to represent a relational calculus expression. Then in Section 19.7.2 we show how heuristic optimization rules are applied to convert an initial query tree into an equivalent query tree, which represents a different relational algebra expression that is more efficient to execute but gives the same result as the original tree. We also discuss the equivalence of various relational algebra expressions. Finally, Section 19.7.3 discusses the generation of query execution plans.
19.7.1 Notation for Query Trees and Query Graphs A query tree is a tree data structure that corresponds to a relational algebra expres- sion. It represents the input relations of the query as leaf nodes of the tree, and rep- resents the relational algebra operations as internal nodes. An execution of the query tree consists of executing an internal node operation whenever its operands are available and then replacing that internal node by the relation that results from executing the operation. The order of execution of operations starts at the leaf nodes, which represents the input database relations for the query, and ends at the root node, which represents the final operation of the query. The execution terminates when the root node operation is executed and produces the result relation for the query.
Figure 19.4a shows a query tree (the same as shown in Figure 6.9) for query Q2 in Chapters 4 to 6: For every project located in ‘Stafford’, retrieve the project number, the controlling department number, and the department manager’s last name, address, and birthdate. This query is specified on the COMPANY relational schema in Figure 3.5 and corresponds to the following relational algebra expression:
πPnumber, Dnum, Lname, Address, Bdate (((σPlocation=‘Stafford’(PROJECT))
Dnum=Dnumber(DEPARTMENT)) Mgr_ssn=Ssn(EMPLOYEE))
This corresponds to the following SQL query:
Q2: SELECT P.Pnumber, P.Dnum, E.Lname, E.Address, E.Bdate FROM PROJECT AS P, DEPARTMENT AS D, EMPLOYEE AS E WHERE P.Dnum=D.Dnumber AND D.Mgr_ssn=E.Ssn AND
P.Plocation= ‘Stafford’;
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(b)
(a)
E
DP
P.Pnumber, P.Dnum, E.Lname, E.Address, E.Bdate π
P.Dnum=D.Dnumber AND D.Mgr_ssn=E.Ssn AND P.Plocation=‘Stafford’ σ
(c)
EDP
[P.Pnumber, P.Dnum] [E.Lname, E.Address, E.Bdate]
P.Dnum=D.Dnumber
P.Plocation=‘Stafford’
D.Mgr_ssn=E.Ssn
‘Stafford’
XX
XX
(1)
(2)
(3)
P.Pnumber,P.Dnum,E.Lname,E.Address,E.Bdateπ
D.Mgr_ssn=E.Ssn
P.Dnum=D.Dnumber
σ P.Plocation= ‘Stafford’
E
D
P
EMPLOYEE
DEPARTMENT
PROJECT
Figure 19.4 Two query trees for the query Q2. (a) Query tree corresponding to the relational algebra expression for Q2. (b) Initial (canonical) query tree for SQL query Q2. (c) Query graph for Q2.
In Figure 19.4a, the leaf nodes P, D, and E represent the three relations PROJECT, DEPARTMENT, and EMPLOYEE, respectively, and the internal tree nodes represent the relational algebra operations of the expression. When this query tree is executed, the node marked (1) in Figure 19.4a must begin execution before node (2) because some resulting tuples of operation (1) must be available before we can begin execut- ing operation (2). Similarly, node (2) must begin executing and producing results before node (3) can start execution, and so on.
As we can see, the query tree represents a specific order of operations for executing a query. A more neutral data structure for representation of a query is the query graph notation. Figure 19.4c (the same as shown in Figure 6.13) shows the query
19.7 Using Heuristics in Query Optimization 703
graph for query Q2. Relations in the query are represented by relation nodes, which are displayed as single circles. Constant values, typically from the query selection conditions, are represented by constant nodes, which are displayed as double circles or ovals. Selection and join conditions are represented by the graph edges, as shown in Figure 19.4c. Finally, the attributes to be retrieved from each relation are dis- played in square brackets above each relation.
The query graph representation does not indicate an order on which operations to perform first. There is only a single graph corresponding to each query.15 Although some optimization techniques were based on query graphs, it is now generally accepted that query trees are preferable because, in practice, the query optimizer needs to show the order of operations for query execution, which is not possible in query graphs.
19.7.2 Heuristic Optimization of Query Trees In general, many different relational algebra expressions—and hence many different query trees—can be equivalent; that is, they can represent the same query.16
The query parser will typically generate a standard initial query tree to correspond to an SQL query, without doing any optimization. For example, for a SELECT- PROJECT-JOIN query, such as Q2, the initial tree is shown in Figure 19.4(b). The CARTESIAN PRODUCT of the relations specified in the FROM clause is first applied; then the selection and join conditions of the WHERE clause are applied, followed by the projection on the SELECT clause attributes. Such a canonical query tree repre- sents a relational algebra expression that is very inefficient if executed directly, because of the CARTESIAN PRODUCT (×) operations. For example, if the PROJECT, DEPARTMENT, and EMPLOYEE relations had record sizes of 100, 50, and 150 bytes and contained 100, 20, and 5,000 tuples, respectively, the result of the CARTESIAN PRODUCT would contain 10 million tuples of record size 300 bytes each. However, the initial query tree in Figure 19.4(b) is in a simple standard form that can be eas- ily created from the SQL query. It will never be executed. The heuristic query opti- mizer will transform this initial query tree into an equivalent final query tree that is efficient to execute.
The optimizer must include rules for equivalence among relational algebra expres- sions that can be applied to transform the initial tree into the final, optimized query tree. First we discuss informally how a query tree is transformed by using heuristics, and then we discuss general transformation rules and show how they can be used in an algebraic heuristic optimizer.
Example of Transforming a Query. Consider the following query Q on the data- base in Figure 3.5: Find the last names of employees born after 1957 who work on a project named ‘Aquarius’. This query can be specified in SQL as follows:
15Hence, a query graph corresponds to a relational calculus expression as shown in Section 6.6.5. 16The same query may also be stated in various ways in a high-level query language such as SQL (see Chapters 4 and 5).
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Q: SELECT Lname FROM EMPLOYEE, WORKS_ON, PROJECT WHERE Pname=‘Aquarius’ AND Pnumber=Pno AND Essn=Ssn
AND Bdate > ‘1957-12-31’;
The initial query tree for Q is shown in Figure 19.5(a). Executing this tree directly first creates a very large file containing the CARTESIAN PRODUCT of the entire EMPLOYEE, WORKS_ON, and PROJECT files. That is why the initial query tree is never executed, but is transformed into another equivalent tree that is efficient to
(a) Lname
Pname=‘Aquarius’ AND Pnumber=Pno AND Essn=Ssn AND Bdate>‘1957-12-31’
PROJECT
WORKS_ONEMPLOYEE
(b) Lname
Pnumber=Pno
Bdate>‘1957-12-31’
Pname=‘Aquarius’Essn=Ssn
π
π
σ
σ
σσ
σ
EMPLOYEE
PROJECT
WORKS_ON
X
X
X
X
Figure 19.5 Steps in converting a query tree during heuristic optimization. (a) Initial (canonical) query tree for SQL query Q. (b) Moving SELECT operations down the query tree. (c) Applying the more restrictive SELECT operation first. (d) Replacing CARTESIAN PRODUCT and SELECT with JOIN operations. (e) Moving PROJECT operations down the query tree.
19.7 Using Heuristics in Query Optimization 705
(e) π Lname
σBdate>‘1957-12-31’
σ Pname=‘Aquarius’
π Pnumber π Essn,Pno
π Essn π Ssn, Lname
EMPLOYEE
WORKS_ON
PROJECT
(d) π Lname
σ Bdate>‘1957-12-31’
σ Pname=‘Aquarius’ EMPLOYEEWORKS_ON
PROJECT
Essn=Ssn
Pnumber=Pno
Pnumber=Pno
Essn=Ssn
(c)
σ Essn=Ssn
π Lname
σ Pnumber=Pno σ Bdate>‘1957-12-31’
σ Pname=‘Aquarius’
EMPLOYEE
WORKS_ON
PROJECT
X
X
execute. This particular query needs only one record from the PROJECT relation— for the ‘Aquarius’ project—and only the EMPLOYEE records for those whose date of birth is after ‘1957-12-31’. Figure 19.5(b) shows an improved query tree that first applies the SELECT operations to reduce the number of tuples that appear in the CARTESIAN PRODUCT.
A further improvement is achieved by switching the positions of the EMPLOYEE and PROJECT relations in the tree, as shown in Figure 19.5(c). This uses the information that Pnumber is a key attribute of the PROJECT relation, and hence the SELECT operation on the PROJECT relation will retrieve a single record only. We can further improve the query tree by replacing any CARTESIAN PRODUCT operation that is followed by a join condition with a JOIN operation, as shown in Figure 19.5(d). Another improvement is to keep only the attributes needed by subsequent opera- tions in the intermediate relations, by including PROJECT (π) operations as early as possible in the query tree, as shown in Figure 19.5(e). This reduces the attributes (columns) of the intermediate relations, whereas the SELECT operations reduce the number of tuples (records).
As the preceding example demonstrates, a query tree can be transformed step by step into an equivalent query tree that is more efficient to execute. However, we must make sure that the transformation steps always lead to an equivalent query tree. To do this, the query optimizer must know which transformation rules preserve this equivalence. We discuss some of these transformation rules next.
General Transformation Rules for Relational Algebra Operations. There are many rules for transforming relational algebra operations into equivalent ones. For query optimization purposes, we are interested in the meaning of the operations and the resulting relations. Hence, if two relations have the same set of attributes in a different order but the two relations represent the same information, we consider the relations to be equivalent. In Section 3.1.2 we gave an alternative definition of relation that makes the order of attributes unimportant; we will use this definition here. We will state some transformation rules that are useful in query optimization, without proving them:
1. Cascade of σ A conjunctive selection condition can be broken up into a cas- cade (that is, a sequence) of individual σ operations: σc1 AND c2 AND . . . AND cn
(R)� σc1 (σc2
(...(σcn (R))...))
2. Commutativity of σ. The σ operation is commutative: σc1
(σc2 (R)) � σc2
(σc1 (R))
3. Cascade of π. In a cascade (sequence) of π operations, all but the last one can be ignored:
πList1 (πList2
(...(πListn (R))...)) � πList1
(R)
4. Commuting σ with π. If the selection condition c involves only those attrib- utes A1, . . . , An in the projection list, the two operations can be commuted:
πA1, A2, ..., An (σc (R)) � σc (πA1, A2, ..., An
(R))
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19.7 Using Heuristics in Query Optimization 707
5. Commutativity of (and ×). The join operation is commutative, as is the × operation: R c S ≡ S c R R × S ≡ S × R
Notice that although the order of attributes may not be the same in the rela- tions resulting from the two joins (or two Cartesian products), the meaning is the same because the order of attributes is not important in the alternative definition of relation.
6. Commuting σ with (or ×). If all the attributes in the selection condition c involve only the attributes of one of the relations being joined—say, R—the two operations can be commuted as follows:
σc (R S) ≡ (σc (R)) S Alternatively, if the selection condition c can be written as (c1 AND c2), where condition c1 involves only the attributes of R and condition c2 involves only the attributes of S, the operations commute as follows:
σc (R S) � (σc1 (R)) (σc 2
(S))
The same rules apply if the is replaced by a × operation.
7. Commuting π with (or ×). Suppose that the projection list is L = {A1, ..., An, B1, ..., Bm} , where A1, ..., An are attributes of R and B1, ..., Bm are attrib- utes of S. If the join condition c involves only attributes in L, the two opera- tions can be commuted as follows:
πL (R c S) � (πA1, ..., An (R)) c (πB1, ..., Bm
(S))
If the join condition c contains additional attributes not in L, these must be added to the projection list, and a final π operation is needed. For example, if attributes An+1, ..., An+k of R and Bm+1, ..., Bm+p of S are involved in the join condition c but are not in the projection list L, the operations commute as follows:
πL (R c S) � πL ((πA1, ..., An, An+1, ..., An+k (R)) c (πB1, ..., Bm, Bm+1, ..., Bm+p
(S)))
For ×, there is no condition c, so the first transformation rule always applies by replacing c with ×.
8. Commutativity of set operations. The set operations ∪ and ∩ are commu- tative but − is not.
9. Associativity of , ×, ∪, and ∩. These four operations are individually associative; that is, if θ stands for any one of these four operations (through- out the expression), we have:
(R θ S) θ T ≡ R θ (S θ T)
10. Commuting σ with set operations. The σ operation commutes with ∪, ∩, and −. If θ stands for any one of these three operations (throughout the expression), we have:
σc (R θ S) ≡ (σc (R)) θ (σc (S))
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11. The π operation commutes with ∪. πL (R ∪ S) ≡ (πL (R)) ∪ (πL (S))
12. Converting a (σ, ×) sequence into . If the condition c of a σ that follows a × corresponds to a join condition, convert the (σ, ×) sequence into a as follows:
(σc (R × S)) ≡ (R c S)
There are other possible transformations. For example, a selection or join condition c can be converted into an equivalent condition by using the following standard rules from Boolean algebra (DeMorgan’s laws):
NOT (c1 AND c2) ≡ (NOT c1) OR (NOT c2) NOT (c1 OR c2) ≡ (NOT c1) AND (NOT c2)
Additional transformations discussed in Chapters 4, 5, and 6 are not repeated here. We discuss next how transformations can be used in heuristic optimization.
Outline of a Heuristic Algebraic Optimization Algorithm. We can now out- line the steps of an algorithm that utilizes some of the above rules to transform an initial query tree into a final tree that is more efficient to execute (in most cases). The algorithm will lead to transformations similar to those discussed in our exam- ple in Figure 19.5. The steps of the algorithm are as follows:
1. Using Rule 1, break up any SELECT operations with conjunctive conditions into a cascade of SELECT operations. This permits a greater degree of free- dom in moving SELECT operations down different branches of the tree.
2. Using Rules 2, 4, 6, and 10 concerning the commutativity of SELECT with other operations, move each SELECT operation as far down the query tree as is permitted by the attributes involved in the select condition. If the condi- tion involves attributes from only one table, which means that it represents a selection condition, the operation is moved all the way to the leaf node that represents this table. If the condition involves attributes from two tables, which means that it represents a join condition, the condition is moved to a location down the tree after the two tables are combined.
3. Using Rules 5 and 9 concerning commutativity and associativity of binary operations, rearrange the leaf nodes of the tree using the following criteria. First, position the leaf node relations with the most restrictive SELECT oper- ations so they are executed first in the query tree representation. The defini- tion of most restrictive SELECT can mean either the ones that produce a relation with the fewest tuples or with the smallest absolute size.17 Another possibility is to define the most restrictive SELECT as the one with the small- est selectivity; this is more practical because estimates of selectivities are often available in the DBMS catalog. Second, make sure that the ordering of leaf nodes does not cause CARTESIAN PRODUCT operations; for example, if
17Either definition can be used, since these rules are heuristic.
19.7 Using Heuristics in Query Optimization 709
the two relations with the most restrictive SELECT do not have a direct join condition between them, it may be desirable to change the order of leaf nodes to avoid Cartesian products.18
4. Using Rule 12, combine a CARTESIAN PRODUCT operation with a subse- quent SELECT operation in the tree into a JOIN operation, if the condition represents a join condition.
5. Using Rules 3, 4, 7, and 11 concerning the cascading of PROJECT and the commuting of PROJECT with other operations, break down and move lists of projection attributes down the tree as far as possible by creating new PROJECT operations as needed. Only those attributes needed in the query result and in subsequent operations in the query tree should be kept after each PROJECT operation.
6. Identify subtrees that represent groups of operations that can be executed by a single algorithm.
In our example, Figure 19.5(b) shows the tree in Figure 19.5(a) after applying steps 1 and 2 of the algorithm; Figure 19.5(c) shows the tree after step 3; Figure 19.5(d) after step 4; and Figure 19.5(e) after step 5. In step 6 we may group together the operations in the subtree whose root is the operation πEssn into a single algorithm. We may also group the remaining operations into another subtree, where the tuples resulting from the first algorithm replace the subtree whose root is the operation πEssn, because the first grouping means that this subtree is executed first.
Summary of Heuristics for Algebraic Optimization. The main heuristic is to apply first the operations that reduce the size of intermediate results. This includes performing as early as possible SELECT operations to reduce the number of tuples and PROJECT operations to reduce the number of attributes—by moving SELECT and PROJECT operations as far down the tree as possible. Additionally, the SELECT and JOIN operations that are most restrictive—that is, result in relations with the fewest tuples or with the smallest absolute size—should be executed before other similar operations. The latter rule is accomplished through reordering the leaf nodes of the tree among themselves while avoiding Cartesian products, and adjust- ing the rest of the tree appropriately.
19.7.3 Converting Query Trees into Query Execution Plans An execution plan for a relational algebra expression represented as a query tree includes information about the access methods available for each relation as well as the algorithms to be used in computing the relational operators represented in the tree. As a simple example, consider query Q1 from Chapter 4, whose corresponding relational algebra expression is
πFname, Lname, Address(σDname=‘Research’(DEPARTMENT) Dnumber=Dno EMPLOYEE)
18Note that a CARTESIAN PRODUCT is acceptable in some cases—for example, if each relation has only a single tuple because each had a previous select condition on a key field.
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π Fname, Lname, Address
σ Dname=‘Research’
DEPARTMENT
EMPLOYEE
Dnumber=Dno
Figure 19.6 A query tree for query Q1.
The query tree is shown in Figure 19.6. To convert this into an execution plan, the optimizer might choose an index search for the SELECT operation on DEPARTMENT (assuming one exists), a single-loop join algorithm that loops over the records in the result of the SELECT operation on DEPARTMENT for the join operation (assuming an index exists on the Dno attribute of EMPLOYEE), and a scan of the JOIN result for input to the PROJECT operator. Additionally, the approach taken for executing the query may specify a materialized or a pipelined evaluation, although in general a pipelined evaluation is preferred whenever feasible.
With materialized evaluation, the result of an operation is stored as a temporary relation (that is, the result is physically materialized). For instance, the JOIN opera- tion can be computed and the entire result stored as a temporary relation, which is then read as input by the algorithm that computes the PROJECT operation, which would produce the query result table. On the other hand, with pipelined evaluation, as the resulting tuples of an operation are produced, they are forwarded directly to the next operation in the query sequence. For example, as the selected tuples from DEPARTMENT are produced by the SELECT operation, they are placed in a buffer; the JOIN operation algorithm would then consume the tuples from the buffer, and those tuples that result from the JOIN operation are pipelined to the pro- jection operation algorithm. The advantage of pipelining is the cost savings in not having to write the intermediate results to disk and not having to read them back for the next operation.
19.8 Using Selectivity and Cost Estimates in Query Optimization
A query optimizer does not depend solely on heuristic rules; it also estimates and compares the costs of executing a query using different execution strategies and algorithms, and it then chooses the strategy with the lowest cost estimate. For this approach to work, accurate cost estimates are required so that different strategies can be compared fairly and realistically. In addition, the optimizer must limit the num- ber of execution strategies to be considered; otherwise, too much time will be spent making cost estimates for the many possible execution strategies. Hence, this approach is more suitable for compiled queries where the optimization is done at compile time and the resulting execution strategy code is stored and executed directly at runtime. For interpreted queries, where the entire process shown in
19.8 Using Selectivity and Cost Estimates in Query Optimization 711
Figure 19.1 occurs at runtime, a full-scale optimization may slow down the response time. A more elaborate optimization is indicated for compiled queries, whereas a partial, less time-consuming optimization works best for interpreted queries.
This approach is generally referred to as cost-based query optimization.19 It uses traditional optimization techniques that search the solution space to a problem for a solution that minimizes an objective (cost) function. The cost functions used in query optimization are estimates and not exact cost functions, so the optimization may select a query execution strategy that is not the optimal (absolute best) one. In Section 19.8.1 we discuss the components of query execution cost. In Section 19.8.2 we discuss the type of information needed in cost functions. This information is kept in the DBMS catalog. In Section 19.8.3 we give examples of cost functions for the SELECT operation, and in Section 19.8.4 we discuss cost functions for two-way JOIN operations. Section 19.8.5 discusses multiway joins, and Section 19.8.6 gives an example.
19.8.1 Cost Components for Query Execution The cost of executing a query includes the following components:
1. Access cost to secondary storage. This is the cost of transferring (reading and writing) data blocks between secondary disk storage and main memory buffers. This is also known as disk I/O (input/output) cost. The cost of search- ing for records in a disk file depends on the type of access structures on that file, such as ordering, hashing, and primary or secondary indexes. In addi- tion, factors such as whether the file blocks are allocated contiguously on the same disk cylinder or scattered on the disk affect the access cost.
2. Disk storage cost. This is the cost of storing on disk any intermediate files that are generated by an execution strategy for the query.
3. Computation cost. This is the cost of performing in-memory operations on the records within the data buffers during query execution. Such operations include searching for and sorting records, merging records for a join or a sort operation, and performing computations on field values. This is also known as CPU (central processing unit) cost.
4. Memory usage cost. This is the cost pertaining to the number of main mem- ory buffers needed during query execution.
5. Communication cost. This is the cost of shipping the query and its results from the database site to the site or terminal where the query originated. In distributed databases (see Chapter 25), it would also include the cost of trans- ferring tables and results among various computers during query evaluation.
For large databases, the main emphasis is often on minimizing the access cost to sec- ondary storage. Simple cost functions ignore other factors and compare different query execution strategies in terms of the number of block transfers between disk
19This approach was first used in the optimizer for the SYSTEM R in an experimental DBMS developed at IBM (Selinger et al. 1979).
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and main memory buffers. For smaller databases, where most of the data in the files involved in the query can be completely stored in memory, the emphasis is on min- imizing computation cost. In distributed databases, where many sites are involved (see Chapter 25), communication cost must be minimized also. It is difficult to include all the cost components in a (weighted) cost function because of the diffi- culty of assigning suitable weights to the cost components. That is why some cost functions consider a single factor only—disk access. In the next section we discuss some of the information that is needed for formulating cost functions.
19.8.2 Catalog Information Used in Cost Functions To estimate the costs of various execution strategies, we must keep track of any information that is needed for the cost functions. This information may be stored in the DBMS catalog, where it is accessed by the query optimizer. First, we must know the size of each file. For a file whose records are all of the same type, the number of records (tuples) (r), the (average) record size (R), and the number of file blocks (b) (or close estimates of them) are needed. The blocking factor (bfr) for the file may also be needed. We must also keep track of the primary file organization for each file. The primary file organization records may be unordered, ordered by an attribute with or without a primary or clustering index, or hashed (static hashing or one of the dynamic hashing methods) on a key attribute. Information is also kept on all primary, secondary, or clustering indexes and their indexing attributes. The number of levels (x) of each multilevel index (primary, secondary, or clustering) is needed for cost functions that estimate the number of block accesses that occur during query execution. In some cost functions the number of first-level index blocks (bI1) is needed.
Another important parameter is the number of distinct values (d) of an attribute and the attribute selectivity (sl), which is the fraction of records satisfying an equal- ity condition on the attribute. This allows estimation of the selection cardinality (s = sl * r) of an attribute, which is the average number of records that will satisfy an equality selection condition on that attribute. For a key attribute, d = r, sl = 1/r and s = 1. For a nonkey attribute, by making an assumption that the d distinct values are uniformly distributed among the records, we estimate sl = (1/d) and so s = (r/d).20
Information such as the number of index levels is easy to maintain because it does not change very often. However, other information may change frequently; for example, the number of records r in a file changes every time a record is inserted or deleted. The query optimizer will need reasonably close but not necessarily com- pletely up-to-the-minute values of these parameters for use in estimating the cost of various execution strategies.
For a nonkey attribute with d distinct values, it is often the case that the records are not uniformly distributed among these values. For example, suppose that a com- pany has 5 departments numbered 1 through 5, and 200 employees who are distrib-
20More accurate optimizers store histograms of the distribution of records over the data values for an attribute.
19.8 Using Selectivity and Cost Estimates in Query Optimization 713
uted among the departments as follows: (1, 5), (2, 25), (3, 70), (4, 40), (5, 60). In such cases, the optimizer can store a histogram that reflects the distribution of employee records over different departments in a table with the two attributes (Dno, Selectivity), which would contain the following values for our example: (1, 0.025), (2, 0.125), (3, 0.35), (4, 0.2), (5, 0.3). The selectivity values stored in the histogram can also be estimates if the employee table changes frequently.
In the next two sections we examine how some of these parameters are used in cost functions for a cost-based query optimizer.
19.8.3 Examples of Cost Functions for SELECT We now give cost functions for the selection algorithms S1 to S8 discussed in Section 19.3.1 in terms of number of block transfers between memory and disk. Algorithm S9 involves an intersection of record pointers after they have been retrieved by some other means, such as algorithm S6, and so the cost function will be based on the cost for S6. These cost functions are estimates that ignore compu- tation time, storage cost, and other factors. The cost for method Si is referred to as CSi block accesses.
■ S1—Linear search (brute force) approach. We search all the file blocks to retrieve all records satisfying the selection condition; hence, CS1a = b. For an equality condition on a key attribute, only half the file blocks are searched on the average before finding the record, so a rough estimate for CS1b = (b/2) if the record is found; if no record is found that satisfies the condition, CS1b = b.
■ S2—Binary search. This search accesses approximately CS2 = log2b + ⎡(s/bfr)⎤ − 1 file blocks. This reduces to log2b if the equality condition is on a unique (key) attribute, because s = 1 in this case.
■ S3a—Using a primary index to retrieve a single record. For a primary index, retrieve one disk block at each index level, plus one disk block from the data file. Hence, the cost is one more disk block than the number of index levels: CS3a = x + 1.
■ S3b—Using a hash key to retrieve a single record. For hashing, only one disk block needs to be accessed in most cases. The cost function is approxi- mately CS3b = 1 for static hashing or linear hashing, and it is 2 disk block accesses for extendible hashing (see Section 17.8).
■ S4—Using an ordering index to retrieve multiple records. If the compari- son condition is >, >=, <, or <= on a key field with an ordering index, roughly half the file records will satisfy the condition. This gives a cost func- tion of CS4 = x + (b/2). This is a very rough estimate, and although it may be correct on the average, it may be quite inaccurate in individual cases. A more accurate estimate is possible if the distribution of records is stored in a his- togram.
■ S5—Using a clustering index to retrieve multiple records. One disk block is accessed at each index level, which gives the address of the first file disk block in the cluster. Given an equality condition on the indexing attribute, s
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records will satisfy the condition, where s is the selection cardinality of the indexing attribute. This means that ⎡(s/bfr)⎤ file blocks will be in the cluster of file blocks that hold all the selected records, giving CS5 = x + ⎡(s/bfr)⎤.
■ S6—Using a secondary (B+-tree) index. For a secondary index on a key (unique) attribute, the cost is x + 1 disk block accesses. For a secondary index on a nonkey (nonunique) attribute, s records will satisfy an equality condi- tion, where s is the selection cardinality of the indexing attribute. However, because the index is nonclustering, each of the records may reside on a differ- ent disk block, so the (worst case) cost estimate is CS6a = x + 1 + s. The addi- tional 1 is to account for the disk block that contains the record pointers after the index is searched (see Figure 18.5). If the comparison condition is >, >=, <, or <= and half the file records are assumed to satisfy the condition, then (very roughly) half the first-level index blocks are accessed, plus half the file records via the index. The cost estimate for this case, approximately, is CS6b = x + (bI1/2) + (r/2). The r/2 factor can be refined if better selectivity estimates are available through a histogram. The latter method CS6b can be very costly.
■ S7—Conjunctive selection. We can use either S1 or one of the methods S2 to S6 discussed above. In the latter case, we use one condition to retrieve the records and then check in the main memory buffers whether each retrieved record satisfies the remaining conditions in the conjunction. If multiple indexes exist, the search of each index can produce a set of record pointers (record ids) in the main memory buffers. The intersection of the sets of record pointers (referred to in S9) can be computed in main memory, and then the resulting records are retrieved based on their record ids.
■ S8—Conjunctive selection using a composite index. Same as S3a, S5, or S6a, depending on the type of index.
Example of Using the Cost Functions. In a query optimizer, it is common to enumerate the various possible strategies for executing a query and to estimate the costs for different strategies. An optimization technique, such as dynamic program- ming, may be used to find the optimal (least) cost estimate efficiently, without hav- ing to consider all possible execution strategies. We do not discuss optimization algorithms here; rather, we use a simple example to illustrate how cost estimates may be used. Suppose that the EMPLOYEE file in Figure 3.5 has rE = 10,000 records stored in bE = 2000 disk blocks with blocking factor bfrE = 5 records/block and the following access paths:
1. A clustering index on Salary, with levels xSalary = 3 and average selection car- dinality sSalary = 20. (This corresponds to a selectivity of slSalary = 0.002).
2. A secondary index on the key attribute Ssn, with xSsn = 4 (sSsn = 1, slSsn = 0.0001).
3. A secondary index on the nonkey attribute Dno, with xDno = 2 and first-level index blocks bI1Dno = 4. There are dDno = 125 distinct values for Dno, so the selectivity of Dno is slDno = (1/dDno) = 0.008, and the selection cardinality is sDno = (rE * slDno) = (rE/dDno) = 80.
19.8 Using Selectivity and Cost Estimates in Query Optimization 715
4. A secondary index on Sex, with xSex = 1. There are dSex = 2 values for the Sex attribute, so the average selection cardinality is sSex = (rE /dSex) = 5000. (Note that in this case, a histogram giving the percentage of male and female employees may be useful, unless they are approximately equal.)
We illustrate the use of cost functions with the following examples:
OP1: σSsn=‘123456789’(EMPLOYEE)
OP2: σDno>5(EMPLOYEE)
OP3: σDno=5(EMPLOYEE)
OP4: σDno=5 AND SALARY>30000 AND Sex=‘F’(EMPLOYEE)
The cost of the brute force (linear search or file scan) option S1 will be estimated as CS1a = bE = 2000 (for a selection on a nonkey attribute) or CS1b = (bE /2) = 1000 (average cost for a selection on a key attribute). For OP1 we can use either method S1 or method S6a; the cost estimate for S6a is CS6a = xSsn + 1 = 4 + 1 = 5, and it is chosen over method S1, whose average cost is CS1b = 1000. For OP2 we can use either method S1 (with estimated cost CS1a = 2000) or method S6b (with estimated cost CS6b = xDno + (bI1Dno/2) + (rE /2) = 2 + (4/2) + (10,000/2) = 5004), so we choose the linear search approach for OP2. For OP3 we can use either method S1 (with esti- mated cost CS1a = 2000) or method S6a (with estimated cost CS6a = xDno + sDno = 2 + 80 = 82), so we choose method S6a.
Finally, consider OP4, which has a conjunctive selection condition. We need to esti- mate the cost of using any one of the three components of the selection condition to retrieve the records, plus the linear search approach. The latter gives cost estimate CS1a = 2000. Using the condition (Dno = 5) first gives the cost estimate CS6a = 82. Using the condition (Salary > 30,000) first gives a cost estimate CS4 = xSalary + (bE /2) = 3 + (2000/2) = 1003. Using the condition (Sex = ‘F’) first gives a cost estimate CS6a = xSex + sSex = 1 + 5000 = 5001. The optimizer would then choose method S6a on the secondary index on Dno because it has the lowest cost estimate. The condition (Dno = 5) is used to retrieve the records, and the remaining part of the conjunctive condition (Salary > 30,000 AND Sex = ‘F’) is checked for each selected record after it is retrieved into memory. Only the records that satisfy these additional conditions are included in the result of the operation.
19.8.4 Examples of Cost Functions for JOIN To develop reasonably accurate cost functions for JOIN operations, we need to have an estimate for the size (number of tuples) of the file that results after the JOIN oper- ation. This is usually kept as a ratio of the size (number of tuples) of the resulting join file to the size of the CARTESIAN PRODUCT file, if both are applied to the same input files, and it is called the join selectivity ( js). If we denote the number of tuples of a relation R by |R|, we have:
js = |(R c S)| / |(R × S)| = |(R c S)| / (|R| * |S|)
If there is no join condition c, then js = 1 and the join is the same as the CARTESIAN PRODUCT. If no tuples from the relations satisfy the join condition, then js = 0. In
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general, 0 js 1. For a join where the condition c is an equality comparison R.A = S.B, we get the following two special cases:
1. If A is a key of R, then |(R c S)| ≤ |S|, so js ≤ (1/|R|). This is because each record in file S will be joined with at most one record in file R, since A is a key of R. A special case of this condition is when attribute B is a foreign key of S that references the primary key A of R. In addition, if the foreign key B has the NOT NULL constraint, then js = (1/|R|), and the result file of the join will contain |S| records.
2. If B is a key of S, then |(R c S)| ≤ |R|, so js ≤ (1/|S|).
Having an estimate of the join selectivity for commonly occurring join conditions enables the query optimizer to estimate the size of the resulting file after the join operation, given the sizes of the two input files, by using the formula |(R c S)| = js
* |R| * |S|. We can now give some sample approximate cost functions for estimating the cost of some of the join algorithms given in Section 19.3.2. The join operations are of the form:
R A=B S
where A and B are domain-compatible attributes of R and S, respectively. Assume that R has bR blocks and that S has bS blocks:
■ J1—Nested-loop join. Suppose that we use R for the outer loop; then we get the following cost function to estimate the number of block accesses for this method, assuming three memory buffers. We assume that the blocking factor for the resulting file is bfrRS and that the join selectivity is known:
CJ1 = bR + (bR * bS) + (( js * |R| * |S|)/bfrRS)
The last part of the formula is the cost of writing the resulting file to disk. This cost formula can be modified to take into account different numbers of memory buffers, as presented in Section 19.3.2. If nB main memory buffers are available to perform the join, the cost formula becomes:
CJ1 = bR + ( ⎡bR/(nB – 2)⎤ * bS) + ((js * |R| * |S|)/bfrRS) ■ J2—Single-loop join (using an access structure to retrieve the matching
record(s)). If an index exists for the join attribute B of S with index levels xB, we can retrieve each record s in R and then use the index to retrieve all the matching records t from S that satisfy t[B] = s[A]. The cost depends on the type of index. For a secondary index where sB is the selection cardinality for the join attribute B of S,21 we get:
CJ2a = bR + (|R| * (xB + 1 + sB)) + (( js * |R| * |S|)/bfrRS)
For a clustering index where sB is the selection cardinality of B, we get
CJ2b = bR + (|R| * (xB + (sB/bfrB))) + (( js * |R| * |S|)/bfrRS)
For a primary index, we get
21Selection cardinality was defined as the average number of records that satisfy an equality condition on an attribute, which is the average number of records that have the same value for the attribute and hence will be joined to a single record in the other file.
19.8 Using Selectivity and Cost Estimates in Query Optimization 717
CJ2c = bR + (|R| * (xB + 1)) + (( j s * |R| * |S|)/bfrRS)
If a hash key exists for one of the two join attributes—say, B of S—we get
CJ2d = bR + (|R| * h) + (( j s * |R| * |S|)/bfrRS)
where h ≥ 1 is the average number of block accesses to retrieve a record, given its hash key value. Usually, h is estimated to be 1 for static and linear hashing and 2 for extendible hashing.
■ J3—Sort-merge join. If the files are already sorted on the join attributes, the cost function for this method is
CJ3a = bR + bS + (( j s * |R| * |S|)/bfrRS)
If we must sort the files, the cost of sorting must be added. We can use the formulas from Section 19.2 to estimate the sorting cost.
Example of Using the Cost Functions. Suppose that we have the EMPLOYEE file described in the example in the previous section, and assume that the DEPARTMENT file in Figure 3.5 consists of rD = 125 records stored in bD = 13 disk blocks. Consider the following two join operations:
OP6: EMPLOYEE Dno=Dnumber DEPARTMENT OP7: DEPARTMENT Mgr_ssn=Ssn EMPLOYEE
Suppose that we have a primary index on Dnumber of DEPARTMENT with xDnumber= 1 level and a secondary index on Mgr_ssn of DEPARTMENT with selection cardinality sMgr_ssn= 1 and levels xMgr_ssn= 2. Assume that the join selectivity for OP6 is jsOP6 = (1/|DEPARTMENT|) = 1/125 because Dnumber is a key of DEPARTMENT. Also assume that the blocking factor for the resulting join file is bfrED= 4 records per block. We can estimate the worst-case costs for the JOIN operation OP6 using the applicable methods J1 and J2 as follows:
1. Using method J1 with EMPLOYEE as outer loop:
CJ1 = bE + (bE * bD) + (( jsOP6 * rE * rD)/bfrED)
= 2000 + (2000 * 13) + (((1/125) * 10,000 * 125)/4) = 30,500
2. Using method J1 with DEPARTMENT as outer loop:
CJ1 = bD + (bE * bD) + (( jsOP6 * rE * rD)/bfrED)
= 13 + (13 * 2000) + (((1/125) * 10,000 * 125/4) = 28,513
3. Using method J2 with EMPLOYEE as outer loop:
CJ2c = bE + (rE * (xDnumber+ 1)) + (( jsOP6 * rE * rD)/bfrED = 2000 + (10,000 * 2) + (((1/125) * 10,000 * 125/4) = 24,500
4. Using method J2 with DEPARTMENT as outer loop:
CJ2a = bD + (rD * (xDno + sDno)) + (( jsOP6 * rE * rD)/bfrED)
= 13 + (125 * (2 + 80)) + (((1/125) * 10,000 * 125/4) = 12,763
Case 4 has the lowest cost estimate and will be chosen. Notice that in case 2 above, if 15 memory buffers (or more) were available for executing the join instead of just 3, 13 of them could be used to hold the entire DEPARTMENT relation (outer loop
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R1 R2
R3
R4
R4 R3
R2
R1
Figure 19.7 Two left-deep (JOIN) query trees.
relation) in memory, one could be used as buffer for the result, and one would be used to hold one block at a time of the EMPLOYEE file (inner loop file), and the cost for case 2 could be drastically reduced to just bE + bD + (( jsOP6 * rE * rD)/bfrED) or 4,513, as discussed in Section 19.3.2. If some other number of main memory buffers was available, say nB = 10, then the cost for case 2 would be calculated as follows, which would also give better performance than case 4:
CJ1 = bD + ( ⎡bD/(nB – 2)⎤ * bE) + ((js * |R| * |S|)/bfrRS) = 13 + ( ⎡13/8⎤ * 2000) + (((1/125) * 10,000 * 125/4) = 28,513 = 13 + (2 * 2000) + 2500 = 6,513
As an exercise, the reader should perform a similar analysis for OP7.
19.8.5 Multiple Relation Queries and JOIN Ordering The algebraic transformation rules in Section 19.7.2 include a commutative rule and an associative rule for the join operation. With these rules, many equivalent join expressions can be produced. As a result, the number of alternative query trees grows very rapidly as the number of joins in a query increases. A query that joins n relations will often have n − 1 join operations, and hence can have a large number of different join orders. Estimating the cost of every possible join tree for a query with a large number of joins will require a substantial amount of time by the query opti- mizer. Hence, some pruning of the possible query trees is needed. Query optimizers typically limit the structure of a (join) query tree to that of left-deep (or right-deep) trees. A left-deep tree is a binary tree in which the right child of each nonleaf node is always a base relation. The optimizer would choose the particular left-deep tree with the lowest estimated cost. Two examples of left-deep trees are shown in Figure 19.7. (Note that the trees in Figure 19.5 are also left-deep trees.)
With left-deep trees, the right child is considered to be the inner relation when exe- cuting a nested-loop join, or the probing relation when executing a single-loop join. One advantage of left-deep (or right-deep) trees is that they are amenable to pipelining, as discussed in Section 19.6. For instance, consider the first left-deep tree in Figure 19.7 and assume that the join algorithm is the single-loop method; in this case, a disk page of tuples of the outer relation is used to probe the inner relation for
19.8 Using Selectivity and Cost Estimates in Query Optimization 719
matching tuples. As resulting tuples (records) are produced from the join of R1 and R2, they can be used to probe R3 to locate their matching records for joining. Likewise, as resulting tuples are produced from this join, they could be used to probe R4. Another advantage of left-deep (or right-deep) trees is that having a base relation as one of the inputs of each join allows the optimizer to utilize any access paths on that relation that may be useful in executing the join.
If materialization is used instead of pipelining (see Sections 19.6 and 19.7.3), the join results could be materialized and stored as temporary relations. The key idea from the optimizer’s standpoint with respect to join ordering is to find an ordering that will reduce the size of the temporary results, since the temporary results (pipelined or materialized) are used by subsequent operators and hence affect the execution cost of those operators.
19.8.6 Example to Illustrate Cost-Based Query Optimization We will consider query Q2 and its query tree shown in Figure 19.4(a) to illustrate cost-based query optimization:
Q2: SELECT Pnumber, Dnum, Lname, Address, Bdate FROM PROJECT, DEPARTMENT, EMPLOYEE WHERE Dnum=Dnumber AND Mgr_ssn=Ssn AND
Plocation=‘Stafford’;
Suppose we have the information about the relations shown in Figure 19.8. The LOW_VALUE and HIGH_VALUE statistics have been normalized for clarity. The tree in Figure 19.4(a) is assumed to represent the result of the algebraic heuristic opti- mization process and the start of cost-based optimization (in this example, we assume that the heuristic optimizer does not push the projection operations down the tree).
The first cost-based optimization to consider is join ordering. As previously men- tioned, we assume the optimizer considers only left-deep trees, so the potential join orders—without CARTESIAN PRODUCT—are:
1. PROJECT DEPARTMENT EMPLOYEE
2. DEPARTMENT PROJECT EMPLOYEE
3. DEPARTMENT EMPLOYEE PROJECT
4. EMPLOYEE DEPARTMENT PROJECT
Assume that the selection operation has already been applied to the PROJECT rela- tion. If we assume a materialized approach, then a new temporary relation is created after each join operation. To examine the cost of join order (1), the first join is between PROJECT and DEPARTMENT. Both the join method and the access methods for the input relations must be determined. Since DEPARTMENT has no index according to Figure 19.8, the only available access method is a table scan (that is, a linear search). The PROJECT relation will have the selection operation performed before the join, so two options exist: table scan (linear search) or utiliz- ing its PROJ_PLOC index, so the optimizer must compare their estimated costs.
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(a) Table_name
PROJECT PROJECT
PROJECT
DEPARTMENT
DEPARTMENT EMPLOYEE
EMPLOYEE
EMPLOYEE
200 2000
50
50
50 10000
50
500
1 1
1
1
1 1
1
1
200 2000
50
50
50 10000
50
500
Dnum
Dnumber
Plocation
Pnumber
Dno
Salary
Mgr_ssn
Ssn
Column_name Num_distinct Low_value High_value
(c) Index_name
*Blevel is the number of levels without the leaf level.
PROJ_PLOC EMP_SSN
EMP_SAL
1 1
1
4 50
50
200 10000
500NONUNIQUE
NONUNIQUE
UNIQUE
Uniqueness Blevel* Leaf_blocks Distinct_keys
(b) Table_name
PROJECT DEPARTMENT
EMPLOYEE
100 5
200010000
2000
50
Num_rows Blocks
Figure 19.8 Sample statistical information for relations in Q2. (a) Column information. (b) Table information. (c) Index information.
The statistical information on the PROJ_PLOC index (see Figure 19.8) shows the number of index levels x = 2 (root plus leaf levels). The index is nonunique (because Plocation is not a key of PROJECT), so the optimizer assumes a uniform data distribution and estimates the number of record pointers for each Plocation value to be 10. This is computed from the tables in Figure 19.8 by multiplying Selectivity * Num_rows, where Selectivity is estimated by 1/Num_distinct. So the cost of using the index and accessing the records is estimated to be 12 block accesses (2 for the index and 10 for the data blocks). The cost of a table scan is estimated to be 100 block accesses, so the index access is more efficient as expected.
In the materialized approach, a temporary file TEMP1 of size 1 block is created to hold the result of the selection operation. The file size is calculated by determining the blocking factor using the formula Num_rows/Blocks, which gives 2000/100 or 20 rows per block. Hence, the 10 records selected from the PROJECT relation will fit
19.9 Overview of Query Optimization in Oracle 721
into a single block. Now we can compute the estimated cost of the first join. We will consider only the nested-loop join method, where the outer relation is the tempo- rary file, TEMP1, and the inner relation is DEPARTMENT. Since the entire TEMP1 file fits in the available buffer space, we need to read each of the DEPARTMENT table’s five blocks only once, so the join cost is six block accesses plus the cost of writing the temporary result file, TEMP2. The optimizer would have to determine the size of TEMP2. Since the join attribute Dnumber is the key for DEPARTMENT, any Dnum value from TEMP1 will join with at most one record from DEPARTMENT, so the number of rows in TEMP2 will be equal to the number of rows in TEMP1, which is 10. The optimizer would determine the record size for TEMP2 and the number of blocks needed to store these 10 rows. For brevity, assume that the blocking factor for TEMP2 is five rows per block, so a total of two blocks are needed to store TEMP2.
Finally, the cost of the last join needs to be estimated. We can use a single-loop join on TEMP2 since in this case the index EMP_SSN (see Figure 19.8) can be used to probe and locate matching records from EMPLOYEE. Hence, the join method would involve reading in each block of TEMP2 and looking up each of the five Mgr_ssn val- ues using the EMP_SSN index. Each index lookup would require a root access, a leaf access, and a data block access (x+1, where the number of levels x is 2). So, 10 lookups require 30 block accesses. Adding the two block accesses for TEMP2 gives a total of 32 block accesses for this join.
For the final projection, assume pipelining is used to produce the final result, which does not require additional block accesses, so the total cost for join order (1) is esti- mated as the sum of the previous costs. The optimizer would then estimate costs in a similar manner for the other three join orders and choose the one with the lowest estimate. We leave this as an exercise for the reader.
19.9 Overview of Query Optimization in Oracle
The Oracle DBMS22 provides two different approaches to query optimization: rule- based and cost-based. With the rule-based approach, the optimizer chooses execu- tion plans based on heuristically ranked operations. Oracle maintains a table of 15 ranked access paths, where a lower ranking implies a more efficient approach. The access paths range from table access by ROWID (the most efficient)—where ROWID specifies the record’s physical address that includes the data file, data block, and row offset within the block—to a full table scan (the least efficient)—where all rows in the table are searched by doing multiblock reads. However, the rule-based approach is being phased out in favor of the cost-based approach, where the optimizer exam- ines alternative access paths and operator algorithms and chooses the execution plan with the lowest estimated cost. The estimated query cost is proportional to the expected elapsed time needed to execute the query with the given execution plan.
22The discussion in this section is primarily based on version 7 of Oracle. More optimization techniques have been added to subsequent versions.
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The Oracle optimizer calculates this cost based on the estimated usage of resources, such as I/O, CPU time, and memory needed. The goal of cost-based optimization in Oracle is to minimize the elapsed time to process the entire query.
An interesting addition to the Oracle query optimizer is the capability for an appli- cation developer to specify hints to the optimizer.23 The idea is that an application developer might know more information about the data than the optimizer. For example, consider the EMPLOYEE table shown in Figure 3.6. The Sex column of that table has only two distinct values. If there are 10,000 employees, then the optimizer would estimate that half are male and half are female, assuming a uniform data dis- tribution. If a secondary index exists, it would more than likely not be used. However, if the application developer knows that there are only 100 male employ- ees, a hint could be specified in an SQL query whose WHERE-clause condition is Sex = ‘M’ so that the associated index would be used in processing the query. Various hints can be specified, such as:
■ The optimization approach for an SQL statement
■ The access path for a table accessed by the statement
■ The join order for a join statement
■ A particular join operation in a join statement
The cost-based optimization of Oracle 8 and later versions is a good example of the sophisticated approach taken to optimize SQL queries in commercial RDBMSs.
19.10 Semantic Query Optimization A different approach to query optimization, called semantic query optimization, has been suggested. This technique, which may be used in combination with the techniques discussed previously, uses constraints specified on the database schema—such as unique attributes and other more complex constraints—in order to modify one query into another query that is more efficient to execute. We will not discuss this approach in detail but we will illustrate it with a simple example. Consider the SQL query:
SELECT E.Lname, M.Lname FROM EMPLOYEE AS E, EMPLOYEE AS M WHERE E.Super_ssn=M.Ssn AND E.Salary > M.Salary
This query retrieves the names of employees who earn more than their supervisors. Suppose that we had a constraint on the database schema that stated that no employee can earn more than his or her direct supervisor. If the semantic query optimizer checks for the existence of this constraint, it does not need to execute the query at all because it knows that the result of the query will be empty. This may save considerable time if the constraint checking can be done efficiently. However, searching through many constraints to find those that are applicable to a given
23Such hints have also been called query annotations.
Review Questions 723
query and that may semantically optimize it can also be quite time-consuming. With the inclusion of active rules and additional metadata in database systems (see Chapter 26), semantic query optimization techniques are being gradually incorpo- rated into the DBMSs.
19.11 Summary In this chapter we gave an overview of the techniques used by DBMSs in processing and optimizing high-level queries. We first discussed how SQL queries are trans- lated into relational algebra and then how various relational algebra operations may be executed by a DBMS. We saw that some operations, particularly SELECT and JOIN, may have many execution options. We also discussed how operations can be combined during query processing to create pipelined or stream-based execution instead of materialized execution.
Following that, we described heuristic approaches to query optimization, which use heuristic rules and algebraic techniques to improve the efficiency of query execu- tion. We showed how a query tree that represents a relational algebra expression can be heuristically optimized by reorganizing the tree nodes and transforming it into another equivalent query tree that is more efficient to execute. We also gave equivalence-preserving transformation rules that may be applied to a query tree. Then we introduced query execution plans for SQL queries, which add method exe- cution plans to the query tree operations.
We discussed the cost-based approach to query optimization. We showed how cost functions are developed for some database access algorithms and how these cost functions are used to estimate the costs of different execution strategies. We pre- sented an overview of the Oracle query optimizer, and we mentioned the technique of semantic query optimization.
Review Questions 19.1. Discuss the reasons for converting SQL queries into relational algebra
queries before optimization is done.
19.2. Discuss the different algorithms for implementing each of the following relational operators and the circumstances under which each algorithm can be used: SELECT, JOIN, PROJECT, UNION, INTERSECT, SET DIFFERENCE, CARTESIAN PRODUCT.
19.3. What is a query execution plan?
19.4. What is meant by the term heuristic optimization? Discuss the main heuris- tics that are applied during query optimization.
19.5. How does a query tree represent a relational algebra expression? What is meant by an execution of a query tree? Discuss the rules for transformation of query trees and identify when each rule should be applied during opti- mization.
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19.6. How many different join orders are there for a query that joins 10 relations?
19.7. What is meant by cost-based query optimization?
19.8. What is the difference between pipelining and materialization?
19.9. Discuss the cost components for a cost function that is used to estimate query execution cost. Which cost components are used most often as the basis for cost functions?
19.10. Discuss the different types of parameters that are used in cost functions. Where is this information kept?
19.11. List the cost functions for the SELECT and JOIN methods discussed in Section 19.8.
19.12. What is meant by semantic query optimization? How does it differ from other query optimization techniques?
Exercises 19.13. Consider SQL queries Q1, Q8, Q1B, and Q4 in Chapter 4 and Q27 in
Chapter 5.
a. Draw at least two query trees that can represent each of these queries. Under what circumstances would you use each of your query trees?
b. Draw the initial query tree for each of these queries, and then show how the query tree is optimized by the algorithm outlined in Section 19.7.
c. For each query, compare your own query trees of part (a) and the initial and final query trees of part (b).
19.14. A file of 4096 blocks is to be sorted with an available buffer space of 64 blocks. How many passes will be needed in the merge phase of the external sort-merge algorithm?
19.15. Develop cost functions for the PROJECT, UNION, INTERSECTION, SET DIF- FERENCE, and CARTESIAN PRODUCT algorithms discussed in Section 19.4.
19.16. Develop cost functions for an algorithm that consists of two SELECTs, a JOIN, and a final PROJECT, in terms of the cost functions for the individual operations.
19.17. Can a nondense index be used in the implementation of an aggregate opera- tor? Why or why not?
19.18. Calculate the cost functions for different options of executing the JOIN oper- ation OP7 discussed in Section 19.3.2.
19.19. Develop formulas for the hybrid hash-join algorithm for calculating the size of the buffer for the first bucket. Develop more accurate cost estimation for- mulas for the algorithm.
Selected Bibliography 725
19.20. Estimate the cost of operations OP6 and OP7, using the formulas developed in Exercise 19.9.
19.21. Extend the sort-merge join algorithm to implement the LEFT OUTER JOIN operation.
19.22. Compare the cost of two different query plans for the following query:
σSalary > 40000(EMPLOYEE Dno=DnumberDEPARTMENT)
Use the database statistics in Figure 19.8.
Selected Bibliography A detailed algorithm for relational algebra optimization is given by Smith and Chang (1975). The Ph.D. thesis of Kooi (1980) provides a foundation for query pro- cessing techniques. A survey paper by Jarke and Koch (1984) gives a taxonomy of query optimization and includes a bibliography of work in this area. A survey by Graefe (1993) discusses query execution in database systems and includes an exten- sive bibliography.
Whang (1985) discusses query optimization in OBE (Office-By-Example), which is a system based on the language QBE. Cost-based optimization was introduced in the SYSTEM R experimental DBMS and is discussed in Astrahan et al. (1976). Selinger et al. (1979) is a classic paper that discussed cost-based optimization of multiway joins in SYSTEM R. Join algorithms are discussed in Gotlieb (1975), Blasgen and Eswaran (1976), and Whang et al. (1982). Hashing algorithms for implementing joins are described and analyzed in DeWitt et al. (1984), Bratbergsengen (1984), Shapiro (1986), Kitsuregawa et al. (1989), and Blakeley and Martin (1990), among others. Approaches to finding a good join order are pre- sented in Ioannidis and Kang (1990) and in Swami and Gupta (1989). A discussion of the implications of left-deep and bushy join trees is presented in Ioannidis and Kang (1991). Kim (1982) discusses transformations of nested SQL queries into canonical representations. Optimization of aggregate functions is discussed in Klug (1982) and Muralikrishna (1992). Salzberg et al. (1990) describe a fast external sort- ing algorithm. Estimating the size of temporary relations is crucial for query opti- mization. Sampling-based estimation schemes are presented in Haas et al. (1995) and in Haas and Swami (1995). Lipton et al. (1990) also discuss selectivity estima- tion. Having the database system store and use more detailed statistics in the form of histograms is the topic of Muralikrishna and DeWitt (1988) and Poosala et al. (1996).
Kim et al. (1985) discuss advanced topics in query optimization. Semantic query optimization is discussed in King (1981) and Malley and Zdonick (1986). Work on semantic query optimization is reported in Chakravarthy et al. (1990), Shenoy and Ozsoyoglu (1989), and Siegel et al. (1992).
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Physical Database Design and Tuning
In the last chapter we discussed various techniques bywhich queries can be processed efficiently by the DBMS. These techniques are mostly internal to the DBMS and invisible to the pro- grammer. In this chapter we discuss additional issues that affect the performance of an application running on a DBMS. In particular, we discuss some of the options available to database administrators and programmers for storing databases, and some of the heuristics, rules, and techniques that they can use to tune the database for performance improvement. First, in Section 20.1, we discuss the issues that arise in physical database design dealing with storage and access of data. Then, in Section 20.2, we discuss how to improve database performance through tuning, indexing of data, database design, and the queries themselves.
20.1 Physical Database Design in Relational Databases
In this section, we begin by discussing the physical design factors that affect the per- formance of applications and transactions, and then we comment on the specific guidelines for RDBMSs.
20.1.1 Factors That Influence Physical Database Design Physical design is an activity where the goal is not only to create the appropriate structuring of data in storage, but also to do so in a way that guarantees good per- formance. For a given conceptual schema, there are many physical design alterna- tives in a given DBMS. It is not possible to make meaningful physical design
20chapter 20
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decisions and performance analyses until the database designer knows the mix of queries, transactions, and applications that are expected to run on the database. This is called the job mix for the particular set of database system applications. The database administrators/designers must analyze these applications, their expected frequencies of invocation, any timing constraints on their execution speed, the expected frequency of update operations, and any unique constraints on attributes. We discuss each of these factors next.
A. Analyzing the Database Queries and Transactions. Before undertaking the physical database design, we must have a good idea of the intended use of the database by defining in a high-level form the queries and transactions that are expected to run on the database. For each retrieval query, the following informa- tion about the query would be needed:
1. The files that will be accessed by the query.1
2. The attributes on which any selection conditions for the query are specified.
3. Whether the selection condition is an equality, inequality, or a range condi- tion.
4. The attributes on which any join conditions or conditions to link multiple tables or objects for the query are specified.
5. The attributes whose values will be retrieved by the query.
The attributes listed in items 2 and 4 above are candidates for the definition of access structures, such as indexes, hash keys, or sorting of the file.
For each update operation or update transaction, the following information would be needed:
1. The files that will be updated.
2. The type of operation on each file (insert, update, or delete).
3. The attributes on which selection conditions for a delete or update are spec- ified.
4. The attributes whose values will be changed by an update operation.
Again, the attributes listed in item 3 are candidates for access structures on the files, because they would be used to locate the records that will be updated or deleted. On the other hand, the attributes listed in item 4 are candidates for avoiding an access structure, since modifying them will require updating the access structures.
B. Analyzing the Expected Frequency of Invocation of Queries and Transactions. Besides identifying the characteristics of expected retrieval queries and update transactions, we must consider their expected rates of invocation. This frequency information, along with the attribute information collected on each query and transaction, is used to compile a cumulative list of the expected fre- quency of use for all queries and transactions. This is expressed as the expected fre- quency of using each attribute in each file as a selection attribute or a join attribute,
1For simplicity we use the term files here, but this can also mean tables or relations.
20.1 Physical Database Design in Relational Databases 729
over all the queries and transactions. Generally, for large volumes of processing, the informal 80–20 rule can be used: approximately 80 percent of the processing is accounted for by only 20 percent of the queries and transactions. Therefore, in prac- tical situations, it is rarely necessary to collect exhaustive statistics and invocation rates on all the queries and transactions; it is sufficient to determine the 20 percent or so most important ones.
C. Analyzing the Time Constraints of Queries and Transactions. Some queries and transactions may have stringent performance constraints. For example, a transaction may have the constraint that it should terminate within 5 seconds on 95 percent of the occasions when it is invoked, and that it should never take more than 20 seconds. Such timing constraints place further priorities on the attributes that are candidates for access paths. The selection attributes used by queries and transactions with time constraints become higher-priority candidates for primary access structures for the files, because the primary access structures are generally the most efficient for locating records in a file.
D. Analyzing the Expected Frequencies of Update Operations. A minimum number of access paths should be specified for a file that is frequently updated, because updating the access paths themselves slows down the update operations. For example, if a file that has frequent record insertions has 10 indexes on 10 different attributes, each of these indexes must be updated whenever a new record is inserted. The overhead for updating 10 indexes can slow down the insert operations.
E. Analyzing the Uniqueness Constraints on Attributes. Access paths should be specified on all candidate key attributes—or sets of attributes—that are either the primary key of a file or unique attributes. The existence of an index (or other access path) makes it sufficient to only search the index when checking this uniqueness constraint, since all values of the attribute will exist in the leaf nodes of the index. For example, when inserting a new record, if a key attribute value of the new record already exists in the index, the insertion of the new record should be rejected, since it would violate the uniqueness constraint on the attribute.
Once the preceding information is compiled, it is possible to address the physical database design decisions, which consist mainly of deciding on the storage struc- tures and access paths for the database files.
20.1.2 Physical Database Design Decisions Most relational systems represent each base relation as a physical database file. The access path options include specifying the type of primary file organization for each relation and the attributes of which indexes that should be defined. At most, one of the indexes on each file may be a primary or a clustering index. Any number of additional secondary indexes can be created.2
2The reader should review the various types of indexes described in Section 18.1. For a clearer under- standing of this discussion, it is also helpful to be familiar with the algorithms for query processing dis- cussed in Chapter 19.
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Design Decisions about Indexing. The attributes whose values are required in equality or range conditions (selection operation) are those that are keys or that participate in join conditions (join operation) requiring access paths, such as indexes.
The performance of queries largely depends upon what indexes or hashing schemes exist to expedite the processing of selections and joins. On the other hand, during insert, delete, or update operations, the existence of indexes adds to the overhead. This overhead must be justified in terms of the gain in efficiency by expediting queries and transactions.
The physical design decisions for indexing fall into the following categories:
1. Whether to index an attribute. The general rules for creating an index on an attribute are that the attribute must either be a key (unique), or there must be some query that uses that attribute either in a selection condition (equal- ity or range of values) or in a join condition. One reason for creating multi- ple indexes is that some operations can be processed by just scanning the indexes, without having to access the actual data file (see Section 19.5).
2. What attribute or attributes to index on. An index can be constructed on a single attribute, or on more than one attribute if it is a composite index. If multiple attributes from one relation are involved together in several queries, (for example, (Garment_style_#, Color) in a garment inventory database), a multiattribute (composite) index is warranted. The ordering of attributes within a multiattribute index must correspond to the queries. For instance, the above index assumes that queries would be based on an ordering of col- ors within a Garment_style_# rather than vice versa.
3. Whether to set up a clustered index. At most, one index per table can be a primary or clustering index, because this implies that the file be physically ordered on that attribute. In most RDBMSs, this is specified by the keyword CLUSTER. (If the attribute is a key, a primary index is created, whereas a clustering index is created if the attribute is not a key—see Section 18.1.) If a table requires several indexes, the decision about which one should be the primary or clustering index depends upon whether keeping the table ordered on that attribute is needed. Range queries benefit a great deal from clustering. If several attributes require range queries, relative benefits must be evaluated before deciding which attribute to cluster on. If a query is to be answered by doing an index search only (without retrieving data records), the corresponding index should not be clustered, since the main benefit of clustering is achieved when retrieving the records themselves. A clustering index may be set up as a multiattribute index if range retrieval by that com- posite key is useful in report creation (for example, an index on Zip_code, Store_id, and Product_id may be a clustering index for sales data).
4. Whether to use a hash index over a tree index. In general, RDBMSs use B+- trees for indexing. However, ISAM and hash indexes are also provided in some systems (see Chapter 18). B+-trees support both equality and range queries on the attribute used as the search key. Hash indexes work well with
20.1 Physical Database Design in Relational Databases 731
equality conditions, particularly during joins to find a matching record(s), but they do not support range queries.
5. Whether to use dynamic hashing for the file. For files that are very volatile—that is, those that grow and shrink continuously—one of the dynamic hashing schemes discussed in Section 17.9 would be suitable. Currently, they are not offered by many commercial RDBMSs.
How to Create an Index. Many RDBMSs have a similar type of command for creating an index, although it is not part of the SQL standard. The general form of this command is:
CREATE [ UNIQUE ] INDEX <index name> ON <table name> ( <column name> [ <order> ] { , <column name> [ <order> ] } ) [ CLUSTER ] ;
The keywords UNIQUE and CLUSTER are optional. The keyword CLUSTER is used when the index to be created should also sort the data file records on the indexing attribute. Thus, specifying CLUSTER on a key (unique) attribute would create some variation of a primary index, whereas specifying CLUSTER on a nonkey (nonunique) attribute would create some variation of a clustering index. The value for <order> can be either ASC (ascending) or DESC (descending), and specifies whether the data file should be ordered in ascending or descending values of the indexing attribute. The default is ASC. For example, the following would create a clustering (ascending) index on the nonkey attribute Dno of the EMPLOYEE file:
CREATE INDEX DnoIndex ON EMPLOYEE (Dno) CLUSTER ;
Denormalization as a Design Decision for Speeding Up Queries. The ulti- mate goal during normalization (see Chapters 15 and 16) is to separate attributes into tables to minimize redundancy, and thereby avoid the update anomalies that lead to an extra processing overhead to maintain consistency in the database. The ideals that are typically followed are the third or Boyce-Codd normal forms (see Chapter 15).
The above ideals are sometimes sacrificed in favor of faster execution of frequently occurring queries and transactions. This process of storing the logical database design (which may be in BCNF or 4NF) in a weaker normal form, say 2NF or 1NF, is called denormalization. Typically, the designer includes certain attributes from a table S into another table R. The reason is that the attributes from S that are included in R are frequently needed—along with other attributes in R—for answer- ing queries or producing reports. By including these attributes, a join of R with S is avoided for these frequently occurring queries and reports. This reintroduces redundancy in the base tables by including the same attributes in both tables R and S. A partial functional dependency or a transitive dependency now exists in the table R, thereby creating the associated redundancy problems (see Chapter 15). A tradeoff exists between the additional updating needed for maintaining consistency of
732 Chapter 20 Physical Database Design and Tuning
redundant attributes versus the effort needed to perform a join to incorporate the additional attributes needed in the result. For example, consider the following rela- tion:
ASSIGN (Emp_id, Proj_id, Emp_name, Emp_job_title, Percent_assigned, Proj_name, Proj_mgr_id, Proj_mgr_name),
which corresponds exactly to the headers in a report called The Employee Assignment Roster.
This relation is only in 1NF because of the following functional dependencies:
Proj_id → Proj_name, Proj_mgr_id Proj_mgr_id → Proj_mgr_name Emp_id → Emp_name, Emp_job_title
This relation may be preferred over the design in 2NF (and 3NF) consisting of the following three relations:
EMP (Emp_id, Emp_name, Emp_job_title) PROJ (Proj_id, Proj_name, Proj_mgr_id) EMP_PROJ (Emp_id, Proj_id, Percent_assigned)
This is because to produce the The Employee Assignment Roster report (with all fields shown in ASSIGN above), the latter multirelation design requires two NATURAL JOIN (indicated with *) operations (between EMP and EMP_PROJ, and between PROJ and EMP_PROJ), plus a final JOIN between PROJ and EMP to retrieve the Proj_mgr_name from the Proj_mgr_id. Thus the following JOINs would be needed (the final join would also require renaming (aliasing) of the last EMP table, which is not shown):
((EMP_PROJ * EMP) * PROJ) PROJ.Proj_mgr_id = EMP.Emp_id EMP
It is also possible to create a view for the ASSIGN table. This does not mean that the join operations will be avoided, but that the user need not specify the joins. If the view table is materialized, the joins would be avoided, but if the virtual view table is not stored as a materialized file, the join computations would still be necessary. Other forms of denormalization consist of storing extra tables to maintain original functional dependencies that are lost during BCNF decomposition. For example, Figure 15.14 shows the TEACH(Student, Course, Instructor) relation with the func- tional dependencies {{Student, Course} → Instructor, Instructor → Course}. A lossless decomposition of TEACH into T1(Student, Instructor) and T2(Instructor, Course) does not allow queries of the form what course did student Smith take from instructor Navathe to be answered without joining T1 and T2. Therefore, storing T1, T2, and TEACH may be a possible solution, which reduces the design from BCNF to 3NF. Here, TEACH is a materialized join of the other two tables, representing an extreme redundancy. Any updates to T1 and T2 would have to be applied to TEACH. An alter- nate strategy is to create T1 and T2 as updatable base tables, and to create TEACH as a view (virtual table) on T1 and T2 that can only be queried.
20.2 An Overview of Database Tuning in Relational Systems 733
20.2 An Overview of Database Tuning in Relational Systems
After a database is deployed and is in operation, actual use of the applications, trans- actions, queries, and views reveals factors and problem areas that may not have been accounted for during the initial physical design. The inputs to physical design listed in Section 20.1.1 can be revised by gathering actual statistics about usage patterns. Resource utilization as well as internal DBMS processing—such as query optimiza- tion—can be monitored to reveal bottlenecks, such as contention for the same data or devices. Volumes of activity and sizes of data can be better estimated. Therefore, it is necessary to monitor and revise the physical database design constantly—an activ- ity referred to as database tuning. The goals of tuning are as follows:
■ To make applications run faster.
■ To improve (lower) the response time of queries and transactions.
■ To improve the overall throughput of transactions.
The dividing line between physical design and tuning is very thin. The same design decisions that we discussed in Section 20.1.2 are revisited during database tuning, which is a continual adjustment of the physical design. We give a brief overview of the tuning process below.3 The inputs to the tuning process include statistics related to the same factors mentioned in Section 20.1.1. In particular, DBMSs can internally collect the following statistics:
■ Sizes of individual tables.
■ Number of distinct values in a column.
■ The number of times a particular query or transaction is submitted and exe- cuted in an interval of time.
■ The times required for different phases of query and transaction processing (for a given set of queries or transactions).
These and other statistics create a profile of the contents and use of the database. Other information obtained from monitoring the database system activities and processes includes the following:
■ Storage statistics. Data about allocation of storage into tablespaces, index- spaces, and buffer pools.
■ I/O and device performance statistics. Total read/write activity (paging) on disk extents and disk hot spots.
■ Query/transaction processing statistics. Execution times of queries and transactions, and optimization times during query optimization.
3Interested readers should consult Shasha and Bonnet (2002) for a detailed discussion of tuning.
734 Chapter 20 Physical Database Design and Tuning
■ Locking/logging related statistics. Rates of issuing different types of locks, transaction throughput rates, and log records activity.4
■ Index statistics. Number of levels in an index, number of noncontiguous leaf pages, and so on.
Some of the above statistics relate to transactions, concurrency control, and recov- ery, which are discussed in Chapters 21 through 23. Tuning a database involves deal- ing with the following types of problems:
■ How to avoid excessive lock contention, thereby increasing concurrency among transactions.
■ How to minimize the overhead of logging and unnecessary dumping of data.
■ How to optimize the buffer size and scheduling of processes.
■ How to allocate resources such as disks, RAM, and processes for most effi- cient utilization.
Most of the previously mentioned problems can be solved by the DBA by setting appropriate physical DBMS parameters, changing configurations of devices, chang- ing operating system parameters, and other similar activities. The solutions tend to be closely tied to specific systems. The DBAs are typically trained to handle these tuning problems for the specific DBMS. We briefly discuss the tuning of various physical database design decisions below.
20.2.1 Tuning Indexes The initial choice of indexes may have to be revised for the following reasons:
■ Certain queries may take too long to run for lack of an index.
■ Certain indexes may not get utilized at all.
■ Certain indexes may undergo too much updating because the index is on an attribute that undergoes frequent changes.
Most DBMSs have a command or trace facility, which can be used by the DBA to ask the system to show how a query was executed—what operations were performed in what order and what secondary access structures (indexes) were used. By analyzing these execution plans, it is possible to diagnose the causes of the above problems. Some indexes may be dropped and some new indexes may be created based on the tuning analysis.
The goal of tuning is to dynamically evaluate the requirements, which sometimes fluctuate seasonally or during different times of the month or week, and to reorgan- ize the indexes and file organizations to yield the best overall performance. Dropping and building new indexes is an overhead that can be justified in terms of performance improvements. Updating of a table is generally suspended while an
4The reader should preview Chapters 21–23 for an explanation of these terms.
20.2 An Overview of Database Tuning in Relational Systems 735
index is dropped or created; this loss of service must be accounted for. Besides drop- ping or creating indexes and changing from a nonclustered to a clustered index and vice versa, rebuilding the index may improve performance. Most RDBMSs use B+-trees for an index. If there are many deletions on the index key, index pages may contain wasted space, which can be claimed during a rebuild operation. Similarly, too many insertions may cause overflows in a clustered index that affect perfor- mance. Rebuilding a clustered index amounts to reorganizing the entire table ordered on that key.
The available options for indexing and the way they are defined, created, and reor- ganized varies from system to system. As an illustration, consider the sparse and dense indexes in Chapter 18. A sparse index such as a primary index (see Section 18.1) will have one index pointer for each page (disk block) in the data file; a dense index such as a unique secondary index will have an index pointer for each record. Sybase provides clustering indexes as sparse indexes in the form of B+-trees, whereas INGRES provides sparse clustering indexes as ISAM files and dense clustering indexes as B+-trees. In some versions of Oracle and DB2, the option of setting up a clustering index is limited to a dense index (with many more index entries), and the DBA has to work with this limitation.
20.2.2 Tuning the Database Design In Section 20.1.2, we discussed the need for a possible denormalization, which is a departure from keeping all tables as BCNF relations. If a given physical database design does not meet the expected objectives, the DBA may revert to the logical database design, make adjustments such as denormalizations to the logical schema, and remap it to a new set of physical tables and indexes.
As discussed, the entire database design has to be driven by the processing require- ments as much as by data requirements. If the processing requirements are dynam- ically changing, the design needs to respond by making changes to the conceptual schema if necessary and to reflect those changes into the logical schema and physi- cal design. These changes may be of the following nature:
■ Existing tables may be joined (denormalized) because certain attributes from two or more tables are frequently needed together: This reduces the normalization level from BCNF to 3NF, 2NF, or 1NF.5
■ For the given set of tables, there may be alternative design choices, all of which achieve 3NF or BCNF. We illustrated alternative equivalent designs in Chapter 16. One normalized design may be replaced by another.
■ A relation of the form R(K,A, B, C, D, ...)—with K as a set of key attributes— that is in BCNF can be stored in multiple tables that are also in BCNF—for example, R1(K, A, B), R2(K, C, D, ), R3(K, ...)—by replicating the key K in each table. Such a process is known as vertical partitioning. Each table groups
5Note that 3NF and 2NF address different types of problem dependencies that are independent of each other; hence, the normalization (or denormalization) order between them is arbitrary.
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sets of attributes that are accessed together. For example, the table EMPLOYEE(Ssn, Name, Phone, Grade, Salary) may be split into two tables: EMP1(Ssn, Name, Phone) and EMP2(Ssn, Grade, Salary). If the original table has a large number of rows (say 100,000) and queries about phone numbers and salary information are totally distinct and occur with very different fre- quencies, then this separation of tables may work better.
■ Attribute(s) from one table may be repeated in another even though this cre- ates redundancy and a potential anomaly. For example, Part_name may be replicated in tables wherever the Part# appears (as foreign key), but there may be one master table called PART_MASTER(Part#, Part_name, ...) where the Partname is guaranteed to be up-to-date.
■ Just as vertical partitioning splits a table vertically into multiple tables, horizontal partitioning takes horizontal slices of a table and stores them as distinct tables. For example, product sales data may be separated into ten tables based on ten product lines. Each table has the same set of columns (attributes) but contains a distinct set of products (tuples). If a query or transaction applies to all product data, it may have to run against all the tables and the results may have to be combined.
These types of adjustments designed to meet the high volume of queries or transac- tions, with or without sacrificing the normal forms, are commonplace in practice.
20.2.3 Tuning Queries We already discussed how query performance is dependent upon the appropriate selection of indexes, and how indexes may have to be tuned after analyzing queries that give poor performance by using the commands in the RDBMS that show the execution plan of the query. There are mainly two indications that suggest that query tuning may be needed:
1. A query issues too many disk accesses (for example, an exact match query scans an entire table).
2. The query plan shows that relevant indexes are not being used.
Some typical instances of situations prompting query tuning include the following:
1. Many query optimizers do not use indexes in the presence of arithmetic expressions (such as Salary/365 > 10.50), numerical comparisons of attrib- utes of different sizes and precision (such as Aqty = Bqty where Aqty is of type INTEGER and Bqty is of type SMALLINTEGER), NULL comparisons (such as Bdate IS NULL), and substring comparisons (such as Lname LIKE ‘%mann’).
2. Indexes are often not used for nested queries using IN; for example, the fol- lowing query:
SELECT Ssn FROM EMPLOYEE WHERE Dno IN ( SELECT Dnumber FROM DEPARTMENT
WHERE Mgr_ssn = ‘333445555’ );
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may not use the index on Dno in EMPLOYEE, whereas using Dno = Dnumber in the WHERE-clause with a single block query may cause the index to be used.
3. Some DISTINCTs may be redundant and can be avoided without changing the result. A DISTINCT often causes a sort operation and must be avoided as much as possible.
4. Unnecessary use of temporary result tables can be avoided by collapsing multiple queries into a single query unless the temporary relation is needed for some intermediate processing.
5. In some situations involving the use of correlated queries, temporaries are useful. Consider the following query, which retrieves the highest paid employee in each department:
SELECT Ssn FROM EMPLOYEE E WHERE Salary = SELECT MAX (Salary)
FROM EMPLOYEE AS M WHERE M.Dno = E.Dno;
This has the potential danger of searching all of the inner EMPLOYEE table M for each tuple from the outer EMPLOYEE table E. To make the execution more efficient, the process can be broken into two queries, where the first query just computes the maximum salary in each department as follows:
SELECT MAX (Salary) AS High_salary, Dno INTO TEMP FROM EMPLOYEE GROUP BY Dno; SELECT EMPLOYEE.Ssn FROM EMPLOYEE, TEMP WHERE EMPLOYEE.Salary = TEMP.High_salary
AND EMPLOYEE.Dno = TEMP.Dno;
6. If multiple options for a join condition are possible, choose one that uses a clustering index and avoid those that contain string comparisons. For exam- ple, assuming that the Name attribute is a candidate key in EMPLOYEE and STUDENT, it is better to use EMPLOYEE.Ssn = STUDENT.Ssn as a join condi- tion rather than EMPLOYEE.Name = STUDENT.Name if Ssn has a clustering index in one or both tables.
7. One idiosyncrasy with some query optimizers is that the order of tables in the FROM-clause may affect the join processing. If that is the case, one may have to switch this order so that the smaller of the two relations is scanned and the larger relation is used with an appropriate index.
8. Some query optimizers perform worse on nested queries compared to their equivalent unnested counterparts. There are four types of nested queries:
■ Uncorrelated subqueries with aggregates in an inner query.
■ Uncorrelated subqueries without aggregates.
■ Correlated subqueries with aggregates in an inner query.
738 Chapter 20 Physical Database Design and Tuning
■ Correlated subqueries without aggregates.
Of the four types above, the first one typically presents no problem, since most query optimizers evaluate the inner query once. However, for a query of the second type, such as the example in item 2, most query optimizers may not use an index on Dno in EMPLOYEE. However, the same optimizers may do so if the query is written as an unnested query. Transformation of correlated subqueries may involve setting temporary tables. Detailed exam- ples are outside our scope here.6
9. Finally, many applications are based on views that define the data of interest to those applications. Sometimes, these views become overkill, because a query may be posed directly against a base table, rather than going through a view that is defined by a JOIN.
20.2.4 Additional Query Tuning Guidelines Additional techniques for improving queries apply in certain situations as follows:
1. A query with multiple selection conditions that are connected via OR may not be prompting the query optimizer to use any index. Such a query may be split up and expressed as a union of queries, each with a condition on an attribute that causes an index to be used. For example,
SELECT Fname, Lname, Salary, Age7
FROM EMPLOYEE WHERE Age > 45 OR Salary < 50000;
may be executed using sequential scan giving poor performance. Splitting it up as
SELECT Fname, Lname, Salary, Age FROM EMPLOYEE WHERE Age > 45 UNION SELECT Fname, Lname, Salary, Age FROM EMPLOYEE WHERE Salary < 50000;
may utilize indexes on Age as well as on Salary.
2. To help expedite a query, the following transformations may be tried:
■ NOT condition may be transformed into a positive expression.
■ Embedded SELECT blocks using IN, = ALL, = ANY, and = SOME may be replaced by joins.
■ If an equality join is set up between two tables, the range predicate (selec- tion condition) on the joining attribute set up in one table may be repeated for the other table.
6For further details, see Shasha and Bonnet (2002). 7We modified the schema and used Age in EMPLOYEE instead of Bdate.
Review Questions 739
3. WHERE conditions may be rewritten to utilize the indexes on multiple columns. For example,
SELECT Region#, Prod_type, Month, Sales FROM SALES_STATISTICS WHERE Region# = 3 AND ((Prod_type BETWEEN 1 AND 3) OR (Prod_type
BETWEEN 8 AND 10));
may use an index only on Region# and search through all leaf pages of the index for a match on Prod_type. Instead, using
SELECT Region#, Prod_type, Month, Sales FROM SALES_STATISTICS WHERE (Region# = 3 AND (Prod_type BETWEEN 1 AND 3))
OR (Region# = 3 AND (Prod_type BETWEEN 8 AND 10));
may use a composite index on (Region#, Prod_type) and work much more efficiently.
In this section, we have covered many of the common instances where the ineffi- ciency of a query may be fixed by some simple corrective action such as using a tem- porary table, avoiding certain types of query constructs, or avoiding the use of views. The goal is to have the RDBMS use existing single attribute or composite attribute indexes as much as possible. This avoids full scans of data blocks or entire scanning of index leaf nodes. Redundant processes like sorting must be avoided at any cost. The problems and the remedies will depend upon the workings of a query optimizer within an RDBMS. Detailed literature exists in database tuning guidelines for database administration by the RDBMS vendors. Major relational DBMS ven- dors like Oracle, IBM and Microsoft encourage their large customers to share ideas of tuning at the annual expos and other forums so that the entire industry benefits by using performance enhancement techniques. These techniques are typically available in trade literature and on various Web sites.
20.3 Summary In this chapter, we discussed the factors that affect physical database design deci- sions and provided guidelines for choosing among physical design alternatives. We discussed changes to logical design such as denormalization, as well as modifica- tions of indexes, and changes to queries to illustrate different techniques for data- base performance tuning. These are only a representative sample of a large number of measures and techniques adopted in the design of large commercial applications of relational DBMSs.
Review Questions 20.1. What are the important factors that influence physical database design?
20.2. Discuss the decisions made during physical database design.
20.3. Discuss the guidelines for physical database design in RDBMSs.
740 Chapter 20 Physical Database Design and Tuning
20.4. Discuss the types of modifications that may be applied to the logical data- base design of a relational database.
20.5. Under what situations would denormalization of a database schema be used? Give examples of denormalization.
20.6. Discuss the tuning of indexes for relational databases.
20.7. Discuss the considerations for reevaluating and modifying SQL queries.
20.8. Illustrate the types of changes to SQL queries that may be worth considering for improving the performance during database tuning.
Selected Bibliography Wiederhold (1987) covers issues related to physical design. O’Neil and O’Neil (2001) has a detailed discussion of physical design and transaction issues in refer- ence to commercial RDBMSs. Navathe and Kerschberg (1986) discuss all phases of database design and point out the role of data dictionaries. Rozen and Shasha (1991) and Carlis and March (1984) present different models for the problem of physical database design. Shasha and Bonnet (2002) has an elaborate discussion of guidelines for database tuning. Niemiec (2008) is one among several books available for Oracle database administration and tuning; Schneider (2006) is focused on designing and tuning MySQL databases.
part 9 Transaction Processing,
Concurrency Control, and Recovery
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Introduction to Transaction Processing Concepts
and Theory
The concept of transaction provides a mechanismfor describing logical units of database processing. Transaction processing systems are systems with large databases and hundreds of concurrent users executing database transactions. Examples of such systems include airline reservations, banking, credit card processing, online retail purchasing, stock markets, supermarket checkouts, and many other applications. These systems require high availability and fast response time for hundreds of concurrent users. In this chapter we present the concepts that are needed in transaction processing sys- tems. We define the concept of a transaction, which is used to represent a logical unit of database processing that must be completed in its entirety to ensure correct- ness. A transaction is typically implemented by a computer program, which includes database commands such as retrievals, insertions, deletions, and updates. We introduced some of the basic techniques for database programming in Chapters 13 and 14.
In this chapter, we focus on the basic concepts and theory that are needed to ensure the correct executions of transactions. We discuss the concurrency control problem, which occurs when multiple transactions submitted by various users interfere with one another in a way that produces incorrect results. We also discuss the problems that can occur when transactions fail, and how the database system can recover from various types of failures.
This chapter is organized as follows. Section 21.1 informally discusses why concur- rency control and recovery are necessary in a database system. Section 21.2 defines the term transaction and discusses additional concepts related to transaction pro- cessing in database systems. Section 21.3 presents the important properties of atom- icity, consistency preservation, isolation, and durability or permanency—called the
21chapter 21
744 Chapter 21 Introduction to Transaction Processing Concepts and Theory
ACID properties—that are considered desirable in transaction processing systems. Section 21.4 introduces the concept of schedules (or histories) of executing transac- tions and characterizes the recoverability of schedules. Section 21.5 discusses the notion of serializability of concurrent transaction execution, which can be used to define correct execution sequences (or schedules) of concurrent transactions. In Section 21.6, we present some of the commands that support the transaction con- cept in SQL. Section 21.7 summarizes the chapter.
The two following chapters continue with more details on the actual methods and techniques used to support transaction processing. Chapter 22 gives an overview of the basic concurrency control protocols and Chapter 23 introduces recovery techniques.
21.1 Introduction to Transaction Processing In this section we discuss the concepts of concurrent execution of transactions and recovery from transaction failures. Section 21.1.1 compares single-user and multi- user database systems and demonstrates how concurrent execution of transactions can take place in multiuser systems. Section 21.1.2 defines the concept of transac- tion and presents a simple model of transaction execution based on read and write database operations. This model is used as the basis for defining and formalizing concurrency control and recovery concepts. Section 21.1.3 uses informal examples to show why concurrency control techniques are needed in multiuser systems. Finally, Section 21.1.4 discusses why techniques are needed to handle recovery from system and transaction failures by discussing the different ways in which transac- tions can fail while executing.
21.1.1 Single-User versus Multiuser Systems One criterion for classifying a database system is according to the number of users who can use the system concurrently. A DBMS is single-user if at most one user at a time can use the system, and it is multiuser if many users can use the system—and hence access the database—concurrently. Single-user DBMSs are mostly restricted to personal computer systems; most other DBMSs are multiuser. For example, an airline reservations system is used by hundreds of travel agents and reservation clerks concurrently. Database systems used in banks, insurance agencies, stock exchanges, supermarkets, and many other applications are multiuser systems. In these systems, hundreds or thousands of users are typically operating on the data- base by submitting transactions concurrently to the system.
Multiple users can access databases—and use computer systems—simultaneously because of the concept of multiprogramming, which allows the operating system of the computer to execute multiple programs—or processes—at the same time. A single central processing unit (CPU) can only execute at most one process at a time. However, multiprogramming operating systems execute some commands from one process, then suspend that process and execute some commands from the next
21.1 Introduction to Transaction Processing 745
A A
B B C D
CPU1 CPU2
t1 t2 t3 t4 Time
Figure 21.1 Interleaved process- ing versus parallel processing of con- current transactions.
process, and so on. A process is resumed at the point where it was suspended when- ever it gets its turn to use the CPU again. Hence, concurrent execution of processes is actually interleaved, as illustrated in Figure 21.1, which shows two processes, A and B, executing concurrently in an interleaved fashion. Interleaving keeps the CPU busy when a process requires an input or output (I/O) operation, such as reading a block from disk. The CPU is switched to execute another process rather than remaining idle during I/O time. Interleaving also prevents a long process from delaying other processes.
If the computer system has multiple hardware processors (CPUs), parallel process- ing of multiple processes is possible, as illustrated by processes C and D in Figure 21.1. Most of the theory concerning concurrency control in databases is developed in terms of interleaved concurrency, so for the remainder of this chapter we assume this model. In a multiuser DBMS, the stored data items are the primary resources that may be accessed concurrently by interactive users or application programs, which are constantly retrieving information from and modifying the database.
21.1.2 Transactions, Database Items, Read and Write Operations, and DBMS Buffers
A transaction is an executing program that forms a logical unit of database process- ing. A transaction includes one or more database access operations—these can include insertion, deletion, modification, or retrieval operations. The database operations that form a transaction can either be embedded within an application program or they can be specified interactively via a high-level query language such as SQL. One way of specifying the transaction boundaries is by specifying explicit begin transaction and end transaction statements in an application program; in this case, all database access operations between the two are considered as forming one transaction. A single application program may contain more than one transac- tion if it contains several transaction boundaries. If the database operations in a transaction do not update the database but only retrieve data, the transaction is called a read-only transaction; otherwise it is known as a read-write transaction.
746 Chapter 21 Introduction to Transaction Processing Concepts and Theory
The database model that is used to present transaction processing concepts is quite simple when compared to the data models that we discussed earlier in the book, such as the relational model or the object model. A database is basically represented as a collection of named data items. The size of a data item is called its granularity. A data item can be a database record, but it can also be a larger unit such as a whole disk block, or even a smaller unit such as an individual field (attribute) value of some record in the database. The transaction processing concepts we discuss are inde- pendent of the data item granularity (size) and apply to data items in general. Each data item has a unique name, but this name is not typically used by the programmer; rather, it is just a means to uniquely identify each data item. For example, if the data item granularity is one disk block, then the disk block address can be used as the data item name. Using this simplified database model, the basic database access operations that a transaction can include are as follows:
■ read_item(X). Reads a database item named X into a program variable. To simplify our notation, we assume that the program variable is also named X.
■ write_item(X). Writes the value of program variable X into the database item named X.
As we discussed in Chapter 17, the basic unit of data transfer from disk to main memory is one block. Executing a read_item(X) command includes the following steps:
1. Find the address of the disk block that contains item X.
2. Copy that disk block into a buffer in main memory (if that disk block is not already in some main memory buffer).
3. Copy item X from the buffer to the program variable named X.
Executing a write_item(X) command includes the following steps:
1. Find the address of the disk block that contains item X.
2. Copy that disk block into a buffer in main memory (if that disk block is not already in some main memory buffer).
3. Copy item X from the program variable named X into its correct location in the buffer.
4. Store the updated block from the buffer back to disk (either immediately or at some later point in time).
It is step 4 that actually updates the database on disk. In some cases the buffer is not immediately stored to disk, in case additional changes are to be made to the buffer. Usually, the decision about when to store a modified disk block whose contents are in a main memory buffer is handled by the recovery manager of the DBMS in coop- eration with the underlying operating system. The DBMS will maintain in the database cache a number of data buffers in main memory. Each buffer typically holds the contents of one database disk block, which contains some of the database items being processed. When these buffers are all occupied, and additional database disk blocks must be copied into memory, some buffer replacement policy is used to
21.1 Introduction to Transaction Processing 747
(a)
read_item(X ); X := X – N; write_item(X ); read_item(Y ); Y := Y + N; write_item(Y );
(b)
read_item(X ); X := X + M; write_item(X );
T1 T2 Figure 21.2 Two sample transac- tions. (a) Transaction T1. (b) Transaction T2.
choose which of the current buffers is to be replaced. If the chosen buffer has been modified, it must be written back to disk before it is reused.1
A transaction includes read_item and write_item operations to access and update the database. Figure 21.2 shows examples of two very simple transactions. The read-set of a transaction is the set of all items that the transaction reads, and the write-set is the set of all items that the transaction writes. For example, the read-set of T1 in Figure 21.2 is {X, Y} and its write-set is also {X, Y}.
Concurrency control and recovery mechanisms are mainly concerned with the database commands in a transaction. Transactions submitted by the various users may execute concurrently and may access and update the same database items. If this concurrent execution is uncontrolled, it may lead to problems, such as an incon- sistent database. In the next section we informally introduce some of the problems that may occur.
21.1.3 Why Concurrency Control Is Needed Several problems can occur when concurrent transactions execute in an uncon- trolled manner. We illustrate some of these problems by referring to a much simpli- fied airline reservations database in which a record is stored for each airline flight. Each record includes the number of reserved seats on that flight as a named (uniquely identifiable) data item, among other information. Figure 21.2(a) shows a transac- tion T1 that transfers N reservations from one flight whose number of reserved seats is stored in the database item named X to another flight whose number of reserved seats is stored in the database item named Y. Figure 21.2(b) shows a simpler trans- action T2 that just reserves M seats on the first flight (X) referenced in transaction T1.
2 To simplify our example, we do not show additional portions of the transac- tions, such as checking whether a flight has enough seats available before reserving additional seats.
1We will not discuss buffer replacement policies here because they are typically discussed in operating systems textbooks. 2A similar, more commonly used example assumes a bank database, with one transaction doing a trans- fer of funds from account X to account Y and the other transaction doing a deposit to account X.
748 Chapter 21 Introduction to Transaction Processing Concepts and Theory
When a database access program is written, it has the flight number, flight date, and the number of seats to be booked as parameters; hence, the same program can be used to execute many different transactions, each with a different flight number, date, and number of seats to be booked. For concurrency control purposes, a trans- action is a particular execution of a program on a specific date, flight, and number of seats. In Figure 21.2(a) and (b), the transactions T1 and T2 are specific executions of the programs that refer to the specific flights whose numbers of seats are stored in data items X and Y in the database. Next we discuss the types of problems we may encounter with these two simple transactions if they run concurrently.
The Lost Update Problem. This problem occurs when two transactions that access the same database items have their operations interleaved in a way that makes the value of some database items incorrect. Suppose that transactions T1 and T2 are submitted at approximately the same time, and suppose that their operations are interleaved as shown in Figure 21.3(a); then the final value of item X is incorrect because T2 reads the value of X before T1 changes it in the database, and hence the updated value resulting from T1 is lost. For example, if X = 80 at the start (originally there were 80 reservations on the flight), N = 5 (T1 transfers 5 seat reservations from the flight corresponding to X to the flight corresponding to Y), and M = 4 (T2 reserves 4 seats on X), the final result should be X = 79. However, in the interleaving of operations shown in Figure 21.3(a), it is X = 84 because the update in T1 that removed the five seats from X was lost.
The Temporary Update (or Dirty Read) Problem. This problem occurs when one transaction updates a database item and then the transaction fails for some rea- son (see Section 21.1.4). Meanwhile, the updated item is accessed (read) by another transaction before it is changed back to its original value. Figure 21.3(b) shows an example where T1 updates item X and then fails before completion, so the system must change X back to its original value. Before it can do so, however, transaction T2 reads the temporary value of X, which will not be recorded permanently in the data- base because of the failure of T1. The value of item X that is read by T2 is called dirty data because it has been created by a transaction that has not completed and com- mitted yet; hence, this problem is also known as the dirty read problem.
The Incorrect Summary Problem. If one transaction is calculating an aggregate summary function on a number of database items while other transactions are updating some of these items, the aggregate function may calculate some values before they are updated and others after they are updated. For example, suppose that a transaction T3 is calculating the total number of reservations on all the flights; meanwhile, transaction T1 is executing. If the interleaving of operations shown in Figure 21.3(c) occurs, the result of T3 will be off by an amount N because T3 reads the value of X after N seats have been subtracted from it but reads the value of Y before those N seats have been added to it.
21.1 Introduction to Transaction Processing 749
(a)
read_item(X ); X := X – N;
write_item(X ); read_item(Y );
read_item(X ); X := X + M;
write_item(X );
Time
Item X has an incorrect value because its update by T1 is lost (overwritten).
Y := Y + N; write_item(Y );
(b)
read_item(X ); X := X – N; write_item(X );
read_item(X ); X := X + M; write_item(X );
Time
Transaction T1 fails and must change the value of X back to its old value; meanwhile T2 has read the temporary incorrect value of X.
read_item(Y );
T1
T1
(c)
read_item(X ); X := X – N; write_item(X );
read_item(Y ); Y := Y + N; write_item(Y );
read_item(X ); sum := sum + X; read_item(Y ); sum := sum + Y;
T3 reads X after N is subtracted and reads Y before N is added; a wrong summary is the result (off by N ).
T3
T2
sum := 0; read_item(A); sum := sum + A;
T1 T2 Figure 21.3 Some problems that occur when concurrent execution is uncontrolled. (a) The lost update problem. (b) The temporary update problem. (c) The incorrect summary problem.
750 Chapter 21 Introduction to Transaction Processing Concepts and Theory
The Unrepeatable Read Problem. Another problem that may occur is called unrepeatable read, where a transaction T reads the same item twice and the item is changed by another transaction T� between the two reads. Hence, T receives different values for its two reads of the same item. This may occur, for example, if during an airline reservation transaction, a customer inquires about seat availability on several flights. When the customer decides on a particular flight, the transaction then reads the number of seats on that flight a second time before completing the reservation, and it may end up reading a different value for the item.
21.1.4 Why Recovery Is Needed Whenever a transaction is submitted to a DBMS for execution, the system is respon- sible for making sure that either all the operations in the transaction are completed successfully and their effect is recorded permanently in the database, or that the transaction does not have any effect on the database or any other transactions. In the first case, the transaction is said to be committed, whereas in the second case, the transaction is aborted. The DBMS must not permit some operations of a trans- action T to be applied to the database while other operations of T are not, because the whole transaction is a logical unit of database processing. If a transaction fails after executing some of its operations but before executing all of them, the opera- tions already executed must be undone and have no lasting effect.
Types of Failures. Failures are generally classified as transaction, system, and media failures. There are several possible reasons for a transaction to fail in the mid- dle of execution:
1. A computer failure (system crash). A hardware, software, or network error occurs in the computer system during transaction execution. Hardware crashes are usually media failures—for example, main memory failure.
2. A transaction or system error. Some operation in the transaction may cause it to fail, such as integer overflow or division by zero. Transaction failure may also occur because of erroneous parameter values or because of a logical programming error.3 Additionally, the user may interrupt the transaction during its execution.
3. Local errors or exception conditions detected by the transaction. During transaction execution, certain conditions may occur that necessitate cancel- lation of the transaction. For example, data for the transaction may not be found. An exception condition,4 such as insufficient account balance in a banking database, may cause a transaction, such as a fund withdrawal, to be canceled. This exception could be programmed in the transaction itself, and in such a case would not be considered as a transaction failure.
3In general, a transaction should be thoroughly tested to ensure that it does not have any bugs (logical programming errors). 4Exception conditions, if programmed correctly, do not constitute transaction failures.
21.2 Transaction and System Concepts 751
4. Concurrency control enforcement. The concurrency control method (see Chapter 22) may decide to abort a transaction because it violates serializabil- ity (see Section 21.5), or it may abort one or more transactions to resolve a state of deadlock among several transactions (see Section 22.1.3). Transactions aborted because of serializability violations or deadlocks are typically restarted automatically at a later time.
5. Disk failure. Some disk blocks may lose their data because of a read or write malfunction or because of a disk read/write head crash. This may happen during a read or a write operation of the transaction.
6. Physical problems and catastrophes. This refers to an endless list of prob- lems that includes power or air-conditioning failure, fire, theft, sabotage, overwriting disks or tapes by mistake, and mounting of a wrong tape by the operator.
Failures of types 1, 2, 3, and 4 are more common than those of types 5 or 6. Whenever a failure of type 1 through 4 occurs, the system must keep sufficient information to quickly recover from the failure. Disk failure or other catastrophic failures of type 5 or 6 do not happen frequently; if they do occur, recovery is a major task. We discuss recovery from failure in Chapter 23.
The concept of transaction is fundamental to many techniques for concurrency control and recovery from failures.
21.2 Transaction and System Concepts In this section we discuss additional concepts relevant to transaction processing. Section 21.2.1 describes the various states a transaction can be in, and discusses other operations needed in transaction processing. Section 21.2.2 discusses the sys- tem log, which keeps information about transactions and data items that will be needed for recovery. Section 21.2.3 describes the concept of commit points of trans- actions, and why they are important in transaction processing.
21.2.1 Transaction States and Additional Operations A transaction is an atomic unit of work that should either be completed in its entirety or not done at all. For recovery purposes, the system needs to keep track of when each transaction starts, terminates, and commits or aborts (see Section 21.2.3). Therefore, the recovery manager of the DBMS needs to keep track of the following operations:
■ BEGIN_TRANSACTION. This marks the beginning of transaction execution.
■ READ or WRITE. These specify read or write operations on the database items that are executed as part of a transaction.
■ END_TRANSACTION. This specifies that READ and WRITE transaction oper- ations have ended and marks the end of transaction execution. However, at this point it may be necessary to check whether the changes introduced by
752 Chapter 21 Introduction to Transaction Processing Concepts and Theory
Active
Begin transaction
End transaction Commit
AbortAbort
Read, Write
Partially committed
Failed Terminated
Committed
Figure 21.4 State transition diagram illustrating the states for transaction execution.
the transaction can be permanently applied to the database (committed) or whether the transaction has to be aborted because it violates serializability (see Section 21.5) or for some other reason.
■ COMMIT_TRANSACTION. This signals a successful end of the transaction so that any changes (updates) executed by the transaction can be safely committed to the database and will not be undone.
■ ROLLBACK (or ABORT). This signals that the transaction has ended unsuc- cessfully, so that any changes or effects that the transaction may have applied to the database must be undone.
Figure 21.4 shows a state transition diagram that illustrates how a transaction moves through its execution states. A transaction goes into an active state immediately after it starts execution, where it can execute its READ and WRITE operations. When the transaction ends, it moves to the partially committed state. At this point, some recovery protocols need to ensure that a system failure will not result in an inability to record the changes of the transaction permanently (usually by recording changes in the system log, discussed in the next section).5 Once this check is successful, the transaction is said to have reached its commit point and enters the committed state. Commit points are discussed in more detail in Section 21.2.3. When a transaction is committed, it has concluded its execution successfully and all its changes must be recorded permanently in the database, even if a system failure occurs.
However, a transaction can go to the failed state if one of the checks fails or if the transaction is aborted during its active state. The transaction may then have to be rolled back to undo the effect of its WRITE operations on the database. The terminated state corresponds to the transaction leaving the system. The transaction information that is maintained in system tables while the transaction has been run- ning is removed when the transaction terminates. Failed or aborted transactions may be restarted later—either automatically or after being resubmitted by the user—as brand new transactions.
5Optimistic concurrency control (see Section 22.4) also requires that certain checks are made at this point to ensure that the transaction did not interfere with other executing transactions.
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21.2.2 The System Log To be able to recover from failures that affect transactions, the system maintains a log6 to keep track of all transaction operations that affect the values of database items, as well as other transaction information that may be needed to permit recov- ery from failures. The log is a sequential, append-only file that is kept on disk, so it is not affected by any type of failure except for disk or catastrophic failure. Typically, one (or more) main memory buffers hold the last part of the log file, so that log entries are first added to the main memory buffer. When the log buffer is filled, or when certain other conditions occur, the log buffer is appended to the end of the log file on disk. In addition, the log file from disk is periodically backed up to archival storage (tape) to guard against catastrophic failures. The following are the types of entries—called log records—that are written to the log file and the corresponding action for each log record. In these entries, T refers to a unique transaction-id that is generated automatically by the system for each transaction and that is used to identify each transaction:
1. [start_transaction, T]. Indicates that transaction T has started execution.
2. [write_item, T, X, old_value, new_value]. Indicates that transaction T has changed the value of database item X from old_value to new_value.
3. [read_item, T, X]. Indicates that transaction T has read the value of database item X.
4. [commit, T]. Indicates that transaction T has completed successfully, and affirms that its effect can be committed (recorded permanently) to the data- base.
5. [abort, T]. Indicates that transaction T has been aborted.
Protocols for recovery that avoid cascading rollbacks (see Section 21.4.2)—which include nearly all practical protocols—do not require that READ operations are writ- ten to the system log. However, if the log is also used for other purposes—such as auditing (keeping track of all database operations)—then such entries can be included. Additionally, some recovery protocols require simpler WRITE entries only include one of new_value and old_value instead of including both (see Section 21.4.2).
Notice that we are assuming that all permanent changes to the database occur within transactions, so the notion of recovery from a transaction failure amounts to either undoing or redoing transaction operations individually from the log. If the system crashes, we can recover to a consistent database state by examining the log and using one of the techniques described in Chapter 23. Because the log contains a record of every WRITE operation that changes the value of some database item, it is possible to undo the effect of these WRITE operations of a transaction T by tracing backward through the log and resetting all items changed by a WRITE operation of T to their old_values. Redo of an operation may also be necessary if a transaction has its updates recorded in the log but a failure occurs before the system can be sure that
6The log has sometimes been called the DBMS journal.
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all these new_values have been written to the actual database on disk from the main memory buffers.7
21.2.3 Commit Point of a Transaction A transaction T reaches its commit point when all its operations that access the database have been executed successfully and the effect of all the transaction opera- tions on the database have been recorded in the log. Beyond the commit point, the transaction is said to be committed, and its effect must be permanently recorded in the database. The transaction then writes a commit record [commit, T] into the log. If a system failure occurs, we can search back in the log for all transactions T that have written a [start_transaction, T] record into the log but have not written their [commit, T] record yet; these transactions may have to be rolled back to undo their effect on the database during the recovery process. Transactions that have written their commit record in the log must also have recorded all their WRITE operations in the log, so their effect on the database can be redone from the log records.
Notice that the log file must be kept on disk. As discussed in Chapter 17, updating a disk file involves copying the appropriate block of the file from disk to a buffer in main memory, updating the buffer in main memory, and copying the buffer to disk. It is common to keep one or more blocks of the log file in main memory buffers, called the log buffer, until they are filled with log entries and then to write them back to disk only once, rather than writing to disk every time a log entry is added. This saves the overhead of multiple disk writes of the same log file buffer. At the time of a system crash, only the log entries that have been written back to disk are considered in the recovery process because the contents of main memory may be lost. Hence, before a transaction reaches its commit point, any portion of the log that has not been written to the disk yet must now be written to the disk. This process is called force-writing the log buffer before committing a transaction.
21.3 Desirable Properties of Transactions Transactions should possess several properties, often called the ACID properties; they should be enforced by the concurrency control and recovery methods of the DBMS. The following are the ACID properties:
■ Atomicity. A transaction is an atomic unit of processing; it should either be performed in its entirety or not performed at all.
■ Consistency preservation. A transaction should be consistency preserving, meaning that if it is completely executed from beginning to end without interference from other transactions, it should take the database from one consistent state to another.
■ Isolation. A transaction should appear as though it is being executed in iso- lation from other transactions, even though many transactions are executing
7Undo and redo are discussed more fully in Chapter 23.
21.4 Characterizing Schedules Based on Recoverability 755
concurrently. That is, the execution of a transaction should not be interfered with by any other transactions executing concurrently.
■ Durability or permanency. The changes applied to the database by a com- mitted transaction must persist in the database. These changes must not be lost because of any failure.
The atomicity property requires that we execute a transaction to completion. It is the responsibility of the transaction recovery subsystem of a DBMS to ensure atomicity. If a transaction fails to complete for some reason, such as a system crash in the midst of transaction execution, the recovery technique must undo any effects of the transaction on the database. On the other hand, write operations of a committed transaction must be eventually written to disk.
The preservation of consistency is generally considered to be the responsibility of the programmers who write the database programs or of the DBMS module that enforces integrity constraints. Recall that a database state is a collection of all the stored data items (values) in the database at a given point in time. A consistent state of the database satisfies the constraints specified in the schema as well as any other constraints on the database that should hold. A database program should be written in a way that guarantees that, if the database is in a consistent state before executing the transaction, it will be in a consistent state after the complete execution of the transaction, assuming that no interference with other transactions occurs.
The isolation property is enforced by the concurrency control subsystem of the DBMS.8 If every transaction does not make its updates (write operations) visible to other transactions until it is committed, one form of isolation is enforced that solves the temporary update problem and eliminates cascading rollbacks (see Chapter 23) but does not eliminate all other problems. There have been attempts to define the level of isolation of a transaction. A transaction is said to have level 0 (zero) isola- tion if it does not overwrite the dirty reads of higher-level transactions. Level 1 (one) isolation has no lost updates, and level 2 isolation has no lost updates and no dirty reads. Finally, level 3 isolation (also called true isolation) has, in addition to level 2 properties, repeatable reads.9
And last, the durability property is the responsibility of the recovery subsystem of the DBMS. We will introduce how recovery protocols enforce durability and atomicity in the next section and then discuss this in more detail in Chapter 23.
21.4 Characterizing Schedules Based on Recoverability
When transactions are executing concurrently in an interleaved fashion, then the order of execution of operations from all the various transactions is known as a schedule (or history). In this section, first we define the concept of schedules, and
8We will discuss concurrency control protocols in Chapter 22. 9The SQL syntax for isolation level discussed later in Section 21.6 is closely related to these levels.
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then we characterize the types of schedules that facilitate recovery when failures occur. In Section 21.5, we characterize schedules in terms of the interference of par- ticipating transactions, leading to the concepts of serializability and serializable schedules.
21.4.1 Schedules (Histories) of Transactions A schedule (or history) S of n transactions T1, T2, ..., Tn is an ordering of the oper- ations of the transactions. Operations from different transactions can be interleaved in the schedule S. However, for each transaction Ti that participates in the schedule S, the operations of Ti in S must appear in the same order in which they occur in Ti. The order of operations in S is considered to be a total ordering, meaning that for any two operations in the schedule, one must occur before the other. It is possible theoretically to deal with schedules whose operations form partial orders (as we discuss later), but we will assume for now total ordering of the operations in a schedule.
For the purpose of recovery and concurrency control, we are mainly interested in the read_item and write_item operations of the transactions, as well as the commit and abort operations. A shorthand notation for describing a schedule uses the symbols b, r, w, e, c, and a for the operations begin_transaction, read_item, write_item, end_transac- tion, commit, and abort, respectively, and appends as a subscript the transaction id (transaction number) to each operation in the schedule. In this notation, the data- base item X that is read or written follows the r and w operations in parentheses. In some schedules, we will only show the read and write operations, whereas in other schedules, we will show all the operations. For example, the schedule in Figure 21.3(a), which we shall call Sa, can be written as follows in this notation:
Sa: r1(X); r2(X); w1(X); r1(Y); w2(X); w1(Y);
Similarly, the schedule for Figure 21.3(b), which we call Sb, can be written as follows, if we assume that transaction T1 aborted after its read_item(Y) operation:
Sb: r1(X); w1(X); r2(X); w2(X); r1(Y); a1;
Two operations in a schedule are said to conflict if they satisfy all three of the fol- lowing conditions: (1) they belong to different transactions; (2) they access the same item X; and (3) at least one of the operations is a write_item(X). For example, in schedule Sa, the operations r1(X) and w2(X) conflict, as do the operations r2(X) and w1(X), and the operations w1(X) and w2(X). However, the operations r1(X) and r2(X) do not conflict, since they are both read operations; the operations w2(X) and w1(Y) do not conflict because they operate on distinct data items X and Y; and the operations r1(X) and w1(X) do not conflict because they belong to the same transaction.
Intuitively, two operations are conflicting if changing their order can result in a dif- ferent outcome. For example, if we change the order of the two operations r1(X); w2(X) to w2(X); r1(X), then the value of X that is read by transaction T1 changes, because in the second order the value of X is changed by w2(X) before it is read by
21.4 Characterizing Schedules Based on Recoverability 757
r1(X), whereas in the first order the value is read before it is changed. This is called a read-write conflict. The other type is called a write-write conflict, and is illustrated by the case where we change the order of two operations such as w1(X); w2(X) to w2(X); w1(X). For a write-write conflict, the last value of X will differ because in one case it is written by T2 and in the other case by T1. Notice that two read operations are not conflicting because changing their order makes no difference in outcome.
The rest of this section covers some theoretical definitions concerning schedules. A schedule S of n transactions T1, T2, ..., Tn is said to be a complete schedule if the following conditions hold:
1. The operations in S are exactly those operations in T1, T2, ..., Tn, including a commit or abort operation as the last operation for each transaction in the schedule.
2. For any pair of operations from the same transaction Ti, their relative order of appearance in S is the same as their order of appearance in Ti.
3. For any two conflicting operations, one of the two must occur before the other in the schedule.10
The preceding condition (3) allows for two nonconflicting operations to occur in the schedule without defining which occurs first, thus leading to the definition of a schedule as a partial order of the operations in the n transactions.11 However, a total order must be specified in the schedule for any pair of conflicting operations (condition 3) and for any pair of operations from the same transaction (condition 2). Condition 1 simply states that all operations in the transactions must appear in the complete schedule. Since every transaction has either committed or aborted, a complete schedule will not contain any active transactions at the end of the schedule.
In general, it is difficult to encounter complete schedules in a transaction processing system because new transactions are continually being submitted to the system. Hence, it is useful to define the concept of the committed projection C(S) of a schedule S, which includes only the operations in S that belong to committed trans- actions—that is, transactions Ti whose commit operation ci is in S.
21.4.2 Characterizing Schedules Based on Recoverability For some schedules it is easy to recover from transaction and system failures, whereas for other schedules the recovery process can be quite involved. In some cases, it is even not possible to recover correctly after a failure. Hence, it is important to characterize the types of schedules for which recovery is possible, as well as those for which recovery is relatively simple. These characterizations do not actually pro- vide the recovery algorithm; they only attempt to theoretically characterize the dif- ferent types of schedules.
10Theoretically, it is not necessary to determine an order between pairs of nonconflicting operations. 11In practice, most schedules have a total order of operations. If parallel processing is employed, it is theoretically possible to have schedules with partially ordered nonconflicting operations.
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First, we would like to ensure that, once a transaction T is committed, it should never be necessary to roll back T. This ensures that the durability property of trans- actions is not violated (see Section 21.3). The schedules that theoretically meet this criterion are called recoverable schedules; those that do not are called nonrecoverable and hence should not be permitted by the DBMS. The definition of recoverable schedule is as follows: A schedule S is recoverable if no transaction T in S commits until all transactions T� that have written some item X that T reads have committed. A transaction T reads from transaction T� in a schedule S if some item X is first written by T� and later read by T. In addition, T� should not have been aborted before T reads item X, and there should be no transactions that write X after T� writes it and before T reads it (unless those transactions, if any, have aborted before T reads X).
Some recoverable schedules may require a complex recovery process as we shall see, but if sufficient information is kept (in the log), a recovery algorithm can be devised for any recoverable schedule. The (partial) schedules Sa and Sb from the preceding section are both recoverable, since they satisfy the above definition. Consider the schedule Sa� given below, which is the same as schedule Sa except that two commit operations have been added to Sa:
Sa�: r1(X); r2(X); w1(X); r1(Y); w2(X); c2; w1(Y); c1;
Sa� is recoverable, even though it suffers from the lost update problem; this problem is handled by serializability theory (see Section 21.5). However, consider the two (partial) schedules Sc and Sd that follow:
Sc: r1(X); w1(X); r2(X); r1(Y); w2(X); c2; a1;
Sd: r1(X); w1(X); r2(X); r1(Y); w2(X); w1(Y); c1; c2;
Se: r1(X); w1(X); r2(X); r1(Y); w2(X); w1(Y); a1; a2;
Sc is not recoverable because T2 reads item X from T1, but T2 commits before T1 commits. The problem occurs if T1 aborts after the c2 operation in Sc, then the value of X that T2 read is no longer valid and T2 must be aborted after it is committed, leading to a schedule that is not recoverable. For the schedule to be recoverable, the c2 operation in Sc must be postponed until after T1 commits, as shown in Sd. If T1 aborts instead of committing, then T2 should also abort as shown in Se, because the value of X it read is no longer valid. In Se, aborting T2 is acceptable since it has not committed yet, which is not the case for the nonrecoverable schedule Sc.
In a recoverable schedule, no committed transaction ever needs to be rolled back, and so the definition of committed transaction as durable is not violated. However, it is possible for a phenomenon known as cascading rollback (or cascading abort) to occur in some recoverable schedules, where an uncommitted transaction has to be rolled back because it read an item from a transaction that failed. This is illustrated in schedule Se, where transaction T2 has to be rolled back because it read item X from T1, and T1 then aborted.
Because cascading rollback can be quite time-consuming—since numerous transac- tions can be rolled back (see Chapter 23)—it is important to characterize the sched-
21.5 Characterizing Schedules Based on Serializability 759
ules where this phenomenon is guaranteed not to occur. A schedule is said to be cascadeless, or to avoid cascading rollback, if every transaction in the schedule reads only items that were written by committed transactions. In this case, all items read will not be discarded, so no cascading rollback will occur. To satisfy this criterion, the r2(X) command in schedules Sd and Se must be postponed until after T1 has commit- ted (or aborted), thus delaying T2 but ensuring no cascading rollback if T1 aborts.
Finally, there is a third, more restrictive type of schedule, called a strict schedule, in which transactions can neither read nor write an item X until the last transaction that wrote X has committed (or aborted). Strict schedules simplify the recovery process. In a strict schedule, the process of undoing a write_item(X) operation of an aborted transaction is simply to restore the before image (old_value or BFIM) of data item X. This simple procedure always works correctly for strict schedules, but it may not work for recoverable or cascadeless schedules. For example, consider schedule Sf :
Sf : w1(X, 5); w2(X, 8); a1;
Suppose that the value of X was originally 9, which is the before image stored in the system log along with the w1(X, 5) operation. If T1 aborts, as in Sf , the recovery pro- cedure that restores the before image of an aborted write operation will restore the value of X to 9, even though it has already been changed to 8 by transaction T2, thus leading to potentially incorrect results. Although schedule Sf is cascadeless, it is not a strict schedule, since it permits T2 to write item X even though the transaction T1 that last wrote X had not yet committed (or aborted). A strict schedule does not have this problem.
It is important to note that any strict schedule is also cascadeless, and any cascade- less schedule is also recoverable. Suppose we have i transactions T1, T2, ..., Ti, and their number of operations are n1, n2, ..., ni, respectively. If we make a set of all pos- sible schedules of these transactions, we can divide the schedules into two disjoint subsets: recoverable and nonrecoverable. The cascadeless schedules will be a subset of the recoverable schedules, and the strict schedules will be a subset of the cascade- less schedules. Thus, all strict schedules are cascadeless, and all cascadeless schedules are recoverable.
21.5 Characterizing Schedules Based on Serializability
In the previous section, we characterized schedules based on their recoverability properties. Now we characterize the types of schedules that are always considered to be correct when concurrent transactions are executing. Such schedules are known as serializable schedules. Suppose that two users—for example, two airline reservations agents—submit to the DBMS transactions T1 and T2 in Figure 21.2 at approxi- mately the same time. If no interleaving of operations is permitted, there are only two possible outcomes:
1. Execute all the operations of transaction T1 (in sequence) followed by all the operations of transaction T2 (in sequence).
(a)
Schedule A Schedule B
read_item(X ); X := X – N;
write_item(X ); read_item(Y );
read_item(X ); X := X + M; write_item(X );
Time Y := Y + N; write_item(Y );
(b)
read_item(X ); X := X + M; write_item(X );
Time read_item(X ); X := X – N;
write_item(X ); read_item(Y );
Y := Y + N; write_item(Y );
(c) T1 T2
Schedule C Schedule D
read_item(X ); X := X – N;
write_item(X ); read_item(Y );
read_item(X ); X := X + M;
write_item(X );
Time
Y := Y + N; write_item(Y );
read_item(X ); X := X + M; write_item(X );
read_item(X ); X := X – N; write_item(X );
read_item(Y ); Y := Y + N; write_item(Y );
T1 T2
T1 T2 T1 T2
Time
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Figure 21.5 Examples of serial and nonserial schedules involving transactions T1 and T2. (a) Serial schedule A: T1 followed by T2. (b) Serial schedule B: T2 followed by T1. (c) Two nonserial schedules C and D with interleaving of operations.
2. Execute all the operations of transaction T2 (in sequence) followed by all the operations of transaction T1 (in sequence).
These two schedules—called serial schedules—are shown in Figure 21.5(a) and (b), respectively. If interleaving of operations is allowed, there will be many possible orders in which the system can execute the individual operations of the transac- tions. Two possible schedules are shown in Figure 21.5(c). The concept of serializability of schedules is used to identify which schedules are correct when transaction executions have interleaving of their operations in the schedules. This section defines serializability and discusses how it may be used in practice.
21.5 Characterizing Schedules Based on Serializability 761
21.5.1 Serial, Nonserial, and Conflict-Serializable Schedules Schedules A and B in Figure 21.5(a) and (b) are called serial because the operations of each transaction are executed consecutively, without any interleaved operations from the other transaction. In a serial schedule, entire transactions are performed in serial order: T1 and then T2 in Figure 21.5(a), and T2 and then T1 in Figure 21.5(b). Schedules C and D in Figure 21.5(c) are called nonserial because each sequence interleaves operations from the two transactions.
Formally, a schedule S is serial if, for every transaction T participating in the sched- ule, all the operations of T are executed consecutively in the schedule; otherwise, the schedule is called nonserial. Therefore, in a serial schedule, only one transaction at a time is active—the commit (or abort) of the active transaction initiates execution of the next transaction. No interleaving occurs in a serial schedule. One reasonable assumption we can make, if we consider the transactions to be independent, is that every serial schedule is considered correct. We can assume this because every transac- tion is assumed to be correct if executed on its own (according to the consistency preservation property of Section 21.3). Hence, it does not matter which transaction is executed first. As long as every transaction is executed from beginning to end in isolation from the operations of other transactions, we get a correct end result on the database.
The problem with serial schedules is that they limit concurrency by prohibiting interleaving of operations. In a serial schedule, if a transaction waits for an I/O operation to complete, we cannot switch the CPU processor to another transaction, thus wasting valuable CPU processing time. Additionally, if some transaction T is quite long, the other transactions must wait for T to complete all its operations before starting. Hence, serial schedules are considered unacceptable in practice. However, if we can determine which other schedules are equivalent to a serial sched- ule, we can allow these schedules to occur.
To illustrate our discussion, consider the schedules in Figure 21.5, and assume that the initial values of database items are X = 90 and Y = 90 and that N = 3 and M = 2. After executing transactions T1 and T2, we would expect the database values to be X = 89 and Y = 93, according to the meaning of the transactions. Sure enough, execut- ing either of the serial schedules A or B gives the correct results. Now consider the nonserial schedules C and D. Schedule C (which is the same as Figure 21.3(a)) gives the results X = 92 and Y = 93, in which the X value is erroneous, whereas schedule D gives the correct results.
Schedule C gives an erroneous result because of the lost update problem discussed in Section 21.1.3; transaction T2 reads the value of X before it is changed by transac- tion T1, so only the effect of T2 on X is reflected in the database. The effect of T1 on X is lost, overwritten by T2, leading to the incorrect result for item X. However, some nonserial schedules give the correct expected result, such as schedule D. We would like to determine which of the nonserial schedules always give a correct result and which may give erroneous results. The concept used to characterize schedules in this manner is that of serializability of a schedule.
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S1
read_item(X ); X := X + 10; write_item(X );
S2
read_item(X ); X := X * 1.1; write_item (X );
Figure 21.6 Two schedules that are result equivalent for the initial value of X = 100 but are not result equivalent in general.
The definition of serializable schedule is as follows: A schedule S of n transactions is serializable if it is equivalent to some serial schedule of the same n transactions. We will define the concept of equivalence of schedules shortly. Notice that there are n! possible serial schedules of n transactions and many more possible nonserial sched- ules. We can form two disjoint groups of the nonserial schedules—those that are equivalent to one (or more) of the serial schedules and hence are serializable, and those that are not equivalent to any serial schedule and hence are not serializable.
Saying that a nonserial schedule S is serializable is equivalent to saying that it is cor- rect, because it is equivalent to a serial schedule, which is considered correct. The remaining question is: When are two schedules considered equivalent?
There are several ways to define schedule equivalence. The simplest but least satis- factory definition involves comparing the effects of the schedules on the database. Two schedules are called result equivalent if they produce the same final state of the database. However, two different schedules may accidentally produce the same final state. For example, in Figure 21.6, schedules S1 and S2 will produce the same final database state if they execute on a database with an initial value of X = 100; however, for other initial values of X, the schedules are not result equivalent. Additionally, these schedules execute different transactions, so they definitely should not be con- sidered equivalent. Hence, result equivalence alone cannot be used to define equiv- alence of schedules. The safest and most general approach to defining schedule equivalence is not to make any assumptions about the types of operations included in the transactions. For two schedules to be equivalent, the operations applied to each data item affected by the schedules should be applied to that item in both schedules in the same order. Two definitions of equivalence of schedules are gener- ally used: conflict equivalence and view equivalence. We discuss conflict equivalence next, which is the more commonly used definition.
The definition of conflict equivalence of schedules is as follows: Two schedules are said to be conflict equivalent if the order of any two conflicting operations is the same in both schedules. Recall from Section 21.4.1 that two operations in a schedule are said to conflict if they belong to different transactions, access the same database item, and either both are write_item operations or one is a write_item and the other a read_item. If two conflicting operations are applied in different orders in two sched- ules, the effect can be different on the database or on the transactions in the sched- ule, and hence the schedules are not conflict equivalent. For example, as we discussed in Section 21.4.1, if a read and write operation occur in the order r1(X), w2(X) in schedule S1, and in the reverse order w2(X), r1(X) in schedule S2, the value read by r1(X) can be different in the two schedules. Similarly, if two write operations
21.5 Characterizing Schedules Based on Serializability 763
occur in the order w1(X), w2(X) in S1, and in the reverse order w2(X), w1(X) in S2, the next r(X) operation in the two schedules will read potentially different values; or if these are the last operations writing item X in the schedules, the final value of item X in the database will be different.
Using the notion of conflict equivalence, we define a schedule S to be conflict seri- alizable12 if it is (conflict) equivalent to some serial schedule S�. In such a case, we can reorder the nonconflicting operations in S until we form the equivalent serial schedule S�. According to this definition, schedule D in Figure 21.5(c) is equivalent to the serial schedule A in Figure 21.5(a). In both schedules, the read_item(X) of T2 reads the value of X written by T1, while the other read_item operations read the database values from the initial database state. Additionally, T1 is the last transaction to write Y, and T2 is the last transaction to write X in both schedules. Because A is a serial schedule and schedule D is equivalent to A, D is a serializable schedule. Notice that the operations r1(Y) and w1(Y) of schedule D do not conflict with the opera- tions r2(X) and w2(X), since they access different data items. Therefore, we can move r1(Y), w1(Y) before r2(X), w2(X), leading to the equivalent serial schedule T1, T2.
Schedule C in Figure 21.5(c) is not equivalent to either of the two possible serial schedules A and B, and hence is not serializable. Trying to reorder the operations of schedule C to find an equivalent serial schedule fails because r2(X) and w1(X) con- flict, which means that we cannot move r2(X) down to get the equivalent serial schedule T1, T2. Similarly, because w1(X) and w2(X) conflict, we cannot move w1(X) down to get the equivalent serial schedule T2, T1.
Another, more complex definition of equivalence—called view equivalence, which leads to the concept of view serializability—is discussed in Section 21.5.4.
21.5.2 Testing for Conflict Serializability of a Schedule There is a simple algorithm for determining whether a particular schedule is con- flict serializable or not. Most concurrency control methods do not actually test for serializability. Rather protocols, or rules, are developed that guarantee that any schedule that follows these rules will be serializable. We discuss the algorithm for testing conflict serializability of schedules here to gain a better understanding of these concurrency control protocols, which are discussed in Chapter 22.
Algorithm 21.1 can be used to test a schedule for conflict serializability. The algo- rithm looks at only the read_item and write_item operations in a schedule to construct a precedence graph (or serialization graph), which is a directed graph G = (N, E) that consists of a set of nodes N = {T1, T2, ..., Tn } and a set of directed edges E = {e1, e2, ..., em }. There is one node in the graph for each transaction Ti in the schedule. Each edge ei in the graph is of the form (Tj → Tk ), 1 ≤ j ≤ n, 1 ≤ k n, where Tj is the starting node of ei and Tk is the ending node of ei. Such an edge from node Tj to
12We will use serializable to mean conflict serializable. Another definition of serializable used in practice (see Section 21.6) is to have repeatable reads, no dirty reads, and no phantom records (see Section 22.7.1 for a discussion on phantoms).
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node Tk is created by the algorithm if one of the operations in Tj appears in the schedule before some conflicting operation in Tk.
Algorithm 21.1. Testing Conflict Serializability of a Schedule S 1. For each transaction Ti participating in schedule S, create a node labeled Ti
in the precedence graph.
2. For each case in S where Tj executes a read_item(X) after Ti executes a write_item(X), create an edge (Ti → Tj) in the precedence graph.
3. For each case in S where Tj executes a write_item(X) after Ti executes a read_item(X), create an edge (Ti → Tj) in the precedence graph.
4. For each case in S where Tj executes a write_item(X) after Ti executes a write_item(X), create an edge (Ti → Tj) in the precedence graph.
5. The schedule S is serializable if and only if the precedence graph has no cycles.
The precedence graph is constructed as described in Algorithm 21.1. If there is a cycle in the precedence graph, schedule S is not (conflict) serializable; if there is no cycle, S is serializable. A cycle in a directed graph is a sequence of edges C = ((Tj → Tk), (Tk → Tp), ..., (Ti → Tj)) with the property that the starting node of each edge—except the first edge—is the same as the ending node of the previous edge, and the starting node of the first edge is the same as the ending node of the last edge (the sequence starts and ends at the same node).
In the precedence graph, an edge from Ti to Tj means that transaction Ti must come before transaction Tj in any serial schedule that is equivalent to S, because two con- flicting operations appear in the schedule in that order. If there is no cycle in the precedence graph, we can create an equivalent serial schedule S� that is equivalent to S, by ordering the transactions that participate in S as follows: Whenever an edge exists in the precedence graph from Ti to Tj, Ti must appear before Tj in the equiva- lent serial schedule S�.13 Notice that the edges (Ti → Tj) in a precedence graph can optionally be labeled by the name(s) of the data item(s) that led to creating the edge. Figure 21.7 shows such labels on the edges.
In general, several serial schedules can be equivalent to S if the precedence graph for S has no cycle. However, if the precedence graph has a cycle, it is easy to show that we cannot create any equivalent serial schedule, so S is not serializable. The prece- dence graphs created for schedules A to D, respectively, in Figure 21.5 appear in Figure 21.7(a) to (d). The graph for schedule C has a cycle, so it is not serializable. The graph for schedule D has no cycle, so it is serializable, and the equivalent serial schedule is T1 followed by T2. The graphs for schedules A and B have no cycles, as expected, because the schedules are serial and hence serializable.
Another example, in which three transactions participate, is shown in Figure 21.8. Figure 21.8(a) shows the read_item and write_item operations in each transaction. Two schedules E and F for these transactions are shown in Figure 21.8(b) and (c),
13This process of ordering the nodes of an acrylic graph is known as topological sorting.
21.5 Characterizing Schedules Based on Serializability 765
T1(a)
(c)
(b)
(d)
T2
T1
X
X
X
X
T2
T1 T2
T1 T2
X
Figure 21.7 Constructing the precedence graphs for schedules A to D from Figure 21.5 to test for conflict serializability. (a) Precedence graph for serial schedule A. (b) Precedence graph for serial schedule B. (c) Precedence graph for schedule C (not serializable). (d) Precedence graph for schedule D (serializable, equivalent to schedule A).
respectively, and the precedence graphs for schedules E and F are shown in parts (d) and (e). Schedule E is not serializable because the corresponding precedence graph has cycles. Schedule F is serializable, and the serial schedule equivalent to F is shown in Figure 21.8(e). Although only one equivalent serial schedule exists for F, in gen- eral there may be more than one equivalent serial schedule for a serializable sched- ule. Figure 21.8(f ) shows a precedence graph representing a schedule that has two equivalent serial schedules. To find an equivalent serial schedule, start with a node that does not have any incoming edges, and then make sure that the node order for every edge is not violated.
21.5.3 How Serializability Is Used for Concurrency Control As we discussed earlier, saying that a schedule S is (conflict) serializable—that is, S is (conflict) equivalent to a serial schedule—is tantamount to saying that S is correct. Being serializable is distinct from being serial, however. A serial schedule represents inefficient processing because no interleaving of operations from different transac- tions is permitted. This can lead to low CPU utilization while a transaction waits for disk I/O, or for another transaction to terminate, thus slowing down processing considerably. A serializable schedule gives the benefits of concurrent execution without giving up any correctness. In practice, it is quite difficult to test for the seri- alizability of a schedule. The interleaving of operations from concurrent transac- tions—which are usually executed as processes by the operating system—is typically determined by the operating system scheduler, which allocates resources to
766 Chapter 21 Introduction to Transaction Processing Concepts and Theory
Transaction T1 read_item(X );
write_item(X );
read_item(Y );
write_item(Y );
read_item(X ); write_item(X );
read_item(Y ); write_item(Y );
Transaction T3 read_item(Y );
read_item(Z );
write_item(Y );
write_item(Z );
read_item(Y ); read_item(Z );
write_item(Y); write_item(Z );
Transaction T2 read_item(Z );
read_item(Y );
write_item(Y );
read_item(X );
write_item(X );
read_item(Z ); read_item(Y ); write_item(Y );
read_item(X );
write_item(X );
(b)
(a)
Schedule E
Time
read_item(X ); write_item(X );
read_item(Y ); write_item(Y );
read_item(Y ); read_item(Z );
write_item(Y ); write_item(Z );
read_item(Z );
read_item(Y ); write_item(Y ); read_item(X ); write_item(X );
(c)
Schedule F
Time
Transaction T1 Transaction T2 Transaction T3
Transaction T1 Transaction T2 Transaction T3
Figure 21.8 Another example of serializability testing. (a) The read and write operations of three transactions T1, T2, and T3. (b) Schedule E. (c) Schedule F.
all processes. Factors such as system load, time of transaction submission, and pri- orities of processes contribute to the ordering of operations in a schedule. Hence, it is difficult to determine how the operations of a schedule will be interleaved before- hand to ensure serializability.
21.5 Characterizing Schedules Based on Serializability 767
(d)
X
Y
Y Y, Z
T1
Equivalent serial schedules
None
Reason
Cycle X(T1 T2),Y(T2 T1) Cycle X(T1 T2),YZ (T2 T3),Y(T3 T1)
(e) X,Y
Y Y, Z
Equivalent serial schedules
(f) Equivalent serial schedules
T2
T3
T1 T2
T3
T1 T2
T3
T2T3 T1
T2T3 T1
T1T3 T2
If transactions are executed at will and then the resulting schedule is tested for seri- alizability, we must cancel the effect of the schedule if it turns out not to be serializ- able. This is a serious problem that makes this approach impractical. Hence, the approach taken in most practical systems is to determine methods or protocols that ensure serializability, without having to test the schedules themselves. The approach taken in most commercial DBMSs is to design protocols (sets of rules) that—if fol- lowed by every individual transaction or if enforced by a DBMS concurrency con- trol subsystem—will ensure serializability of all schedules in which the transactions participate.
Another problem appears here: When transactions are submitted continuously to the system, it is difficult to determine when a schedule begins and when it ends. Serializability theory can be adapted to deal with this problem by considering only the committed projection of a schedule S. Recall from Section 21.4.1 that the committed projection C(S) of a schedule S includes only the operations in S that belong to committed transactions. We can theoretically define a schedule S to be serializable if its committed projection C(S) is equivalent to some serial schedule, since only committed transactions are guaranteed by the DBMS.
Figure 21.8 (continued) Another example of serializability testing. (d) Precedence graph for schedule E. (e) Precedence graph for schedule F. (f) Precedence graph with two equivalent serial schedules.
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In Chapter 22, we discuss a number of different concurrency control protocols that guarantee serializability. The most common technique, called two-phase locking, is based on locking data items to prevent concurrent transactions from interfering with one another, and enforcing an additional condition that guarantees serializ- ability. This is used in the majority of commercial DBMSs. Other protocols have been proposed;14 these include timestamp ordering, where each transaction is assigned a unique timestamp and the protocol ensures that any conflicting opera- tions are executed in the order of the transaction timestamps; multiversion protocols, which are based on maintaining multiple versions of data items; and optimistic (also called certification or validation) protocols, which check for possible serializability violations after the transactions terminate but before they are permitted to commit.
21.5.4 View Equivalence and View Serializability In Section 21.5.1 we defined the concepts of conflict equivalence of schedules and conflict serializability. Another less restrictive definition of equivalence of schedules is called view equivalence. This leads to another definition of serializability called view serializability. Two schedules S and S� are said to be view equivalent if the fol- lowing three conditions hold:
1. The same set of transactions participates in S and S�, and S and S� include the same operations of those transactions.
2. For any operation ri(X) of Ti in S, if the value of X read by the operation has been written by an operation wj(X) of Tj (or if it is the original value of X before the schedule started), the same condition must hold for the value of X read by operation ri(X) of Ti in S�.
3. If the operation wk(Y) of Tk is the last operation to write item Y in S, then wk(Y) of Tk must also be the last operation to write item Y in S�.
The idea behind view equivalence is that, as long as each read operation of a trans- action reads the result of the same write operation in both schedules, the write operations of each transaction must produce the same results. The read operations are hence said to see the same view in both schedules. Condition 3 ensures that the final write operation on each data item is the same in both schedules, so the data- base state should be the same at the end of both schedules. A schedule S is said to be view serializable if it is view equivalent to a serial schedule.
The definitions of conflict serializability and view serializability are similar if a con- dition known as the constrained write assumption (or no blind writes) holds on all transactions in the schedule. This condition states that any write operation wi(X) in Ti is preceded by a ri(X) in Ti and that the value written by wi(X) in Ti depends only on the value of X read by ri(X). This assumes that computation of the new value of X is a function f(X) based on the old value of X read from the database. A blind write is a write operation in a transaction T on an item X that is not depen- dent on the value of X, so it is not preceded by a read of X in the transaction T.
14These other protocols have not been incorporated much into commercial systems; most relational DBMSs use some variation of the two-phase locking protocol.
21.5 Characterizing Schedules Based on Serializability 769
The definition of view serializability is less restrictive than that of conflict serializ- ability under the unconstrained write assumption, where the value written by an operation wi(X) in Ti can be independent of its old value from the database. This is possible when blind writes are allowed, and it is illustrated by the following schedule Sg of three transactions T1: r1(X); w1(X); T2: w2(X); and T3: w3(X):
Sg: r1(X); w2(X); w1(X); w3(X); c1; c2; c3;
In Sg the operations w2(X) and w3(X) are blind writes, since T2 and T3 do not read the value of X. The schedule Sg is view serializable, since it is view equivalent to the serial schedule T1, T2, T3. However, Sg is not conflict serializable, since it is not conflict equivalent to any serial schedule. It has been shown that any conflict- serializable schedule is also view serializable but not vice versa, as illustrated by the preceding example. There is an algorithm to test whether a schedule S is view serial- izable or not. However, the problem of testing for view serializability has been shown to be NP-hard, meaning that finding an efficient polynomial time algorithm for this problem is highly unlikely.
21.5.5 Other Types of Equivalence of Schedules Serializability of schedules is sometimes considered to be too restrictive as a condi- tion for ensuring the correctness of concurrent executions. Some applications can produce schedules that are correct by satisfying conditions less stringent than either conflict serializability or view serializability. An example is the type of transactions known as debit-credit transactions—for example, those that apply deposits and withdrawals to a data item whose value is the current balance of a bank account. The semantics of debit-credit operations is that they update the value of a data item X by either subtracting from or adding to the value of the data item. Because addi- tion and subtraction operations are commutative—that is, they can be applied in any order—it is possible to produce correct schedules that are not serializable. For example, consider the following transactions, each of which may be used to transfer an amount of money between two bank accounts:
T1: r1(X); X := X − 10; w1(X); r1(Y); Y := Y + 10; w1(Y); T2: r2(Y); Y := Y − 20; w2(Y); r2(X); X := X + 20; w2(X);
Consider the following nonserializable schedule Sh for the two transactions:
Sh: r1(X); w1(X); r2(Y); w2(Y); r1(Y); w1(Y); r2(X); w2(X);
With the additional knowledge, or semantics, that the operations between each ri(I) and wi(I) are commutative, we know that the order of executing the sequences con- sisting of (read, update, write) is not important as long as each (read, update, write) sequence by a particular transaction Ti on a particular item I is not interrupted by conflicting operations. Hence, the schedule Sh is considered to be correct even though it is not serializable. Researchers have been working on extending concur- rency control theory to deal with cases where serializability is considered to be too restrictive as a condition for correctness of schedules. Also, in certain domains of applications such as computer aided design (CAD) of complex systems like aircraft,
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design transactions last over a long time period. In such applications, more relaxed schemes of concurrency control have been proposed to maintain consistency of the database.
21.6 Transaction Support in SQL In this section, we give a brief introduction to transaction support in SQL. There are many more details, and the newer standards have more commands for transaction processing. The basic definition of an SQL transaction is similar to our already defined concept of a transaction. That is, it is a logical unit of work and is guaran- teed to be atomic. A single SQL statement is always considered to be atomic—either it completes execution without an error or it fails and leaves the database unchanged.
With SQL, there is no explicit Begin_Transaction statement. Transaction initiation is done implicitly when particular SQL statements are encountered. However, every transaction must have an explicit end statement, which is either a COMMIT or a ROLLBACK. Every transaction has certain characteristics attributed to it. These characteristics are specified by a SET TRANSACTION statement in SQL. The charac- teristics are the access mode, the diagnostic area size, and the isolation level.
The access mode can be specified as READ ONLY or READ WRITE. The default is READ WRITE, unless the isolation level of READ UNCOMMITTED is specified (see below), in which case READ ONLY is assumed. A mode of READ WRITE allows select, update, insert, delete, and create commands to be executed. A mode of READ ONLY, as the name implies, is simply for data retrieval.
The diagnostic area size option, DIAGNOSTIC SIZE n, specifies an integer value n, which indicates the number of conditions that can be held simultaneously in the diagnostic area. These conditions supply feedback information (errors or excep- tions) to the user or program on the n most recently executed SQL statement.
The isolation level option is specified using the statement ISOLATION LEVEL <isolation>, where the value for <isolation> can be READ UNCOMMITTED, READ COMMITTED, REPEATABLE READ, or SERIALIZABLE.15 The default isolation level is SERIALIZABLE, although some systems use READ COMMITTED as their default. The use of the term SERIALIZABLE here is based on not allowing violations that cause dirty read, unrepeatable read, and phantoms,16 and it is thus not identical to the way serializability was defined earlier in Section 21.5. If a transaction executes at a lower isolation level than SERIALIZABLE, then one or more of the following three viola- tions may occur:
1. Dirty read. A transaction T1 may read the update of a transaction T2, which has not yet committed. If T2 fails and is aborted, then T1 would have read a value that does not exist and is incorrect.
15These are similar to the isolation levels discussed briefly at the end of Section 21.3. 16The dirty read and unrepeatable read problems were discussed in Section 21.1.3. Phantoms are dis- cussed in Section 22.7.1.
21.6 Transaction Support in SQL 771
Table 21.1 Possible Violations Based on Isolation Levels as Defined in SQL
Type of Violation
Isolation Level Dirty Read Nonrepeatable Read Phantom
READ UNCOMMITTED Yes Yes Yes READ COMMITTED No Yes Yes REPEATABLE READ No No Yes SERIALIZABLE No No No
2. Nonrepeatable read. A transaction T1 may read a given value from a table. If another transaction T2 later updates that value and T1 reads that value again, T1 will see a different value.
3. Phantoms. A transaction T1 may read a set of rows from a table, perhaps based on some condition specified in the SQL WHERE-clause. Now suppose that a transaction T2 inserts a new row that also satisfies the WHERE-clause condition used in T1, into the table used by T1. If T1 is repeated, then T1 will see a phantom, a row that previously did not exist.
Table 21.1 summarizes the possible violations for the different isolation levels. An entry of Yes indicates that a violation is possible and an entry of No indicates that it is not possible. READ UNCOMMITTED is the most forgiving, and SERIALIZABLE is the most restrictive in that it avoids all three of the problems mentioned above.
A sample SQL transaction might look like the following:
EXEC SQL WHENEVER SQLERROR GOTO UNDO; EXEC SQL SET TRANSACTION
READ WRITE DIAGNOSTIC SIZE 5 ISOLATION LEVEL SERIALIZABLE;
EXEC SQL INSERT INTO EMPLOYEE (Fname, Lname, Ssn, Dno, Salary) VALUES ('Robert', 'Smith', '991004321', 2, 35000);
EXEC SQL UPDATE EMPLOYEE SET Salary = Salary * 1.1 WHERE Dno = 2;
EXEC SQL COMMIT; GOTO THE_END; UNDO: EXEC SQL ROLLBACK; THE_END: ... ;
The above transaction consists of first inserting a new row in the EMPLOYEE table and then updating the salary of all employees who work in department 2. If an error occurs on any of the SQL statements, the entire transaction is rolled back. This implies that any updated salary (by this transaction) would be restored to its previ- ous value and that the newly inserted row would be removed.
As we have seen, SQL provides a number of transaction-oriented features. The DBA or database programmers can take advantage of these options to try improving
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transaction performance by relaxing serializability if that is acceptable for their applications.
21.7 Summary In this chapter we discussed DBMS concepts for transaction processing. We intro- duced the concept of a database transaction and the operations relevant to transac- tion processing. We compared single-user systems to multiuser systems and then presented examples of how uncontrolled execution of concurrent transactions in a multiuser system can lead to incorrect results and database values. We also discussed the various types of failures that may occur during transaction execution.
Next we introduced the typical states that a transaction passes through during execu- tion, and discussed several concepts that are used in recovery and concurrency con- trol methods. The system log keeps track of database accesses, and the system uses this information to recover from failures. A transaction either succeeds and reaches its commit point or it fails and has to be rolled back. A committed transaction has its changes permanently recorded in the database. We presented an overview of the desirable properties of transactions—atomicity, consistency preservation, isolation, and durability—which are often referred to as the ACID properties.
Then we defined a schedule (or history) as an execution sequence of the operations of several transactions with possible interleaving. We characterized schedules in terms of their recoverability. Recoverable schedules ensure that, once a transaction commits, it never needs to be undone. Cascadeless schedules add an additional con- dition to ensure that no aborted transaction requires the cascading abort of other transactions. Strict schedules provide an even stronger condition that allows a sim- ple recovery scheme consisting of restoring the old values of items that have been changed by an aborted transaction.
We defined equivalence of schedules and saw that a serializable schedule is equiva- lent to some serial schedule. We defined the concepts of conflict equivalence and view equivalence, which led to definitions for conflict serializability and view serial- izability. A serializable schedule is considered correct. We presented an algorithm for testing the (conflict) serializability of a schedule. We discussed why testing for serializability is impractical in a real system, although it can be used to define and verify concurrency control protocols, and we briefly mentioned less restrictive defi- nitions of schedule equivalence. Finally, we gave a brief overview of how transaction concepts are used in practice within SQL.
Review Questions 21.1. What is meant by the concurrent execution of database transactions in a
multiuser system? Discuss why concurrency control is needed, and give informal examples.
Exercises 773
21.2. Discuss the different types of failures. What is meant by catastrophic failure?
21.3. Discuss the actions taken by the read_item and write_item operations on a database.
21.4. Draw a state diagram and discuss the typical states that a transaction goes through during execution.
21.5. What is the system log used for? What are the typical kinds of records in a system log? What are transaction commit points, and why are they impor- tant?
21.6. Discuss the atomicity, durability, isolation, and consistency preservation properties of a database transaction.
21.7. What is a schedule (history)? Define the concepts of recoverable, cascadeless, and strict schedules, and compare them in terms of their recoverability.
21.8. Discuss the different measures of transaction equivalence. What is the differ- ence between conflict equivalence and view equivalence?
21.9. What is a serial schedule? What is a serializable schedule? Why is a serial schedule considered correct? Why is a serializable schedule considered cor- rect?
21.10. What is the difference between the constrained write and the unconstrained write assumptions? Which is more realistic?
21.11. Discuss how serializability is used to enforce concurrency control in a data- base system. Why is serializability sometimes considered too restrictive as a measure of correctness for schedules?
21.12. Describe the four levels of isolation in SQL.
21.13. Define the violations caused by each of the following: dirty read, nonrepeat- able read, and phantoms.
Exercises 21.14. Change transaction T2 in Figure 21.2(b) to read
read_item(X); X := X + M; if X > 90 then exit else write_item(X);
Discuss the final result of the different schedules in Figure 21.3(a) and (b), where M = 2 and N = 2, with respect to the following questions: Does adding the above condition change the final outcome? Does the outcome obey the implied consistency rule (that the capacity of X is 90)?
21.15. Repeat Exercise 21.14, adding a check in T1 so that Y does not exceed 90.
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21.16. Add the operation commit at the end of each of the transactions T1 and T2 in Figure 21.2, and then list all possible schedules for the modified transactions. Determine which of the schedules are recoverable, which are cascadeless, and which are strict.
21.17. List all possible schedules for transactions T1 and T2 in Figure 21.2, and determine which are conflict serializable (correct) and which are not.
21.18. How many serial schedules exist for the three transactions in Figure 21.8(a)? What are they? What is the total number of possible schedules?
21.19. Write a program to create all possible schedules for the three transactions in Figure 21.8(a), and to determine which of those schedules are conflict serial- izable and which are not. For each conflict-serializable schedule, your pro- gram should print the schedule and list all equivalent serial schedules.
21.20. Why is an explicit transaction end statement needed in SQL but not an explicit begin statement?
21.21. Describe situations where each of the different isolation levels would be use- ful for transaction processing.
21.22. Which of the following schedules is (conflict) serializable? For each serializ- able schedule, determine the equivalent serial schedules.
a. r1(X); r3(X); w1(X); r2(X); w3(X);
b. r1(X); r3(X); w3(X); w1(X); r2(X);
c. r3(X); r2(X); w3(X); r1(X); w1(X);
d. r3(X); r2(X); r1(X); w3(X); w1(X);
21.23. Consider the three transactions T1, T2, and T3, and the schedules S1 and S2 given below. Draw the serializability (precedence) graphs for S1 and S2, and state whether each schedule is serializable or not. If a schedule is serializable, write down the equivalent serial schedule(s).
T1: r1 (X); r1 (Z); w1 (X); T2: r2 (Z); r2 (Y); w2 (Z); w2 (Y); T3: r3 (X); r3 (Y); w3 (Y); S1: r1 (X); r2 (Z); r1 (Z); r3 (X); r3 (Y); w1 (X); w3 (Y); r2 (Y); w2 (Z); w2 (Y); S2: r1 (X); r2 (Z); r3 (X); r1 (Z); r2 (Y); r3 (Y); w1 (X); w2 (Z); w3 (Y); w2 (Y);
21.24. Consider schedules S3, S4, and S5 below. Determine whether each schedule is strict, cascadeless, recoverable, or nonrecoverable. (Determine the strictest recoverability condition that each schedule satisfies.)
S3: r1 (X); r2 (Z); r1 (Z); r3 (X); r3 (Y); w1 (X); c1; w3 (Y); c3; r2 (Y); w2 (Z); w2 (Y); c2;
S4: r1 (X); r2 (Z); r1 (Z); r3 (X); r3 (Y); w1 (X); w3 (Y); r2 (Y); w2 (Z); w2 (Y); c1; c2; c3;
S5: r1 (X); r2 (Z); r3 (X); r1 (Z); r2 (Y); r3 (Y); w1 (X); c1; w2 (Z); w3 (Y); w2 (Y); c3; c2;
Selected Bibliography The concept of serializability and related ideas to maintain consistency in a database were introduced in Gray et al. (1975). The concept of the database transaction was first discussed in Gray (1981). Gray won the coveted ACM Turing Award in 1998 for his work on database transactions and implementation of transactions in relational DBMSs. Bernstein, Hadzilacos, and Goodman (1988) focus on concurrency control and recovery techniques in both centralized and distributed database systems; it is an excellent reference. Papadimitriou (1986) offers a more theoretical perspective. A large reference book of more than a thousand pages by Gray and Reuter (1993) offers a more practical perspective of transaction processing concepts and tech- niques. Elmagarmid (1992) offers collections of research papers on transaction pro- cessing for advanced applications. Transaction support in SQL is described in Date and Darwen (1997). View serializability is defined in Yannakakis (1984). Recoverability of schedules and reliability in databases is discussed in Hadzilacos (1983, 1988).
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Concurrency Control Techniques
In this chapter we discuss a number of concurrencycontrol techniques that are used to ensure the nonin- terference or isolation property of concurrently executing transactions. Most of these techniques ensure serializability of schedules—which we defined in Section 21.5—using concurrency control protocols (sets of rules) that guarantee serializ- ability. One important set of protocols—known as two-phase locking protocols— employ the technique of locking data items to prevent multiple transactions from accessing the items concurrently; a number of locking protocols are described in Sections 22.1 and 22.3.2. Locking protocols are used in most commercial DBMSs. Another set of concurrency control protocols use timestamps. A timestamp is a unique identifier for each transaction, generated by the system. Timestamps values are generated in the same order as the transaction start times. Concurrency control protocols that use timestamp ordering to ensure serializability are introduced in Section 22.2. In Section 22.3 we discuss multiversion concurrency control proto- cols that use multiple versions of a data item. One multiversion protocol extends timestamp order to multiversion timestamp ordering (Section 22.3.1), and another extends two-phase locking (Section 22.3.2). In Section 22.4 we present a protocol based on the concept of validation or certification of a transaction after it executes its operations; these are sometimes called optimistic protocols, and also assume that multiple versions of a data item can exist.
Another factor that affects concurrency control is the granularity of the data items—that is, what portion of the database a data item represents. An item can be as small as a single attribute (field) value or as large as a disk block, or even a whole file or the entire database. We discuss granularity of items and a multiple granular- ity concurrency control protocol, which is an extension of two-phase locking, in Section 22.5. In Section 22.6 we describe concurrency control issues that arise when
22chapter 22
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indexes are used to process transactions, and in Section 22.7 we discuss some addi- tional concurrency control concepts. Section 22.8 summarizes the chapter.
It is sufficient to read Sections 22.1, 22.5, 22.6, and 22.7, and possibly 22.3.2, if your main interest is an introduction to the concurrency control techniques that are based on locking, which are used most often in practice. The other techniques are mainly of theoretical interest.
22.1 Two-Phase Locking Techniques for Concurrency Control
Some of the main techniques used to control concurrent execution of transactions are based on the concept of locking data items. A lock is a variable associated with a data item that describes the status of the item with respect to possible operations that can be applied to it. Generally, there is one lock for each data item in the data- base. Locks are used as a means of synchronizing the access by concurrent transac- tions to the database items. In Section 22.1.1 we discuss the nature and types of locks. Then, in Section 22.1.2 we present protocols that use locking to guarantee serializability of transaction schedules. Finally, in Section 22.1.3 we describe two problems associated with the use of locks—deadlock and starvation—and show how these problems are handled in concurrency control protocols.
22.1.1 Types of Locks and System Lock Tables Several types of locks are used in concurrency control. To introduce locking con- cepts gradually, first we discuss binary locks, which are simple, but are also too restrictive for database concurrency control purposes, and so are not used in practice. Then we discuss shared/exclusive locks—also known as read/write locks—which provide more general locking capabilities and are used in practical database locking schemes. In Section 22.3.2 we describe an additional type of lock called a certify lock, and show how it can be used to improve performance of locking protocols.
Binary Locks. A binary lock can have two states or values: locked and unlocked (or 1 and 0, for simplicity). A distinct lock is associated with each database item X. If the value of the lock on X is 1, item X cannot be accessed by a database operation that requests the item. If the value of the lock on X is 0, the item can be accessed when requested, and the lock value is changed to 1. We refer to the current value (or state) of the lock associated with item X as lock(X).
Two operations, lock_item and unlock_item, are used with binary locking. A transaction requests access to an item X by first issuing a lock_item(X) operation. If LOCK(X) = 1, the transaction is forced to wait. If LOCK(X) = 0, it is set to 1 (the transaction locks the item) and the transaction is allowed to access item X. When the transaction is through using the item, it issues an unlock_item(X) operation, which sets LOCK(X) back to 0 (unlocks the item) so that X may be accessed by other transactions. Hence, a binary lock enforces mutual exclusion on the data item. A description of the lock_item(X) and unlock_item(X) operations is shown in Figure 22.1.
22.1 Two-Phase Locking Techniques for Concurrency Control 779
lock_item(X): B: if LOCK(X) = 0 (* item is unlocked *)
then LOCK(X) ←1 (* lock the item *) else
begin wait (until LOCK(X) = 0
and the lock manager wakes up the transaction); go to B end;
unlock_item(X): LOCK(X) ← 0; (* unlock the item *) if any transactions are waiting
then wakeup one of the waiting transactions;
Figure 22.1 Lock and unlock oper- ations for binary locks.
Notice that the lock_item and unlock_item operations must be implemented as indi- visible units (known as critical sections in operating systems); that is, no interleav- ing should be allowed once a lock or unlock operation is started until the operation terminates or the transaction waits. In Figure 22.1, the wait command within the lock_item(X) operation is usually implemented by putting the transaction in a wait- ing queue for item X until X is unlocked and the transaction can be granted access to it. Other transactions that also want to access X are placed in the same queue. Hence, the wait command is considered to be outside the lock_item operation.
It is quite simple to implement a binary lock; all that is needed is a binary-valued variable, LOCK, associated with each data item X in the database. In its simplest form, each lock can be a record with three fields: <Data_item_name, LOCK, Locking_transaction> plus a queue for transactions that are waiting to access the item. The system needs to maintain only these records for the items that are currently locked in a lock table, which could be organized as a hash file on the item name. Items not in the lock table are considered to be unlocked. The DBMS has a lock manager sub- system to keep track of and control access to locks.
If the simple binary locking scheme described here is used, every transaction must obey the following rules:
1. A transaction T must issue the operation lock_item(X) before any read_item(X) or write_item(X) operations are performed in T.
2. A transaction T must issue the operation unlock_item(X) after all read_item(X) and write_item(X) operations are completed in T.
3. A transaction T will not issue a lock_item(X) operation if it already holds the lock on item X.1
4. A transaction T will not issue an unlock_item(X) operation unless it already holds the lock on item X.
1This rule may be removed if we modify the lock_item (X) operation in Figure 22.1 so that if the item is currently locked by the requesting transaction, the lock is granted.
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These rules can be enforced by the lock manager module of the DBMS. Between the lock_item(X) and unlock_item(X) operations in transaction T, T is said to hold the lock on item X. At most one transaction can hold the lock on a particular item. Thus no two transactions can access the same item concurrently.
Shared/Exclusive (or Read/Write) Locks. The preceding binary locking scheme is too restrictive for database items because at most, one transaction can hold a lock on a given item. We should allow several transactions to access the same item X if they all access X for reading purposes only. This is because read operations on the same item by different transactions are not conflicting (see Section 21.4.1). However, if a transaction is to write an item X, it must have exclusive access to X. For this purpose, a different type of lock called a multiple-mode lock is used. In this scheme—called shared/exclusive or read/write locks—there are three locking operations: read_lock(X), write_lock(X), and unlock(X). A lock associated with an item X, LOCK(X), now has three possible states: read-locked, write-locked, or unlocked. A read-locked item is also called share-locked because other transactions are allowed to read the item, whereas a write-locked item is called exclusive-locked because a single transaction exclusively holds the lock on the item.
One method for implementing the preceding operations on a read/write lock is to keep track of the number of transactions that hold a shared (read) lock on an item in the lock table. Each record in the lock table will have four fields: <Data_item_name, LOCK, No_of_reads, Locking_transaction(s)>. Again, to save space, the system needs to maintain lock records only for locked items in the lock table. The value (state) of LOCK is either read-locked or write-locked, suitably coded (if we assume no records are kept in the lock table for unlocked items). If LOCK(X)=write-locked, the value of locking_transaction(s) is a single transaction that holds the exclusive (write) lock on X. If LOCK(X)=read-locked, the value of locking transaction(s) is a list of one or more transactions that hold the shared (read) lock on X. The three operations read_lock(X), write_lock(X), and unlock(X) are described in Figure 22.2.2 As before, each of the three locking operations should be considered indivisible; no interleav- ing should be allowed once one of the operations is started until either the opera- tion terminates by granting the lock or the transaction is placed in a waiting queue for the item.
When we use the shared/exclusive locking scheme, the system must enforce the fol- lowing rules:
1. A transaction T must issue the operation read_lock(X) or write_lock(X) before any read_item(X) operation is performed in T.
2. A transaction T must issue the operation write_lock(X) before any write_item(X) operation is performed in T.
2These algorithms do not allow upgrading or downgrading of locks, as described later in this section. The reader can extend the algorithms to allow these additional operations.
read_lock(X): B: if LOCK(X) = “unlocked”
then begin LOCK(X) ← “read-locked”; no_of_reads(X) ← 1 end
else if LOCK(X) = “read-locked” then no_of_reads(X) ← no_of_reads(X) + 1
else begin wait (until LOCK(X) = “unlocked”
and the lock manager wakes up the transaction); go to B end;
write_lock(X): B: if LOCK(X) = “unlocked”
then LOCK(X) ← “write-locked” else begin
wait (until LOCK(X) = “unlocked” and the lock manager wakes up the transaction);
go to B end;
unlock (X): if LOCK(X) = “write-locked”
then begin LOCK(X) ← “unlocked”; wakeup one of the waiting transactions, if any end
else it LOCK(X) = “read-locked” then begin
no_of_reads(X) ← no_of_reads(X) −1; if no_of_reads(X) = 0
then begin LOCK(X) = “unlocked”; wakeup one of the waiting transactions, if any end
end;
22.1 Two-Phase Locking Techniques for Concurrency Control 781
Figure 22.2 Locking and unlocking operations for two- mode (read-write or shared-exclusive) locks.
3. A transaction T must issue the operation unlock(X) after all read_item(X) and write_item(X) operations are completed in T.3
4. A transaction T will not issue a read_lock(X) operation if it already holds a read (shared) lock or a write (exclusive) lock on item X. This rule may be relaxed, as we discuss shortly.
3This rule may be relaxed to allow a transaction to unlock an item, then lock it again later.
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5. A transaction T will not issue a write_lock(X) operation if it already holds a read (shared) lock or write (exclusive) lock on item X. This rule may also be relaxed, as we discuss shortly.
6. A transaction T will not issue an unlock(X) operation unless it already holds a read (shared) lock or a write (exclusive) lock on item X.
Conversion of Locks. Sometimes it is desirable to relax conditions 4 and 5 in the preceding list in order to allow lock conversion; that is, a transaction that already holds a lock on item X is allowed under certain conditions to convert the lock from one locked state to another. For example, it is possible for a transaction T to issue a read_lock(X) and then later to upgrade the lock by issuing a write_lock(X) operation. If T is the only transaction holding a read lock on X at the time it issues the write_lock(X) operation, the lock can be upgraded; otherwise, the transaction must wait. It is also possible for a transaction T to issue a write_lock(X) and then later to downgrade the lock by issuing a read_lock(X) operation. When upgrading and downgrading of locks is used, the lock table must include transaction identifiers in the record structure for each lock (in the locking_transaction(s) field) to store the information on which transactions hold locks on the item. The descriptions of the read_lock(X) and write_lock(X) operations in Figure 22.2 must be changed appropri- ately to allow for lock upgrading and downgrading. We leave this as an exercise for the reader.
Using binary locks or read/write locks in transactions, as described earlier, does not guarantee serializability of schedules on its own. Figure 22.3 shows an example where the preceding locking rules are followed but a nonserializable schedule may result. This is because in Figure 22.3(a) the items Y in T1 and X in T2 were unlocked too early. This allows a schedule such as the one shown in Figure 22.3(c) to occur, which is not a serializable schedule and hence gives incorrect results. To guarantee serializability, we must follow an additional protocol concerning the positioning of locking and unlocking operations in every transaction. The best-known protocol, two-phase locking, is described in the next section.
22.1.2 Guaranteeing Serializability by Two-Phase Locking A transaction is said to follow the two-phase locking protocol if all locking opera- tions (read_lock, write_lock) precede the first unlock operation in the transaction.4
Such a transaction can be divided into two phases: an expanding or growing (first) phase, during which new locks on items can be acquired but none can be released; and a shrinking (second) phase, during which existing locks can be released but no new locks can be acquired. If lock conversion is allowed, then upgrading of locks (from read-locked to write-locked) must be done during the expanding phase, and downgrading of locks (from write-locked to read-locked) must be done in the
4This is unrelated to the two-phase commit protocol for recovery in distributed databases (see Chapter 25).
22.1 Two-Phase Locking Techniques for Concurrency Control 783
(a) T1 Initial values: X=20, Y=30
Result serial schedule T1 followed by T2: X=50, Y=80
Result of serial schedule T2 followed by T1: X=70, Y=50
read_lock(Y ); read_item(Y ); unlock(Y ); write_lock(X ); read_item(X ); X := X + Y; write_item(X ); unlock(X );
write_lock(X ); read_item(X ); X := X + Y; write_item(X ); unlock(X );
read_lock(X ); read_item(X ); unlock(X ); write_lock(Y ); read_item(Y ); Y := X + Y; write_item(Y ); unlock(Y );
read_lock(X ); read_item(X ); unlock(X ); write_lock(Y ); read_item(Y ); Y := X + Y; write_item(Y ); unlock(Y );
(b)
(c)
Time
read_lock(Y ); read_item(Y ); unlock(Y );
Result of schedule S: X=50, Y=50 (nonserializable)
T2
T1 T2
Figure 22.3 Transactions that do not obey two-phase lock- ing. (a) Two transactions T1 and T2. (b) Results of possible serial schedules of T1 and T2. (c) A nonserializable schedule S that uses locks.
shrinking phase. Hence, a read_lock(X) operation that downgrades an already held write lock on X can appear only in the shrinking phase.
Transactions T1 and T2 in Figure 22.3(a) do not follow the two-phase locking proto- col because the write_lock(X) operation follows the unlock(Y) operation in T1, and similarly the write_lock(Y) operation follows the unlock(X) operation in T2. If we enforce two-phase locking, the transactions can be rewritten as T1� and T2�, as shown in Figure 22.4. Now, the schedule shown in Figure 22.3(c) is not permitted for T1� and T2� (with their modified order of locking and unlocking operations) under the rules of locking described in Section 22.1.1 because T1� will issue its write_lock(X) before it unlocks item Y; consequently, when T2� issues its read_lock(X), it is forced to wait until T1� releases the lock by issuing an unlock (X) in the schedule.
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read_lock(Y ); read_item(Y ); write_lock(X ); unlock(Y ) read_item(X ); X := X + Y; write_item(X ); unlock(X );
read_lock(X ); read_item(X ); write_lock(Y ); unlock(X ) read_item(Y ); Y := X + Y; write_item(Y ); unlock(Y );
T1� T2�
Figure 22.4 Transactions T1� and T2�, which are the same as T1 and T2 in Figure 22.3, but follow the two-phase locking protocol. Note that they can produce a deadlock.
It can be proved that, if every transaction in a schedule follows the two-phase lock- ing protocol, the schedule is guaranteed to be serializable, obviating the need to test for serializability of schedules. The locking protocol, by enforcing two-phase lock- ing rules, also enforces serializability.
Two-phase locking may limit the amount of concurrency that can occur in a sched- ule because a transaction T may not be able to release an item X after it is through using it if T must lock an additional item Y later; or conversely, T must lock the additional item Y before it needs it so that it can release X. Hence, X must remain locked by T until all items that the transaction needs to read or write have been locked; only then can X be released by T. Meanwhile, another transaction seeking to access X may be forced to wait, even though T is done with X; conversely, if Y is locked earlier than it is needed, another transaction seeking to access Y is forced to wait even though T is not using Y yet. This is the price for guaranteeing serializabil- ity of all schedules without having to check the schedules themselves.
Although the two-phase locking protocol guarantees serializability (that is, every schedule that is permitted is serializable), it does not permit all possible serializable schedules (that is, some serializable schedules will be prohibited by the protocol).
Basic, Conservative, Strict, and Rigorous Two-Phase Locking. There are a number of variations of two-phase locking (2PL). The technique just described is known as basic 2PL. A variation known as conservative 2PL (or static 2PL) requires a transaction to lock all the items it accesses before the transaction begins execution, by predeclaring its read-set and write-set. Recall from Section 21.1.2 that the read-set of a transaction is the set of all items that the transaction reads, and the write-set is the set of all items that it writes. If any of the predeclared items needed cannot be locked, the transaction does not lock any item; instead, it waits until all the items are available for locking. Conservative 2PL is a deadlock-free protocol, as we will see in Section 22.1.3 when we discuss the deadlock problem. However, it is difficult to use in practice because of the need to predeclare the read-set and write- set, which is not possible in many situations.
In practice, the most popular variation of 2PL is strict 2PL, which guarantees strict schedules (see Section 21.4). In this variation, a transaction T does not release any of
22.1 Two-Phase Locking Techniques for Concurrency Control 785
its exclusive (write) locks until after it commits or aborts. Hence, no other transac- tion can read or write an item that is written by T unless T has committed, leading to a strict schedule for recoverability. Strict 2PL is not deadlock-free. A more restric- tive variation of strict 2PL is rigorous 2PL, which also guarantees strict schedules. In this variation, a transaction T does not release any of its locks (exclusive or shared) until after it commits or aborts, and so it is easier to implement than strict 2PL. Notice the difference between conservative and rigorous 2PL: the former must lock all its items before it starts, so once the transaction starts it is in its shrinking phase; the latter does not unlock any of its items until after it terminates (by com- mitting or aborting), so the transaction is in its expanding phase until it ends.
In many cases, the concurrency control subsystem itself is responsible for generat- ing the read_lock and write_lock requests. For example, suppose the system is to enforce the strict 2PL protocol. Then, whenever transaction T issues a read_item(X), the system calls the read_lock(X) operation on behalf of T. If the state of LOCK(X) is write_locked by some other transaction T�, the system places T in the waiting queue for item X; otherwise, it grants the read_lock(X) request and permits the read_item(X) operation of T to execute. On the other hand, if transaction T issues a write_item(X), the system calls the write_lock(X) operation on behalf of T. If the state of LOCK(X) is write_locked or read_locked by some other transaction T�, the system places T in the waiting queue for item X; if the state of LOCK(X) is read_locked and T itself is the only transaction holding the read lock on X, the system upgrades the lock to write_locked and permits the write_item(X) operation by T. Finally, if the state of LOCK(X) is unlocked, the system grants the write_lock(X) request and per- mits the write_item(X) operation to execute. After each action, the system must update its lock table appropriately.
The use of locks can cause two additional problems: deadlock and starvation. We discuss these problems and their solutions in the next section.
22.1.3 Dealing with Deadlock and Starvation Deadlock occurs when each transaction T in a set of two or more transactions is waiting for some item that is locked by some other transaction T� in the set. Hence, each transaction in the set is in a waiting queue, waiting for one of the other trans- actions in the set to release the lock on an item. But because the other transaction is also waiting, it will never release the lock. A simple example is shown in Figure 22.5(a), where the two transactions T1� and T2� are deadlocked in a partial schedule; T1� is in the waiting queue for X, which is locked by T2�, while T2� is in the waiting queue for Y, which is locked by T1�. Meanwhile, neither T1� nor T2� nor any other transaction can access items X and Y.
Deadlock Prevention Protocols. One way to prevent deadlock is to use a deadlock prevention protocol.5 One deadlock prevention protocol, which is used
5These protocols are not generally used in practice, either because of unrealistic assumptions or because of their possible overhead. Deadlock detection and timeouts (covered in the following sections) are more practical.
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(a) T1� (b)
read_lock(Y ); read_item(Y );
Time
write_lock(X );
read_lock(X ); read_item(X );
write_lock(Y );
T2�
T2�T1�
X
Y
Figure 22.5 Illustrating the deadlock problem. (a) A partial schedule of T1� and T2� that is in a state of deadlock. (b) A wait-for graph for the partial schedule in (a).
in conservative two-phase locking, requires that every transaction lock all the items it needs in advance (which is generally not a practical assumption)—if any of the items cannot be obtained, none of the items are locked. Rather, the transaction waits and then tries again to lock all the items it needs. Obviously this solution further limits concurrency. A second protocol, which also limits concurrency, involves ordering all the items in the database and making sure that a transaction that needs several items will lock them according to that order. This requires that the program- mer (or the system) is aware of the chosen order of the items, which is also not prac- tical in the database context.
A number of other deadlock prevention schemes have been proposed that make a decision about what to do with a transaction involved in a possible deadlock situa- tion: Should it be blocked and made to wait or should it be aborted, or should the transaction preempt and abort another transaction? Some of these techniques use the concept of transaction timestamp TS(T), which is a unique identifier assigned to each transaction. The timestamps are typically based on the order in which trans- actions are started; hence, if transaction T1 starts before transaction T2, then TS(T1) < TS(T2). Notice that the older transaction (which starts first) has the smaller time- stamp value. Two schemes that prevent deadlock are called wait-die and wound- wait. Suppose that transaction Ti tries to lock an item X but is not able to because X is locked by some other transaction Tj with a conflicting lock. The rules followed by these schemes are:
■ Wait-die. If TS(Ti) < TS(Tj), then (Ti older than Tj) Ti is allowed to wait; otherwise (Ti younger than Tj) abort Ti (Ti dies) and restart it later with the same timestamp.
■ Wound-wait. If TS(Ti) < TS(Tj), then (Ti older than Tj) abort Tj (Ti wounds Tj) and restart it later with the same timestamp; otherwise (Ti younger than Tj) Ti is allowed to wait.
In wait-die, an older transaction is allowed to wait for a younger transaction, whereas a younger transaction requesting an item held by an older transaction is aborted and restarted. The wound-wait approach does the opposite: A younger transaction is allowed to wait for an older one, whereas an older transaction requesting an item
22.1 Two-Phase Locking Techniques for Concurrency Control 787
held by a younger transaction preempts the younger transaction by aborting it. Both schemes end up aborting the younger of the two transactions (the transaction that started later) that may be involved in a deadlock, assuming that this will waste less processing. It can be shown that these two techniques are deadlock-free, since in wait-die, transactions only wait for younger transactions so no cycle is created. Similarly, in wound-wait, transactions only wait for older transactions so no cycle is created. However, both techniques may cause some transactions to be aborted and restarted needlessly, even though those transactions may never actually cause a deadlock.
Another group of protocols that prevent deadlock do not require timestamps. These include the no waiting (NW) and cautious waiting (CW) algorithms. In the no waiting algorithm, if a transaction is unable to obtain a lock, it is immediately aborted and then restarted after a certain time delay without checking whether a deadlock will actually occur or not. In this case, no transaction ever waits, so no deadlock will occur. However, this scheme can cause transactions to abort and restart needlessly. The cautious waiting algorithm was proposed to try to reduce the number of needless aborts/restarts. Suppose that transaction Ti tries to lock an item X but is not able to do so because X is locked by some other transaction Tj with a conflicting lock. The cautious waiting rules are as follows:
■ Cautious waiting. If Tj is not blocked (not waiting for some other locked item), then Ti is blocked and allowed to wait; otherwise abort Ti.
It can be shown that cautious waiting is deadlock-free, because no transaction will ever wait for another blocked transaction. By considering the time b(T) at which each blocked transaction T was blocked, if the two transactions Ti and Tj above both become blocked, and Ti is waiting for Tj, then b(Ti) < b(Tj), since Ti can only wait for Tj at a time when Tj is not blocked itself. Hence, the blocking times form a total ordering on all blocked transactions, so no cycle that causes deadlock can occur.
Deadlock Detection. A second, more practical approach to dealing with deadlock is deadlock detection, where the system checks if a state of deadlock actually exists. This solution is attractive if we know there will be little interference among the transactions—that is, if different transactions will rarely access the same items at the same time. This can happen if the transactions are short and each transaction locks only a few items, or if the transaction load is light. On the other hand, if transac- tions are long and each transaction uses many items, or if the transaction load is quite heavy, it may be advantageous to use a deadlock prevention scheme.
A simple way to detect a state of deadlock is for the system to construct and main- tain a wait-for graph. One node is created in the wait-for graph for each transaction that is currently executing. Whenever a transaction Ti is waiting to lock an item X that is currently locked by a transaction Tj, a directed edge (Ti → Tj) is created in the wait-for graph. When Tj releases the lock(s) on the items that Ti was waiting for, the directed edge is dropped from the wait-for graph. We have a state of dead- lock if and only if the wait-for graph has a cycle. One problem with this approach is the matter of determining when the system should check for a deadlock. One possi-
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bility is to check for a cycle every time an edge is added to the wait-for graph, but this may cause excessive overhead. Criteria such as the number of currently execut- ing transactions or the period of time several transactions have been waiting to lock items may be used instead to check for a cycle. Figure 22.5(b) shows the wait-for graph for the (partial) schedule shown in Figure 22.5(a).
If the system is in a state of deadlock, some of the transactions causing the deadlock must be aborted. Choosing which transactions to abort is known as victim selec- tion. The algorithm for victim selection should generally avoid selecting transac- tions that have been running for a long time and that have performed many updates, and it should try instead to select transactions that have not made many changes (younger transactions).
Timeouts. Another simple scheme to deal with deadlock is the use of timeouts. This method is practical because of its low overhead and simplicity. In this method, if a transaction waits for a period longer than a system-defined timeout period, the system assumes that the transaction may be deadlocked and aborts it—regardless of whether a deadlock actually exists or not.
Starvation. Another problem that may occur when we use locking is starvation, which occurs when a transaction cannot proceed for an indefinite period of time while other transactions in the system continue normally. This may occur if the waiting scheme for locked items is unfair, giving priority to some transactions over others. One solution for starvation is to have a fair waiting scheme, such as using a first-come-first-served queue; transactions are enabled to lock an item in the order in which they originally requested the lock. Another scheme allows some transac- tions to have priority over others but increases the priority of a transaction the longer it waits, until it eventually gets the highest priority and proceeds. Starvation can also occur because of victim selection if the algorithm selects the same transac- tion as victim repeatedly, thus causing it to abort and never finish execution. The algorithm can use higher priorities for transactions that have been aborted multiple times to avoid this problem. The wait-die and wound-wait schemes discussed previ- ously avoid starvation, because they restart a transaction that has been aborted with its same original timestamp, so the possibility that the same transaction is aborted repeatedly is slim.
22.2 Concurrency Control Based on Timestamp Ordering
The use of locks, combined with the 2PL protocol, guarantees serializability of schedules. The serializable schedules produced by 2PL have their equivalent serial schedules based on the order in which executing transactions lock the items they acquire. If a transaction needs an item that is already locked, it may be forced to wait until the item is released. Some transactions may be aborted and restarted because of the deadlock problem. A different approach that guarantees serializability involves using transaction timestamps to order transaction execution for an equiva-
22.2 Concurrency Control Based on Timestamp Ordering 789
lent serial schedule. In Section 22.2.1 we discuss timestamps, and in Section 22.2.2 we discuss how serializability is enforced by ordering transactions based on their timestamps.
22.2.1 Timestamps Recall that a timestamp is a unique identifier created by the DBMS to identify a transaction. Typically, timestamp values are assigned in the order in which the transactions are submitted to the system, so a timestamp can be thought of as the transaction start time. We will refer to the timestamp of transaction T as TS(T). Concurrency control techniques based on timestamp ordering do not use locks; hence, deadlocks cannot occur.
Timestamps can be generated in several ways. One possibility is to use a counter that is incremented each time its value is assigned to a transaction. The transaction time- stamps are numbered 1, 2, 3, ... in this scheme. A computer counter has a finite max- imum value, so the system must periodically reset the counter to zero when no transactions are executing for some short period of time. Another way to implement timestamps is to use the current date/time value of the system clock and ensure that no two timestamp values are generated during the same tick of the clock.
22.2.2 The Timestamp Ordering Algorithm The idea for this scheme is to order the transactions based on their timestamps. A schedule in which the transactions participate is then serializable, and the only equivalent serial schedule permitted has the transactions in order of their timestamp values. This is called timestamp ordering (TO). Notice how this differs from 2PL, where a schedule is serializable by being equivalent to some serial schedule allowed by the locking protocols. In timestamp ordering, however, the schedule is equivalent to the particular serial order corresponding to the order of the transaction time- stamps. The algorithm must ensure that, for each item accessed by conflicting opera- tions in the schedule, the order in which the item is accessed does not violate the timestamp order. To do this, the algorithm associates with each database item X two timestamp (TS) values:
1. read_TS(X). The read timestamp of item X is the largest timestamp among all the timestamps of transactions that have successfully read item X—that is, read_TS(X) = TS(T), where T is the youngest transaction that has read X successfully.
2. write_TS(X). The write timestamp of item X is the largest of all the time- stamps of transactions that have successfully written item X—that is, write_TS(X) = TS(T), where T is the youngest transaction that has written X successfully.
Basic Timestamp Ordering (TO). Whenever some transaction T tries to issue a read_item(X) or a write_item(X) operation, the basic TO algorithm compares the timestamp of T with read_TS(X) and write_TS(X) to ensure that the timestamp
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order of transaction execution is not violated. If this order is violated, then transac- tion T is aborted and resubmitted to the system as a new transaction with a new timestamp. If T is aborted and rolled back, any transaction T1 that may have used a value written by T must also be rolled back. Similarly, any transaction T2 that may have used a value written by T1 must also be rolled back, and so on. This effect is known as cascading rollback and is one of the problems associated with basic TO, since the schedules produced are not guaranteed to be recoverable. An additional protocol must be enforced to ensure that the schedules are recoverable, cascadeless, or strict. We first describe the basic TO algorithm here. The concurrency control algorithm must check whether conflicting operations violate the timestamp order- ing in the following two cases:
1. Whenever a transaction T issues a write_item(X) operation, the following is checked:
a. If read_TS(X) > TS(T) or if write_TS(X) > TS(T), then abort and roll back T and reject the operation. This should be done because some younger transaction with a timestamp greater than TS(T)—and hence after T in the timestamp ordering—has already read or written the value of item X before T had a chance to write X, thus violating the timestamp ordering.
b. If the condition in part (a) does not occur, then execute the write_item(X) operation of T and set write_TS(X) to TS(T).
2. Whenever a transaction T issues a read_item(X) operation, the following is checked:
a. If write_TS(X) > TS(T), then abort and roll back T and reject the opera- tion. This should be done because some younger transaction with time- stamp greater than TS(T)—and hence after T in the timestamp ordering—has already written the value of item X before T had a chance to read X.
b. If write_TS(X) ≤ TS(T), then execute the read_item(X) operation of T and set read_TS(X) to the larger of TS(T) and the current read_TS(X).
Whenever the basic TO algorithm detects two conflicting operations that occur in the incorrect order, it rejects the later of the two operations by aborting the transaction that issued it. The schedules produced by basic TO are hence guaranteed to be conflict serializable, like the 2PL protocol. However, some schedules are possible under each protocol that are not allowed under the other. Thus, neither protocol allows all possible serializable schedules. As mentioned earlier, deadlock does not occur with timestamp ordering. However, cyclic restart (and hence starvation) may occur if a transaction is continually aborted and restarted.
Strict Timestamp Ordering (TO). A variation of basic TO called strict TO ensures that the schedules are both strict (for easy recoverability) and (conflict) serializable. In this variation, a transaction T that issues a read_item(X) or write_item(X) such that TS(T) > write_TS(X) has its read or write operation delayed until the transaction T � that wrote the value of X (hence TS(T �) = write_TS(X)) has committed or aborted. To implement this algorithm, it is necessary to simulate the
22.3 Multiversion Concurrency Control Techniques 791
locking of an item X that has been written by transaction T � until T � is either com- mitted or aborted. This algorithm does not cause deadlock, since T waits for T � only if TS(T) > TS(T �).
Thomas’s Write Rule. A modification of the basic TO algorithm, known as Thomas’s write rule, does not enforce conflict serializability, but it rejects fewer write operations by modifying the checks for the write_item(X) operation as follows:
1. If read_TS(X) > TS(T), then abort and roll back T and reject the operation.
2. If write_TS(X) > TS(T), then do not execute the write operation but continue processing. This is because some transaction with timestamp greater than TS(T)—and hence after T in the timestamp ordering—has already written the value of X. Thus, we must ignore the write_item(X) operation of T because it is already outdated and obsolete. Notice that any conflict arising from this situation would be detected by case (1).
3. If neither the condition in part (1) nor the condition in part (2) occurs, then execute the write_item(X) operation of T and set write_TS(X) to TS(T).
22.3 Multiversion Concurrency Control Techniques
Other protocols for concurrency control keep the old values of a data item when the item is updated. These are known as multiversion concurrency control, because several versions (values) of an item are maintained. When a transaction requires access to an item, an appropriate version is chosen to maintain the serializability of the currently executing schedule, if possible. The idea is that some read operations that would be rejected in other techniques can still be accepted by reading an older version of the item to maintain serializability. When a transaction writes an item, it writes a new version and the old version(s) of the item are retained. Some multiver- sion concurrency control algorithms use the concept of view serializability rather than conflict serializability.
An obvious drawback of multiversion techniques is that more storage is needed to maintain multiple versions of the database items. However, older versions may have to be maintained anyway—for example, for recovery purposes. In addition, some database applications require older versions to be kept to maintain a history of the evolution of data item values. The extreme case is a temporal database (see Secton 26.2), which keeps track of all changes and the times at which they occurred. In such cases, there is no additional storage penalty for multiversion techniques, since older versions are already maintained.
Several multiversion concurrency control schemes have been proposed. We discuss two schemes here, one based on timestamp ordering and the other based on 2PL. In addition, the validation concurrency control method (see Section 22.4) also main- tains multiple versions.
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22.3.1 Multiversion Technique Based on Timestamp Ordering In this method, several versions X1, X2, ..., Xk of each data item X are maintained. For each version, the value of version Xi and the following two timestamps are kept:
1. read_TS(Xi). The read timestamp of Xi is the largest of all the timestamps of transactions that have successfully read version Xi.
2. write_TS(Xi). The write timestamp of Xi is the timestamp of the transac- tion that wrote the value of version Xi.
Whenever a transaction T is allowed to execute a write_item(X) operation, a new ver- sion Xk+1 of item X is created, with both the write_TS(Xk+1) and the read_TS(Xk+1) set to TS(T). Correspondingly, when a transaction T is allowed to read the value of version Xi, the value of read_TS(Xi) is set to the larger of the current read_TS(Xi) and TS(T).
To ensure serializability, the following rules are used:
1. If transaction T issues a write_item(X) operation, and version i of X has the highest write_TS(Xi) of all versions of X that is also less than or equal to TS(T), and read_TS(Xi) > TS(T), then abort and roll back transaction T; otherwise, create a new version Xj of X with read_TS(Xj) = write_TS(Xj) = TS(T).
2. If transaction T issues a read_item(X) operation, find the version i of X that has the highest write_TS(Xi) of all versions of X that is also less than or equal to TS(T); then return the value of Xi to transaction T, and set the value of read_TS(Xi) to the larger of TS(T) and the current read_TS(Xi).
As we can see in case 2, a read_item(X) is always successful, since it finds the appro- priate version Xi to read based on the write_TS of the various existing versions of X. In case 1, however, transaction T may be aborted and rolled back. This happens if T attempts to write a version of X that should have been read by another transaction T� whose timestamp is read_TS(Xi); however, T� has already read version Xi, which was written by the transaction with timestamp equal to write_TS(Xi). If this conflict occurs, T is rolled back; otherwise, a new version of X, written by transaction T, is created. Notice that if T is rolled back, cascading rollback may occur. Hence, to ensure recoverability, a transaction T should not be allowed to commit until after all the transactions that have written some version that T has read have committed.
22.3.2 Multiversion Two-Phase Locking Using Certify Locks In this multiple-mode locking scheme, there are three locking modes for an item: read, write, and certify, instead of just the two modes (read, write) discussed previ- ously. Hence, the state of LOCK(X) for an item X can be one of read-locked, write- locked, certify-locked, or unlocked. In the standard locking scheme, with only read and write locks (see Section 22.1.1), a write lock is an exclusive lock. We can describe the relationship between read and write locks in the standard scheme by means of the lock compatibility table shown in Figure 22.6(a). An entry of Yes means that if a transaction T holds the type of lock specified in the column header
22.3 Multiversion Concurrency Control Techniques 793
(b) Read Write
Read
Write
Certify
Yes No No
No No No
Yes Yes No
Certify
(a) Read Write
Read
Write No No
Yes No
Figure 22.6 Lock compatibility tables. (a) A compatibility table for read/write locking scheme. (b) A compatibility table for read/write/certify locking scheme.
on item X and if transaction T� requests the type of lock specified in the row header on the same item X, then T� can obtain the lock because the locking modes are com- patible. On the other hand, an entry of No in the table indicates that the locks are not compatible, so T� must wait until T releases the lock.
In the standard locking scheme, once a transaction obtains a write lock on an item, no other transactions can access that item. The idea behind multiversion 2PL is to allow other transactions T� to read an item X while a single transaction T holds a write lock on X. This is accomplished by allowing two versions for each item X; one version must always have been written by some committed transaction. The second version X� is created when a transaction T acquires a write lock on the item. Other transactions can continue to read the committed version of X while T holds the write lock. Transaction T can write the value of X� as needed, without affecting the value of the committed version X. However, once T is ready to commit, it must obtain a certify lock on all items that it currently holds write locks on before it can commit. The certify lock is not compatible with read locks, so the transaction may have to delay its commit until all its write-locked items are released by any reading transac- tions in order to obtain the certify locks. Once the certify locks—which are exclusive locks—are acquired, the committed version X of the data item is set to the value of version X�, version X� is discarded, and the certify locks are then released. The lock compatibility table for this scheme is shown in Figure 22.6(b).
In this multiversion 2PL scheme, reads can proceed concurrently with a single write operation—an arrangement not permitted under the standard 2PL schemes. The cost is that a transaction may have to delay its commit until it obtains exclusive cer- tify locks on all the items it has updated. It can be shown that this scheme avoids cas- cading aborts, since transactions are only allowed to read the version X that was written by a committed transaction. However, deadlocks may occur if upgrading of a read lock to a write lock is allowed, and these must be handled by variations of the techniques discussed in Section 22.1.3.
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22.4 Validation (Optimistic) Concurrency Control Techniques
In all the concurrency control techniques we have discussed so far, a certain degree of checking is done before a database operation can be executed. For example, in locking, a check is done to determine whether the item being accessed is locked. In timestamp ordering, the transaction timestamp is checked against the read and write timestamps of the item. Such checking represents overhead during transac- tion execution, with the effect of slowing down the transactions.
In optimistic concurrency control techniques, also known as validation or certification techniques, no checking is done while the transaction is executing. Several theoretical concurrency control methods are based on the validation tech- nique. We will describe only one scheme here. In this scheme, updates in the trans- action are not applied directly to the database items until the transaction reaches its end. During transaction execution, all updates are applied to local copies of the data items that are kept for the transaction.6 At the end of transaction execution, a validation phase checks whether any of the transaction’s updates violate serializ- ability. Certain information needed by the validation phase must be kept by the sys- tem. If serializability is not violated, the transaction is committed and the database is updated from the local copies; otherwise, the transaction is aborted and then restarted later.
There are three phases for this concurrency control protocol:
1. Read phase. A transaction can read values of committed data items from the database. However, updates are applied only to local copies (versions) of the data items kept in the transaction workspace.
2. Validation phase. Checking is performed to ensure that serializability will not be violated if the transaction updates are applied to the database.
3. Write phase. If the validation phase is successful, the transaction updates are applied to the database; otherwise, the updates are discarded and the trans- action is restarted.
The idea behind optimistic concurrency control is to do all the checks at once; hence, transaction execution proceeds with a minimum of overhead until the vali- dation phase is reached. If there is little interference among transactions, most will be validated successfully. However, if there is much interference, many transactions that execute to completion will have their results discarded and must be restarted later. Under these circumstances, optimistic techniques do not work well. The tech- niques are called optimistic because they assume that little interference will occur and hence that there is no need to do checking during transaction execution.
The optimistic protocol we describe uses transaction timestamps and also requires that the write_sets and read_sets of the transactions be kept by the system. Additionally, start and end times for some of the three phases need to be kept for
6Note that this can be considered as keeping multiple versions of items!
22.5 Granularity of Data Items and Multiple Granularity Locking 795
each transaction. Recall that the write_set of a transaction is the set of items it writes, and the read_set is the set of items it reads. In the validation phase for transaction Ti, the protocol checks that Ti does not interfere with any committed transactions or with any other transactions currently in their validation phase. The validation phase for Ti checks that, for each such transaction Tj that is either committed or is in its validation phase, one of the following conditions holds:
1. Transaction Tj completes its write phase before Ti starts its read phase.
2. Ti starts its write phase after Tj completes its write phase, and the read_set of Ti has no items in common with the write_set of Tj.
3. Both the read_set and write_set of Ti have no items in common with the write_set of Tj, and Tj completes its read phase before Ti completes its read phase.
When validating transaction Ti, the first condition is checked first for each transac- tion Tj, since (1) is the simplest condition to check. Only if condition 1 is false is condition 2 checked, and only if (2) is false is condition 3—the most complex to evaluate—checked. If any one of these three conditions holds, there is no interfer- ence and Ti is validated successfully. If none of these three conditions holds, the val- idation of transaction Ti fails and it is aborted and restarted later because interference may have occurred.
22.5 Granularity of Data Items and Multiple Granularity Locking
All concurrency control techniques assume that the database is formed of a number of named data items. A database item could be chosen to be one of the following:
■ A database record
■ A field value of a database record
■ A disk block
■ A whole file
■ The whole database
The granularity can affect the performance of concurrency control and recovery. In Section 22.5.1, we discuss some of the tradeoffs with regard to choosing the granu- larity level used for locking, and in Section 22.5.2 we discuss a multiple granularity locking scheme, where the granularity level (size of the data item) may be changed dynamically.
22.5.1 Granularity Level Considerations for Locking The size of data items is often called the data item granularity. Fine granularity refers to small item sizes, whereas coarse granularity refers to large item sizes. Several tradeoffs must be considered in choosing the data item size. We will discuss data item size in the context of locking, although similar arguments can be made for other concurrency control techniques.
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db
r111 r11j r121 r12j r1n1 r1nj r211 r21k r221 r22k r2m1 r2mk. . . . . . . . .
. . .
. . . . . . . . . . . .
. . .
. . .
p11 p12
f1
p1n p21 p22 p2m
f2
Figure 22.7 A granularity hierarchy for illustrating multiple granularity level locking.
First, notice that the larger the data item size is, the lower the degree of concurrency permitted. For example, if the data item size is a disk block, a transaction T that needs to lock a record B must lock the whole disk block X that contains B because a lock is associated with the whole data item (block). Now, if another transaction S wants to lock a different record C that happens to reside in the same block X in a conflicting lock mode, it is forced to wait. If the data item size was a single record, transaction S would be able to proceed, because it would be locking a different data item (record).
On the other hand, the smaller the data item size is, the more the number of items in the database. Because every item is associated with a lock, the system will have a larger number of active locks to be handled by the lock manager. More lock and unlock operations will be performed, causing a higher overhead. In addition, more storage space will be required for the lock table. For timestamps, storage is required for the read_TS and write_TS for each data item, and there will be similar overhead for handling a large number of items.
Given the above tradeoffs, an obvious question can be asked: What is the best item size? The answer is that it depends on the types of transactions involved. If a typical transaction accesses a small number of records, it is advantageous to have the data item granularity be one record. On the other hand, if a transaction typically accesses many records in the same file, it may be better to have block or file granularity so that the transaction will consider all those records as one (or a few) data items.
22.5.2 Multiple Granularity Level Locking Since the best granularity size depends on the given transaction, it seems appropri- ate that a database system should support multiple levels of granularity, where the granularity level can be different for various mixes of transactions. Figure 22.7 shows a simple granularity hierarchy with a database containing two files, each file containing several disk pages, and each page containing several records. This can be used to illustrate a multiple granularity level 2PL protocol, where a lock can be requested at any level. However, additional types of locks will be needed to support such a protocol efficiently.
22.5 Granularity of Data Items and Multiple Granularity Locking 797
Consider the following scenario, with only shared and exclusive lock types, that refers to the example in Figure 22.7. Suppose transaction T1 wants to update all the records in file f1, and T1 requests and is granted an exclusive lock for f1. Then all of f1’s pages (p11 through p1n)—and the records contained on those pages—are locked in exclu- sive mode. This is beneficial for T1 because setting a single file-level lock is more effi- cient than setting n page-level locks or having to lock each individual record. Now suppose another transaction T2 only wants to read record r1nj from page p1n of file f1; then T2 would request a shared record-level lock on r1nj. However, the database sys- tem (that is, the transaction manager or more specifically the lock manager) must verify the compatibility of the requested lock with already held locks. One way to ver- ify this is to traverse the tree from the leaf r1nj to p1n to f1 to db. If at any time a con- flicting lock is held on any of those items, then the lock request for r1nj is denied and T2 is blocked and must wait. This traversal would be fairly efficient.
However, what if transaction T2’s request came before transaction T1’s request? In this case, the shared record lock is granted to T2 for r1nj, but when T1’s file-level lock is requested, it is quite difficult for the lock manager to check all nodes (pages and records) that are descendants of node f1 for a lock conflict. This would be very inef- ficient and would defeat the purpose of having multiple granularity level locks.
To make multiple granularity level locking practical, additional types of locks, called intention locks, are needed. The idea behind intention locks is for a transaction to indicate, along the path from the root to the desired node, what type of lock (shared or exclusive) it will require from one of the node’s descendants. There are three types of intention locks:
1. Intention-shared (IS) indicates that one or more shared locks will be requested on some descendant node(s).
2. Intention-exclusive (IX) indicates that one or more exclusive locks will be requested on some descendant node(s).
3. Shared-intention-exclusive (SIX) indicates that the current node is locked in shared mode but that one or more exclusive locks will be requested on some descendant node(s).
The compatibility table of the three intention locks, and the shared and exclusive locks, is shown in Figure 22.8. Besides the introduction of the three types of inten- tion locks, an appropriate locking protocol must be used. The multiple granularity locking (MGL) protocol consists of the following rules:
1. The lock compatibility (based on Figure 22.8) must be adhered to.
2. The root of the tree must be locked first, in any mode.
3. A node N can be locked by a transaction T in S or IS mode only if the parent node N is already locked by transaction T in either IS or IX mode.
4. A node N can be locked by a transaction T in X, IX, or SIX mode only if the parent of node N is already locked by transaction T in either IX or SIX mode.
5. A transaction T can lock a node only if it has not unlocked any node (to enforce the 2PL protocol).
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IS
IX
S
SIX
X
IS
Yes
Yes
Yes
Yes
No
IX
Yes
No
Yes
No
No
S
No
Yes
Yes
No
No
SIX
No
No
Yes
No
No
X
No
No
No
No
No Figure 22.8 Lock compatibility matrix for multiple granularity locking.
6. A transaction T can unlock a node, N, only if none of the children of node N are currently locked by T.
Rule 1 simply states that conflicting locks cannot be granted. Rules 2, 3, and 4 state the conditions when a transaction may lock a given node in any of the lock modes. Rules 5 and 6 of the MGL protocol enforce 2PL rules to produce serializable sched- ules. To illustrate the MGL protocol with the database hierarchy in Figure 22.7, con- sider the following three transactions:
1. T1 wants to update record r111 and record r211.
2. T2 wants to update all records on page p12.
3. T3 wants to read record r11j and the entire f2 file.
Figure 22.9 shows a possible serializable schedule for these three transactions. Only the lock and unlock operations are shown. The notation <lock_type>(<item>) is used to display the locking operations in the schedule.
The multiple granularity level protocol is especially suited when processing a mix of transactions that include (1) short transactions that access only a few items (records or fields) and (2) long transactions that access entire files. In this environment, less transaction blocking and less locking overhead is incurred by such a protocol when compared to a single level granularity locking approach.
22.6 Using Locks for Concurrency Control in Indexes
Two-phase locking can also be applied to indexes (see Chapter 18), where the nodes of an index correspond to disk pages. However, holding locks on index pages until the shrinking phase of 2PL could cause an undue amount of transaction blocking because searching an index always starts at the root. Therefore, if a transaction wants to insert a record (write operation), the root would be locked in exclusive mode, so all other conflicting lock requests for the index must wait until the transaction enters its shrinking phase. This blocks all other transactions from accessing the index, so in practice other approaches to locking an index must be used.
22.6 Using Locks for Concurrency Control in Indexes 799
IX(db) IX(f1)
T1
IX(p11) X(r111)
IX(f2) IX(p21) X(p211)
unlock(r211) unlock(p21) unlock(f2)
unlock(r111) unlock(p11) unlock(f1) unlock(db)
T3
IS(db) IS(f1) IS(p11)
S(r11j)
S(f2)
unlock(r11j) unlock(p11) unlock(f1) unlock(f2) unlock(db)
IX(db)
T2
IX(f1) X(p12)
unlock(p12) unlock(f1) unlock(db)
Figure 22.9 Lock operations to illustrate a serializable schedule.
The tree structure of the index can be taken advantage of when developing a con- currency control scheme. For example, when an index search (read operation) is being executed, a path in the tree is traversed from the root to a leaf. Once a lower- level node in the path has been accessed, the higher-level nodes in that path will not be used again. So once a read lock on a child node is obtained, the lock on the par- ent can be released. When an insertion is being applied to a leaf node (that is, when a key and a pointer are inserted), then a specific leaf node must be locked in exclu- sive mode. However, if that node is not full, the insertion will not cause changes to higher-level index nodes, which implies that they need not be locked exclusively.
A conservative approach for insertions would be to lock the root node in exclusive mode and then to access the appropriate child node of the root. If the child node is
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not full, then the lock on the root node can be released. This approach can be applied all the way down the tree to the leaf, which is typically three or four levels from the root. Although exclusive locks are held, they are soon released. An alterna- tive, more optimistic approach would be to request and hold shared locks on the nodes leading to the leaf node, with an exclusive lock on the leaf. If the insertion causes the leaf to split, insertion will propagate to one or more higher-level nodes. Then, the locks on the higher-level nodes can be upgraded to exclusive mode.
Another approach to index locking is to use a variant of the B+-tree, called the B- link tree. In a B-link tree, sibling nodes on the same level are linked at every level. This allows shared locks to be used when requesting a page and requires that the lock be released before accessing the child node. For an insert operation, the shared lock on a node would be upgraded to exclusive mode. If a split occurs, the parent node must be relocked in exclusive mode. One complication is for search operations executed concurrently with the update. Suppose that a concurrent update operation follows the same path as the search, and inserts a new entry into the leaf node. Additionally, suppose that the insert causes that leaf node to split. When the insert is done, the search process resumes, following the pointer to the desired leaf, only to find that the key it is looking for is not present because the split has moved that key into a new leaf node, which would be the right sibling of the original leaf node. However, the search process can still succeed if it follows the pointer (link) in the original leaf node to its right sibling, where the desired key has been moved.
Handling the deletion case, where two or more nodes from the index tree merge, is also part of the B-link tree concurrency protocol. In this case, locks on the nodes to be merged are held as well as a lock on the parent of the two nodes to be merged.
22.7 Other Concurrency Control Issues In this section we discuss some other issues relevant to concurrency control. In Section 22.7.1, we discuss problems associated with insertion and deletion of records and the so-called phantom problem, which may occur when records are inserted. This problem was described as a potential problem requiring a concur- rency control measure in Section 21.6. In Section 22.7.2 we discuss problems that may occur when a transaction outputs some data to a monitor before it commits, and then the transaction is later aborted.
22.7.1 Insertion, Deletion, and Phantom Records When a new data item is inserted in the database, it obviously cannot be accessed until after the item is created and the insert operation is completed. In a locking environment, a lock for the item can be created and set to exclusive (write) mode; the lock can be released at the same time as other write locks would be released, based on the concurrency control protocol being used. For a timestamp-based pro- tocol, the read and write timestamps of the new item are set to the timestamp of the creating transaction.
22.7 Other Concurrency Control Issues 801
Next, consider a deletion operation that is applied on an existing data item. For locking protocols, again an exclusive (write) lock must be obtained before the trans- action can delete the item. For timestamp ordering, the protocol must ensure that no later transaction has read or written the item before allowing the item to be deleted.
A situation known as the phantom problem can occur when a new record that is being inserted by some transaction T satisfies a condition that a set of records accessed by another transaction T� must satisfy. For example, suppose that transac- tion T is inserting a new EMPLOYEE record whose Dno = 5, while transaction T� is accessing all EMPLOYEE records whose Dno = 5 (say, to add up all their Salary values to calculate the personnel budget for department 5). If the equivalent serial order is T followed by T�, then T� must read the new EMPLOYEE record and include its Salary in the sum calculation. For the equivalent serial order T� followed by T, the new salary should not be included. Notice that although the transactions logically con- flict, in the latter case there is really no record (data item) in common between the two transactions, since T� may have locked all the records with Dno = 5 before T inserted the new record. This is because the record that causes the conflict is a phantom record that has suddenly appeared in the database on being inserted. If other operations in the two transactions conflict, the conflict due to the phantom record may not be recognized by the concurrency control protocol.
One solution to the phantom record problem is to use index locking, as discussed in Section 22.6. Recall from Chapter 18 that an index includes entries that have an attribute value, plus a set of pointers to all records in the file with that value. For example, an index on Dno of EMPLOYEE would include an entry for each distinct Dno value, plus a set of pointers to all EMPLOYEE records with that value. If the index entry is locked before the record itself can be accessed, then the conflict on the phantom record can be detected, because transaction T� would request a read lock on the index entry for Dno = 5, and T would request a write lock on the same entry before they could place the locks on the actual records. Since the index locks conflict, the phantom conflict would be detected.
A more general technique, called predicate locking, would lock access to all records that satisfy an arbitrary predicate (condition) in a similar manner; however, predi- cate locks have proved to be difficult to implement efficiently.
22.7.2 Interactive Transactions Another problem occurs when interactive transactions read input and write output to an interactive device, such as a monitor screen, before they are committed. The problem is that a user can input a value of a data item to a transaction T that is based on some value written to the screen by transaction T�, which may not have committed. This dependency between T and T� cannot be modeled by the system concurrency control method, since it is only based on the user interacting with the two transactions.
An approach to dealing with this problem is to postpone output of transactions to the screen until they have committed.
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22.7.3 Latches Locks held for a short duration are typically called latches. Latches do not follow the usual concurrency control protocol such as two-phase locking. For example, a latch can be used to guarantee the physical integrity of a page when that page is being written from the buffer to disk. A latch would be acquired for the page, the page written to disk, and then the latch released.
22.8 Summary In this chapter we discussed DBMS techniques for concurrency control. We started by discussing lock-based protocols, which are by far the most commonly used in practice. We described the two-phase locking (2PL) protocol and a number of its variations: basic 2PL, strict 2PL, conservative 2PL, and rigorous 2PL. The strict and rigorous variations are more common because of their better recoverability proper- ties. We introduced the concepts of shared (read) and exclusive (write) locks, and showed how locking can guarantee serializability when used in conjunction with the two-phase locking rule. We also presented various techniques for dealing with the deadlock problem, which can occur with locking. In practice, it is common to use timeouts and deadlock detection (wait-for graphs).
We presented other concurrency control protocols that are not used often in prac- tice but are important for the theoretical alternatives they show for solving this problem. These include the timestamp ordering protocol, which ensures serializ- ability based on the order of transaction timestamps. Timestamps are unique, system-generated transaction identifiers. We discussed Thomas’s write rule, which improves performance but does not guarantee conflict serializability. The strict timestamp ordering protocol was also presented. We discussed two multiversion protocols, which assume that older versions of data items can be kept in the data- base. One technique, called multiversion two-phase locking (which has been used in practice), assumes that two versions can exist for an item and attempts to increase concurrency by making write and read locks compatible (at the cost of introducing an additional certify lock mode). We also presented a multiversion protocol based on timestamp ordering, and an example of an optimistic protocol, which is also known as a certification or validation protocol.
Then we turned our attention to the important practical issue of data item granu- larity. We described a multigranularity locking protocol that allows the change of granularity (item size) based on the current transaction mix, with the goal of improving the performance of concurrency control. An important practical issue was then presented, which is to develop locking protocols for indexes so that indexes do not become a hindrance to concurrent access. Finally, we introduced the phan- tom problem and problems with interactive transactions, and briefly described the concept of latches and how it differs from locks.
Review Questions 803
Review Questions 22.1. What is the two-phase locking protocol? How does it guarantee serializabil-
ity?
22.2. What are some variations of the two-phase locking protocol? Why is strict or rigorous two-phase locking often preferred?
22.3. Discuss the problems of deadlock and starvation, and the different approaches to dealing with these problems.
22.4. Compare binary locks to exclusive/shared locks. Why is the latter type of locks preferable?
22.5. Describe the wait-die and wound-wait protocols for deadlock prevention.
22.6. Describe the cautious waiting, no waiting, and timeout protocols for dead- lock prevention.
22.7. What is a timestamp? How does the system generate timestamps?
22.8. Discuss the timestamp ordering protocol for concurrency control. How does strict timestamp ordering differ from basic timestamp ordering?
22.9. Discuss two multiversion techniques for concurrency control.
22.10. What is a certify lock? What are the advantages and disadvantages of using certify locks?
22.11. How do optimistic concurrency control techniques differ from other con- currency control techniques? Why are they also called validation or certifica- tion techniques? Discuss the typical phases of an optimistic concurrency control method.
22.12. How does the granularity of data items affect the performance of concur- rency control? What factors affect selection of granularity size for data items?
22.13. What type of lock is needed for insert and delete operations?
22.14. What is multiple granularity locking? Under what circumstances is it used?
22.15. What are intention locks?
22.16. When are latches used?
22.17. What is a phantom record? Discuss the problem that a phantom record can cause for concurrency control.
22.18. How does index locking resolve the phantom problem?
22.19. What is a predicate lock?
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Exercises 22.20. Prove that the basic two-phase locking protocol guarantees conflict serializ-
ability of schedules. (Hint: Show that if a serializability graph for a schedule has a cycle, then at least one of the transactions participating in the schedule does not obey the two-phase locking protocol.)
22.21. Modify the data structures for multiple-mode locks and the algorithms for read_lock(X), write_lock(X), and unlock(X) so that upgrading and downgrad- ing of locks are possible. (Hint: The lock needs to check the transaction id(s) that hold the lock, if any.)
22.22. Prove that strict two-phase locking guarantees strict schedules.
22.23. Prove that the wait-die and wound-wait protocols avoid deadlock and star- vation.
22.24. Prove that cautious waiting avoids deadlock.
22.25. Apply the timestamp ordering algorithm to the schedules in Figure 21.8(b) and (c), and determine whether the algorithm will allow the execution of the schedules.
22.26. Repeat Exercise 22.25, but use the multiversion timestamp ordering method.
22.27. Why is two-phase locking not used as a concurrency control method for indexes such as B+-trees?
22.28. The compatibility matrix in Figure 22.8 shows that IS and IX locks are com- patible. Explain why this is valid.
22.29. The MGL protocol states that a transaction T can unlock a node N, only if none of the children of node N are still locked by transaction T. Show that without this condition, the MGL protocol would be incorrect.
Selected Bibliography The two-phase locking protocol and the concept of predicate locks were first pro- posed by Eswaran et al. (1976). Bernstein et al. (1987), Gray and Reuter (1993), and Papadimitriou (1986) focus on concurrency control and recovery. Kumar (1996) focuses on performance of concurrency control methods. Locking is discussed in Gray et al. (1975), Lien and Weinberger (1978), Kedem and Silbershatz (1980), and Korth (1983). Deadlocks and wait-for graphs were formalized by Holt (1972), and the wait-wound and wound-die schemes are presented in Rosenkrantz et al. (1978). Cautious waiting is discussed in Hsu and Zhang (1992). Helal et al. (1993) com- pares various locking approaches. Timestamp-based concurrency control tech- niques are discussed in Bernstein and Goodman (1980) and Reed (1983). Optimistic concurrency control is discussed in Kung and Robinson (1981) and Bassiouni (1988). Papadimitriou and Kanellakis (1979) and Bernstein and
Selected Bibliography 805
Goodman (1983) discuss multiversion techniques. Multiversion timestamp order- ing was proposed in Reed (1979, 1983), and multiversion two-phase locking is dis- cussed in Lai and Wilkinson (1984). A method for multiple locking granularities was proposed in Gray et al. (1975), and the effects of locking granularities are ana- lyzed in Ries and Stonebraker (1977). Bhargava and Reidl (1988) presents an approach for dynamically choosing among various concurrency control and recov- ery methods. Concurrency control methods for indexes are presented in Lehman and Yao (1981) and in Shasha and Goodman (1988). A performance study of vari- ous B+-tree concurrency control algorithms is presented in Srinivasan and Carey (1991).
Other work on concurrency control includes semantic-based concurrency control (Badrinath and Ramamritham, 1992), transaction models for long-running activi- ties (Dayal et al., 1991), and multilevel transaction management (Hasse and Weikum, 1991).
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807
Database Recovery Techniques
In this chapter we discuss some of the techniques thatcan be used for database recovery from failures. In Section 21.1.4 we discussed the different causes of failure, such as system crashes and transaction errors. Also, in Section 21.2, we covered many of the concepts that are used by recovery processes, such as the system log and commit points.
This chapter presents additional concepts that are relevant to recovery protocols, and provides an overview of the various database recovery algorithms We start in Section 23.1 with an outline of a typical recovery procedure and a categorization of recovery algorithms, and then we discuss several recovery concepts, including write- ahead logging, in-place versus shadow updates, and the process of rolling back (undoing) the effect of an incomplete or failed transaction. In Section 23.2 we pre- sent recovery techniques based on deferred update, also known as the NO- UNDO/REDO technique, where the data on disk is not updated until after a transaction commits. In Section 23.3 we discuss recovery techniques based on immediate update, where data can be updated on disk during transaction execution; these include the UNDO/REDO and UNDO/NO-REDO algorithms. We discuss the technique known as shadowing or shadow paging, which can be categorized as a NO-UNDO/NO-REDO algorithm in Section 23.4. An example of a practical DBMS recovery scheme, called ARIES, is presented in Section 23.5. Recovery in multidata- bases is briefly discussed in Section 23.6. Finally, techniques for recovery from cata- strophic failure are discussed in Section 23.7. Section 23.8 summarizes the chapter.
Our emphasis is on conceptually describing several different approaches to recov- ery. For descriptions of recovery features in specific systems, the reader should con- sult the bibliographic notes at the end of the chapter and the online and printed user manuals for those systems. Recovery techniques are often intertwined with the
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concurrency control mechanisms. Certain recovery techniques are best used with specific concurrency control methods. We will discuss recovery concepts indepen- dently of concurrency control mechanisms, but we will discuss the circumstances under which a particular recovery mechanism is best used with a certain concur- rency control protocol.
23.1 Recovery Concepts
23.1.1 Recovery Outline and Categorization of Recovery Algorithms
Recovery from transaction failures usually means that the database is restored to the most recent consistent state just before the time of failure. To do this, the system must keep information about the changes that were applied to data items by the various transactions. This information is typically kept in the system log, as we dis- cussed in Section 21.2.2. A typical strategy for recovery may be summarized infor- mally as follows:
1. If there is extensive damage to a wide portion of the database due to cata- strophic failure, such as a disk crash, the recovery method restores a past copy of the database that was backed up to archival storage (typically tape or other large capacity offline storage media) and reconstructs a more current state by reapplying or redoing the operations of committed transactions from the backed up log, up to the time of failure.
2. When the database on disk is not physically damaged, and a noncatastrophic failure of types 1 through 4 in Section 21.1.4 has occurred, the recovery strategy is to identify any changes that may cause an inconsistency in the database. For example, a transaction that has updated some database items on disk but has not been committed needs to have its changes reversed by undoing its write operations. It may also be necessary to redo some opera- tions in order to restore a consistent state of the database; for example, if a transaction has committed but some of its write operations have not yet been written to disk. For noncatastrophic failure, the recovery protocol does not need a complete archival copy of the database. Rather, the entries kept in the online system log on disk are analyzed to determine the appropriate actions for recovery.
Conceptually, we can distinguish two main techniques for recovery from noncata- strophic transaction failures: deferred update and immediate update. The deferred update techniques do not physically update the database on disk until after a trans- action reaches its commit point; then the updates are recorded in the database. Before reaching commit, all transaction updates are recorded in the local transac- tion workspace or in the main memory buffers that the DBMS maintains (the DBMS main memory cache). Before commit, the updates are recorded persistently in the log, and then after commit, the updates are written to the database on disk. If a transaction fails before reaching its commit point, it will not have changed the
23.1 Recovery Concepts 809
database in any way, so UNDO is not needed. It may be necessary to REDO the effect of the operations of a committed transaction from the log, because their effect may not yet have been recorded in the database on disk. Hence, deferred update is also known as the NO-UNDO/REDO algorithm. We discuss this tech- nique in Section 23.2.
In the immediate update techniques, the database may be updated by some opera- tions of a transaction before the transaction reaches its commit point. However, these operations must also be recorded in the log on disk by force-writing before they are applied to the database on disk, making recovery still possible. If a transaction fails after recording some changes in the database on disk but before reaching its commit point, the effect of its operations on the database must be undone; that is, the transaction must be rolled back. In the general case of immediate update, both undo and redo may be required during recovery. This technique, known as the UNDO/REDO algorithm, requires both operations during recovery, and is used most often in practice. A variation of the algorithm where all updates are required to be recorded in the database on disk before a transaction commits requires undo only, so it is known as the UNDO/NO-REDO algorithm. We discuss these techniques in Section 23.3.
The UNDO and REDO operations are required to be idempotent—that is, executing an operation multiple times is equivalent to executing it just once. In fact, the whole recovery process should be idempotent because if the system were to fail during the recovery process, the next recovery attempt might UNDO and REDO certain write_item operations that had already been executed during the first recovery process. The result of recovery from a system crash during recovery should be the same as the result of recovering when there is no crash during recovery!
23.1.2 Caching (Buffering) of Disk Blocks The recovery process is often closely intertwined with operating system functions— in particular, the buffering of database disk pages in the DBMS main memory cache. Typically, multiple disk pages that include the data items to be updated are cached into main memory buffers and then updated in memory before being writ- ten back to disk. The caching of disk pages is traditionally an operating system func- tion, but because of its importance to the efficiency of recovery procedures, it is handled by the DBMS by calling low-level operating systems routines.
In general, it is convenient to consider recovery in terms of the database disk pages (blocks). Typically a collection of in-memory buffers, called the DBMS cache, is kept under the control of the DBMS for the purpose of holding these buffers. A directory for the cache is used to keep track of which database items are in the buffers.1 This can be a table of <Disk_page_address, Buffer_location, ... > entries. When the DBMS requests action on some item, first it checks the cache directory to determine whether the disk page containing the item is in the DBMS cache. If it is
1This is somewhat similar to the concept of page tables used by the operating system.
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not, the item must be located on disk, and the appropriate disk pages are copied into the cache. It may be necessary to replace (or flush) some of the cache buffers to make space available for the new item. Some page replacement strategy similar to these used in operating systems, such as least recently used (LRU) or first-in-first- out (FIFO), or a new strategy that is DBMS-specific can be used to select the buffers for replacement, such as DBMIN or Least-Likely-to-Use (see bibliographic notes).
The entries in the DBMS cache directory hold additional information relevant to buffer management. Associated with each buffer in the cache is a dirty bit, which can be included in the directory entry, to indicate whether or not the buffer has been modified. When a page is first read from the database disk into a cache buffer, a new entry is inserted in the cache directory with the new disk page address, and the dirty bit is set to 0 (zero). As soon as the buffer is modified, the dirty bit for the corresponding directory entry is set to 1 (one). Additional information, such as the transaction id(s) of the transaction(s) that modified the buffer can also be kept in the directory. When the buffer contents are replaced (flushed) from the cache, the contents must first be written back to the corresponding disk page only if its dirty bit is 1. Another bit, called the pin-unpin bit, is also needed—a page in the cache is pinned (bit value 1 (one)) if it cannot be written back to disk as yet. For example, the recovery protocol may restrict certain buffer pages from being written back to the disk until the transactions that changed this buffer have committed.
Two main strategies can be employed when flushing a modified buffer back to disk. The first strategy, known as in-place updating, writes the buffer to the same original disk location, thus overwriting the old value of any changed data items on disk.2
Hence, a single copy of each database disk block is maintained. The second strategy, known as shadowing, writes an updated buffer at a different disk location, so mul- tiple versions of data items can be maintained, but this approach is not typically used in practice.
In general, the old value of the data item before updating is called the before image (BFIM), and the new value after updating is called the after image (AFIM). If shad- owing is used, both the BFIM and the AFIM can be kept on disk; hence, it is not strictly necessary to maintain a log for recovering. We briefly discuss recovery based on shadowing in Section 23.4.
23.1.3 Write-Ahead Logging, Steal/No-Steal, and Force/No-Force
When in-place updating is used, it is necessary to use a log for recovery (see Section 21.2.2). In this case, the recovery mechanism must ensure that the BFIM of the data item is recorded in the appropriate log entry and that the log entry is flushed to disk before the BFIM is overwritten with the AFIM in the database on disk. This process is generally known as write-ahead logging, and is necessary to be able to UNDO the operation if this is required during recovery. Before we can describe a protocol for
2In-place updating is used in most systems in practice.
23.1 Recovery Concepts 811
write-ahead logging, we need to distinguish between two types of log entry infor- mation included for a write command: the information needed for UNDO and the information needed for REDO. A REDO-type log entry includes the new value (AFIM) of the item written by the operation since this is needed to redo the effect of the operation from the log (by setting the item value in the database on disk to its AFIM). The UNDO-type log entries include the old value (BFIM) of the item since this is needed to undo the effect of the operation from the log (by setting the item value in the database back to its BFIM). In an UNDO/REDO algorithm, both types of log entries are combined. Additionally, when cascading rollback is possible, read_item entries in the log are considered to be UNDO-type entries (see Section 23.1.5).
As mentioned, the DBMS cache holds the cached database disk blocks in main memory buffers, which include not only data blocks, but also index blocks and log blocks from the disk. When a log record is written, it is stored in the current log buffer in the DBMS cache. The log is simply a sequential (append-only) disk file, and the DBMS cache may contain several log blocks in main memory buffers (typi- cally, the last n log blocks of the log file). When an update to a data block—stored in the DBMS cache—is made, an associated log record is written to the last log buffer in the DBMS cache. With the write-ahead logging approach, the log buffers (blocks) that contain the associated log records for a particular data block update must first be written to disk before the data block itself can be written back to disk from its main memory buffer.
Standard DBMS recovery terminology includes the terms steal/no-steal and force/no-force, which specify the rules that govern when a page from the database can be written to disk from the cache:
1. If a cache buffer page updated by a transaction cannot be written to disk before the transaction commits, the recovery method is called a no-steal approach. The pin-unpin bit will be used to indicate if a page cannot be written back to disk. On the other hand, if the recovery protocol allows writ- ing an updated buffer before the transaction commits, it is called steal. Steal is used when the DBMS cache (buffer) manager needs a buffer frame for another transaction and the buffer manager replaces an existing page that had been updated but whose transaction has not committed. The no-steal rule means that UNDO will never be needed during recovery, since a commit- ted transaction will not have any of its updates on disk before it commits.
2. If all pages updated by a transaction are immediately written to disk before the transaction commits, it is called a force approach. Otherwise, it is called no-force. The force rule means that REDO will never be needed during recov- ery, since any committed transaction will have all its updates on disk before it is committed.
The deferred update (NO-UNDO) recovery scheme discussed in Section 23.2 follows a no-steal approach. However, typical database systems employ a steal/no-force strat- egy. The advantage of steal is that it avoids the need for a very large buffer space to store all updated pages in memory. The advantage of no-force is that an updated
812 Chapter 23 Database Recovery Techniques
page of a committed transaction may still be in the buffer when another transaction needs to update it, thus eliminating the I/O cost to write that page multiple times to disk, and possibly to have to read it again from disk. This may provide a substantial saving in the number of disk I/O operations when a specific page is updated heavily by multiple transactions.
To permit recovery when in-place updating is used, the appropriate entries required for recovery must be permanently recorded in the log on disk before changes are applied to the database. For example, consider the following write-ahead logging (WAL) protocol for a recovery algorithm that requires both UNDO and REDO:
1. The before image of an item cannot be overwritten by its after image in the database on disk until all UNDO-type log records for the updating transac- tion—up to this point—have been force-written to disk.
2. The commit operation of a transaction cannot be completed until all the REDO-type and UNDO-type log records for that transaction have been force- written to disk.
To facilitate the recovery process, the DBMS recovery subsystem may need to main- tain a number of lists related to the transactions being processed in the system. These include a list for active transactions that have started but not committed as yet, and it may also include lists of all committed and aborted transactions since the last checkpoint (see the next section). Maintaining these lists makes the recovery process more efficient.
23.1.4 Checkpoints in the System Log and Fuzzy Checkpointing
Another type of entry in the log is called a checkpoint.3 A [checkpoint, list of active transactions] record is written into the log periodically at that point when the system writes out to the database on disk all DBMS buffers that have been modified. As a consequence of this, all transactions that have their [commit, T ] entries in the log before a [checkpoint] entry do not need to have their WRITE operations redone in case of a system crash, since all their updates will be recorded in the database on disk during checkpointing. As part of checkpointing, the list of transaction ids for active transactions at the time of the checkpoint is included in the checkpoint record, so that these transactions can be easily identified during recovery.
The recovery manager of a DBMS must decide at what intervals to take a check- point. The interval may be measured in time—say, every m minutes—or in the number t of committed transactions since the last checkpoint, where the values of m or t are system parameters. Taking a checkpoint consists of the following actions:
1. Suspend execution of transactions temporarily.
2. Force-write all main memory buffers that have been modified to disk.
3The term checkpoint has been used to describe more restrictive situations in some systems, such as DB2. It has also been used in the literature to describe entirely different concepts.
23.1 Recovery Concepts 813
3. Write a [checkpoint] record to the log, and force-write the log to disk.
4. Resume executing transactions.
As a consequence of step 2, a checkpoint record in the log may also include addi- tional information, such as a list of active transaction ids, and the locations (addresses) of the first and most recent (last) records in the log for each active trans- action. This can facilitate undoing transaction operations in the event that a trans- action must be rolled back.
The time needed to force-write all modified memory buffers may delay transaction processing because of step 1. To reduce this delay, it is common to use a technique called fuzzy checkpointing. In this technique, the system can resume transaction processing after a [begin_checkpoint] record is written to the log without having to wait for step 2 to finish. When step 2 is completed, an [end_checkpoint, ...] record is written in the log with the relevant information collected during checkpointing. However, until step 2 is completed, the previous checkpoint record should remain valid. To accomplish this, the system maintains a file on disk that contains a pointer to the valid checkpoint, which continues to point to the previous checkpoint record in the log. Once step 2 is concluded, that pointer is changed to point to the new checkpoint in the log.
23.1.5 Transaction Rollback and Cascading Rollback If a transaction fails for whatever reason after updating the database, but before the transaction commits, it may be necessary to roll back the transaction. If any data item values have been changed by the transaction and written to the database, they must be restored to their previous values (BFIMs). The undo-type log entries are used to restore the old values of data items that must be rolled back.
If a transaction T is rolled back, any transaction S that has, in the interim, read the value of some data item X written by T must also be rolled back. Similarly, once S is rolled back, any transaction R that has read the value of some data item Y written by S must also be rolled back; and so on. This phenomenon is called cascading roll- back, and can occur when the recovery protocol ensures recoverable schedules but does not ensure strict or cascadeless schedules (see Section 21.4.2). Understandably, cascading rollback can be quite complex and time-consuming. That is why almost all recovery mechanisms are designed so that cascading rollback is never required.
Figure 23.1 shows an example where cascading rollback is required. The read and write operations of three individual transactions are shown in Figure 23.1(a). Figure 23.1(b) shows the system log at the point of a system crash for a particular execution schedule of these transactions. The values of data items A, B, C, and D, which are used by the transactions, are shown to the right of the system log entries. We assume that the original item values, shown in the first line, are A = 30, B = 15, C = 40, and D = 20. At the point of system failure, transaction T3 has not reached its conclusion and must be rolled back. The WRITE operations of T3, marked by a single * in Figure 23.1(b), are the T3 operations that are undone during transaction rollback. Figure 23.1(c) graphically shows the operations of the different transactions along the time axis.
814 Chapter 23 Database Recovery Techniques
(a)
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* T3 is rolled back because it did not reach its commit point.
** T2 is rolled back because it reads the value of item B written by T3.
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Figure 23.1 Illustrating cascading rollback (a process that never occurs in strict or cascadeless schedules). (a) The read and write operations of three transactions. (b) System log at point of crash. (c) Operations before the crash.
23.2 NO-UNDO/REDO Recovery Based on Deferred Update 815
We must now check for cascading rollback. From Figure 23.1(c) we see that transac- tion T2 reads the value of item B that was written by transaction T3; this can also be determined by examining the log. Because T3 is rolled back, T2 must now be rolled back, too. The WRITE operations of T2, marked by ** in the log, are the ones that are undone. Note that only write_item operations need to be undone during transaction rollback; read_item operations are recorded in the log only to determine whether cascading rollback of additional transactions is necessary.
In practice, cascading rollback of transactions is never required because practical recovery methods guarantee cascadeless or strict schedules. Hence, there is also no need to record any read_item operations in the log because these are needed only for determining cascading rollback.
23.1.6 Transaction Actions That Do Not Affect the Database
In general, a transaction will have actions that do not affect the database, such as generating and printing messages or reports from information retrieved from the database. If a transaction fails before completion, we may not want the user to get these reports, since the transaction has failed to complete. If such erroneous reports are produced, part of the recovery process would have to inform the user that these reports are wrong, since the user may take an action based on these reports that affects the database. Hence, such reports should be generated only after the transac- tion reaches its commit point. A common method of dealing with such actions is to issue the commands that generate the reports but keep them as batch jobs, which are executed only after the transaction reaches its commit point. If the transaction fails, the batch jobs are canceled.
23.2 NO-UNDO/REDO Recovery Based on Deferred Update
The idea behind deferred update is to defer or postpone any actual updates to the database on disk until the transaction completes its execution successfully and reaches its commit point.4
During transaction execution, the updates are recorded only in the log and in the cache buffers. After the transaction reaches its commit point and the log is force- written to disk, the updates are recorded in the database. If a transaction fails before reaching its commit point, there is no need to undo any operations because the transaction has not affected the database on disk in any way. Therefore, only REDO- type log entries are needed in the log, which include the new value (AFIM) of the item written by a write operation. The UNDO-type log entries are not needed since no undoing of operations will be required during recovery. Although this may sim- plify the recovery process, it cannot be used in practice unless transactions are short
4Hence deferred update can generally be characterized as a no-steal approach.
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and each transaction changes few items. For other types of transactions, there is the potential for running out of buffer space because transaction changes must be held in the cache buffers until the commit point.
We can state a typical deferred update protocol as follows:
1. A transaction cannot change the database on disk until it reaches its commit point.
2. A transaction does not reach its commit point until all its REDO-type log entries are recorded in the log and the log buffer is force-written to disk.
Notice that step 2 of this protocol is a restatement of the write-ahead logging (WAL) protocol. Because the database is never updated on disk until after the transaction commits, there is never a need to UNDO any operations. REDO is needed in case the system fails after a transaction commits but before all its changes are recorded in the database on disk. In this case, the transaction operations are redone from the log entries during recovery.
For multiuser systems with concurrency control, the concurrency control and recovery processes are interrelated. Consider a system in which concurrency control uses strict two-phase locking, so the locks on items remain in effect until the trans- action reaches its commit point. After that, the locks can be released. This ensures strict and serializable schedules. Assuming that [checkpoint] entries are included in the log, a possible recovery algorithm for this case, which we call RDU_M (Recovery using Deferred Update in a Multiuser environment), is given next.
Procedure RDU_M (NO-UNDO/REDO with checkpoints). Use two lists of transactions maintained by the system: the committed transactions T since the last checkpoint (commit list), and the active transactions T� (active list). REDO all the WRITE operations of the committed transactions from the log, in the order in which they were written into the log. The transactions that are active and did not commit are effectively canceled and must be resubmitted.
The REDO procedure is defined as follows:
Procedure REDO (WRITE_OP). Redoing a write_item operation WRITE_OP con- sists of examining its log entry [write_item, T, X, new_value] and setting the value of item X in the database to new_value, which is the after image (AFIM).
Figure 23.2 illustrates a timeline for a possible schedule of executing transactions. When the checkpoint was taken at time t1, transaction T1 had committed, whereas transactions T3 and T4 had not. Before the system crash at time t2, T3 and T2 were committed but not T4 and T5. According to the RDU_M method, there is no need to redo the write_item operations of transaction T1—or any transactions committed before the last checkpoint time t1. The write_item operations of T2 and T3 must be redone, however, because both transactions reached their commit points after the last checkpoint. Recall that the log is force-written before committing a transaction. Transactions T4 and T5 are ignored: They are effectively canceled or rolled back because none of their write_item operations were recorded in the database on disk under the deferred update protocol.
23.3 Recovery Techniques Based on Immediate Update 817
System crash TimeCheckpoint
T2 T1
T3
T5
T4
t1 t2
Figure 23.2 An example of a recovery timeline to illustrate the effect of checkpointing.
We can make the NO-UNDO/REDO recovery algorithm more efficient by noting that, if a data item X has been updated—as indicated in the log entries—more than once by committed transactions since the last checkpoint, it is only necessary to REDO the last update of X from the log during recovery because the other updates would be overwritten by this last REDO. In this case, we start from the end of the log; then, whenever an item is redone, it is added to a list of redone items. Before REDO is applied to an item, the list is checked; if the item appears on the list, it is not redone again, since its last value has already been recovered.
If a transaction is aborted for any reason (say, by the deadlock detection method), it is simply resubmitted, since it has not changed the database on disk. A drawback of the method described here is that it limits the concurrent execution of transactions because all write-locked items remain locked until the transaction reaches its commit point. Additionally, it may require excessive buffer space to hold all updated items until the transactions commit. The method’s main benefit is that transaction oper- ations never need to be undone, for two reasons:
1. A transaction does not record any changes in the database on disk until after it reaches its commit point—that is, until it completes its execution success- fully. Hence, a transaction is never rolled back because of failure during transaction execution.
2. A transaction will never read the value of an item that is written by an uncommitted transaction, because items remain locked until a transaction reaches its commit point. Hence, no cascading rollback will occur.
Figure 23.3 shows an example of recovery for a multiuser system that utilizes the recovery and concurrency control method just described.
23.3 Recovery Techniques Based on Immediate Update
In these techniques, when a transaction issues an update command, the database on disk can be updated immediately, without any need to wait for the transaction to reach its commit point. Notice that it is not a requirement that every update be
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(a) T1 read_item(A) read_item(D)
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T2 and T3 are ignored because they did not reach their commit points.
T4 is redone because its commit point is after the last system checkpoint.
System crash
T2 T3 T4
Figure 23.3 An example of recov- ery using deferred update with concurrent transactions. (a) The READ and WRITE operations of four transactions. (b) System log at the point of crash.
applied immediately to disk; it is just possible that some updates are applied to disk before the transaction commits.
Provisions must be made for undoing the effect of update operations that have been applied to the database by a failed transaction. This is accomplished by rolling back the transaction and undoing the effect of the transaction’s write_item operations. Therefore, the UNDO-type log entries, which include the old value (BFIM) of the item, must be stored in the log. Because UNDO can be needed during recovery, these methods follow a steal strategy for deciding when updated main memory buffers can be written back to disk (see Section 23.1.3). Theoretically, we can distinguish two main categories of immediate update algorithms. If the recovery technique ensures that all updates of a transaction are recorded in the database on disk before the transaction commits, there is never a need to REDO any operations of committed transactions. This is called the UNDO/NO-REDO recovery algorithm. In this method, all updates by a transaction must be recorded on disk before the transaction commits, so that REDO is never needed. Hence, this method must utilize the force
23.3 Recovery Techniques Based on Immediate Update 819
strategy for deciding when updated main memory buffers are written back to disk (see Section 23.1.3).
If the transaction is allowed to commit before all its changes are written to the data- base, we have the most general case, known as the UNDO/REDO recovery algo- rithm. In this case, the steal/no-force strategy is applied (see Section 23.1.3). This is also the most complex technique. We will outline an UNDO/REDO recovery algo- rithm and leave it as an exercise for the reader to develop the UNDO/NO-REDO vari- ation. In Section 23.5, we describe a more practical approach known as the ARIES recovery technique.
When concurrent execution is permitted, the recovery process again depends on the protocols used for concurrency control. The procedure RIU_M (Recovery using Immediate Updates for a Multiuser environment) outlines a recovery algorithm for concurrent transactions with immediate update (UNDO/REDO recovery). Assume that the log includes checkpoints and that the concurrency control protocol pro- duces strict schedules—as, for example, the strict two-phase locking protocol does. Recall that a strict schedule does not allow a transaction to read or write an item unless the transaction that last wrote the item has committed (or aborted and rolled back). However, deadlocks can occur in strict two-phase locking, thus requiring abort and UNDO of transactions. For a strict schedule, UNDO of an operation requires changing the item back to its old value (BFIM).
Procedure RIU_M (UNDO/REDO with checkpoints).
1. Use two lists of transactions maintained by the system: the committed trans- actions since the last checkpoint and the active transactions.
2. Undo all the write_item operations of the active (uncommitted) transactions, using the UNDO procedure. The operations should be undone in the reverse of the order in which they were written into the log.
3. Redo all the write_item operations of the committed transactions from the log, in the order in which they were written into the log, using the REDO proce- dure defined earlier.
The UNDO procedure is defined as follows:
Procedure UNDO (WRITE_OP). Undoing a write_item operation write_op con- sists of examining its log entry [write_item, T, X, old_value, new_value] and set- ting the value of item X in the database to old_value, which is the before image (BFIM). Undoing a number of write_item operations from one or more trans- actions from the log must proceed in the reverse order from the order in which the operations were written in the log.
As we discussed for the NO-UNDO/REDO procedure, step 3 is more efficiently done by starting from the end of the log and redoing only the last update of each item X. Whenever an item is redone, it is added to a list of redone items and is not redone again. A similar procedure can be devised to improve the efficiency of step 2 so that an item can be undone at most once during recovery. In this case, the earliest UNDO is applied first by scanning the log in the forward direction (starting from the
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Current directory (after updating pages 2, 5)
Database disk blocks (pages)
Shadow directory (not updated)
Page 5 (old)
Page 1
Page 4
Page 2 (old)
Page 3
Page 6
Page 2 (new)
Page 5 (new)
1
2
3
4
5
6
1
2
3
4
5
6
Figure 23.4 An example of shadow paging.
beginning of the log). Whenever an item is undone, it is added to a list of undone items and is not undone again.
23.4 Shadow Paging This recovery scheme does not require the use of a log in a single-user environment. In a multiuser environment, a log may be needed for the concurrency control method. Shadow paging considers the database to be made up of a number of fixed- size disk pages (or disk blocks)—say, n—for recovery purposes. A directory with n entries5 is constructed, where the ith entry points to the ith database page on disk. The directory is kept in main memory if it is not too large, and all references—reads or writes—to database pages on disk go through it. When a transaction begins exe- cuting, the current directory—whose entries point to the most recent or current database pages on disk—is copied into a shadow directory. The shadow directory is then saved on disk while the current directory is used by the transaction.
During transaction execution, the shadow directory is never modified. When a write_item operation is performed, a new copy of the modified database page is cre- ated, but the old copy of that page is not overwritten. Instead, the new page is writ- ten elsewhere—on some previously unused disk block. The current directory entry is modified to point to the new disk block, whereas the shadow directory is not modified and continues to point to the old unmodified disk block. Figure 23.4 illus- trates the concepts of shadow and current directories. For pages updated by the transaction, two versions are kept. The old version is referenced by the shadow directory and the new version by the current directory.
5The directory is similar to the page table maintained by the operating system for each process.
23.5 The ARIES Recovery Algorithm 821
To recover from a failure during transaction execution, it is sufficient to free the modified database pages and to discard the current directory. The state of the data- base before transaction execution is available through the shadow directory, and that state is recovered by reinstating the shadow directory. The database thus is returned to its state prior to the transaction that was executing when the crash occurred, and any modified pages are discarded. Committing a transaction corre- sponds to discarding the previous shadow directory. Since recovery involves neither undoing nor redoing data items, this technique can be categorized as a NO- UNDO/NO-REDO technique for recovery.
In a multiuser environment with concurrent transactions, logs and checkpoints must be incorporated into the shadow paging technique. One disadvantage of shadow paging is that the updated database pages change location on disk. This makes it dif- ficult to keep related database pages close together on disk without complex storage management strategies. Furthermore, if the directory is large, the overhead of writ- ing shadow directories to disk as transactions commit is significant. A further com- plication is how to handle garbage collection when a transaction commits. The old pages referenced by the shadow directory that have been updated must be released and added to a list of free pages for future use. These pages are no longer needed after the transaction commits. Another issue is that the operation to migrate between cur- rent and shadow directories must be implemented as an atomic operation.
23.5 The ARIES Recovery Algorithm We now describe the ARIES algorithm as an example of a recovery algorithm used in database systems. It is used in many relational database-related products of IBM. ARIES uses a steal/no-force approach for writing, and it is based on three concepts: write-ahead logging, repeating history during redo, and logging changes during undo. We discussed write-ahead logging in Section 23.1.3. The second concept, repeating history, means that ARIES will retrace all actions of the database system prior to the crash to reconstruct the database state when the crash occurred. Transactions that were uncommitted at the time of the crash (active transactions) are undone. The third concept, logging during undo, will prevent ARIES from repeating the completed undo operations if a failure occurs during recovery, which causes a restart of the recovery process.
The ARIES recovery procedure consists of three main steps: analysis, REDO, and UNDO. The analysis step identifies the dirty (updated) pages in the buffer6 and the set of transactions active at the time of the crash. The appropriate point in the log where the REDO operation should start is also determined. The REDO phase actu- ally reapplies updates from the log to the database. Generally, the REDO operation is applied only to committed transactions. However, this is not the case in ARIES. Certain information in the ARIES log will provide the start point for REDO, from
6The actual buffers may be lost during a crash, since they are in main memory. Additional tables stored in the log during checkpointing (Dirty Page Table, Transaction Table) allows ARIES to identify this infor- mation (as discussed later in this section).
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which REDO operations are applied until the end of the log is reached. Additionally, information stored by ARIES and in the data pages will allow ARIES to determine whether the operation to be redone has actually been applied to the database and therefore does not need to be reapplied. Thus, only the necessary REDO operations are applied during recovery. Finally, during the UNDO phase, the log is scanned backward and the operations of transactions that were active at the time of the crash are undone in reverse order. The information needed for ARIES to accomplish its recovery procedure includes the log, the Transaction Table, and the Dirty Page Table. Additionally, checkpointing is used. These tables are maintained by the trans- action manager and written to the log during checkpointing.
In ARIES, every log record has an associated log sequence number (LSN) that is monotonically increasing and indicates the address of the log record on disk. Each LSN corresponds to a specific change (action) of some transaction. Also, each data page will store the LSN of the latest log record corresponding to a change for that page. A log record is written for any of the following actions: updating a page (write), committing a transaction (commit), aborting a transaction (abort), undoing an update (undo), and ending a transaction (end). The need for including the first three actions in the log has been discussed, but the last two need some explanation. When an update is undone, a compensation log record is written in the log. When a transaction ends, whether by committing or aborting, an end log record is written.
Common fields in all log records include the previous LSN for that transaction, the transaction ID, and the type of log record. The previous LSN is important because it links the log records (in reverse order) for each transaction. For an update (write) action, additional fields in the log record include the page ID for the page that con- tains the item, the length of the updated item, its offset from the beginning of the page, the before image of the item, and its after image.
Besides the log, two tables are needed for efficient recovery: the Transaction Table and the Dirty Page Table, which are maintained by the transaction manager. When a crash occurs, these tables are rebuilt in the analysis phase of recovery. The Transaction Table contains an entry for each active transaction, with information such as the transaction ID, transaction status, and the LSN of the most recent log record for the transaction. The Dirty Page Table contains an entry for each dirty page in the buffer, which includes the page ID and the LSN corresponding to the earliest update to that page.
Checkpointing in ARIES consists of the following: writing a begin_checkpoint record to the log, writing an end_checkpoint record to the log, and writing the LSN of the begin_checkpoint record to a special file. This special file is accessed during recovery to locate the last checkpoint information. With the end_checkpoint record, the con- tents of both the Transaction Table and Dirty Page Table are appended to the end of the log. To reduce the cost, fuzzy checkpointing is used so that the DBMS can con- tinue to execute transactions during checkpointing (see Section 23.1.4). Additionally, the contents of the DBMS cache do not have to be flushed to disk dur- ing checkpoint, since the Transaction Table and Dirty Page Table—which are appended to the log on disk—contain the information needed for recovery. Note
23.5 The ARIES Recovery Algorithm 823
that if a crash occurs during checkpointing, the special file will refer to the previous checkpoint, which is used for recovery.
After a crash, the ARIES recovery manager takes over. Information from the last checkpoint is first accessed through the special file. The analysis phase starts at the begin_checkpoint record and proceeds to the end of the log. When the end_checkpoint record is encountered, the Transaction Table and Dirty Page Table are accessed (recall that these tables were written in the log during checkpointing). During analysis, the log records being analyzed may cause modifications to these two tables. For instance, if an end log record was encountered for a transaction T in the Transaction Table, then the entry for T is deleted from that table. If some other type of log record is encountered for a transaction T�, then an entry for T� is inserted into the Transaction Table, if not already present, and the last LSN field is modified. If the log record corresponds to a change for page P, then an entry would be made for page P (if not present in the table) and the associated LSN field would be modified. When the analysis phase is complete, the necessary information for REDO and UNDO has been compiled in the tables.
The REDO phase follows next. To reduce the amount of unnecessary work, ARIES starts redoing at a point in the log where it knows (for sure) that previous changes to dirty pages have already been applied to the database on disk. It can determine this by finding the smallest LSN, M, of all the dirty pages in the Dirty Page Table, which indicates the log position where ARIES needs to start the REDO phase. Any changes corresponding to an LSN < M, for redoable transactions, must have already been propagated to disk or already been overwritten in the buffer; otherwise, those dirty pages with that LSN would be in the buffer (and the Dirty Page Table). So, REDO starts at the log record with LSN = M and scans forward to the end of the log. For each change recorded in the log, the REDO algorithm would verify whether or not the change has to be reapplied. For example, if a change recorded in the log pertains to page P that is not in the Dirty Page Table, then this change is already on disk and does not need to be reapplied. Or, if a change recorded in the log (with LSN = N, say) pertains to page P and the Dirty Page Table contains an entry for P with LSN greater than N, then the change is already present. If neither of these two conditions hold, page P is read from disk and the LSN stored on that page, LSN(P), is compared with N. If N < LSN(P), then the change has been applied and the page does not need to be rewritten to disk.
Once the REDO phase is finished, the database is in the exact state that it was in when the crash occurred. The set of active transactions—called the undo_set—has been identified in the Transaction Table during the analysis phase. Now, the UNDO phase proceeds by scanning backward from the end of the log and undoing the appropriate actions. A compensating log record is written for each action that is undone. The UNDO reads backward in the log until every action of the set of trans- actions in the undo_set has been undone. When this is completed, the recovery process is finished and normal processing can begin again.
Consider the recovery example shown in Figure 23.5. There are three transactions: T1, T2, and T3. T1 updates page C, T2 updates pages B and C, and T3 updates page A.
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TRANSACTION TABLE
Last_lsn Status(b)
(c)
(a) Lsn
1
Last_lsn Tran_id Type Page_id Other_information
Transaction_id
TRANSACTION TABLE DIRTY PAGE TABLE
Transaction_id
T1 3
Last_lsn
commit
Status Page_id
C
Lsn
1
T3
T2 8
6 in progress
commit
A
B
6
2
T2
T1
DIRTY PAGE TABLE
Page_id
C
Lsn
1
B 22
3 commit
in progress
8
7
6
5
4
3
2
0
7
2
0
end checkpoint
begin checkpoint
1
0
T1 T2 T1
T3 T2 T2
update
commit
update
update
commit
update B
C
A
C . . .
. . .
. . .
. . .
. . .
. . .
Figure 23.5 An example of recovery in ARIES. (a) The log at point of crash. (b) The Transaction and Dirty Page Tables at time of checkpoint. (c) The Transaction and Dirty Page Tables after the analysis phase.
Figure 23.5(a) shows the partial contents of the log, and Figure 23.5(b) shows the contents of the Transaction Table and Dirty Page Table. Now, suppose that a crash occurs at this point. Since a checkpoint has occurred, the address of the associated begin_checkpoint record is retrieved, which is location 4. The analysis phase starts from location 4 until it reaches the end. The end_checkpoint record would contain the Transaction Table and Dirty Page Table in Figure 23.5(b), and the analysis phase will further reconstruct these tables. When the analysis phase encounters log record 6, a new entry for transaction T3 is made in the Transaction Table and a new entry for page A is made in the Dirty Page Table. After log record 8 is analyzed, the status of transaction T2 is changed to committed in the Transaction Table. Figure 23.5(c) shows the two tables after the analysis phase.
23.6 Recovery in Multidatabase Systems 825
For the REDO phase, the smallest LSN in the Dirty Page Table is 1. Hence the REDO will start at log record 1 and proceed with the REDO of updates. The LSNs {1, 2, 6, 7} corresponding to the updates for pages C, B, A, and C, respectively, are not less than the LSNs of those pages (as shown in the Dirty Page Table). So those data pages will be read again and the updates reapplied from the log (assuming the actual LSNs stored on those data pages are less then the corresponding log entry). At this point, the REDO phase is finished and the UNDO phase starts. From the Transaction Table (Figure 23.5(c)), UNDO is applied only to the active transaction T3. The UNDO phase starts at log entry 6 (the last update for T3) and proceeds backward in the log. The backward chain of updates for transaction T3 (only log record 6 in this exam- ple) is followed and undone.
23.6 Recovery in Multidatabase Systems So far, we have implicitly assumed that a transaction accesses a single database. In some cases, a single transaction, called a multidatabase transaction, may require access to multiple databases. These databases may even be stored on different types of DBMSs; for example, some DBMSs may be relational, whereas others are object- oriented, hierarchical, or network DBMSs. In such a case, each DBMS involved in the multidatabase transaction may have its own recovery technique and transaction manager separate from those of the other DBMSs. This situation is somewhat simi- lar to the case of a distributed database management system (see Chapter 25), where parts of the database reside at different sites that are connected by a communication network.
To maintain the atomicity of a multidatabase transaction, it is necessary to have a two-level recovery mechanism. A global recovery manager, or coordinator, is needed to maintain information needed for recovery, in addition to the local recov- ery managers and the information they maintain (log, tables). The coordinator usu- ally follows a protocol called the two-phase commit protocol, whose two phases can be stated as follows:
■ Phase 1. When all participating databases signal the coordinator that the part of the multidatabase transaction involving each has concluded, the coordinator sends a message prepare for commit to each participant to get ready for committing the transaction. Each participating database receiving that message will force-write all log records and needed information for local recovery to disk and then send a ready to commit or OK signal to the coordinator. If the force-writing to disk fails or the local transaction cannot commit for some reason, the participating database sends a cannot commit or not OK signal to the coordinator. If the coordinator does not receive a reply from the database within a certain time out interval, it assumes a not OK response.
■ Phase 2. If all participating databases reply OK, and the coordinator’s vote is also OK, the transaction is successful, and the coordinator sends a commit signal for the transaction to the participating databases. Because all the local
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effects of the transaction and information needed for local recovery have been recorded in the logs of the participating databases, recovery from fail- ure is now possible. Each participating database completes transaction com- mit by writing a [commit] entry for the transaction in the log and permanently updating the database if needed. On the other hand, if one or more of the participating databases or the coordinator have a not OK response, the transaction has failed, and the coordinator sends a message to roll back or UNDO the local effect of the transaction to each participating database. This is done by undoing the transaction operations, using the log.
The net effect of the two-phase commit protocol is that either all participating data- bases commit the effect of the transaction or none of them do. In case any of the participants—or the coordinator—fails, it is always possible to recover to a state where either the transaction is committed or it is rolled back. A failure during or before Phase 1 usually requires the transaction to be rolled back, whereas a failure during Phase 2 means that a successful transaction can recover and commit.
23.7 Database Backup and Recovery from Catastrophic Failures
So far, all the techniques we have discussed apply to noncatastrophic failures. A key assumption has been that the system log is maintained on the disk and is not lost as a result of the failure. Similarly, the shadow directory must be stored on disk to allow recovery when shadow paging is used. The recovery techniques we have dis- cussed use the entries in the system log or the shadow directory to recover from fail- ure by bringing the database back to a consistent state.
The recovery manager of a DBMS must also be equipped to handle more cata- strophic failures such as disk crashes. The main technique used to handle such crashes is a database backup, in which the whole database and the log are periodi- cally copied onto a cheap storage medium such as magnetic tapes or other large capacity offline storage devices. In case of a catastrophic system failure, the latest backup copy can be reloaded from the tape to the disk, and the system can be restarted.
Data from critical applications such as banking, insurance, stock market, and other databases is periodically backed up in its entirety and moved to physically separate safe locations. Subterranean storage vaults have been used to protect such data from flood, storm, earthquake, or fire damage. Events like the 9/11 terrorist attack in New York (in 2001) and the Katrina hurricane disaster in New Orleans (in 2005) have created a greater awareness of disaster recovery of business-critical databases.
To avoid losing all the effects of transactions that have been executed since the last backup, it is customary to back up the system log at more frequent intervals than full database backup by periodically copying it to magnetic tape. The system log is usually substantially smaller than the database itself and hence can be backed up more frequently. Therefore, users do not lose all transactions they have performed
23.8 Summary 827
since the last database backup. All committed transactions recorded in the portion of the system log that has been backed up to tape can have their effect on the data- base redone. A new log is started after each database backup. Hence, to recover from disk failure, the database is first recreated on disk from its latest backup copy on tape. Following that, the effects of all the committed transactions whose operations have been recorded in the backed-up copies of the system log are reconstructed.
23.8 Summary In this chapter we discussed the techniques for recovery from transaction failures. The main goal of recovery is to ensure the atomicity property of a transaction. If a transaction fails before completing its execution, the recovery mechanism has to make sure that the transaction has no lasting effects on the database. First we gave an informal outline for a recovery process and then we discussed system concepts for recovery. These included a discussion of caching, in-place updating versus shad- owing, before and after images of a data item, UNDO versus REDO recovery opera- tions, steal/no-steal and force/no-force policies, system checkpointing, and the write-ahead logging protocol.
Next we discussed two different approaches to recovery: deferred update and imme- diate update. Deferred update techniques postpone any actual updating of the data- base on disk until a transaction reaches its commit point. The transaction force-writes the log to disk before recording the updates in the database. This approach, when used with certain concurrency control methods, is designed never to require transaction rollback, and recovery simply consists of redoing the opera- tions of transactions committed after the last checkpoint from the log. The disad- vantage is that too much buffer space may be needed, since updates are kept in the buffers and are not applied to disk until a transaction commits. Deferred update can lead to a recovery algorithm known as NO-UNDO/REDO. Immediate update tech- niques may apply changes to the database on disk before the transaction reaches a successful conclusion. Any changes applied to the database must first be recorded in the log and force-written to disk so that these operations can be undone if neces- sary. We also gave an overview of a recovery algorithm for immediate update known as UNDO/REDO. Another algorithm, known as UNDO/NO-REDO, can also be devel- oped for immediate update if all transaction actions are recorded in the database before commit.
We discussed the shadow paging technique for recovery, which keeps track of old database pages by using a shadow directory. This technique, which is classified as NO-UNDO/NO-REDO, does not require a log in single-user systems but still needs the log for multiuser systems. We also presented ARIES, a specific recovery scheme used in many of IBM’s relational database products. Then we discussed the two- phase commit protocol, which is used for recovery from failures involving multi- database transactions. Finally, we discussed recovery from catastrophic failures, which is typically done by backing up the database and the log to tape. The log can be backed up more frequently than the database, and the backup log can be used to redo operations starting from the last database backup.
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Review Questions 23.1. Discuss the different types of transaction failures. What is meant by cata-
strophic failure?
23.2. Discuss the actions taken by the read_item and write_item operations on a database.
23.3. What is the system log used for? What are the typical kinds of entries in a system log? What are checkpoints, and why are they important? What are transaction commit points, and why are they important?
23.4. How are buffering and caching techniques used by the recovery subsystem?
23.5. What are the before image (BFIM) and after image (AFIM) of a data item? What is the difference between in-place updating and shadowing, with respect to their handling of BFIM and AFIM?
23.6. What are UNDO-type and REDO-type log entries?
23.7. Describe the write-ahead logging protocol.
23.8. Identify three typical lists of transactions that are maintained by the recovery subsystem.
23.9. What is meant by transaction rollback? What is meant by cascading rollback? Why do practical recovery methods use protocols that do not permit cascad- ing rollback? Which recovery techniques do not require any rollback?
23.10. Discuss the UNDO and REDO operations and the recovery techniques that use each.
23.11. Discuss the deferred update technique of recovery. What are the advantages and disadvantages of this technique? Why is it called the NO-UNDO/REDO method?
23.12. How can recovery handle transaction operations that do not affect the data- base, such as the printing of reports by a transaction?
23.13. Discuss the immediate update recovery technique in both single-user and multiuser environments. What are the advantages and disadvantages of immediate update?
23.14. What is the difference between the UNDO/REDO and the UNDO/NO-REDO algorithms for recovery with immediate update? Develop the outline for an UNDO/NO-REDO algorithm.
23.15. Describe the shadow paging recovery technique. Under what circumstances does it not require a log?
23.16. Describe the three phases of the ARIES recovery method.
23.17. What are log sequence numbers (LSNs) in ARIES? How are they used? What information do the Dirty Page Table and Transaction Table contain? Describe how fuzzy checkpointing is used in ARIES.
Exercises 829
[checkpoint]
[start_transaction, T1]
[start_transaction, T2]
[start_transaction, T3]
[read_item, T1, A]
[read_item, T1, D]
[read_item, T4, D]
[read_item, T2, D]
[read_item, T2, B]
[write_item, T1, D, 20, 25]
[write_item, T2, B, 12, 18]
[read_item, T4, A]
[write_item, T4, D, 25, 15]
[write_item, T3, C, 30, 40]
[write_item, T2, D, 15, 25]
[write_item, T4, A, 30, 20]
[commit, T1]
[commit, T4]
[start_transaction, T4]
System crash
Figure 23.6 A sample schedule and its corresponding log.
23.18. What do the terms steal/no-steal and force/no-force mean with regard to buffer management for transaction processing?
23.19. Describe the two-phase commit protocol for multidatabase transactions.
23.20. Discuss how disaster recovery from catastrophic failures is handled.
Exercises 23.21. Suppose that the system crashes before the [read_item, T3, A] entry is written
to the log in Figure 23.1(b). Will that make any difference in the recovery process?
23.22. Suppose that the system crashes before the [write_item, T2, D, 25, 26] entry is written to the log in Figure 23.1(b). Will that make any difference in the recovery process?
23.23. Figure 23.6 shows the log corresponding to a particular schedule at the point of a system crash for four transactions T1, T2, T3, and T4. Suppose that we use the immediate update protocol with checkpointing. Describe the recovery process from the system crash. Specify which transactions are rolled back, which operations in the log are redone and which (if any) are undone, and whether any cascading rollback takes place.
830 Chapter 23 Database Recovery Techniques
23.24. Suppose that we use the deferred update protocol for the example in Figure 23.6. Show how the log would be different in the case of deferred update by removing the unnecessary log entries; then describe the recovery process, using your modified log. Assume that only REDO operations are applied, and specify which operations in the log are redone and which are ignored.
23.25. How does checkpointing in ARIES differ from checkpointing as described in Section 23.1.4?
23.26. How are log sequence numbers used by ARIES to reduce the amount of REDO work needed for recovery? Illustrate with an example using the infor- mation shown in Figure 23.5. You can make your own assumptions as to when a page is written to disk.
23.27. What implications would a no-steal/force buffer management policy have on checkpointing and recovery?
Choose the correct answer for each of the following multiple-choice questions:
23.28. Incremental logging with deferred updates implies that the recovery system must necessarily
a. store the old value of the updated item in the log.
b. store the new value of the updated item in the log.
c. store both the old and new value of the updated item in the log.
d. store only the Begin Transaction and Commit Transaction records in the log.
23.29. The write-ahead logging (WAL) protocol simply means that
a. writing of a data item should be done ahead of any logging operation.
b. the log record for an operation should be written before the actual data is written.
c. all log records should be written before a new transaction begins execu- tion.
d. the log never needs to be written to disk.
23.30. In case of transaction failure under a deferred update incremental logging scheme, which of the following will be needed?
a. an undo operation
b. a redo operation
c. an undo and redo operation
d. none of the above
23.31. For incremental logging with immediate updates, a log record for a transac- tion would contain
a. a transaction name, a data item name, and the old and new value of the item.
Exercises 831
b. a transaction name, a data item name, and the old value of the item.
c. a transaction name, a data item name, and the new value of the item.
d. a transaction name and a data item name.
23.32. For correct behavior during recovery, undo and redo operations must be
a. commutative.
b. associative.
c. idempotent.
d. distributive.
23.33. When a failure occurs, the log is consulted and each operation is either undone or redone. This is a problem because
a. searching the entire log is time consuming.
b. many redos are unnecessary.
c. both (a) and (b).
d. none of the above.
23.34. When using a log-based recovery scheme, it might improve performance as well as providing a recovery mechanism by
a. writing the log records to disk when each transaction commits.
b. writing the appropriate log records to disk during the transaction’s execu- tion.
c. waiting to write the log records until multiple transactions commit and writing them as a batch.
d. never writing the log records to disk.
23.35. There is a possibility of a cascading rollback when
a. a transaction writes items that have been written only by a committed transaction.
b. a transaction writes an item that is previously written by an uncommitted transaction.
c. a transaction reads an item that is previously written by an uncommitted transaction.
d. both (b) and (c).
23.36. To cope with media (disk) failures, it is necessary
a. for the DBMS to only execute transactions in a single user environment.
b. to keep a redundant copy of the database.
c. to never abort a transaction.
d. all of the above.
23.37. If the shadowing approach is used for flushing a data item back to disk, then
a. the item is written to disk only after the transaction commits.
b. the item is written to a different location on disk.
c. the item is written to disk before the transaction commits.
d. the item is written to the same disk location from which it was read.
Selected Bibliography The books by Bernstein et al. (1987) and Papadimitriou (1986) are devoted to the theory and principles of concurrency control and recovery. The book by Gray and Reuter (1993) is an encyclopedic work on concurrency control, recovery, and other transaction-processing issues.
Verhofstad (1978) presents a tutorial and survey of recovery techniques in database systems. Categorizing algorithms based on their UNDO/REDO characteristics is dis- cussed in Haerder and Reuter (1983) and in Bernstein et al. (1983). Gray (1978) dis- cusses recovery, along with other system aspects of implementing operating systems for databases. The shadow paging technique is discussed in Lorie (1977), Verhofstad (1978), and Reuter (1980). Gray et al. (1981) discuss the recovery mechanism in SYSTEM R. Lockemann and Knutsen (1968), Davies (1973), and Bjork (1973) are early papers that discuss recovery. Chandy et al. (1975) discuss transaction rollback. Lilien and Bhargava (1985) discuss the concept of integrity block and its use to improve the efficiency of recovery.
Recovery using write-ahead logging is analyzed in Jhingran and Khedkar (1992) and is used in the ARIES system (Mohan et al. 1992). More recent work on recovery includes compensating transactions (Korth et al. 1990) and main memory database recovery (Kumar 1991). The ARIES recovery algorithms (Mohan et al. 1992) have been quite successful in practice. Franklin et al. (1992) discusses recovery in the EXODUS system. Two books by Kumar and Hsu (1998) and Kumar and Song (1998) discuss recovery in detail and contain descriptions of recovery methods used in a number of existing relational database products. Examples of page replacement strategies that are specific for databases are discussed in Chou and DeWitt (1985) and Pazos et al. (2006).
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part10 Additional Database Topics:
Security and Distribution
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835
Database Security
This chapter discusses techniques for securing data-bases against a variety of threats. It also presents schemes of providing access privileges to authorized users. Some of the security threats to databases—such as SQL Injection—will be presented. At the end of the chapter we also summarize how a commercial RDBMS—specifically, the Oracle sys- tem—provides different types of security. We start in Section 24.1 with an introduc- tion to security issues and the threats to databases, and we give an overview of the control measures that are covered in the rest of this chapter. We also comment on the relationship between data security and privacy as it applies to personal informa- tion. Section 24.2 discusses the mechanisms used to grant and revoke privileges in relational database systems and in SQL, mechanisms that are often referred to as discretionary access control. In Section 24.3, we present an overview of the mech- anisms for enforcing multiple levels of security—a particular concern in database system security that is known as mandatory access control. Section 24.3 also intro- duces the more recently developed strategies of role-based access control, and label-based and row-based security. Section 24.3 also provides a brief discussion of XML access control. Section 24.4 discusses a major threat to databases called SQL Injection, and discusses some of the proposed preventive measures against it. Section 24.5 briefly discusses the security problem in statistical databases. Section 24.6 introduces the topic of flow control and mentions problems associated with covert channels. Section 24.7 provides a brief summary of encryption and symmet- ric key and asymmetric (public) key infrastructure schemes. It also discusses digital certificates. Section 24.8 introduces privacy-preserving techniques, and Section 24.9 presents the current challenges to database security. In Section 24.10, we discuss Oracle label-based security. Finally, Section 24.11 summarizes the chapter. Readers who are interested only in basic database security mechanisms will find it sufficient to cover the material in Sections 24.1 and 24.2.
24chapter 24
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24.1 Introduction to Database Security Issues1
24.1.1 Types of Security Database security is a broad area that addresses many issues, including the following:
■ Various legal and ethical issues regarding the right to access certain informa- tion—for example, some information may be deemed to be private and can- not be accessed legally by unauthorized organizations or persons. In the United States, there are numerous laws governing privacy of information.
■ Policy issues at the governmental, institutional, or corporate level as to what kinds of information should not be made publicly available—for example, credit ratings and personal medical records.
■ System-related issues such as the system levels at which various security func- tions should be enforced—for example, whether a security function should be handled at the physical hardware level, the operating system level, or the DBMS level.
■ The need in some organizations to identify multiple security levels and to categorize the data and users based on these classifications—for example, top secret, secret, confidential, and unclassified. The security policy of the organization with respect to permitting access to various classifications of data must be enforced.
Threats to Databases. Threats to databases can result in the loss or degradation of some or all of the following commonly accepted security goals: integrity, avail- ability, and confidentiality.
■ Loss of integrity. Database integrity refers to the requirement that informa- tion be protected from improper modification. Modification of data includes creation, insertion, updating, changing the status of data, and dele- tion. Integrity is lost if unauthorized changes are made to the data by either intentional or accidental acts. If the loss of system or data integrity is not corrected, continued use of the contaminated system or corrupted data could result in inaccuracy, fraud, or erroneous decisions.
■ Loss of availability. Database availability refers to making objects available to a human user or a program to which they have a legitimate right.
■ Loss of confidentiality. Database confidentiality refers to the protection of data from unauthorized disclosure. The impact of unauthorized disclosure of confidential information can range from violation of the Data Privacy Act to the jeopardization of national security. Unauthorized, unanticipated, or unintentional disclosure could result in loss of public confidence, embar- rassment, or legal action against the organization.
1The substantial contribution of Fariborz Farahmand and Bharath Rengarajan to this and subsequent sections in this chapter is much appreciated.
24.1 Introduction to Database Security Issues 837
To protect databases against these types of threats, it is common to implement four kinds of control measures: access control, inference control, flow control, and encryp- tion. We discuss each of these in this chapter.
In a multiuser database system, the DBMS must provide techniques to enable cer- tain users or user groups to access selected portions of a database without gaining access to the rest of the database. This is particularly important when a large inte- grated database is to be used by many different users within the same organization. For example, sensitive information such as employee salaries or performance reviews should be kept confidential from most of the database system’s users. A DBMS typically includes a database security and authorization subsystem that is responsible for ensuring the security of portions of a database against unauthorized access. It is now customary to refer to two types of database security mechanisms:
■ Discretionary security mechanisms. These are used to grant privileges to users, including the capability to access specific data files, records, or fields in a specified mode (such as read, insert, delete, or update).
■ Mandatory security mechanisms. These are used to enforce multilevel security by classifying the data and users into various security classes (or lev- els) and then implementing the appropriate security policy of the organiza- tion. For example, a typical security policy is to permit users at a certain classification (or clearance) level to see only the data items classified at the user’s own (or lower) classification level. An extension of this is role-based security, which enforces policies and privileges based on the concept of orga- nizational roles.
We discuss discretionary security in Section 24.2 and mandatory and role-based security in Section 24.3.
24.1.2 Control Measures Four main control measures are used to provide security of data in databases:
■ Access control
■ Inference control
■ Flow control
■ Data encryption
A security problem common to computer systems is that of preventing unautho- rized persons from accessing the system itself, either to obtain information or to make malicious changes in a portion of the database. The security mechanism of a DBMS must include provisions for restricting access to the database system as a whole. This function, called access control, is handled by creating user accounts and passwords to control the login process by the DBMS. We discuss access control tech- niques in Section 24.1.3.
Statistical databases are used to provide statistical information or summaries of values based on various criteria. For example, a database for population statistics
838 Chapter 24 Database Security
may provide statistics based on age groups, income levels, household size, education levels, and other criteria. Statistical database users such as government statisticians or market research firms are allowed to access the database to retrieve statistical information about a population but not to access the detailed confidential informa- tion about specific individuals. Security for statistical databases must ensure that information about individuals cannot be accessed. It is sometimes possible to deduce or infer certain facts concerning individuals from queries that involve only summary statistics on groups; consequently, this must not be permitted either. This problem, called statistical database security, is discussed briefly in Section 24.4. The corresponding control measures are called inference control measures.
Another security issue is that of flow control, which prevents information from flowing in such a way that it reaches unauthorized users. It is discussed in Section 24.6. Channels that are pathways for information to flow implicitly in ways that vio- late the security policy of an organization are called covert channels. We briefly dis- cuss some issues related to covert channels in Section 24.6.1.
A final control measure is data encryption, which is used to protect sensitive data (such as credit card numbers) that is transmitted via some type of communications network. Encryption can be used to provide additional protection for sensitive por- tions of a database as well. The data is encoded using some coding algorithm. An unauthorized user who accesses encoded data will have difficulty deciphering it, but authorized users are given decoding or decrypting algorithms (or keys) to decipher the data. Encrypting techniques that are very difficult to decode without a key have been developed for military applications. Section 24.7 briefly discusses encryption techniques, including popular techniques such as public key encryption, which is heavily used to support Web-based transactions against databases, and digital signa- tures, which are used in personal communications.
A comprehensive discussion of security in computer systems and databases is out- side the scope of this textbook. We give only a brief overview of database security techniques here. The interested reader can refer to several of the references dis- cussed in the Selected Bibliography at the end of this chapter for a more compre- hensive discussion.
24.1.3 Database Security and the DBA As we discussed in Chapter 1, the database administrator (DBA) is the central authority for managing a database system. The DBA’s responsibilities include grant- ing privileges to users who need to use the system and classifying users and data in accordance with the policy of the organization. The DBA has a DBA account in the DBMS, sometimes called a system or superuser account, which provides powerful capabilities that are not made available to regular database accounts and users.2
DBA-privileged commands include commands for granting and revoking privileges
2This account is similar to the root or superuser accounts that are given to computer system administra- tors, which allow access to restricted operating system commands.
24.1 Introduction to Database Security Issues 839
to individual accounts, users, or user groups and for performing the following types of actions:
1. Account creation. This action creates a new account and password for a user or a group of users to enable access to the DBMS.
2. Privilege granting. This action permits the DBA to grant certain privileges to certain accounts.
3. Privilege revocation. This action permits the DBA to revoke (cancel) certain privileges that were previously given to certain accounts.
4. Security level assignment. This action consists of assigning user accounts to the appropriate security clearance level.
The DBA is responsible for the overall security of the database system. Action 1 in the preceding list is used to control access to the DBMS as a whole, whereas actions 2 and 3 are used to control discretionary database authorization, and action 4 is used to control mandatory authorization.
24.1.4 Access Control, User Accounts, and Database Audits Whenever a person or a group of persons needs to access a database system, the individual or group must first apply for a user account. The DBA will then create a new account number and password for the user if there is a legitimate need to access the database. The user must log in to the DBMS by entering the account number and password whenever database access is needed. The DBMS checks that the account number and password are valid; if they are, the user is permitted to use the DBMS and to access the database. Application programs can also be considered users and are required to log in to the database (see Chapter 13).
It is straightforward to keep track of database users and their accounts and pass- words by creating an encrypted table or file with two fields: AccountNumber and Password. This table can easily be maintained by the DBMS. Whenever a new account is created, a new record is inserted into the table. When an account is can- celed, the corresponding record must be deleted from the table.
The database system must also keep track of all operations on the database that are applied by a certain user throughout each login session, which consists of the sequence of database interactions that a user performs from the time of logging in to the time of logging off. When a user logs in, the DBMS can record the user’s account number and associate it with the computer or device from which the user logged in. All operations applied from that computer or device are attributed to the user’s account until the user logs off. It is particularly important to keep track of update operations that are applied to the database so that, if the database is tam- pered with, the DBA can determine which user did the tampering.
To keep a record of all updates applied to the database and of particular users who applied each update, we can modify the system log. Recall from Chapters 21 and 23 that the system log includes an entry for each operation applied to the database that may be required for recovery from a transaction failure or system crash. We can
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expand the log entries so that they also include the account number of the user and the online computer or device ID that applied each operation recorded in the log. If any tampering with the database is suspected, a database audit is performed, which consists of reviewing the log to examine all accesses and operations applied to the database during a certain time period. When an illegal or unauthorized operation is found, the DBA can determine the account number used to perform the operation. Database audits are particularly important for sensitive databases that are updated by many transactions and users, such as a banking database that is updated by many bank tellers. A database log that is used mainly for security purposes is sometimes called an audit trail.
24.1.5 Sensitive Data and Types of Disclosures Sensitivity of data is a measure of the importance assigned to the data by its owner, for the purpose of denoting its need for protection. Some databases contain only sensitive data while other databases may contain no sensitive data at all. Handling databases that fall at these two extremes is relatively easy, because these can be cov- ered by access control, which is explained in the next section. The situation becomes tricky when some of the data is sensitive while other data is not.
Several factors can cause data to be classified as sensitive:
1. Inherently sensitive. The value of the data itself may be so revealing or con- fidential that it becomes sensitive—for example, a person’s salary or that a patient has HIV/AIDS.
2. From a sensitive source. The source of the data may indicate a need for secrecy—for example, an informer whose identity must be kept secret.
3. Declared sensitive. The owner of the data may have explicitly declared it as sensitive.
4. A sensitive attribute or sensitive record. The particular attribute or record may have been declared sensitive—for example, the salary attribute of an employee or the salary history record in a personnel database.
5. Sensitive in relation to previously disclosed data. Some data may not be sensitive by itself but will become sensitive in the presence of some other data—for example, the exact latitude and longitude information for a loca- tion where some previously recorded event happened that was later deemed sensitive.
It is the responsibility of the database administrator and security administrator to collectively enforce the security policies of an organization. This dictates whether access should be permitted to a certain database attribute (also known as a table col- umn or a data element) or not for individual users or for categories of users. Several factors need to be considered before deciding whether it is safe to reveal the data. The three most important factors are data availability, access acceptability, and authenticity assurance.
1. Data availability. If a user is updating a field, then this field becomes inac- cessible and other users should not be able to view this data. This blocking is
24.1 Introduction to Database Security Issues 841
only temporary and only to ensure that no user sees any inaccurate data. This is typically handled by the concurrency control mechanism (see Chapter 22).
2. Access acceptability. Data should only be revealed to authorized users. A database administrator may also deny access to a user request even if the request does not directly access a sensitive data item, on the grounds that the requested data may reveal information about the sensitive data that the user is not authorized to have.
3. Authenticity assurance. Before granting access, certain external characteris- tics about the user may also be considered. For example, a user may only be permitted access during working hours. The system may track previous queries to ensure that a combination of queries does not reveal sensitive data. The latter is particularly relevant to statistical database queries (see Section 24.5).
The term precision, when used in the security area, refers to allowing as much as possible of the data to be available, subject to protecting exactly the subset of data that is sensitive. The definitions of security versus precision are as follows:
■ Security: Means of ensuring that data is kept safe from corruption and that access to it is suitably controlled. To provide security means to disclose only nonsensitive data, and reject any query that references a sensitive field.
■ Precision: To protect all sensitive data while disclosing as much nonsensitive data as possible.
The ideal combination is to maintain perfect security with maximum precision. If we want to maintain security, some sacrifice has to be made with precision. Hence there is typically a tradeoff between security and precision.
24.1.6 Relationship between Information Security versus Information Privacy
The rapid advancement of the use of information technology (IT) in industry, gov- ernment, and academia raises challenging questions and problems regarding the protection and use of personal information. Questions of who has what rights to information about individuals for which purposes become more important as we move toward a world in which it is technically possible to know just about anything about anyone.
Deciding how to design privacy considerations in technology for the future includes philosophical, legal, and practical dimensions. There is a considerable overlap between issues related to access to resources (security) and issues related to appro- priate use of information (privacy). We now define the difference between security versus privacy.
Security in information technology refers to many aspects of protecting a system from unauthorized use, including authentication of users, information encryption, access control, firewall policies, and intrusion detection. For our purposes here, we
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will limit our treatment of security to the concepts associated with how well a sys- tem can protect access to information it contains. The concept of privacy goes beyond security. Privacy examines how well the use of personal information that the system acquires about a user conforms to the explicit or implicit assumptions regarding that use. From an end user perspective, privacy can be considered from two different perspectives: preventing storage of personal information versus ensuring appropriate use of personal information.
For the purposes of this chapter, a simple but useful definition of privacy is the abil- ity of individuals to control the terms under which their personal information is acquired and used. In summary, security involves technology to ensure that informa- tion is appropriately protected. Security is a required building block for privacy to exist. Privacy involves mechanisms to support compliance with some basic principles and other explicitly stated policies. One basic principle is that people should be informed about information collection, told in advance what will be done with their information, and given a reasonable opportunity to approve of such use of the infor- mation. A related concept, trust, relates to both security and privacy, and is seen as increasing when it is perceived that both security and privacy are provided for.
24.2 Discretionary Access Control Based on Granting and Revoking Privileges
The typical method of enforcing discretionary access control in a database system is based on the granting and revoking of privileges. Let us consider privileges in the context of a relational DBMS. In particular, we will discuss a system of privileges somewhat similar to the one originally developed for the SQL language (see Chapters 4 and 5). Many current relational DBMSs use some variation of this tech- nique. The main idea is to include statements in the query language that allow the DBA and selected users to grant and revoke privileges.
24.2.1 Types of Discretionary Privileges In SQL2 and later versions,3 the concept of an authorization identifier is used to refer, roughly speaking, to a user account (or group of user accounts). For simplic- ity, we will use the words user or account interchangeably in place of authorization identifier. The DBMS must provide selective access to each relation in the database based on specific accounts. Operations may also be controlled; thus, having an account does not necessarily entitle the account holder to all the functionality pro- vided by the DBMS. Informally, there are two levels for assigning privileges to use the database system:
■ The account level. At this level, the DBA specifies the particular privileges that each account holds independently of the relations in the database.
■ The relation (or table) level. At this level, the DBA can control the privilege to access each individual relation or view in the database.
3Discretionary privileges were incorporated into SQL2 and are applicable to later versions of SQL.
24.2 Discretionary Access Control Based on Granting and Revoking Privileges 843
The privileges at the account level apply to the capabilities provided to the account itself and can include the CREATE SCHEMA or CREATE TABLE privilege, to create a schema or base relation; the CREATE VIEW privilege; the ALTER privilege, to apply schema changes such as adding or removing attributes from relations; the DROP privilege, to delete relations or views; the MODIFY privilege, to insert, delete, or update tuples; and the SELECT privilege, to retrieve information from the database by using a SELECT query. Notice that these account privileges apply to the account in general. If a certain account does not have the CREATE TABLE privilege, no rela- tions can be created from that account. Account-level privileges are not defined as part of SQL2; they are left to the DBMS implementers to define. In earlier versions of SQL, a CREATETAB privilege existed to give an account the privilege to create tables (relations).
The second level of privileges applies to the relation level, whether they are base relations or virtual (view) relations. These privileges are defined for SQL2. In the following discussion, the term relation may refer either to a base relation or to a view, unless we explicitly specify one or the other. Privileges at the relation level specify for each user the individual relations on which each type of command can be applied. Some privileges also refer to individual columns (attributes) of relations. SQL2 commands provide privileges at the relation and attribute level only. Although this is quite general, it makes it difficult to create accounts with limited privileges. The granting and revoking of privileges generally follow an authorization model for discretionary privileges known as the access matrix model, where the rows of a matrix M represent subjects (users, accounts, programs) and the columns represent objects (relations, records, columns, views, operations). Each position M(i, j) in the matrix represents the types of privileges (read, write, update) that subject i holds on object j.
To control the granting and revoking of relation privileges, each relation R in a data- base is assigned an owner account, which is typically the account that was used when the relation was created in the first place. The owner of a relation is given all privileges on that relation. In SQL2, the DBA can assign an owner to a whole schema by creating the schema and associating the appropriate authorization iden- tifier with that schema, using the CREATE SCHEMA command (see Section 4.1.1). The owner account holder can pass privileges on any of the owned relations to other users by granting privileges to their accounts. In SQL the following types of privi- leges can be granted on each individual relation R:
■ SELECT (retrieval or read) privilege on R. Gives the account retrieval privi- lege. In SQL this gives the account the privilege to use the SELECT statement to retrieve tuples from R.
■ Modification privileges on R. This gives the account the capability to mod- ify the tuples of R. In SQL this includes three privileges: UPDATE, DELETE, and INSERT. These correspond to the three SQL commands (see Section 4.4) for modifying a table R. Additionally, both the INSERT and UPDATE privi- leges can specify that only certain attributes of R can be modified by the account.
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■ References privilege on R. This gives the account the capability to reference (or refer to) a relation R when specifying integrity constraints. This privilege can also be restricted to specific attributes of R.
Notice that to create a view, the account must have the SELECT privilege on all rela- tions involved in the view definition in order to specify the query that corresponds to the view.
24.2.2 Specifying Privileges through the Use of Views The mechanism of views is an important discretionary authorization mechanism in its own right. For example, if the owner A of a relation R wants another account B to be able to retrieve only some fields of R, then A can create a view V of R that includes only those attributes and then grant SELECT on V to B. The same applies to limiting B to retrieving only certain tuples of R; a view V� can be created by defining the view by means of a query that selects only those tuples from R that A wants to allow B to access. We will illustrate this discussion with the example given in Section 24.2.5.
24.2.3 Revoking of Privileges In some cases it is desirable to grant a privilege to a user temporarily. For example, the owner of a relation may want to grant the SELECT privilege to a user for a spe- cific task and then revoke that privilege once the task is completed. Hence, a mech- anism for revoking privileges is needed. In SQL a REVOKE command is included for the purpose of canceling privileges. We will see how the REVOKE command is used in the example in Section 24.2.5.
24.2.4 Propagation of Privileges Using the GRANT OPTION Whenever the owner A of a relation R grants a privilege on R to another account B, the privilege can be given to B with or without the GRANT OPTION. If the GRANT OPTION is given, this means that B can also grant that privilege on R to other accounts. Suppose that B is given the GRANT OPTION by A and that B then grants the privilege on R to a third account C, also with the GRANT OPTION. In this way, privileges on R can propagate to other accounts without the knowledge of the owner of R. If the owner account A now revokes the privilege granted to B, all the privileges that B propagated based on that privilege should automatically be revoked by the system.
It is possible for a user to receive a certain privilege from two or more sources. For example, A4 may receive a certain UPDATE R privilege from both A2 and A3. In such a case, if A2 revokes this privilege from A4, A4 will still continue to have the privilege by virtue of having been granted it from A3. If A3 later revokes the privilege from A4, A4 totally loses the privilege. Hence, a DBMS that allows propagation of privi- leges must keep track of how all the privileges were granted so that revoking of priv- ileges can be done correctly and completely.
24.2 Discretionary Access Control Based on Granting and Revoking Privileges 845
24.2.5 An Example to Illustrate Granting and Revoking of Privileges
Suppose that the DBA creates four accounts—A1, A2, A3, and A4—and wants only A1 to be able to create base relations. To do this, the DBA must issue the following GRANT command in SQL:
GRANT CREATETAB TO A1;
The CREATETAB (create table) privilege gives account A1 the capability to create new database tables (base relations) and is hence an account privilege. This privilege was part of earlier versions of SQL but is now left to each individual system imple- mentation to define.
In SQL2 the same effect can be accomplished by having the DBA issue a CREATE SCHEMA command, as follows:
CREATE SCHEMA EXAMPLE AUTHORIZATION A1;
User account A1 can now create tables under the schema called EXAMPLE. To con- tinue our example, suppose that A1 creates the two base relations EMPLOYEE and DEPARTMENT shown in Figure 24.1; A1 is then the owner of these two relations and hence has all the relation privileges on each of them.
Next, suppose that account A1 wants to grant to account A2 the privilege to insert and delete tuples in both of these relations. However, A1 does not want A2 to be able to propagate these privileges to additional accounts. A1 can issue the following com- mand:
GRANT INSERT, DELETE ON EMPLOYEE, DEPARTMENT TO A2;
Notice that the owner account A1 of a relation automatically has the GRANT OPTION, allowing it to grant privileges on the relation to other accounts. However, account A2 cannot grant INSERT and DELETE privileges on the EMPLOYEE and DEPARTMENT tables because A2 was not given the GRANT OPTION in the preceding command.
Next, suppose that A1 wants to allow account A3 to retrieve information from either of the two tables and also to be able to propagate the SELECT privilege to other accounts. A1 can issue the following command:
GRANT SELECT ON EMPLOYEE, DEPARTMENT TO A3 WITH GRANT OPTION;
DEPARTMENT
DnameDnumber Mgr_ssn
Name Bdate Address Sex Salary Dno
EMPLOYEE
Ssn
Figure 24.1 Schemas for the two relations EMPLOYEE and DEPARTMENT.
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The clause WITH GRANT OPTION means that A3 can now propagate the privilege to other accounts by using GRANT. For example, A3 can grant the SELECT privilege on the EMPLOYEE relation to A4 by issuing the following command:
GRANT SELECT ON EMPLOYEE TO A4;
Notice that A4 cannot propagate the SELECT privilege to other accounts because the GRANT OPTION was not given to A4.
Now suppose that A1 decides to revoke the SELECT privilege on the EMPLOYEE relation from A3; A1 then can issue this command:
REVOKE SELECT ON EMPLOYEE FROM A3;
The DBMS must now revoke the SELECT privilege on EMPLOYEE from A3, and it must also automatically revoke the SELECT privilege on EMPLOYEE from A4. This is because A3 granted that privilege to A4, but A3 does not have the privilege any more.
Next, suppose that A1 wants to give back to A3 a limited capability to SELECT from the EMPLOYEE relation and wants to allow A3 to be able to propagate the privilege. The limitation is to retrieve only the Name, Bdate, and Address attributes and only for the tuples with Dno = 5. A1 then can create the following view:
CREATE VIEW A3EMPLOYEE AS SELECT Name, Bdate, Address FROM EMPLOYEE WHERE Dno = 5;
After the view is created, A1 can grant SELECT on the view A3EMPLOYEE to A3 as follows:
GRANT SELECT ON A3EMPLOYEE TO A3 WITH GRANT OPTION;
Finally, suppose that A1 wants to allow A4 to update only the Salary attribute of EMPLOYEE; A1 can then issue the following command:
GRANT UPDATE ON EMPLOYEE (Salary) TO A4;
The UPDATE and INSERT privileges can specify particular attributes that may be updated or inserted in a relation. Other privileges (SELECT, DELETE) are not attrib- ute specific, because this specificity can easily be controlled by creating the appro- priate views that include only the desired attributes and granting the corresponding privileges on the views. However, because updating views is not always possible (see Chapter 5), the UPDATE and INSERT privileges are given the option to specify the particular attributes of a base relation that may be updated.
24.2.6 Specifying Limits on Propagation of Privileges Techniques to limit the propagation of privileges have been developed, although they have not yet been implemented in most DBMSs and are not a part of SQL. Limiting horizontal propagation to an integer number i means that an account B given the GRANT OPTION can grant the privilege to at most i other accounts.
24.3 Mandatory Access Control and Role-Based Access Control for Multilevel Security 847
Vertical propagation is more complicated; it limits the depth of the granting of privileges. Granting a privilege with a vertical propagation of zero is equivalent to granting the privilege with no GRANT OPTION. If account A grants a privilege to account B with the vertical propagation set to an integer number j > 0, this means that the account B has the GRANT OPTION on that privilege, but B can grant the privilege to other accounts only with a vertical propagation less than j. In effect, ver- tical propagation limits the sequence of GRANT OPTIONS that can be given from one account to the next based on a single original grant of the privilege.
We briefly illustrate horizontal and vertical propagation limits—which are not available currently in SQL or other relational systems—with an example. Suppose that A1 grants SELECT to A2 on the EMPLOYEE relation with horizontal propaga- tion equal to 1 and vertical propagation equal to 2. A2 can then grant SELECT to at most one account because the horizontal propagation limitation is set to 1. Additionally, A2 cannot grant the privilege to another account except with vertical propagation set to 0 (no GRANT OPTION) or 1; this is because A2 must reduce the vertical propagation by at least 1 when passing the privilege to others. In addition, the horizontal propagation must be less than or equal to the originally granted hor- izontal propagation. For example, if account A grants a privilege to account B with the horizontal propagation set to an integer number j > 0, this means that B can grant the privilege to other accounts only with a horizontal propagation less than or equal to j. As this example shows, horizontal and vertical propagation techniques are designed to limit the depth and breadth of propagation of privileges.
24.3 Mandatory Access Control and Role-Based Access Control for Multilevel Security
The discretionary access control technique of granting and revoking privileges on relations has traditionally been the main security mechanism for relational database systems. This is an all-or-nothing method: A user either has or does not have a cer- tain privilege. In many applications, an additional security policy is needed that clas- sifies data and users based on security classes. This approach, known as mandatory access control (MAC), would typically be combined with the discretionary access control mechanisms described in Section 24.2. It is important to note that most commercial DBMSs currently provide mechanisms only for discretionary access control. However, the need for multilevel security exists in government, military, and intelligence applications, as well as in many industrial and corporate applica- tions. Some DBMS vendors—for example, Oracle—have released special versions of their RDBMSs that incorporate mandatory access control for government use.
Typical security classes are top secret (TS), secret (S), confidential (C), and unclas- sified (U), where TS is the highest level and U the lowest. Other more complex secu- rity classification schemes exist, in which the security classes are organized in a lattice. For simplicity, we will use the system with four security classification levels, where TS ≥ S ≥ C ≥ U, to illustrate our discussion. The commonly used model for multilevel security, known as the Bell-LaPadula model, classifies each subject (user,
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account, program) and object (relation, tuple, column, view, operation) into one of the security classifications TS, S, C, or U. We will refer to the clearance (classifica- tion) of a subject S as class(S) and to the classification of an object O as class(O). Two restrictions are enforced on data access based on the subject/object classifica- tions:
1. A subject S is not allowed read access to an object O unless class(S) ≥ class(O). This is known as the simple security property.
2. A subject S is not allowed to write an object O unless class(S) ≤ class(O). This is known as the star property (or *-property).
The first restriction is intuitive and enforces the obvious rule that no subject can read an object whose security classification is higher than the subject’s security clearance. The second restriction is less intuitive. It prohibits a subject from writing an object at a lower security classification than the subject’s security clearance. Violation of this rule would allow information to flow from higher to lower classifi- cations, which violates a basic tenet of multilevel security. For example, a user (sub- ject) with TS clearance may make a copy of an object with classification TS and then write it back as a new object with classification U, thus making it visible throughout the system.
To incorporate multilevel security notions into the relational database model, it is common to consider attribute values and tuples as data objects. Hence, each attrib- ute A is associated with a classification attribute C in the schema, and each attrib- ute value in a tuple is associated with a corresponding security classification. In addition, in some models, a tuple classification attribute TC is added to the relation attributes to provide a classification for each tuple as a whole. The model we describe here is known as the multilevel model, because it allows classifications at multiple security levels. A multilevel relation schema R with n attributes would be represented as:
R(A1, C1, A2, C2, ..., An, Cn, TC)
where each Ci represents the classification attribute associated with attribute Ai.
The value of the tuple classification attribute TC in each tuple t—which is the highest of all attribute classification values within t—provides a general classifica- tion for the tuple itself. Each attribute classification Ci provides a finer security clas- sification for each attribute value within the tuple. The value of TC in each tuple t is the highest of all attribute classification values Ci within t.
The apparent key of a multilevel relation is the set of attributes that would have formed the primary key in a regular (single-level) relation. A multilevel relation will appear to contain different data to subjects (users) with different clearance levels. In some cases, it is possible to store a single tuple in the relation at a higher classifica- tion level and produce the corresponding tuples at a lower-level classification through a process known as filtering. In other cases, it is necessary to store two or more tuples at different classification levels with the same value for the apparent key.
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This leads to the concept of polyinstantiation,4 where several tuples can have the same apparent key value but have different attribute values for users at different clearance levels.
We illustrate these concepts with the simple example of a multilevel relation shown in Figure 24.2(a), where we display the classification attribute values next to each attribute’s value. Assume that the Name attribute is the apparent key, and consider the query SELECT * FROM EMPLOYEE. A user with security clearance S would see the same relation shown in Figure 24.2(a), since all tuple classifications are less than or equal to S. However, a user with security clearance C would not be allowed to see the values for Salary of ‘Brown’ and Job_performance of ‘Smith’, since they have higher classification. The tuples would be filtered to appear as shown in Figure 24.2(b), with Salary and Job_performance appearing as null. For a user with security clearance U, the filtering allows only the Name attribute of ‘Smith’ to appear, with all the other
Name Salary JobPerformance TC Smith U C40000 SFair S Smith U C40000 CExcellent C Brown C S80000 CGood S
EMPLOYEE(d)
Name Salary JobPerformance TC Smith U C40000 SFair S Brown C S80000 CGood S
EMPLOYEE(a)
Name Salary JobPerformance TC
Smith U C40000 CNULL C Brown C CNULL CGood C
EMPLOYEE(b)
Name Salary JobPerformance TC
Smith U UNULL UNULL U
EMPLOYEE(c)
Figure 24.2 A multilevel relation to illus- trate multilevel security. (a) The original EMPLOYEE tuples. (b) Appearance of EMPLOYEE after filtering for classification C users. (c) Appearance of EMPLOYEE after filtering for classification U users. (d) Polyinstantiation of the Smith tuple.
4This is similar to the notion of having multiple versions in the database that represent the same real- world object.
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attributes appearing as null (Figure 24.2(c)). Thus, filtering introduces null values for attribute values whose security classification is higher than the user’s security clearance.
In general, the entity integrity rule for multilevel relations states that all attributes that are members of the apparent key must not be null and must have the same security classification within each individual tuple. Additionally, all other attribute values in the tuple must have a security classification greater than or equal to that of the apparent key. This constraint ensures that a user can see the key if the user is permitted to see any part of the tuple. Other integrity rules, called null integrity and interinstance integrity, informally ensure that if a tuple value at some security level can be filtered (derived) from a higher-classified tuple, then it is sufficient to store the higher-classified tuple in the multilevel relation.
To illustrate polyinstantiation further, suppose that a user with security clearance C tries to update the value of Job_performance of ‘Smith’ in Figure 24.2 to ‘Excellent’; this corresponds to the following SQL update being submitted by that user:
UPDATE EMPLOYEE SET Job_performance = ‘Excellent’ WHERE Name = ‘Smith’;
Since the view provided to users with security clearance C (see Figure 24.2(b)) per- mits such an update, the system should not reject it; otherwise, the user could infer that some nonnull value exists for the Job_performance attribute of ‘Smith’ rather than the null value that appears. This is an example of inferring information through what is known as a covert channel, which should not be permitted in highly secure systems (see Section 24.6.1). However, the user should not be allowed to overwrite the existing value of Job_performance at the higher classification level. The solution is to create a polyinstantiation for the ‘Smith’ tuple at the lower classi- fication level C, as shown in Figure 24.2(d). This is necessary since the new tuple cannot be filtered from the existing tuple at classification S.
The basic update operations of the relational model (INSERT, DELETE, UPDATE) must be modified to handle this and similar situations, but this aspect of the prob- lem is outside the scope of our presentation. We refer the interested reader to the Selected Bibliography at the end of this chapter for further details.
24.3.1 Comparing Discretionary Access Control and Mandatory Access Control
Discretionary access control (DAC) policies are characterized by a high degree of flexibility, which makes them suitable for a large variety of application domains. The main drawback of DAC models is their vulnerability to malicious attacks, such as Trojan horses embedded in application programs. The reason is that discre- tionary authorization models do not impose any control on how information is propagated and used once it has been accessed by users authorized to do so. By con- trast, mandatory policies ensure a high degree of protection—in a way, they prevent
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any illegal flow of information. Therefore, they are suitable for military and high security types of applications, which require a higher degree of protection. However, mandatory policies have the drawback of being too rigid in that they require a strict classification of subjects and objects into security levels, and there- fore they are applicable to few environments. In many practical situations, discre- tionary policies are preferred because they offer a better tradeoff between security and applicability.
24.3.2 Role-Based Access Control Role-based access control (RBAC) emerged rapidly in the 1990s as a proven tech- nology for managing and enforcing security in large-scale enterprise-wide systems. Its basic notion is that privileges and other permissions are associated with organi- zational roles, rather than individual users. Individual users are then assigned to appropriate roles. Roles can be created using the CREATE ROLE and DESTROY ROLE commands. The GRANT and REVOKE commands discussed in Section 24.2 can then be used to assign and revoke privileges from roles, as well as for individual users when needed. For example, a company may have roles such as sales account manager, purchasing agent, mailroom clerk, department manager, and so on. Multiple individuals can be assigned to each role. Security privileges that are com- mon to a role are granted to the role name, and any individual assigned to this role would automatically have those privileges granted.
RBAC can be used with traditional discretionary and mandatory access controls; it ensures that only authorized users in their specified roles are given access to certain data or resources. Users create sessions during which they may activate a subset of roles to which they belong. Each session can be assigned to several roles, but it maps to one user or a single subject only. Many DBMSs have allowed the concept of roles, where privileges can be assigned to roles.
Separation of duties is another important requirement in various commercial DBMSs. It is needed to prevent one user from doing work that requires the involve- ment of two or more people, thus preventing collusion. One method in which sepa- ration of duties can be successfully implemented is with mutual exclusion of roles. Two roles are said to be mutually exclusive if both the roles cannot be used simul- taneously by the user. Mutual exclusion of roles can be categorized into two types, namely authorization time exclusion (static) and runtime exclusion (dynamic). In authorization time exclusion, two roles that have been specified as mutually exclu- sive cannot be part of a user’s authorization at the same time. In runtime exclusion, both these roles can be authorized to one user but cannot be activated by the user at the same time. Another variation in mutual exclusion of roles is that of complete and partial exclusion.
The role hierarchy in RBAC is a natural way to organize roles to reflect the organi- zation’s lines of authority and responsibility. By convention, junior roles at the bottom are connected to progressively senior roles as one moves up the hierarchy. The hierarchic diagrams are partial orders, so they are reflexive, transitive, and
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antisymmetric. In other words, if a user has one role, the user automatically has roles lower in the hierarchy. Defining a role hierarchy involves choosing the type of hierarchy and the roles, and then implementing the hierarchy by granting roles to other roles. Role hierarchy can be implemented in the following manner:
GRANT ROLE full_time TO employee_type1 GRANT ROLE intern TO employee_type2
The above are examples of granting the roles full_time and intern to two types of employees.
Another issue related to security is identity management. Identity refers to a unique name of an individual person. Since the legal names of persons are not necessarily unique, the identity of a person must include sufficient additional information to make the complete name unique. Authorizing this identity and managing the schema of these identities is called Identity Management. Identity Management addresses how organizations can effectively authenticate people and manage their access to confidential information. It has become more visible as a business require- ment across all industries affecting organizations of all sizes. Identity Management administrators constantly need to satisfy application owners while keeping expendi- tures under control and increasing IT efficiency.
Another important consideration in RBAC systems is the possible temporal con- straints that may exist on roles, such as the time and duration of role activations, and timed triggering of a role by an activation of another role. Using an RBAC model is a highly desirable goal for addressing the key security requirements of Web-based applications. Roles can be assigned to workflow tasks so that a user with any of the roles related to a task may be authorized to execute it and may play a cer- tain role only for a certain duration.
RBAC models have several desirable features, such as flexibility, policy neutrality, better support for security management and administration, and other aspects that make them attractive candidates for developing secure Web-based applications. These features are lacking in DAC and MAC models. In addition, RBAC models include the capabilities available in traditional DAC and MAC policies. Furthermore, an RBAC model provides mechanisms for addressing the security issues related to the execution of tasks and workflows, and for specifying user- defined and organization-specific policies. Easier deployment over the Internet has been another reason for the success of RBAC models.
24.3.3 Label-Based Security and Row-Level Access Control Many commercial DBMSs currently use the concept of row-level access control, where sophisticated access control rules can be implemented by considering the data row by row. In row-level access control, each data row is given a label, which is used to store information about data sensitivity. Row-level access control provides finer granularity of data security by allowing the permissions to be set for each row and not just for the table or column. Initially the user is given a default session label by the database administrator. Levels correspond to a hierarchy of data-sensitivity
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levels to exposure or corruption, with the goal of maintaining privacy or security. Labels are used to prevent unauthorized users from viewing or altering certain data. A user having a low authorization level, usually represented by a low number, is denied access to data having a higher-level number. If no such label is given to a row, a row label is automatically assigned to it depending upon the user’s session label.
A policy defined by an administrator is called a Label Security policy. Whenever data affected by the policy is accessed or queried through an application, the policy is automatically invoked. When a policy is implemented, a new column is added to each row in the schema. The added column contains the label for each row that reflects the sensitivity of the row as per the policy. Similar to MAC, where each user has a security clearance, each user has an identity in label-based security. This user’s identity is compared to the label assigned to each row to determine whether the user has access to view the contents of that row. However, the user can write the label value himself, within certain restrictions and guidelines for that specific row. This label can be set to a value that is between the user’s current session label and the user’s minimum level. The DBA has the privilege to set an initial default row label.
The Label Security requirements are applied on top of the DAC requirements for each user. Hence, the user must satisfy the DAC requirements and then the label security requirements to access a row. The DAC requirements make sure that the user is legally authorized to carry on that operation on the schema. In most applica- tions, only some of the tables need label-based security. For the majority of the application tables, the protection provided by DAC is sufficient.
Security policies are generally created by managers and human resources personnel. The policies are high-level, technology neutral, and relate to risks. Policies are a result of management instructions to specify organizational procedures, guiding principles, and courses of action that are considered to be expedient, prudent, or advantageous. Policies are typically accompanied by a definition of penalties and countermeasures if the policy is transgressed. These policies are then interpreted and converted to a set of label-oriented policies by the Label Security administra- tor, who defines the security labels for data and authorizations for users; these labels and authorizations govern access to specified protected objects.
Suppose a user has SELECT privileges on a table. When the user executes a SELECT statement on that table, Label Security will automatically evaluate each row returned by the query to determine whether the user has rights to view the data. For example, if the user has a sensitivity of 20, then the user can view all rows having a security level of 20 or lower. The level determines the sensitivity of the information contained in a row; the more sensitive the row, the higher its security label value. Such Label Security can be configured to perform security checks on UPDATE, DELETE, and INSERT statements as well.
24.3.4 XML Access Control With the worldwide use of XML in commercial and scientific applications, efforts are under way to develop security standards. Among these efforts are digital
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signatures and encryption standards for XML. The XML Signature Syntax and Processing specification describes an XML syntax for representing the associations between cryptographic signatures and XML documents or other electronic resources. The specification also includes procedures for computing and verifying XML signatures. An XML digital signature differs from other protocols for message signing, such as PGP (Pretty Good Privacy—a confidentiality and authentication service that can be used for electronic mail and file storage application), in its sup- port for signing only specific portions of the XML tree (see Chapter 12) rather than the complete document. Additionally, the XML signature specification defines mechanisms for countersigning and transformations—so-called canonicalization to ensure that two instances of the same text produce the same digest for signing even if their representations differ slightly, for example, in typographic white space.
The XML Encryption Syntax and Processing specification defines XML vocabulary and processing rules for protecting confidentiality of XML documents in whole or in part and of non-XML data as well. The encrypted content and additional pro- cessing information for the recipient are represented in well-formed XML so that the result can be further processed using XML tools. In contrast to other commonly used technologies for confidentiality such as SSL (Secure Sockets Layer—a leading Internet security protocol), and virtual private networks, XML encryption also applies to parts of documents and to documents in persistent storage.
24.3.5 Access Control Policies for E-Commerce and the Web Electronic commerce (e-commerce) environments are characterized by any trans- actions that are done electronically. They require elaborate access control policies that go beyond traditional DBMSs. In conventional database environments, access control is usually performed using a set of authorizations stated by security officers or users according to some security policies. Such a simple paradigm is not well suited for a dynamic environment like e-commerce. Furthermore, in an e-commerce environment the resources to be protected are not only traditional data but also knowledge and experience. Such peculiarities call for more flexibility in specifying access control policies. The access control mechanism must be flexible enough to support a wide spectrum of heterogeneous protection objects.
A second related requirement is the support for content-based access control. Content-based access control allows one to express access control policies that take the protection object content into account. In order to support content-based access control, access control policies must allow inclusion of conditions based on the object content.
A third requirement is related to the heterogeneity of subjects, which requires access control policies based on user characteristics and qualifications rather than on spe- cific and individual characteristics (for example, user IDs). A possible solution, to better take into account user profiles in the formulation of access control policies, is to support the notion of credentials. A credential is a set of properties concerning a user that are relevant for security purposes (for example, age or position or role
24.4 SQL Injection 855
within an organization). For instance, by using credentials, one can simply formu- late policies such as Only permanent staff with five or more years of service can access documents related to the internals of the system.
It is believed that the XML is expected to play a key role in access control for e-commerce applications5 because XML is becoming the common representation language for document interchange over the Web, and is also becoming the lan- guage for e-commerce. Thus, on the one hand there is the need to make XML repre- sentations secure, by providing access control mechanisms specifically tailored to the protection of XML documents. On the other hand, access control information (that is, access control policies and user credentials) can be expressed using XML itself. The Directory Services Markup Language (DSML) is a representation of directory service information in XML syntax. It provides a foundation for a stan- dard for communicating with the directory services that will be responsible for pro- viding and authenticating user credentials. The uniform presentation of both protection objects and access control policies can be applied to policies and creden- tials themselves. For instance, some credential properties (such as the user name) may be accessible to everyone, whereas other properties may be visible only to a restricted class of users. Additionally, the use of an XML-based language for specify- ing credentials and access control policies facilitates secure credential submission and export of access control policies.
24.4 SQL Injection SQL Injection is one of the most common threats to a database system. We will dis- cuss it in detail later in this section. Some of the other attacks on databases that are quite frequent are:
■ Unauthorized privilege escalation. This attack is characterized by an indi- vidual attempting to elevate his or her privilege by attacking vulnerable points in the database systems.
■ Privilege abuse. While the previous attack is done by an unauthorized user, this attack is performed by a privileged user. For example, an administrator who is allowed to change student information can use this privilege to update student grades without the instructor’s permission.
■ Denial of service. A Denial of Service (DOS) attack is an attempt to make resources unavailable to its intended users. It is a general attack category in which access to network applications or data is denied to intended users by overflowing the buffer or consuming resources.
■ Weak Authentication. If the user authentication scheme is weak, an attacker can impersonate the identity of a legitimate user by obtaining their login credentials.
5See Thuraisingham et al. (2001).
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24.4.1 SQL Injection Methods As we discussed in Chapter 14, Web programs and applications that access a data- base can send commands and data to the database, as well as display data retrieved from the database through the Web browser. In an SQL Injection attack, the attacker injects a string input through the application, which changes or manipu- lates the SQL statement to the attacker’s advantage. An SQL Injection attack can harm the database in various ways, such as unauthorized manipulation of the data- base, or retrieval of sensitive data. It can also be used to execute system level com- mands that may cause the system to deny service to the application. This section describes types of injection attacks.
SQL Manipulation. A manipulation attack, which is the most common type of injection attack, changes an SQL command in the application—for example, by adding conditions to the WHERE-clause of a query, or by expanding a query with additional query components using set operations such as UNION, INTERSECT, or MINUS. Other types of manipulation attacks are also possible. A typical manipula- tion attack occurs during database login. For example, suppose that a simplistic authentication procedure issues the following query and checks to see if any rows were returned:
SELECT * FROM users WHERE username = ‘jake’ and PASSWORD = ‘jakespasswd’.
The attacker can try to change (or manipulate) the SQL statement, by changing it as follows:
SELECT * FROM users WHERE username = ‘jake’ and (PASSWORD = ‘jakespasswd’ or ‘x’ = ‘x’)
As a result, the attacker who knows that ‘jake’ is a valid login of some user is able to log into the database system as ‘jake’ without knowing his password and is able to do everything that ‘jake’ may be authorized to do to the database system.
Code Injection. This type of attack attempts to add additional SQL statements or commands to the existing SQL statement by exploiting a computer bug, which is caused by processing invalid data. The attacker can inject or introduce code into a computer program to change the course of execution. Code injection is a popular technique for system hacking or cracking to gain information.
Function Call Injection. In this kind of attack, a database function or operating system function call is inserted into a vulnerable SQL statement to manipulate the data or make a privileged system call. For example, it is possible to exploit a function that performs some aspect related to network communication. In addition, func- tions that are contained in a customized database package, or any custom database function, can be executed as part of an SQL query. In particular, dynamically cre- ated SQL queries (see Chapter 13) can be exploited since they are constructed at run time.
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For example, the dual table is used in the FROM clause of SQL in Oracle when a user needs to run SQL that does not logically have a table name. To get today’s date, we can use:
SELECT SYSDATE FROM dual;
The following example demonstrates that even the simplest SQL statements can be vulnerable.
SELECT TRANSLATE (‘user input’, ‘from_string’, ‘to_string’) FROM dual;
Here, TRANSLATE is used to replace a string of characters with another string of characters. The TRANSLATE function above will replace the characters of the ‘from_string’ with the characters in the ‘to_string’ one by one. This means that the f will be replaced with the t, the r with the o, the o with the _, and so on.
This type of SQL statement can be subjected to a function injection attack. Consider the following example:
SELECT TRANSLATE (“ || UTL_HTTP.REQUEST (‘http://129.107.2.1/’) || ’’, ‘98765432’, ‘9876’) FROM dual;
The user can input the string (“ || UTL_HTTP.REQUEST (‘http://129.107.2.1/’) || ’’), where || is the concatenate operator, thus requesting a page from a Web server. UTL_HTTP makes Hypertext Transfer Protocol (HTTP) callouts from SQL. The REQUEST object takes a URL (‘http://129.107.2.1/’ in this example) as a parameter, contacts that site, and returns the data (typically HTML) obtained from that site. The attacker could manipulate the string he inputs, as well as the URL, to include other functions and do other illegal operations. We just used a dummy example to show conversion of ‘98765432’ to ‘9876’, but the user’s intent would be to access the URL and get sensitive information. The attacker can then retrieve useful informa- tion from the database server—located at the URL that is passed as a parameter— and send it to the Web server (that calls the TRANSLATE function).
24.4.2 Risks Associated with SQL Injection SQL injection is harmful and the risks associated with it provide motivation for attackers. Some of the risks associated with SQL injection attacks are explained below.
■ Database Fingerprinting. The attacker can determine the type of database being used in the backend so that he can use database-specific attacks that correspond to weaknesses in a particular DBMS.
■ Denial of Service. The attacker can flood the server with requests, thus denying service to valid users, or they can delete some data.
■ Bypassing Authentication. This is one of the most common risks, in which the attacker can gain access to the database as an authorized user and per- form all the desired tasks.
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■ Identifying Injectable Parameters. In this type of attack, the attacker gath- ers important information about the type and structure of the back-end database of a Web application. This attack is made possible by the fact that the default error page returned by application servers is often overly descriptive.
■ Executing Remote Commands. This provides attackers with a tool to exe- cute arbitrary commands on the database. For example, a remote user can execute stored database procedures and functions from a remote SQL inter- active interface.
■ Performing Privilege Escalation. This type of attack takes advantage of log- ical flaws within the database to upgrade the access level.
24.4.3 Protection Techniques against SQL Injection Protection against SQL injection attacks can be achieved by applying certain pro- gramming rules to all Web-accessible procedures and functions. This section describes some of these techniques.
Bind Variables (Using Parameterized Statements). The use of bind variables (also known as parameters; see Chapter 13) protects against injection attacks and also improves performance.
Consider the following example using Java and JDBC:
PreparedStatement stmt = conn.prepareStatement( “SELECT * FROM EMPLOYEE WHERE EMPLOYEE_ID=? AND PASSWORD=?”);
stmt.setString(1, employee_id);
stmt.setString(2, password);
Instead of embedding the user input into the statement, the input should be bound to a parameter. In this example, the input ‘1’ is assigned (bound) to a bind variable ‘employee_id’ and input ‘2’ to the bind variable ‘password’ instead of directly pass- ing string parameters.
Filtering Input (Input Validation). This technique can be used to remove escape characters from input strings by using the SQL Replace function. For example, the delimiter single quote (‘) can be replaced by two single quotes (‘’). Some SQL Manipulation attacks can be prevented by using this technique, since escape charac- ters can be used to inject manipulation attacks. However, because there can be a large number of escape characters, this technique is not reliable.
Function Security. Database functions, both standard and custom, should be restricted, as they can be exploited in the SQL function injection attacks.
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24.5 Introduction to Statistical Database Security
Statistical databases are used mainly to produce statistics about various popula- tions. The database may contain confidential data about individuals, which should be protected from user access. However, users are permitted to retrieve statistical information about the populations, such as averages, sums, counts, maximums, minimums, and standard deviations. The techniques that have been developed to protect the privacy of individual information are beyond the scope of this book. We will illustrate the problem with a very simple example, which refers to the relation shown in Figure 24.3. This is a PERSON relation with the attributes Name, Ssn, Income, Address, City, State, Zip, Sex, and Last_degree.
A population is a set of tuples of a relation (table) that satisfy some selection condi- tion. Hence, each selection condition on the PERSON relation will specify a partic- ular population of PERSON tuples. For example, the condition Sex = ‘M’ specifies the male population; the condition ((Sex = ‘F’) AND (Last_degree = ‘M.S.’ OR Last_degree = ‘Ph.D.’)) specifies the female population that has an M.S. or Ph.D. degree as their highest degree; and the condition City = ‘Houston’ specifies the pop- ulation that lives in Houston.
Statistical queries involve applying statistical functions to a population of tuples. For example, we may want to retrieve the number of individuals in a population or the average income in the population. However, statistical users are not allowed to retrieve individual data, such as the income of a specific person. Statistical database security techniques must prohibit the retrieval of individual data. This can be achieved by prohibiting queries that retrieve attribute values and by allowing only queries that involve statistical aggregate functions such as COUNT, SUM, MIN, MAX, AVERAGE, and STANDARD DEVIATION. Such queries are sometimes called statistical queries.
It is the responsibility of a database management system to ensure the confidential- ity of information about individuals, while still providing useful statistical sum- maries of data about those individuals to users. Provision of privacy protection of users in a statistical database is paramount; its violation is illustrated in the follow- ing example.
In some cases it is possible to infer the values of individual tuples from a sequence of statistical queries. This is particularly true when the conditions result in a
Name Ssn Income Address City State Zip Sex Last_degree
PERSON Figure 24.3 The PERSON relation schema for illustrating statistical database security.
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population consisting of a small number of tuples. As an illustration, consider the following statistical queries:
Q1: SELECT COUNT (*) FROM PERSON WHERE <condition>;
Q2: SELECT AVG (Income) FROM PERSON WHERE <condition>;
Now suppose that we are interested in finding the Salary of Jane Smith, and we know that she has a Ph.D. degree and that she lives in the city of Bellaire, Texas. We issue the statistical query Q1 with the following condition:
(Last_degree=‘Ph.D.’ AND Sex=‘F’ AND City=‘Bellaire’ AND State=‘Texas’)
If we get a result of 1 for this query, we can issue Q2 with the same condition and find the Salary of Jane Smith. Even if the result of Q1 on the preceding condition is not 1 but is a small number—say 2 or 3—we can issue statistical queries using the functions MAX, MIN, and AVERAGE to identify the possible range of values for the Salary of Jane Smith.
The possibility of inferring individual information from statistical queries is reduced if no statistical queries are permitted whenever the number of tuples in the population specified by the selection condition falls below some threshold. Another technique for prohibiting retrieval of individual information is to prohibit sequences of queries that refer repeatedly to the same population of tuples. It is also possible to introduce slight inaccuracies or noise into the results of statistical queries deliberately, to make it difficult to deduce individual information from the results. Another technique is partitioning of the database. Partitioning implies that records are stored in groups of some minimum size; queries can refer to any complete group or set of groups, but never to subsets of records within a group. The interested reader is referred to the bibliography at the end of this chapter for a discussion of these techniques.
24.6 Introduction to Flow Control Flow control regulates the distribution or flow of information among accessible objects. A flow between object X and object Y occurs when a program reads values from X and writes values into Y. Flow controls check that information contained in some objects does not flow explicitly or implicitly into less protected objects. Thus, a user cannot get indirectly in Y what he or she cannot get directly in X. Active flow control began in the early 1970s. Most flow controls employ some concept of security class; the transfer of information from a sender to a receiver is allowed only if the receiver’s security class is at least as privileged as the sender’s. Examples of a flow con- trol include preventing a service program from leaking a customer’s confidential data, and blocking the transmission of secret military data to an unknown classified user.
A flow policy specifies the channels along which information is allowed to move. The simplest flow policy specifies just two classes of information—confidential (C)
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and nonconfidential (N)—and allows all flows except those from class C to class N. This policy can solve the confinement problem that arises when a service program handles data such as customer information, some of which may be confidential. For example, an income-tax computing service might be allowed to retain a cus- tomer’s address and the bill for services rendered, but not a customer’s income or deductions.
Access control mechanisms are responsible for checking users’ authorizations for resource access: Only granted operations are executed. Flow controls can be enforced by an extended access control mechanism, which involves assigning a secu- rity class (usually called the clearance) to each running program. The program is allowed to read a particular memory segment only if its security class is as high as that of the segment. It is allowed to write in a segment only if its class is as low as that of the segment. This automatically ensures that no information transmitted by the person can move from a higher to a lower class. For example, a military program with a secret clearance can only read from objects that are unclassified and confi- dential and can only write into objects that are secret or top secret.
Two types of flow can be distinguished: explicit flows, occurring as a consequence of assignment instructions, such as Y:= f(X1,Xn,), and implicit flows generated by con- ditional instructions, such as if f(Xm+1, ..., Xn) then Y:= f (X1,Xm).
Flow control mechanisms must verify that only authorized flows, both explicit and implicit, are executed. A set of rules must be satisfied to ensure secure information flows. Rules can be expressed using flow relations among classes and assigned to information, stating the authorized flows within a system. (An information flow from A to B occurs when information associated with A affects the value of infor- mation associated with B. The flow results from operations that cause information transfer from one object to another.) These relations can define, for a class, the set of classes where information (classified in that class) can flow, or can state the specific relations to be verified between two classes to allow information to flow from one to the other. In general, flow control mechanisms implement the controls by assigning a label to each object and by specifying the security class of the object. Labels are then used to verify the flow relations defined in the model.
24.6.1 Covert Channels A covert channel allows a transfer of information that violates the security or the policy. Specifically, a covert channel allows information to pass from a higher clas- sification level to a lower classification level through improper means. Covert chan- nels can be classified into two broad categories: timing channels and storage. The distinguishing feature between the two is that in a timing channel the information is conveyed by the timing of events or processes, whereas storage channels do not require any temporal synchronization, in that information is conveyed by accessing system information or what is otherwise inaccessible to the user.
In a simple example of a covert channel, consider a distributed database system in which two nodes have user security levels of secret (S) and unclassified (U). In order
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for a transaction to commit, both nodes must agree to commit. They mutually can only do operations that are consistent with the *-property, which states that in any transaction, the S site cannot write or pass information to the U site. However, if these two sites collude to set up a covert channel between them, a transaction involving secret data may be committed unconditionally by the U site, but the S site may do so in some predefined agreed-upon way so that certain information may be passed from the S site to the U site, violating the *-property. This may be achieved where the transaction runs repeatedly, but the actions taken by the S site implicitly convey information to the U site. Measures such as locking, which we discussed in Chapters 22 and 23, prevent concurrent writing of the information by users with different security levels into the same objects, preventing the storage-type covert channels. Operating systems and distributed databases provide control over the multiprogramming of operations that allows a sharing of resources without the possibility of encroachment of one program or process into another’s memory or other resources in the system, thus preventing timing-oriented covert channels. In general, covert channels are not a major problem in well-implemented robust data- base implementations. However, certain schemes may be contrived by clever users that implicitly transfer information.
Some security experts believe that one way to avoid covert channels is to disallow programmers to actually gain access to sensitive data that a program will process after the program has been put into operation. For example, a programmer for a bank has no need to access the names or balances in depositors’ accounts. Programmers for brokerage firms do not need to know what buy and sell orders exist for clients. During program testing, access to a form of real data or some sam- ple test data may be justifiable, but not after the program has been accepted for reg- ular use.
24.7 Encryption and Public Key Infrastructures
The previous methods of access and flow control, despite being strong control measures, may not be able to protect databases from some threats. Suppose we com- municate data, but our data falls into the hands of a nonlegitimate user. In this situ- ation, by using encryption we can disguise the message so that even if the transmission is diverted, the message will not be revealed. Encryption is the conver- sion of data into a form, called a ciphertext, which cannot be easily understood by unauthorized persons. It enhances security and privacy when access controls are bypassed, because in cases of data loss or theft, encrypted data cannot be easily understood by unauthorized persons.
With this background, we adhere to following standard definitions:6
■ Ciphertext: Encrypted (enciphered) data.
6These definitions are from NIST (National Institute of Standards and Technology) from http://csrc.nist .gov/publications/nistpubs/800-67/SP800-67.pdf.
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■ Plaintext (or cleartext): Intelligible data that has meaning and can be read or acted upon without the application of decryption.
■ Encryption: The process of transforming plaintext into ciphertext.
■ Decryption: The process of transforming ciphertext back into plaintext.
Encryption consists of applying an encryption algorithm to data using some pre- specified encryption key. The resulting data has to be decrypted using a decryption key to recover the original data.
24.7.1 The Data Encryption and Advanced Encryption Standards
The Data Encryption Standard (DES) is a system developed by the U.S. govern- ment for use by the general public. It has been widely accepted as a cryptographic standard both in the United States and abroad. DES can provide end-to-end encryption on the channel between sender A and receiver B. The DES algorithm is a careful and complex combination of two of the fundamental building blocks of encryption: substitution and permutation (transposition). The algorithm derives its strength from repeated application of these two techniques for a total of 16 cycles. Plaintext (the original form of the message) is encrypted as blocks of 64 bits. Although the key is 64 bits long, in effect the key can be any 56-bit number. After questioning the adequacy of DES, the NIST introduced the Advanced Encryption Standard (AES). This algorithm has a block size of 128 bits, compared with DES’s 56-block size, and can use keys of 128, 192, or 256 bits, compared with DES’s 56-bit key. AES introduces more possible keys, compared with DES, and thus takes a much longer time to crack.
24.7.2 Symmetric Key Algorithms A symmetric key is one key that is used for both encryption and decryption. By using a symmetric key, fast encryption and decryption is possible for routine use with sensitive data in the database. A message encrypted with a secret key can be decrypted only with the same secret key. Algorithms used for symmetric key encryption are called secret-key algorithms. Since secret-key algorithms are mostly used for encrypting the content of a message, they are also called content- encryption algorithms.
The major liability associated with secret-key algorithms is the need for sharing the secret key. A possible method is to derive the secret key from a user-supplied password string by applying the same function to the string at both the sender and receiver; this is known as a password-based encryption algorithm. The strength of the symmetric key encryption depends on the size of the key used. For the same algorithm, encrypting using a longer key is tougher to break than the one using a shorter key.
24.7.3 Public (Asymmetric) Key Encryption In 1976, Diffie and Hellman proposed a new kind of cryptosystem, which they called public key encryption. Public key algorithms are based on mathematical
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functions rather than operations on bit patterns. They address one drawback of symmetric key encryption, namely that both sender and recipient must exchange the common key in a secure manner. In public key systems, two keys are used for encryption/decryption. The public key can be transmitted in a non-secure way, whereas the private key is not transmitted at all. These algorithms—which use two related keys, a public key and a private key, to perform complementary operations (encryption and decryption)—are known as asymmetric key encryption algo- rithms. The use of two keys can have profound consequences in the areas of confi- dentiality, key distribution, and authentication. The two keys used for public key encryption are referred to as the public key and the private key. The private key is kept secret, but it is referred to as a private key rather than a secret key (the key used in conventional encryption) to avoid confusion with conventional encryption. The two keys are mathematically related, since one of the keys is used to perform encryption and the other to perform decryption. However, it is very difficult to derive the private key from the public key.
A public key encryption scheme, or infrastructure, has six ingredients:
1. Plaintext. This is the data or readable message that is fed into the algorithm as input.
2. Encryption algorithm. This algorithm performs various transformations on the plaintext.
3. and 4. Public and private keys. These are a pair of keys that have been selected so that if one is used for encryption, the other is used for decryp- tion. The exact transformations performed by the encryption algorithm depend on the public or private key that is provided as input. For example, if a message is encrypted using the public key, it can only be decrypted using the private key.
5. Ciphertext. This is the scrambled message produced as output. It depends on the plaintext and the key. For a given message, two different keys will pro- duce two different ciphertexts.
6. Decryption algorithm. This algorithm accepts the ciphertext and the matching key and produces the original plaintext.
As the name suggests, the public key of the pair is made public for others to use, whereas the private key is known only to its owner. A general-purpose public key cryptographic algorithm relies on one key for encryption and a different but related key for decryption. The essential steps are as follows:
1. Each user generates a pair of keys to be used for the encryption and decryp- tion of messages.
2. Each user places one of the two keys in a public register or other accessible file. This is the public key. The companion key is kept private.
3. If a sender wishes to send a private message to a receiver, the sender encrypts the message using the receiver’s public key.
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4. When the receiver receives the message, he or she decrypts it using the receiver’s private key. No other recipient can decrypt the message because only the receiver knows his or her private key.
The RSA Public Key Encryption Algorithm. One of the first public key schemes was introduced in 1978 by Ron Rivest, Adi Shamir, and Len Adleman at MIT and is named after them as the RSA scheme. The RSA scheme has since then reigned supreme as the most widely accepted and implemented approach to public key encryption. The RSA encryption algorithm incorporates results from number the- ory, combined with the difficulty of determining the prime factors of a target. The RSA algorithm also operates with modular arithmetic—mod n.
Two keys, d and e, are used for decryption and encryption. An important property is that they can be interchanged. n is chosen as a large integer that is a product of two large distinct prime numbers, a and b, n = a × b. The encryption key e is a randomly chosen number between 1 and n that is relatively prime to (a – 1) × (b – 1). The plaintext block P is encrypted as Pe where Pe = P mod n. Because the exponentiation is performed mod n, factoring Pe to uncover the encrypted plaintext is difficult. However, the decrypting key d is carefully chosen so that (Pe)d mod n = P. The decryption key d can be computed from the condition that d × e = 1 mod ((a – 1) × (b – 1)). Thus, the legitimate receiver who knows d simply computes (Pe)d mod n = P and recovers P without having to factor Pe.
24.7.4 Digital Signatures A digital signature is an example of using encryption techniques to provide authen- tication services in electronic commerce applications. Like a handwritten signature, a digital signature is a means of associating a mark unique to an individual with a body of text. The mark should be unforgettable, meaning that others should be able to check that the signature comes from the originator.
A digital signature consists of a string of symbols. If a person’s digital signature were always the same for each message, then one could easily counterfeit it by simply copying the string of symbols. Thus, signatures must be different for each use. This can be achieved by making each digital signature a function of the message that it is signing, together with a timestamp. To be unique to each signer and counterfeit- proof, each digital signature must also depend on some secret number that is unique to the signer. Thus, in general, a counterfeitproof digital signature must depend on the message and a unique secret number of the signer. The verifier of the signature, however, should not need to know any secret number. Public key tech- niques are the best means of creating digital signatures with these properties.
24.7.5 Digital Certificates A digital certificate is used to combine the value of a public key with the identity of the person or service that holds the corresponding private key into a digitally signed
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statement. Certificates are issued and signed by a certification authority (CA). The entity receiving this certificate from a CA is the subject of that certificate. Instead of requiring each participant in an application to authenticate every user, third-party authentication relies on the use of digital certificates.
The digital certificate itself contains various types of information. For example, both the certification authority and the certificate owner information are included. The following list describes all the information included in the certificate:
1. The certificate owner information, which is represented by a unique identi- fier known as the distinguished name (DN) of the owner. This includes the owner’s name, as well as the owner’s organization and other information about the owner.
2. The certificate also includes the public key of the owner.
3. The date of issue of the certificate is also included.
4. The validity period is specified by ‘Valid From’ and ‘Valid To’ dates, which are included in each certificate.
5. Issuer identifier information is included in the certificate.
6. Finally, the digital signature of the issuing CA for the certificate is included. All the information listed is encoded through a message-digest function, which creates the digital signature. The digital signature basically certifies that the association between the certificate owner and public key is valid.
24.8 Privacy Issues and Preservation Preserving data privacy is a growing challenge for database security and privacy experts. In some perspectives, to preserve data privacy we should even limit per- forming large-scale data mining and analysis. The most commonly used techniques to address this concern are to avoid building mammoth central warehouses as a sin- gle repository of vital information. Another possible measure is to intentionally modify or perturb data.
If all data were available at a single warehouse, violating only a single repository’s security could expose all data. Avoiding central warehouses and using distributed data mining algorithms minimizes the exchange of data needed to develop globally valid models. By modifying, perturbing, and anonymizing data, we can also miti- gate privacy risks associated with data mining. This can be done by removing iden- tity information from the released data and injecting noise into the data. However, by using these techniques, we should pay attention to the quality of the resulting data in the database, which may undergo too many modifications. We must be able to estimate the errors that may be introduced by these modifications.
Privacy is an important area of ongoing research in database management. It is complicated due to its multidisciplinary nature and the issues related to the subjec- tivity in the interpretation of privacy, trust, and so on. As an example, consider medical and legal records and transactions, which must maintain certain privacy
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requirements while they are being defined and enforced. Providing access control and privacy for mobile devices is also receiving increased attention. DBMSs need robust techniques for efficient storage of security-relevant information on small devices, as well as trust negotiation techniques. Where to keep information related to user identities, profiles, credentials, and permissions and how to use it for reliable user identification remains an important problem. Because large-sized streams of data are generated in such environments, efficient techniques for access control must be devised and integrated with processing techniques for continuous queries. Finally, the privacy of user location data, acquired from sensors and communica- tion networks, must be ensured.
24.9 Challenges of Database Security Considering the vast growth in volume and speed of threats to databases and infor- mation assets, research efforts need to be devoted to the following issues: data qual- ity, intellectual property rights, and database survivability. These are only some of the main challenges that researchers in database security are trying to address.
24.9.1 Data Quality The database community needs techniques and organizational solutions to assess and attest the quality of data. These techniques may include simple mechanisms such as quality stamps that are posted on Web sites. We also need techniques that provide more effective integrity semantics verification and tools for the assessment of data quality, based on techniques such as record linkage. Application-level recov- ery techniques are also needed for automatically repairing incorrect data. The ETL (extract, transform, load) tools widely used to load data in data warehouses (see Section 29.4) are presently grappling with these issues.
24.9.2 Intellectual Property Rights With the widespread use of the Internet and intranets, legal and informational aspects of data are becoming major concerns of organizations. To address these concerns, watermarking techniques for relational data have been proposed. The main purpose of digital watermarking is to protect content from unauthorized duplication and distribution by enabling provable ownership of the content. It has traditionally relied upon the availability of a large noise domain within which the object can be altered while retaining its essential properties. However, research is needed to assess the robustness of such techniques and to investigate different approaches aimed at preventing intellectual property rights violations.
24.9.3 Database Survivability Database systems need to operate and continue their functions, even with reduced capabilities, despite disruptive events such as information warfare attacks. A DBMS,
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in addition to making every effort to prevent an attack and detecting one in the event of occurrence, should be able to do the following:
■ Confinement. Take immediate action to eliminate the attacker’s access to the system and to isolate or contain the problem to prevent further spread.
■ Damage assessment. Determine the extent of the problem, including failed functions and corrupted data.
■ Reconfiguration. Reconfigure to allow operation to continue in a degraded mode while recovery proceeds.
■ Repair. Recover corrupted or lost data and repair or reinstall failed system functions to reestablish a normal level of operation.
■ Fault treatment. To the extent possible, identify the weaknesses exploited in the attack and take steps to prevent a recurrence.
The goal of the information warfare attacker is to damage the organization’s opera- tion and fulfillment of its mission through disruption of its information systems. The specific target of an attack may be the system itself or its data. While attacks that bring the system down outright are severe and dramatic, they must also be well timed to achieve the attacker’s goal, since attacks will receive immediate and con- centrated attention in order to bring the system back to operational condition, diag- nose how the attack took place, and install preventive measures.
To date, issues related to database survivability have not been sufficiently investi- gated. Much more research needs to be devoted to techniques and methodologies that ensure database system survivability.
24.10 Oracle Label-Based Security Restricting access to entire tables or isolating sensitive data into separate databases is a costly operation to administer. Oracle Label Security overcomes the need for such measures by enabling row-level access control. It is available in Oracle Database 11g Release 1 (11.1) Enterprise Edition at the time of writing. Each database table or view has a security policy associated with it. This policy executes every time the table or view is queried or altered. Developers can readily add label-based access control to their Oracle Database applications. Label-based security provides an adaptable way of controlling access to sensitive data. Both users and data have labels associated with them. Oracle Label Security uses these labels to provide security.
24.10.1 Virtual Private Database (VPD) Technology Virtual Private Databases (VPDs) is a feature of the Oracle Enterprise Edition that adds predicates to user statements to limit their access in a transparent manner to the user and the application. The VPD concept allows server-enforced, fine-grained access control for a secure application.
VPD provides access control based on policies. These VPD policies enforce object- level access control or row-level security. It provides an application programming
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interface (API) that allows security policies to be attached to database tables or views. Using PL/SQL, a host programming language used in Oracle applications, developers and security administrators can implement security policies with the help of stored procedures.7 VPD policies allow developers to remove access security mechanisms from applications and centralize them within the Oracle Database.
VPD is enabled by associating a security “policy” with a table, view, or synonym. An administrator uses the supplied PL/SQL package, DBMS_RLS, to bind a policy function with a database object. When an object having a security policy associated with it is accessed, the function implementing this policy is consulted. The policy function returns a predicate (a WHERE clause) which is then appended to the user’s SQL statement, thus transparently and dynamically modifying the user’s data access. Oracle Label Security is a technique of enforcing row-level security in the form of a security policy.
24.10.2 Label Security Architecture Oracle Label Security is built on the VPD technology delivered in the Oracle Database 11.1 Enterprise Edition. Figure 24.4 illustrates how data is accessed under Oracle Label Security, showing the sequence of DAC and label security checks.
Figure 24.4 shows the sequence of discretionary access control (DAC) and label security checks. The left part of the figure shows an application user in an Oracle Database 11g Release 1 (11.1) session sending out an SQL request. The Oracle DBMS checks the DAC privileges of the user, making sure that he or she has SELECT privileges on the table. Then it checks whether the table has a Virtual Private Database (VPD) policy associated with it to determine if the table is protected using Oracle Label Security. If it is, the VPD SQL modification (WHERE clause) is added to the original SQL statement to find the set of accessible rows for the user to view. Then Oracle Label Security checks the labels on each row, to determine the subset of rows to which the user has access (as explained in the next section). This modified query gets processed, optimized, and executed.
24.10.3 How Data Labels and User Labels Work Together A user’s label indicates the information the user is permitted to access. It also deter- mines the type of access (read or write) that the user has on that information. A row’s label shows the sensitivity of the information that the row contains as well as the ownership of the information. When a table in the database has a label-based access associated with it, a row can be accessed only if the user’s label meet certain criteria defined in the policy definitions. Access is granted or denied based on the result of comparing the data label and the session label of the user.
Compartments allow a finer classification of sensitivity of the labeled data. All data related to the same project can be labeled with the same compartment. Compartments are optional; a label can contain zero or more compartments.
7Stored procedures are discussed in Section 5.2.2.
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Oracle User
Request for Data in SQL
Check DAC (Discretionary) Access
Control
Check Virtual Private Database (VDP) Policy
Process and Execute Data Request
Enforce Label- Based Security
Oracle Data Server
Table Level Privileges
Table
Data Rows in Table
Label Security Policies
Row Level Access Control
VPD-Based Control
User-Defined VPD Policies
Figure 24.4 Oracle Label Security architecture.
Source: Oracle (2007)
Groups are used to identify organizations as owners of the data with corresponding group labels. Groups are hierarchical; for example, a group can be associated with a parent group.
If a user has a maximum level of SENSITIVE, then the user potentially has access to all data having levels SENSITIVE, CONFIDENTIAL, and UNCLASSIFIED. This user has no access to HIGHLY_SENSITIVE data. Figure 24.5 shows how data labels and user labels work together to provide access control in Oracle Label Security.
As shown in Figure 24.5, User 1 can access the rows 2, 3, and 4 because his maxi- mum level is HS (Highly_Sensitive). He has access to the FIN (Finance) compart- ment, and his access to group WR (Western Region) hierarchically includes group WR_SAL (WR Sales). He cannot access row 1 because he does not have the CHEM (Chemical) compartment. It is important that a user has authorization for all com- partments in a row’s data label to be able to access that row. Based on this example, user 2 can access both rows 3 and 4, and has a maximum level of S, which is less than the HS in row 2. So, although user 2 has access to the FIN compartment, he can only access the group WR_SAL, and thus cannot acces row 1.
24.11 Summary In this chapter we discussed several techniques for enforcing database system secu- rity. We presented different threats to databases in terms of loss of integrity, avail- ability, and confidentiality. We discussed the types of control measures to deal with these problems: access control, inference control, flow control, and encryption. In
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User Labels
HS FIN : WR
S FIN : WR_SAL
Legend for Labels HS = Highly sensitive S = Sensitive C = Confidential U = Unclassified
Maximum Access Level
All compartments to which the user has access
Minimum Access Level
Required
All compartments to which the user must have access
User Label
Data Label
Rows in Table Data Labels
Row 1
Row 2
Row 3
Row 4
S CHEM, FIN : WR
HS FIN : WR_SAL
U FIN
C FIN : WR_SAL
Figure 24.5 Data labels and user labels in Oracle.
Source: Oracle (2007)
the introduction we covered various issues related to security including data sensi- tivity and type of disclosures, providing security vs. precision in the result when a user requests information, and the relationship between information security and privacy.
Security enforcement deals with controlling access to the database system as a whole and controlling authorization to access specific portions of a database. The former is usually done by assigning accounts with passwords to users. The latter can be accomplished by using a system of granting and revoking privileges to individual accounts for accessing specific parts of the database. This approach is generally referred to as discretionary access control (DAC). We presented some SQL com- mands for granting and revoking privileges, and we illustrated their use with exam- ples. Then we gave an overview of mandatory access control (MAC) mechanisms that enforce multilevel security. These require the classifications of users and data values into security classes and enforce the rules that prohibit flow of information from higher to lower security levels. Some of the key concepts underlying the mul- tilevel relational model, including filtering and polyinstantiation, were presented. Role-based access control (RBAC) was introduced, which assigns privileges based on roles that users play. We introduced the notion of role hierarchies, mutual exclu- sion of roles, and row- and label-based security. We briefly discussed the problem of controlling access to statistical databases to protect the privacy of individual infor- mation while concurrently providing statistical access to populations of records. We explained the main ideas behind the threat of SQL Injection, the methods in which it can be induced, and the various types of risks associated with it. Then we gave an
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idea of the various ways SQL injection can be prevented. The issues related to flow control and the problems associated with covert channels were discussed next, as well as encryption and public-private key-based infrastructures. The idea of sym- metric key algorithms and the use of the popular asymmetric key-based public key infrastructure (PKI) scheme was explained. We also covered the concepts of digital signatures and digital certificates. We highlighted the importance of privacy issues and hinted at some privacy preservation techniques. We discussed a variety of chal- lenges to security including data quality, intellectual property rights, and data sur- vivability. We ended the chapter by introducing the implementation of security policies by using a combination of label-based security and virtual private databases in Oracle 11g.
Review Questions 24.1. Discuss what is meant by each of the following terms: database authoriza-
tion, access control, data encryption, privileged (system) account, database audit, audit trail.
24.2. Which account is designated as the owner of a relation? What privileges does the owner of a relation have?
24.3. How is the view mechanism used as an authorization mechanism?
24.4. Discuss the types of privileges at the account level and those at the relation level.
24.5. What is meant by granting a privilege? What is meant by revoking a privilege?
24.6. Discuss the system of propagation of privileges and the restraints imposed by horizontal and vertical propagation limits.
24.7. List the types of privileges available in SQL.
24.8. What is the difference between discretionary and mandatory access control?
24.9. What are the typical security classifications? Discuss the simple security property and the *-property, and explain the justification behind these rules for enforcing multilevel security.
24.10. Describe the multilevel relational data model. Define the following terms: apparent key, polyinstantiation, filtering.
24.11. What are the relative merits of using DAC or MAC?
24.12. What is role-based access control? In what ways is it superior to DAC and MAC?
24.13. What are the two types of mutual exclusion in role-based access control?
24.14. What is meant by row-level access control?
24.15. What is label security? How does an administrator enforce it?
Exercises 873
24.16. What are the different types of SQL injection attacks?
24.17. What risks are associated with SQL injection attacks?
24.18. What preventive measures are possible against SQL injection attacks?
24.19. What is a statistical database? Discuss the problem of statistical database security.
24.20. How is privacy related to statistical database security? What measures can be taken to ensure some degree of privacy in statistical databases?
24.21. What is flow control as a security measure? What types of flow control exist?
24.22. What are covert channels? Give an example of a covert channel.
24.23. What is the goal of encryption? What process is involved in encrypting data and then recovering it at the other end?
24.24. Give an example of an encryption algorithm and explain how it works.
24.25. Repeat the previous question for the popular RSA algorithm.
24.26. What is a symmetric key algorithm for key-based security?
24.27. What is the public key infrastructure scheme? How does it provide security?
24.28. What are digital signatures? How do they work?
24.29. What type of information does a digital certificate include?
Exercises 24.30. How can privacy of data be preserved in a database?
24.31. What are some of the current outstanding challenges for database security?
24.32. Consider the relational database schema in Figure 3.5. Suppose that all the relations were created by (and hence are owned by) user X, who wants to grant the following privileges to user accounts A, B, C, D, and E:
a. Account A can retrieve or modify any relation except DEPENDENT and can grant any of these privileges to other users.
b. Account B can retrieve all the attributes of EMPLOYEE and DEPARTMENT except for Salary, Mgr_ssn, and Mgr_start_date.
c. Account C can retrieve or modify WORKS_ON but can only retrieve the Fname, Minit, Lname, and Ssn attributes of EMPLOYEE and the Pname and Pnumber attributes of PROJECT.
d. Account D can retrieve any attribute of EMPLOYEE or DEPENDENT and can modify DEPENDENT.
e. Account E can retrieve any attribute of EMPLOYEE but only for EMPLOYEE tuples that have Dno = 3.
f. Write SQL statements to grant these privileges. Use views where appropriate.
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24.33. Suppose that privilege (a) of Exercise 24.32 is to be given with GRANT OPTION but only so that account A can grant it to at most five accounts, and each of these accounts can propagate the privilege to other accounts but without the GRANT OPTION privilege. What would the horizontal and verti- cal propagation limits be in this case?
24.34. Consider the relation shown in Figure 24.2(d). How would it appear to a user with classification U? Suppose that a classification U user tries to update the salary of ‘Smith’ to $50,000; what would be the result of this action?
Selected Bibliography Authorization based on granting and revoking privileges was proposed for the SYSTEM R experimental DBMS and is presented in Griffiths and Wade (1976). Several books discuss security in databases and computer systems in general, including the books by Leiss (1982a) and Fernandez et al. (1981), and Fugini et al. (1995). Natan (2005) is a practical book on security and auditing implementation issues in all major RDBMSs.
Many papers discuss different techniques for the design and protection of statistical databases. They include McLeish (1989), Chin and Ozsoyoglu (1981), Leiss (1982), Wong (1984), and Denning (1980). Ghosh (1984) discusses the use of statistical databases for quality control. There are also many papers discussing cryptography and data encryption, including Diffie and Hellman (1979), Rivest et al. (1978), Akl (1983), Pfleeger and Pfleeger (2007), Omura et al. (1990), Stallings (2000), and Iyer at al. (2004).
Halfond et al. (2006) helps understand the concepts of SQL injection attacks and the various threats imposed by them. The white paper Oracle (2007a) explains how Oracle is less prone to SQL injection attack as compared to SQL Server. It also gives a brief explanation as to how these attacks can be prevented from occurring. Further proposed frameworks are discussed in Boyd and Keromytis (2004), Halfond and Orso (2005), and McClure and Krüger (2005).
Multilevel security is discussed in Jajodia and Sandhu (1991), Denning et al. (1987), Smith and Winslett (1992), Stachour and Thuraisingham (1990), Lunt et al. (1990), and Bertino et al. (2001). Overviews of research issues in database security are given by Lunt and Fernandez (1990), Jajodia and Sandhu (1991), Bertino (1998), Castano et al. (1995), and Thuraisingham et al. (2001). The effects of multilevel security on concurrency control are discussed in Atluri et al. (1997). Security in next-generation, semantic, and object-oriented databases is discussed in Rabbiti et al. (1991), Jajodia and Kogan (1990), and Smith (1990). Oh (1999) presents a model for both discre- tionary and mandatory security. Security models for Web-based applications and role-based access control are discussed in Joshi et al. (2001). Security issues for man- agers in the context of e-commerce applications and the need for risk assessment models for selection of appropriate security control measures are discussed in
Selected Bibliography 875
Farahmand et al. (2005). Row-level access control is explained in detail in Oracle (2007b) and Sybase (2005). The latter also provides details on role hierarchy and mutual exclusion. Oracle (2009) explains how Oracle uses the concept of identity management.
Recent advances as well as future challenges for security and privacy of databases are discussed in Bertino and Sandhu (2005). U.S. Govt. (1978), OECD (1980), and NRC (2003) are good references on the view of privacy by important government bodies. Karat et al. (2009) discusses a policy framework for security and privacy. XML and access control are discussed in Naedele (2003). More details can be found on privacy preserving techniques in Vaidya and Clifton (2004), intellectual property rights in Sion et al. (2004), and database survivability in Jajodia et al. (1999). Oracle’s VPD technology and label-based security is discussed in more detail in Oracle (2007b).
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877
Distributed Databases
In this chapter we turn our attention to distributeddatabases (DDBs), distributed database management systems (DDBMSs), and how the client-server architecture is used as a platform for database application development. Distributed databases bring the advantages of distributed computing to the database management domain. A distributed com- puting system consists of a number of processing elements, not necessarily homo- geneous, that are interconnected by a computer network, and that cooperate in performing certain assigned tasks. As a general goal, distributed computing systems partition a big, unmanageable problem into smaller pieces and solve it efficiently in a coordinated manner. The economic viability of this approach stems from two rea- sons: more computing power is harnessed to solve a complex task, and each autonomous processing element can be managed independently to develop its own applications.
DDB technology resulted from a merger of two technologies: database technology, and network and data communication technology. Computer networks allow dis- tributed processing of data. Traditional databases, on the other hand, focus on pro- viding centralized, controlled access to data. Distributed databases allow an integration of information and its processing by applications that may themselves be centralized or distributed.
Several distributed database prototype systems were developed in the 1980s to address the issues of data distribution, distributed query and transaction process- ing, distributed database metadata management, and other topics. However, a full- scale comprehensive DDBMS that implements the functionality and techniques proposed in DDB research never emerged as a commercially viable product. Most major vendors redirected their efforts from developing a pure DDBMS product into developing systems based on client-server concepts, or toward developing technolo- gies for accessing distributed heterogeneous data sources.
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Organizations continue to be interested in the decentralization of processing (at the system level) while achieving an integration of the information resources (at the log- ical level) within their geographically distributed systems of databases, applications, and users. There is now a general endorsement of the client-server approach to application development, and the three-tier approach to Web applications develop- ment (see Section 2.5).
In this chapter we discuss distributed databases, their architectural variations, and concepts central to data distribution and the management of distributed data. Details of the advances in communication technologies facilitating the develop- ment of DDBs are outside the scope of this book; see the texts on data communica- tions and networking listed in the Selected Bibliography at the end of this chapter.
Section 25.1 introduces distributed database management and related concepts. Sections 25.2 and 25.3 introduce different types of distributed database systems and their architectures, including federated and multidatabase systems. The problems of heterogeneity and the needs of autonomy in federated database systems are also highlighted. Detailed issues of distributed database design, involving fragmenting of data and distributing it over multiple sites with possible replication, are discussed in Section 25.4. Sections 25.5 and 25.6 introduce distributed database query and trans- action processing techniques, respectively. Section 25.7 gives an overview of the concurrency control and recovery in distributed databases. Section 25.8 discusses catalog management schemes in distributed databases. In Section 25.9, we briefly discuss current trends in distributed databases such as cloud computing and peer- to-peer databases. Section 25.10 discusses distributed database features of the Oracle RDBMS. Section 25.11 summarizes the chapter.
For a short introduction to the topic of distributed databases, Sections 25.1, 25.2, and 25.3 may be covered.
25.1 Distributed Database Concepts1
We can define a distributed database (DDB) as a collection of multiple logically interrelated databases distributed over a computer network, and a distributed data- base management system (DDBMS) as a software system that manages a distrib- uted database while making the distribution transparent to the user.2
Distributed databases are different from Internet Web files. Web pages are basically a very large collection of files stored on different nodes in a network—the Internet—with interrelationships among the files represented via hyperlinks. The common functions of database management, including uniform query processing and transaction processing, do not apply to this scenario yet. The technology is, however, moving in a direction such that distributed World Wide Web (WWW) databases will become a reality in the future. We have discussed some of the issues of
1The substantial contribution of Narasimhan Srinivasan to this and several other sections in this chapter is appreciated. 2This definition and discussions in this section are based largely on Ozsu and Valduriez (1999).
25.1 Distributed Database Concepts 879
accessing databases on the Web in Chapters 12 and 14. The proliferation of data at millions of Websites in various forms does not qualify as a DDB by the definition given earlier.
25.1.1 Differences between DDB and Multiprocessor Systems We need to distinguish distributed databases from multiprocessor systems that use shared storage (primary memory or disk). For a database to be called distributed, the following minimum conditions should be satisfied:
■ Connection of database nodes over a computer network. There are multi- ple computers, called sites or nodes. These sites must be connected by an underlying communication network to transmit data and commands among sites, as shown later in Figure 25.3(c).
■ Logical interrelation of the connected databases. It is essential that the information in the databases be logically related.
■ Absence of homogeneity constraint among connected nodes. It is not nec- essary that all nodes be identical in terms of data, hardware, and software.
The sites may all be located in physical proximity—say, within the same building or a group of adjacent buildings—and connected via a local area network, or they may be geographically distributed over large distances and connected via a long-haul or wide area network. Local area networks typically use wireless hubs or cables, whereas long-haul networks use telephone lines or satellites. It is also possible to use a combination of networks.
Networks may have different topologies that define the direct communication paths among sites. The type and topology of the network used may have a signifi- cant impact on the performance and hence on the strategies for distributed query processing and distributed database design. For high-level architectural issues, how- ever, it does not matter what type of network is used; what matters is that each site be able to communicate, directly or indirectly, with every other site. For the remain- der of this chapter, we assume that some type of communication network exists among sites, regardless of any particular topology. We will not address any network- specific issues, although it is important to understand that for an efficient operation of a distributed database system (DDBS), network design and performance issues are critical and are an integral part of the overall solution. The details of the under- lying communication network are invisible to the end user.
25.1.2 Transparency The concept of transparency extends the general idea of hiding implementation details from end users. A highly transparent system offers a lot of flexibility to the end user/application developer since it requires little or no awareness of underlying details on their part. In the case of a traditional centralized database, transparency simply pertains to logical and physical data independence for application develop- ers. However, in a DDB scenario, the data and software are distributed over multiple
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sites connected by a computer network, so additional types of transparencies are introduced.
Consider the company database in Figure 3.5 that we have been discussing through- out the book. The EMPLOYEE, PROJECT, and WORKS_ON tables may be frag- mented horizontally (that is, into sets of rows, as we will discuss in Section 25.4) and stored with possible replication as shown in Figure 25.1. The following types of transparencies are possible:
■ Data organization transparency (also known as distribution or network transparency). This refers to freedom for the user from the operational details of the network and the placement of the data in the distributed sys- tem. It may be divided into location transparency and naming transparency. Location transparency refers to the fact that the command used to perform a task is independent of the location of the data and the location of the node where the command was issued. Naming transparency implies that once a name is associated with an object, the named objects can be accessed unam- biguously without additional specification as to where the data is located.
■ Replication transparency. As we show in Figure 25.1, copies of the same data objects may be stored at multiple sites for better availability, perfor- mance, and reliability. Replication transparency makes the user unaware of the existence of these copies.
■ Fragmentation transparency. Two types of fragmentation are possible. Horizontal fragmentation distributes a relation (table) into subrelations
EMPLOYEES
PROJECTS WORKS_ON
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San Francisco and Los Angeles
San Francisco
San Francisco employees
EMPLOYEES
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New York
All
New York employees
EMPLOYEES
PROJECTS
WORKS_ON
Atlanta
Atlanta
Atlanta employees
Chicago (Headquarters)
New York
Los Angeles Atlanta
San Francisco
Communications Network
Figure 25.1 Data distribution and replication among distributed databases.
25.1 Distributed Database Concepts 881
that are subsets of the tuples (rows) in the original relation. Vertical frag- mentation distributes a relation into subrelations where each subrelation is defined by a subset of the columns of the original relation. A global query by the user must be transformed into several fragment queries. Fragmentation transparency makes the user unaware of the existence of fragments.
■ Other transparencies include design transparency and execution trans- parency—referring to freedom from knowing how the distributed database is designed and where a transaction executes.
25.1.3 Autonomy Autonomy determines the extent to which individual nodes or DBs in a connected DDB can operate independently. A high degree of autonomy is desirable for increased flexibility and customized maintenance of an individual node. Autonomy can be applied to design, communication, and execution. Design autonomy refers to independence of data model usage and transaction management techniques among nodes. Communication autonomy determines the extent to which each node can decide on sharing of information with other nodes. Execution autonomy refers to independence of users to act as they please.
25.1.4 Reliability and Availability Reliability and availability are two of the most common potential advantages cited for distributed databases. Reliability is broadly defined as the probability that a sys- tem is running (not down) at a certain time point, whereas availability is the prob- ability that the system is continuously available during a time interval. We can directly relate reliability and availability of the database to the faults, errors, and fail- ures associated with it. A failure can be described as a deviation of a system’s behav- ior from that which is specified in order to ensure correct execution of operations. Errors constitute that subset of system states that causes the failure. Fault is the cause of an error.
To construct a system that is reliable, we can adopt several approaches. One com- mon approach stresses fault tolerance; it recognizes that faults will occur, and designs mechanisms that can detect and remove faults before they can result in a system failure. Another more stringent approach attempts to ensure that the final system does not contain any faults. This is done through an exhaustive design process followed by extensive quality control and testing. A reliable DDBMS toler- ates failures of underlying components and processes user requests so long as data- base consistency is not violated. A DDBMS recovery manager has to deal with failures arising from transactions, hardware, and communication networks. Hardware failures can either be those that result in loss of main memory contents or loss of secondary storage contents. Communication failures occur due to errors associated with messages and line failures. Message errors can include their loss, corruption, or out-of-order arrival at destination.
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25.1.5 Advantages of Distributed Databases Organizations resort to distributed database management for various reasons. Some important advantages are listed below.
1. Improved ease and flexibility of application development. Developing and maintaining applications at geographically distributed sites of an organiza- tion is facilitated owing to transparency of data distribution and control.
2. Increased reliability and availability. This is achieved by the isolation of faults to their site of origin without affecting the other databases connected to the network. When the data and DDBMS software are distributed over several sites, one site may fail while other sites continue to operate. Only the data and software that exist at the failed site cannot be accessed. This improves both reliability and availability. Further improvement is achieved by judiciously replicating data and software at more than one site. In a cen- tralized system, failure at a single site makes the whole system unavailable to all users. In a distributed database, some of the data may be unreachable, but users may still be able to access other parts of the database. If the data in the failed site had been replicated at another site prior to the failure, then the user will not be affected at all.
3. Improved performance. A distributed DBMS fragments the database by keeping the data closer to where it is needed most. Data localization reduces the contention for CPU and I/O services and simultaneously reduces access delays involved in wide area networks. When a large database is distributed over multiple sites, smaller databases exist at each site. As a result, local queries and transactions accessing data at a single site have better perfor- mance because of the smaller local databases. In addition, each site has a smaller number of transactions executing than if all transactions are submit- ted to a single centralized database. Moreover, interquery and intraquery parallelism can be achieved by executing multiple queries at different sites, or by breaking up a query into a number of subqueries that execute in paral- lel. This contributes to improved performance.
4. Easier expansion. In a distributed environment, expansion of the system in terms of adding more data, increasing database sizes, or adding more proces- sors is much easier.
The transparencies we discussed in Section 25.1.2 lead to a compromise between ease of use and the overhead cost of providing transparency. Total transparency provides the global user with a view of the entire DDBS as if it is a single centralized system. Transparency is provided as a complement to autonomy, which gives the users tighter control over local databases. Transparency features may be imple- mented as a part of the user language, which may translate the required services into appropriate operations. Additionally, transparency impacts the features that must be provided by the operating system and the DBMS.
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25.1.6 Additional Functions of Distributed Databases Distribution leads to increased complexity in the system design and implementa- tion. To achieve the potential advantages listed previously, the DDBMS software must be able to provide the following functions in addition to those of a centralized DBMS:
■ Keeping track of data distribution. The ability to keep track of the data dis- tribution, fragmentation, and replication by expanding the DDBMS catalog.
■ Distributed query processing. The ability to access remote sites and trans- mit queries and data among the various sites via a communication network.
■ Distributed transaction management. The ability to devise execution strategies for queries and transactions that access data from more than one site and to synchronize the access to distributed data and maintain the integrity of the overall database.
■ Replicated data management. The ability to decide which copy of a repli- cated data item to access and to maintain the consistency of copies of a repli- cated data item.
■ Distributed database recovery. The ability to recover from individual site crashes and from new types of failures, such as the failure of communication links.
■ Security. Distributed transactions must be executed with the proper man- agement of the security of the data and the authorization/access privileges of users.
■ Distributed directory (catalog) management. A directory contains infor- mation (metadata) about data in the database. The directory may be global for the entire DDB, or local for each site. The placement and distribution of the directory are design and policy issues.
These functions themselves increase the complexity of a DDBMS over a centralized DBMS. Before we can realize the full potential advantages of distribution, we must find satisfactory solutions to these design issues and problems. Including all this additional functionality is hard to accomplish, and finding optimal solutions is a step beyond that.
25.2 Types of Distributed Database Systems The term distributed database management system can describe various systems that differ from one another in many respects. The main thing that all such systems have in common is the fact that data and software are distributed over multiple sites con- nected by some form of communication network. In this section we discuss a num- ber of types of DDBMSs and the criteria and factors that make some of these systems different.
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The first factor we consider is the degree of homogeneity of the DDBMS software. If all servers (or individual local DBMSs) use identical software and all users (clients) use identical software, the DDBMS is called homogeneous; otherwise, it is called heterogeneous. Another factor related to the degree of homogeneity is the degree of local autonomy. If there is no provision for the local site to function as a standalone DBMS, then the system has no local autonomy. On the other hand, if direct access by local transactions to a server is permitted, the system has some degree of local autonomy.
Figure 25.2 shows classification of DDBMS alternatives along orthogonal axes of distribution, autonomy, and heterogeneity. For a centralized database, there is com- plete autonomy, but a total lack of distribution and heterogeneity (Point A in the figure). We see that the degree of local autonomy provides further ground for classi- fication into federated and multidatabase systems. At one extreme of the autonomy spectrum, we have a DDBMS that looks like a centralized DBMS to the user, with zero autonomy (Point B). A single conceptual schema exists, and all access to the system is obtained through a site that is part of the DDBMS—which means that no local autonomy exists. Along the autonomy axis we encounter two types of DDBMSs called federated database system (Point C) and multidatabase system (Point D). In such systems, each server is an independent and autonomous central- ized DBMS that has its own local users, local transactions, and DBA, and hence has
B
Distribution
Heterogeneity
Legend:
A: Traditional centralized database systems
B: Pure distributed database systems
C: Federated database systems
D: Multidatabase or peer to peer database systems
C D
A Autonomy
Figure 25.2 Classification of dis- tributed databases.
25.2 Types of Distributed Database Systems 885
a very high degree of local autonomy. The term federated database system (FDBS) is used when there is some global view or schema of the federation of databases that is shared by the applications (Point C). On the other hand, a multidatabase system has full local autonomy in that it does not have a global schema but interactively constructs one as needed by the application (Point D).3 Both systems are hybrids between distributed and centralized systems, and the distinction we made between them is not strictly followed. We will refer to them as FDBSs in a generic sense. Point D in the diagram may also stand for a system with full local autonomy and full het- erogeneity—this could be a peer-to-peer database system (see Section 25.9.2). In a heterogeneous FDBS, one server may be a relational DBMS, another a network DBMS (such as Computer Associates’ IDMS or HP’S IMAGE/3000), and a third an object DBMS (such as Object Design’s ObjectStore) or hierarchical DBMS (such as IBM’s IMS); in such a case, it is necessary to have a canonical system language and to include language translators to translate subqueries from the canonical language to the language of each server.
We briefly discuss the issues affecting the design of FDBSs next.
25.2.1 Federated Database Management Systems Issues The type of heterogeneity present in FDBSs may arise from several sources. We dis- cuss these sources first and then point out how the different types of autonomies contribute to a semantic heterogeneity that must be resolved in a heterogeneous FDBS.
■ Differences in data models. Databases in an organization come from a vari- ety of data models, including the so-called legacy models (hierarchical and network, see Web Appendixes D and E), the relational data model, the object data model, and even files. The modeling capabilities of the models vary. Hence, to deal with them uniformly via a single global schema or to process them in a single language is challenging. Even if two databases are both from the RDBMS environment, the same information may be represented as an attribute name, as a relation name, or as a value in different databases. This calls for an intelligent query-processing mechanism that can relate informa- tion based on metadata.
■ Differences in constraints. Constraint facilities for specification and imple- mentation vary from system to system. There are comparable features that must be reconciled in the construction of a global schema. For example, the relationships from ER models are represented as referential integrity con- straints in the relational model. Triggers may have to be used to implement certain constraints in the relational model. The global schema must also deal with potential conflicts among constraints.
3The term multidatabase system is not easily applicable to most enterprise IT environments. The notion of constructing a global schema as and when the need arises is not very feasible in practice for enter- prise databases.
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■ Differences in query languages. Even with the same data model, the lan- guages and their versions vary. For example, SQL has multiple versions like SQL-89, SQL-92, SQL-99, and SQL:2008, and each system has its own set of data types, comparison operators, string manipulation features, and so on.
Semantic Heterogeneity. Semantic heterogeneity occurs when there are differ- ences in the meaning, interpretation, and intended use of the same or related data. Semantic heterogeneity among component database systems (DBSs) creates the biggest hurdle in designing global schemas of heterogeneous databases. The design autonomy of component DBSs refers to their freedom of choosing the following design parameters, which in turn affect the eventual complexity of the FDBS:
■ The universe of discourse from which the data is drawn. For example, for two customer accounts, databases in the federation may be from the United States and Japan and have entirely different sets of attributes about customer accounts required by the accounting practices. Currency rate fluctuations would also present a problem. Hence, relations in these two databases that have identical names—CUSTOMER or ACCOUNT—may have some com- mon and some entirely distinct information.
■ Representation and naming. The representation and naming of data ele- ments and the structure of the data model may be prespecified for each local database.
■ The understanding, meaning, and subjective interpretation of data. This is a chief contributor to semantic heterogeneity.
■ Transaction and policy constraints. These deal with serializability criteria, compensating transactions, and other transaction policies.
■ Derivation of summaries. Aggregation, summarization, and other data- processing features and operations supported by the system.
The above problems related to semantic heterogeneity are being faced by all major multinational and governmental organizations in all application areas. In today’s commercial environment, most enterprises are resorting to heterogeneous FDBSs, having heavily invested in the development of individual database systems using diverse data models on different platforms over the last 20 to 30 years. Enterprises are using various forms of software—typically called the middleware, or Web- based packages called application servers (for example, WebLogic or WebSphere) and even generic systems, called Enterprise Resource Planning (ERP) systems (for example, SAP, J. D. Edwards ERP)—to manage the transport of queries and transac- tions from the global application to individual databases (with possible additional processing for business rules) and the data from the heterogeneous database servers to the global application. Detailed discussion of these types of software systems is outside the scope of this book.
Just as providing the ultimate transparency is the goal of any distributed database architecture, local component databases strive to preserve autonomy. Communication autonomy of a component DBS refers to its ability to decide whether to communicate with another component DBS. Execution autonomy
25.3 Distributed Database Architectures 887
refers to the ability of a component DBS to execute local operations without inter- ference from external operations by other component DBSs and its ability to decide the order in which to execute them. The association autonomy of a component DBS implies that it has the ability to decide whether and how much to share its functionality (operations it supports) and resources (data it manages) with other component DBSs. The major challenge of designing FDBSs is to let component DBSs interoperate while still providing the above types of autonomies to them.
25.3 Distributed Database Architectures In this section, we first briefly point out the distinction between parallel and distrib- uted database architectures. While both are prevalent in industry today, there are various manifestations of the distributed architectures that are continuously evolv- ing among large enterprises. The parallel architecture is more common in high- performance computing, where there is a need for multiprocessor architectures to cope with the volume of data undergoing transaction processing and warehousing applications. We then introduce a generic architecture of a distributed database. This is followed by discussions on the architecture of three-tier client-server and federated database systems.
25.3.1 Parallel versus Distributed Architectures There are two main types of multiprocessor system architectures that are common- place:
■ Shared memory (tightly coupled) architecture. Multiple processors share secondary (disk) storage and also share primary memory.
■ Shared disk (loosely coupled) architecture. Multiple processors share sec- ondary (disk) storage but each has their own primary memory.
These architectures enable processors to communicate without the overhead of exchanging messages over a network.4 Database management systems developed using the above types of architectures are termed parallel database management systems rather than DDBMSs, since they utilize parallel processor technology. Another type of multiprocessor architecture is called shared nothing architecture. In this architecture, every processor has its own primary and secondary (disk) memory, no common memory exists, and the processors communicate over a high- speed interconnection network (bus or switch). Although the shared nothing archi- tecture resembles a distributed database computing environment, major differences exist in the mode of operation. In shared nothing multiprocessor systems, there is symmetry and homogeneity of nodes; this is not true of the distributed database environment where heterogeneity of hardware and operating system at each node is very common. Shared nothing architecture is also considered as an environment for
4If both primary and secondary memories are shared, the architecture is also known as shared everything architecture.
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parallel databases. Figure 25.3a illustrates a parallel database (shared nothing), whereas Figure 25.3b illustrates a centralized database with distributed access and Figure 25.3c shows a pure distributed database. We will not expand on parallel architectures and related data management issues here.
(a)
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Figure 25.3 Some different database system architectures. (a) Shared nothing architecture. (b) A networked architecture with a centralized database at one of the sites. (c) A truly distributed database architecture.
25.3 Distributed Database Architectures 889
User
Stored Data
Global Conceptual Schema (GCS)
External View
User
External View
Local Conceptual Schema (LCS) Local Conceptual Schema (LCS)
Local Internal Schema (LIS) Local Internal Schema (LIS)
Stored Data
Site 1 Site nSites 2 to n–1
Figure 25.4 Schema architecture of distributed databases.
25.3.2 General Architecture of Pure Distributed Databases In this section we discuss both the logical and component architectural models of a DDB. In Figure 25.4, which describes the generic schema architecture of a DDB, the enterprise is presented with a consistent, unified view showing the logical structure of underlying data across all nodes. This view is represented by the global concep- tual schema (GCS), which provides network transparency (see Section 25.1.2). To accommodate potential heterogeneity in the DDB, each node is shown as having its own local internal schema (LIS) based on physical organization details at that par- ticular site. The logical organization of data at each site is specified by the local con- ceptual schema (LCS). The GCS, LCS, and their underlying mappings provide the fragmentation and replication transparency discussed in Section 25.1.2. Figure 25.5 shows the component architecture of a DDB. It is an extension of its centralized counterpart (Figure 2.3) in Chapter 2. For the sake of simplicity, common elements are not shown here. The global query compiler references the global conceptual schema from the global system catalog to verify and impose defined constraints. The global query optimizer references both global and local conceptual schemas and generates optimized local queries from global queries. It evaluates all candidate strategies using a cost function that estimates cost based on response time (CPU,
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User
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Stored Data
Global Query Compiler
Global Query Optimizer
Global Transaction Manager
Local Transaction Manager
Local Query Translation
and Execution
Local System Catalog
Stored Data
Local Transaction Manager
Local Query Translation
and Execution
Local System Catalog
Figure 25.5 Component architecture of distributed databases.
I/O, and network latencies) and estimated sizes of intermediate results. The latter is particularly important in queries involving joins. Having computed the cost for each candidate, the optimizer selects the candidate with the minimum cost for exe- cution. Each local DBMS would have their local query optimizer, transaction man- ager, and execution engines as well as the local system catalog, which houses the local schemas. The global transaction manager is responsible for coordinating the execution across multiple sites in conjunction with the local transaction manager at those sites.
25.3.3 Federated Database Schema Architecture Typical five-level schema architecture to support global applications in the FDBS environment is shown in Figure 25.6. In this architecture, the local schema is the
External schema
Federated schema
. . .
. . .
. . .
. . .
. . .
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Local schema
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Local schema
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Export schema
25.3 Distributed Database Architectures 891
Figure 25.6 The five-level schema architecture in a federated database system (FDBS).
Source: Adapted from Sheth and Larson, “Federated Database Systems for Managing Distributed, Heterogeneous, and Autonomous Databases.” ACM Computing Surveys (Vol. 22: No. 3, September 1990).
conceptual schema (full database definition) of a component database, and the component schema is derived by translating the local schema into a canonical data model or common data model (CDM) for the FDBS. Schema translation from the local schema to the component schema is accompanied by generating mappings to transform commands on a component schema into commands on the corres- ponding local schema. The export schema represents the subset of a component schema that is available to the FDBS. The federated schema is the global schema or view, which is the result of integrating all the shareable export schemas. The external schemas define the schema for a user group or an application, as in the three-level schema architecture.5
All the problems related to query processing, transaction processing, and directory and metadata management and recovery apply to FDBSs with additional considera- tions. It is not within our scope to discuss them in detail here.
5For a detailed discussion of the autonomies and the five-level architecture of FDBMSs, see Sheth and Larson (1990).
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Client User interface or presentation tier
(Web browser, HTML, JavaScript, Visual Basic, . . .)
HTTP Protocol
Application server Application (business) logic tier
(Application program, JAVA, C/C++, C#, . . .)
Database server Query and transaction processing tier
(Database access, SQL, PSM, XML, . . .)
ODBC, JDBC, SQL/CLI, SQLJ
Figure 25.7 The three-tier client-server architecture.
25.3.4 An Overview of Three-Tier Client-Server Architecture As we pointed out in the chapter introduction, full-scale DDBMSs have not been developed to support all the types of functionalities that we have discussed so far. Instead, distributed database applications are being developed in the context of the client-server architectures. We introduced the two-tier client-server architecture in Section 2.5. It is now more common to use a three-tier architecture, particularly in Web applications. This architecture is illustrated in Figure 25.7.
In the three-tier client-server architecture, the following three layers exist:
1. Presentation layer (client). This provides the user interface and interacts with the user. The programs at this layer present Web interfaces or forms to the client in order to interface with the application. Web browsers are often utilized, and the languages and specifications used include HTML, XHTML, CSS, Flash, MathML, Scalable Vector Graphics (SVG), Java, JavaScript, Adobe Flex, and others. This layer handles user input, output, and naviga- tion by accepting user commands and displaying the needed information, usually in the form of static or dynamic Web pages. The latter are employed when the interaction involves database access. When a Web interface is used, this layer typically communicates with the application layer via the HTTP protocol.
2. Application layer (business logic). This layer programs the application logic. For example, queries can be formulated based on user input from the client, or query results can be formatted and sent to the client for presenta- tion. Additional application functionality can be handled at this layer, such
25.3 Distributed Database Architectures 893
as security checks, identity verification, and other functions. The application layer can interact with one or more databases or data sources as needed by connecting to the database using ODBC, JDBC, SQL/CLI, or other database access techniques.
3. Database server. This layer handles query and update requests from the application layer, processes the requests, and sends the results. Usually SQL is used to access the database if it is relational or object-relational and stored database procedures may also be invoked. Query results (and queries) may be formatted into XML (see Chapter 12) when transmitted between the application server and the database server.
Exactly how to divide the DBMS functionality between the client, application server, and database server may vary. The common approach is to include the func- tionality of a centralized DBMS at the database server level. A number of relational DBMS products have taken this approach, where an SQL server is provided. The application server must then formulate the appropriate SQL queries and connect to the database server when needed. The client provides the processing for user inter- face interactions. Since SQL is a relational standard, various SQL servers, possibly provided by different vendors, can accept SQL commands through standards such as ODBC, JDBC, and SQL/CLI (see Chapter 13).
In this architecture, the application server may also refer to a data dictionary that includes information on the distribution of data among the various SQL servers, as well as modules for decomposing a global query into a number of local queries that can be executed at the various sites. Interaction between an application server and database server might proceed as follows during the processing of an SQL query:
1. The application server formulates a user query based on input from the client layer and decomposes it into a number of independent site queries. Each site query is sent to the appropriate database server site.
2. Each database server processes the local query and sends the results to the application server site. Increasingly, XML is being touted as the standard for data exchange (see Chapter 12), so the database server may format the query result into XML before sending it to the application server.
3. The application server combines the results of the subqueries to produce the result of the originally required query, formats it into HTML or some other form accepted by the client, and sends it to the client site for display.
The application server is responsible for generating a distributed execution plan for a multisite query or transaction and for supervising distributed execution by send- ing commands to servers. These commands include local queries and transactions to be executed, as well as commands to transmit data to other clients or servers. Another function controlled by the application server (or coordinator) is that of ensuring consistency of replicated copies of a data item by employing distributed (or global) concurrency control techniques. The application server must also ensure the atomicity of global transactions by performing global recovery when certain sites fail.
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If the DDBMS has the capability to hide the details of data distribution from the application server, then it enables the application server to execute global queries and transactions as though the database were centralized, without having to specify the sites at which the data referenced in the query or transaction resides. This prop- erty is called distribution transparency. Some DDBMSs do not provide distribu- tion transparency, instead requiring that applications are aware of the details of data distribution.
25.4 Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design
In this section we discuss techniques that are used to break up the database into log- ical units, called fragments, which may be assigned for storage at the various sites. We also discuss the use of data replication, which permits certain data to be stored in more than one site, and the process of allocating fragments—or replicas of frag- ments—for storage at the various sites. These techniques are used during the process of distributed database design. The information concerning data fragmen- tation, allocation, and replication is stored in a global directory that is accessed by the DDBS applications as needed.
25.4.1 Data Fragmentation In a DDB, decisions must be made regarding which site should be used to store which portions of the database. For now, we will assume that there is no replication; that is, each relation—or portion of a relation—is stored at one site only. We discuss replication and its effects later in this section. We also use the terminology of rela- tional databases, but similar concepts apply to other data models. We assume that we are starting with a relational database schema and must decide on how to dis- tribute the relations over the various sites. To illustrate our discussion, we use the relational database schema in Figure 3.5.
Before we decide on how to distribute the data, we must determine the logical units of the database that are to be distributed. The simplest logical units are the relations themselves; that is, each whole relation is to be stored at a particular site. In our example, we must decide on a site to store each of the relations EMPLOYEE, DEPARTMENT, PROJECT, WORKS_ON, and DEPENDENT in Figure 3.5. In many cases, however, a relation can be divided into smaller logical units for distribution. For example, consider the company database shown in Figure 3.6, and assume there are three computer sites—one for each department in the company.6
We may want to store the database information relating to each department at the computer site for that department. A technique called horizontal fragmentation can be used to partition each relation by department.
6Of course, in an actual situation, there will be many more tuples in the relation than those shown in Figure 3.6.
25.4 Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design 895
Horizontal Fragmentation. A horizontal fragment of a relation is a subset of the tuples in that relation. The tuples that belong to the horizontal fragment are specified by a condition on one or more attributes of the relation. Often, only a sin- gle attribute is involved. For example, we may define three horizontal fragments on the EMPLOYEE relation in Figure 3.6 with the following conditions: (Dno = 5), (Dno = 4), and (Dno = 1)—each fragment contains the EMPLOYEE tuples working for a particular department. Similarly, we may define three horizontal fragments for the PROJECT relation, with the conditions (Dnum = 5), (Dnum = 4), and (Dnum = 1)—each fragment contains the PROJECT tuples controlled by a particu- lar department. Horizontal fragmentation divides a relation horizontally by grouping rows to create subsets of tuples, where each subset has a certain logical meaning. These fragments can then be assigned to different sites in the distributed system. Derived horizontal fragmentation applies the partitioning of a primary relation (DEPARTMENT in our example) to other secondary relations (EMPLOYEE and PROJECT in our example), which are related to the primary via a foreign key. This way, related data between the primary and the secondary relations gets frag- mented in the same way.
Vertical Fragmentation. Each site may not need all the attributes of a relation, which would indicate the need for a different type of fragmentation. Vertical frag- mentation divides a relation “vertically” by columns. A vertical fragment of a rela- tion keeps only certain attributes of the relation. For example, we may want to fragment the EMPLOYEE relation into two vertical fragments. The first fragment includes personal information—Name, Bdate, Address, and Sex—and the second includes work-related information—Ssn, Salary, Super_ssn, and Dno. This vertical fragmentation is not quite proper, because if the two fragments are stored sepa- rately, we cannot put the original employee tuples back together, since there is no common attribute between the two fragments. It is necessary to include the primary key or some candidate key attribute in every vertical fragment so that the full rela- tion can be reconstructed from the fragments. Hence, we must add the Ssn attribute to the personal information fragment.
Notice that each horizontal fragment on a relation R can be specified in the rela- tional algebra by a σCi
(R) operation. A set of horizontal fragments whose conditions C1, C2, ..., Cn include all the tuples in R—that is, every tuple in R satisfies (C1 OR C2 OR ... OR Cn)—is called a complete horizontal fragmentation of R. In many cases a complete horizontal fragmentation is also disjoint; that is, no tuple in R satisfies (Ci AND Cj) for any i ≠ j. Our two earlier examples of horizontal fragmentation for the EMPLOYEE and PROJECT relations were both complete and disjoint. To recon- struct the relation R from a complete horizontal fragmentation, we need to apply the UNION operation to the fragments.
A vertical fragment on a relation R can be specified by a πLi (R) operation in the rela-
tional algebra. A set of vertical fragments whose projection lists L1, L2, ..., Ln include all the attributes in R but share only the primary key attribute of R is called a
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complete vertical fragmentation of R. In this case the projection lists satisfy the fol- lowing two conditions:
■ L1 ∪ L2 ∪ ... ∪ Ln = ATTRS(R). ■ Li ∩ Lj = PK(R) for any i ≠ j, where ATTRS(R) is the set of attributes of R and
PK(R) is the primary key of R.
To reconstruct the relation R from a complete vertical fragmentation, we apply the OUTER UNION operation to the vertical fragments (assuming no horizontal frag- mentation is used). Notice that we could also apply a FULL OUTER JOIN operation and get the same result for a complete vertical fragmentation, even when some hor- izontal fragmentation may also have been applied. The two vertical fragments of the EMPLOYEE relation with projection lists L1 = {Ssn, Name, Bdate, Address, Sex} and L2 = {Ssn, Salary, Super_ssn, Dno} constitute a complete vertical fragmentation of EMPLOYEE.
Two horizontal fragments that are neither complete nor disjoint are those defined on the EMPLOYEE relation in Figure 3.5 by the conditions (Salary > 50000) and (Dno = 4); they may not include all EMPLOYEE tuples, and they may include com- mon tuples. Two vertical fragments that are not complete are those defined by the attribute lists L1 = {Name, Address} and L2 = {Ssn, Name, Salary}; these lists violate both conditions of a complete vertical fragmentation.
Mixed (Hybrid) Fragmentation. We can intermix the two types of fragmenta- tion, yielding a mixed fragmentation. For example, we may combine the horizon- tal and vertical fragmentations of the EMPLOYEE relation given earlier into a mixed fragmentation that includes six fragments. In this case, the original relation can be reconstructed by applying UNION and OUTER UNION (or OUTER JOIN) operations in the appropriate order. In general, a fragment of a relation R can be specified by a SELECT-PROJECT combination of operations πL(σC(R)). If C = TRUE (that is, all tuples are selected) and L ≠ ATTRS(R), we get a vertical frag- ment, and if C ≠ TRUE and L = ATTRS(R), we get a horizontal fragment. Finally, if C ≠ TRUE and L ≠ ATTRS(R), we get a mixed fragment. Notice that a relation can itself be considered a fragment with C = TRUE and L = ATTRS(R). In the following discussion, the term fragment is used to refer to a relation or to any of the preced- ing types of fragments.
A fragmentation schema of a database is a definition of a set of fragments that includes all attributes and tuples in the database and satisfies the condition that the whole database can be reconstructed from the fragments by applying some sequence of OUTER UNION (or OUTER JOIN) and UNION operations. It is also sometimes useful—although not necessary—to have all the fragments be disjoint except for the repetition of primary keys among vertical (or mixed) fragments. In the latter case, all replication and distribution of fragments is clearly specified at a subsequent stage, separately from fragmentation.
An allocation schema describes the allocation of fragments to sites of the DDBS; hence, it is a mapping that specifies for each fragment the site(s) at which it is
25.4 Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design 897
stored. If a fragment is stored at more than one site, it is said to be replicated. We discuss data replication and allocation next.
25.4.2 Data Replication and Allocation Replication is useful in improving the availability of data. The most extreme case is replication of the whole database at every site in the distributed system, thus creating a fully replicated distributed database. This can improve availability remarkably because the system can continue to operate as long as at least one site is up. It also improves performance of retrieval for global queries because the results of such queries can be obtained locally from any one site; hence, a retrieval query can be processed at the local site where it is submitted, if that site includes a server module. The disadvantage of full replication is that it can slow down update operations drasti- cally, since a single logical update must be performed on every copy of the database to keep the copies consistent. This is especially true if many copies of the database exist. Full replication makes the concurrency control and recovery techniques more expen- sive than they would be if there was no replication, as we will see in Section 25.7.
The other extreme from full replication involves having no replication—that is, each fragment is stored at exactly one site. In this case, all fragments must be dis- joint, except for the repetition of primary keys among vertical (or mixed) frag- ments. This is also called nonredundant allocation.
Between these two extremes, we have a wide spectrum of partial replication of the data—that is, some fragments of the database may be replicated whereas others may not. The number of copies of each fragment can range from one up to the total num- ber of sites in the distributed system. A special case of partial replication is occurring heavily in applications where mobile workers—such as sales forces, financial plan- ners, and claims adjustors—carry partially replicated databases with them on laptops and PDAs and synchronize them periodically with the server database.7 A descrip- tion of the replication of fragments is sometimes called a replication schema.
Each fragment—or each copy of a fragment—must be assigned to a particular site in the distributed system. This process is called data distribution (or data alloca- tion). The choice of sites and the degree of replication depend on the performance and availability goals of the system and on the types and frequencies of transactions submitted at each site. For example, if high availability is required, transactions can be submitted at any site, and most transactions are retrieval only, a fully replicated database is a good choice. However, if certain transactions that access particular parts of the database are mostly submitted at a particular site, the corresponding set of fragments can be allocated at that site only. Data that is accessed at multiple sites can be replicated at those sites. If many updates are performed, it may be useful to limit replication. Finding an optimal or even a good solution to distributed data allocation is a complex optimization problem.
7For a proposed scalable approach to synchronize partially replicated databases, see Mahajan et al. (1998).
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25.4.3 Example of Fragmentation, Allocation, and Replication We now consider an example of fragmenting and distributing the company data- base in Figures 3.5 and 3.6. Suppose that the company has three computer sites— one for each current department. Sites 2 and 3 are for departments 5 and 4, respectively. At each of these sites, we expect frequent access to the EMPLOYEE and PROJECT information for the employees who work in that department and the proj- ects controlled by that department. Further, we assume that these sites mainly access the Name, Ssn, Salary, and Super_ssn attributes of EMPLOYEE. Site 1 is used by com- pany headquarters and accesses all employee and project information regularly, in addition to keeping track of DEPENDENT information for insurance purposes.
According to these requirements, the whole database in Figure 3.6 can be stored at site 1. To determine the fragments to be replicated at sites 2 and 3, first we can hori- zontally fragment DEPARTMENT by its key Dnumber. Then we apply derived frag- mentation to the EMPLOYEE, PROJECT, and DEPT_LOCATIONS relations based on their foreign keys for department number—called Dno, Dnum, and Dnumber, respec- tively, in Figure 3.5. We can vertically fragment the resulting EMPLOYEE fragments to include only the attributes {Name, Ssn, Salary, Super_ssn, Dno}. Figure 25.8 shows the mixed fragments EMPD_5 and EMPD_4, which include the EMPLOYEE tuples satisfying the conditions Dno = 5 and Dno = 4, respectively. The horizontal frag- ments of PROJECT, DEPARTMENT, and DEPT_LOCATIONS are similarly fragmented by department number. All these fragments—stored at sites 2 and 3—are replicated because they are also stored at headquarters—site 1.
We must now fragment the WORKS_ON relation and decide which fragments of WORKS_ON to store at sites 2 and 3. We are confronted with the problem that no attribute of WORKS_ON directly indicates the department to which each tuple belongs. In fact, each tuple in WORKS_ON relates an employee e to a project P. We could fragment WORKS_ON based on the department D in which e works or based on the department D� that controls P. Fragmentation becomes easy if we have a con- straint stating that D = D� for all WORKS_ON tuples—that is, if employees can work only on projects controlled by the department they work for. However, there is no such constraint in our database in Figure 3.6. For example, the WORKS_ON tuple <333445555, 10, 10.0> relates an employee who works for department 5 with a project controlled by department 4. In this case, we could fragment WORKS_ON based on the department in which the employee works (which is expressed by the condition C) and then fragment further based on the department that controls the projects that employee is working on, as shown in Figure 25.9.
In Figure 25.9, the union of fragments G1, G2, and G3 gives all WORKS_ON tuples for employees who work for department 5. Similarly, the union of fragments G4, G5, and G6 gives all WORKS_ON tuples for employees who work for department 4. On the other hand, the union of fragments G1, G4, and G7 gives all WORKS_ON tuples for projects controlled by department 5. The condition for each of the fragments G1 through G9 is shown in Figure 25.9 The relations that represent M:N relationships, such as WORKS_ON, often have several possible logical fragmentations. In our dis- tribution in Figure 25.8, we choose to include all fragments that can be joined to
25.4 Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design 899
(a)
(b)
Fname
John B Smith 123456789 30000 333445555 5
Franklin T Wong 333445555 40000 888665555 5
K Narayan 666884444 38000 333445555 5
A English 453453453 25000 333445555 5
Ramesh
Joyce
EMPD_5
Minit Lname Ssn Salary Super_ssn Dno
Data at site 2
Data at site 3
Fname
Alicia J Zelaya 999887777 25000 987654321 4
Jennifer S Wallace 987654321 43000 888665555 4
V Jabbar 987987987 25000 987654321 4Ahmad
EMPD_4
Minit Lname Ssn Salary Super_ssn Dno
Dname
Research 5 333445555 1988-05-22
DEP_5
Dnumber Mgr_ssn Mgr_start_date Dnumber
5 Bellaire
5 Sugarland
5 Houston
DEP_5_LOCS
Location
Dname
Administration 4 987654321 1995-01-01
DEP_4
Dnumber Mgr_ssn Mgr_start_date
Essn
123456789 1
123456789 2
666884444
453453453
453453453
333445555
333445555
333445555
333445555
1
2
2
3
10
20
3
32.5
7.5
20.0
20.0
10.0
10.0
10.0
10.0
40.0
WORKS_ON_5
Pno Hours Pname
Product X 1
Product Y 2
Product Z 3
Bellaire
Sugarland
Houston
PROJS_5
Pnumber Plocation
5
5
5
Dnum
Essn
333445555 10
999887777 30
999887777
987987987
987987987
987654321
987654321
10
30
30
20
10
10.0
30.0
35.0
5.0
20.0
15.0
10.0
WORKS_ON_4
Pno Hours Pname
Computerization 10
New_benefits 30
Stafford
Stafford
PROJS_4
Pnumber Plocation
4
4
Dnum
Dnumber
4 Stafford
DEP_4_LOCS
Location
Figure 25.8 Allocation of fragments to sites. (a) Relation fragments at site 2 corresponding to department 5. (b) Relation fragments at site 3 corre- sponding to department 4.
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Essn
123456789 1 32.5
123456789 2 7.5
3 40.0
1 20.0
2 20.0
2 10.0
3 10.0
666884444
453453453
453453453
333445555
333445555
G1
1C = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 5))
Employees in Department 5
Pno Hours Essn
333445555 10 10.0
G2
C2 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 4))
Pno Hours Essn
333445555 20 10.0
G3
C3 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 1))
Pno Hours
Essn
G4
(b)
(c)
(a)
C4 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 5))
Employees in Department 4
Pno Hours Essn
999887777 30 30.0
999887777 10 10.0
987987987 10 35.0
987987987 30 5.0
987654321 30 20.0
G5
C5 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 4))
Pno Hours Essn
987654321 20 15.0
G6
C6 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 1))
Pno Hours
Essn
G7
C7 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 5))
Employees in Department 1
Pno Hours Essn
G8
C8 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 4))
Pno Hours Essn
888665555 20 Null
G9
C9 = C and (Pno in (SELECT Pnumber FROM PROJECT WHERE Dnum = 1))
Pno Hours
Figure 25.9 Complete and disjoint fragments of the WORKS_ON relation. (a) Fragments of WORKS_ON for employees working in department 5 (C=[Essn in (SELECT Ssn FROM EMPLOYEE WHERE Dno=5)]). (b) Fragments of WORKS_ON for employees working in department 4 (C=[Essn in (SELECT Ssn FROM EMPLOYEE WHERE Dno=4)]). (c) Fragments of WORKS_ON for employees working in department 1 (C=[Essn in (SELECT Ssn FROM EMPLOYEE WHERE Dno=1)]).
25.5 Query Processing and Optimization in Distributed Databases 901
either an EMPLOYEE tuple or a PROJECT tuple at sites 2 and 3. Hence, we place the union of fragments G1, G2, G3, G4, and G7 at site 2 and the union of fragments G4, G5, G6, G2, and G8 at site 3. Notice that fragments G2 and G4 are replicated at both sites. This allocation strategy permits the join between the local EMPLOYEE or PROJECT fragments at site 2 or site 3 and the local WORKS_ON fragment to be per- formed completely locally. This clearly demonstrates how complex the problem of database fragmentation and allocation is for large databases. The Selected Bibliography at the end of this chapter discusses some of the work done in this area.
25.5 Query Processing and Optimization in Distributed Databases
Now we give an overview of how a DDBMS processes and optimizes a query. First we discuss the steps involved in query processing and then elaborate on the commu- nication costs of processing a distributed query. Finally we discuss a special opera- tion, called a semijoin, which is used to optimize some types of queries in a DDBMS. A detailed discussion about optimization algorithms is beyond the scope of this book. We attempt to illustrate optimization principles using suitable examples.8
25.5.1 Distributed Query Processing A distributed database query is processed in stages as follows:
1. Query Mapping. The input query on distributed data is specified formally using a query language. It is then translated into an algebraic query on global relations. This translation is done by referring to the global conceptual schema and does not take into account the actual distribution and replica- tion of data. Hence, this translation is largely identical to the one performed in a centralized DBMS. It is first normalized, analyzed for semantic errors, simplified, and finally restructured into an algebraic query.
2. Localization. In a distributed database, fragmentation results in relations being stored in separate sites, with some fragments possibly being replicated. This stage maps the distributed query on the global schema to separate queries on individual fragments using data distribution and replication information.
3. Global Query Optimization. Optimization consists of selecting a strategy from a list of candidates that is closest to optimal. A list of candidate queries can be obtained by permuting the ordering of operations within a fragment query generated by the previous stage. Time is the preferred unit for measur- ing cost. The total cost is a weighted combination of costs such as CPU cost, I/O costs, and communication costs. Since DDBs are connected by a net- work, often the communication costs over the network are the most signifi- cant. This is especially true when the sites are connected through a wide area network (WAN).
8For a detailed discussion of optimization algorithms, see Ozsu and Valduriez (1999).
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4. Local Query Optimization. This stage is common to all sites in the DDB. The techniques are similar to those used in centralized systems.
The first three stages discussed above are performed at a central control site, while the last stage is performed locally.
25.5.2 Data Transfer Costs of Distributed Query Processing We discussed the issues involved in processing and optimizing a query in a central- ized DBMS in Chapter 19. In a distributed system, several additional factors further complicate query processing. The first is the cost of transferring data over the net- work. This data includes intermediate files that are transferred to other sites for fur- ther processing, as well as the final result files that may have to be transferred to the site where the query result is needed. Although these costs may not be very high if the sites are connected via a high-performance local area network, they become quite significant in other types of networks. Hence, DDBMS query optimization algorithms consider the goal of reducing the amount of data transfer as an optimiza- tion criterion in choosing a distributed query execution strategy.
We illustrate this with two simple sample queries. Suppose that the EMPLOYEE and DEPARTMENT relations in Figure 3.5 are distributed at two sites as shown in Figure 25.10. We will assume in this example that neither relation is fragmented. According to Figure 25.10, the size of the EMPLOYEE relation is 100 * 10,000 = 10
6 bytes, and the size of the DEPARTMENT relation is 35 * 100 = 3500 bytes. Consider the query Q: For each employee, retrieve the employee name and the name of the department for which the employee works. This can be stated as follows in the relational algebra:
Q: πFname,Lname,Dname(EMPLOYEE Dno=Dnumber DEPARTMENT)
The result of this query will include 10,000 records, assuming that every employee is related to a department. Suppose that each record in the query result is 40 bytes long.
Fname
EMPLOYEE
Site 1:
10,000 records each record is 100 bytes long Ssn field is 9 bytes long Dno field is 4 bytes long
Site 2:
Minit Lname Ssn Salary Super_ssn DnoBdate Address Sex
Dname
DEPARTMENT
Dnumber Mgr_ssn Mgr_start_date
Fname field is 15 bytes long Lname field is 15 bytes long
100 records each record is 35 bytes long Dnumber field is 4 bytes long Mgr_ssn field is 9 bytes long
Dname field is 10 bytes long
Figure 25.10 Example to illustrate volume of data transferred.
25.5 Query Processing and Optimization in Distributed Databases 903
The query is submitted at a distinct site 3, which is called the result site because the query result is needed there. Neither the EMPLOYEE nor the DEPARTMENT relations reside at site 3. There are three simple strategies for executing this distributed query:
1. Transfer both the EMPLOYEE and the DEPARTMENT relations to the result site, and perform the join at site 3. In this case, a total of 1,000,000 + 3,500 = 1,003,500 bytes must be transferred.
2. Transfer the EMPLOYEE relation to site 2, execute the join at site 2, and send the result to site 3. The size of the query result is 40 * 10,000 = 400,000 bytes, so 400,000 + 1,000,000 = 1,400,000 bytes must be transferred.
3. Transfer the DEPARTMENT relation to site 1, execute the join at site 1, and send the result to site 3. In this case, 400,000 + 3,500 = 403,500 bytes must be transferred.
If minimizing the amount of data transfer is our optimization criterion, we should choose strategy 3. Now consider another query Q�: For each department, retrieve the department name and the name of the department manager. This can be stated as fol- lows in the relational algebra:
Q�: πFname,Lname,Dname( DEPARTMENT Mgr_ssn=Ssn EMPLOYEE)
Again, suppose that the query is submitted at site 3. The same three strategies for executing query Q apply to Q�, except that the result of Q� includes only 100 records, assuming that each department has a manager:
1. Transfer both the EMPLOYEE and the DEPARTMENT relations to the result site, and perform the join at site 3. In this case, a total of 1,000,000 + 3,500 = 1,003,500 bytes must be transferred.
2. Transfer the EMPLOYEE relation to site 2, execute the join at site 2, and send the result to site 3. The size of the query result is 40 * 100 = 4,000 bytes, so 4,000 + 1,000,000 = 1,004,000 bytes must be transferred.
3. Transfer the DEPARTMENT relation to site 1, execute the join at site 1, and send the result to site 3. In this case, 4,000 + 3,500 = 7,500 bytes must be transferred.
Again, we would choose strategy 3—this time by an overwhelming margin over strategies 1 and 2. The preceding three strategies are the most obvious ones for the case where the result site (site 3) is different from all the sites that contain files involved in the query (sites 1 and 2). However, suppose that the result site is site 2; then we have two simple strategies:
1. Transfer the EMPLOYEE relation to site 2, execute the query, and present the result to the user at site 2. Here, the same number of bytes—1,000,000— must be transferred for both Q and Q�.
2. Transfer the DEPARTMENT relation to site 1, execute the query at site 1, and send the result back to site 2. In this case 400,000 + 3,500 = 403,500 bytes must be transferred for Q and 4,000 + 3,500 = 7,500 bytes for Q�.
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A more complex strategy, which sometimes works better than these simple strate- gies, uses an operation called semijoin. We introduce this operation and discuss dis- tributed execution using semijoins next.
25.5.3 Distributed Query Processing Using Semijoin The idea behind distributed query processing using the semijoin operation is to reduce the number of tuples in a relation before transferring it to another site. Intuitively, the idea is to send the joining column of one relation R to the site where the other relation S is located; this column is then joined with S. Following that, the join attributes, along with the attributes required in the result, are projected out and shipped back to the original site and joined with R. Hence, only the joining column of R is transferred in one direction, and a subset of S with no extraneous tuples or attributes is transferred in the other direction. If only a small fraction of the tuples in S participate in the join, this can be quite an efficient solution to minimizing data transfer.
To illustrate this, consider the following strategy for executing Q or Q�:
1. Project the join attributes of DEPARTMENT at site 2, and transfer them to site 1. For Q, we transfer F = πDnumber(DEPARTMENT), whose size is 4 * 100 = 400 bytes, whereas, for Q�, we transfer F� = πMgr_ssn(DEPARTMENT), whose size is 9 * 100 = 900 bytes.
2. Join the transferred file with the EMPLOYEE relation at site 1, and transfer the required attributes from the resulting file to site 2. For Q, we transfer R = πDno, Fname, Lname(F Dnumber=Dno EMPLOYEE), whose size is 34 * 10,000 = 340,000 bytes, whereas, for Q�, we transfer R� = πMgr_ssn, Fname, Lname (F� Mgr_ssn=Ssn EMPLOYEE), whose size is 39 * 100 = 3,900 bytes.
3. Execute the query by joining the transferred file R or R� with DEPARTMENT, and present the result to the user at site 2.
Using this strategy, we transfer 340,400 bytes for Q and 4,800 bytes for Q�. We lim- ited the EMPLOYEE attributes and tuples transmitted to site 2 in step 2 to only those that will actually be joined with a DEPARTMENT tuple in step 3. For query Q, this turned out to include all EMPLOYEE tuples, so little improvement was achieved. However, for Q� only 100 out of the 10,000 EMPLOYEE tuples were needed.
The semijoin operation was devised to formalize this strategy. A semijoin opera- tion R A=B S, where A and B are domain-compatible attributes of R and S, respec- tively, produces the same result as the relational algebra expression π
R (R
A=B S). In a distributed environment where R and S reside at different sites, the semijoin is typically implemented by first transferring F = π
B (S) to the site where R resides and
then joining F with R, thus leading to the strategy discussed here.
Notice that the semijoin operation is not commutative; that is,
R S ≠S R
25.5 Query Processing and Optimization in Distributed Databases 905
25.5.4 Query and Update Decomposition In a DDBMS with no distribution transparency, the user phrases a query directly in terms of specific fragments. For example, consider another query Q: Retrieve the names and hours per week for each employee who works on some project controlled by department 5, which is specified on the distributed database where the relations at sites 2 and 3 are shown in Figure 25.8, and those at site 1 are shown in Figure 3.6, as in our earlier example. A user who submits such a query must specify whether it ref- erences the PROJS_5 and WORKS_ON_5 relations at site 2 (Figure 25.8) or the PROJECT and WORKS_ON relations at site 1 (Figure 3.6). The user must also main- tain consistency of replicated data items when updating a DDBMS with no replica- tion transparency.
On the other hand, a DDBMS that supports full distribution, fragmentation, and replication transparency allows the user to specify a query or update request on the schema in Figure 3.5 just as though the DBMS were centralized. For updates, the DDBMS is responsible for maintaining consistency among replicated items by using one of the distributed concurrency control algorithms to be discussed in Section 25.7. For queries, a query decomposition module must break up or decompose a query into subqueries that can be executed at the individual sites. Additionally, a strategy for combining the results of the subqueries to form the query result must be generated. Whenever the DDBMS determines that an item referenced in the query is replicated, it must choose or materialize a particular replica during query execution.
To determine which replicas include the data items referenced in a query, the DDBMS refers to the fragmentation, replication, and distribution information stored in the DDBMS catalog. For vertical fragmentation, the attribute list for each fragment is kept in the catalog. For horizontal fragmentation, a condition, some- times called a guard, is kept for each fragment. This is basically a selection condition that specifies which tuples exist in the fragment; it is called a guard because only tuples that satisfy this condition are permitted to be stored in the fragment. For mixed fragments, both the attribute list and the guard condition are kept in the catalog.
In our earlier example, the guard conditions for fragments at site 1 (Figure 3.6) are TRUE (all tuples), and the attribute lists are * (all attributes). For the fragments shown in Figure 25.8, we have the guard conditions and attribute lists shown in Figure 25.11. When the DDBMS decomposes an update request, it can determine which fragments must be updated by examining their guard conditions. For exam- ple, a user request to insert a new EMPLOYEE tuple <‘Alex’, ‘B’, ‘Coleman’, ‘345671239’, ‘22-APR-64’, ‘3306 Sandstone, Houston, TX’, M, 33000, ‘987654321’, 4> would be decomposed by the DDBMS into two insert requests: the first inserts the preceding tuple in the EMPLOYEE fragment at site 1, and the second inserts the pro- jected tuple <‘Alex’, ‘B’, ‘Coleman’, ‘345671239’, 33000, ‘987654321’, 4> in the EMPD4 fragment at site 3.
For query decomposition, the DDBMS can determine which fragments may contain the required tuples by comparing the query condition with the guard
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(a) EMPD5 attribute list: Fname, Minit, Lname, Ssn, Salary, Super_ssn, Dno
guard condition: Dno=5 DEP5
attribute list: * (all attributes Dname, Dnumber, Mgr_ssn, Mgr_start_date) guard condition: Dnumber=5 DEP5_LOCS
attribute list: * (all attributes Dnumber, Location) guard condition: Dnumber=5 PROJS5
attribute list: * (all attributes Pname, Pnumber, Plocation, Dnum) guard condition: Dnum=5 WORKS_ON5
attribute list: * (all attributes Essn, Pno,Hours) guard condition: Essn IN (πSsn (EMPD5)) OR Pno IN (πPnumber (PROJS5))
(b) EMPD4 attribute list: Fname, Minit, Lname, Ssn, Salary, Super_ssn, Dno
guard condition: Dno=4 DEP4
attribute list: * (all attributes Dname, Dnumber, Mgr_ssn, Mgr_start_date) guard condition: Dnumber=4 DEP4_LOCS
attribute list: * (all attributes Dnumber, Location) guard condition: Dnumber=4 PROJS4
attribute list: * (all attributes Pname, Pnumber, Plocation, Dnum) guard condition: Dnum=4 WORKS_ON4
attribute list: * (all attributes Essn, Pno, Hours) guard condition: Essn IN (πSsn (EMPD4))
OR Pno IN (πPnumber (PROJS4))
Figure 25.11 Guard conditions and attributes lists for fragments. (a) Site 2 fragments. (b) Site 3 fragments.
conditions. For example, consider the query Q: Retrieve the names and hours per week for each employee who works on some project controlled by department 5. This can be specified in SQL on the schema in Figure 3.5 as follows:
Q: SELECT Fname, Lname, Hours FROM EMPLOYEE, PROJECT, WORKS_ON WHERE Dnum=5 AND Pnumber=Pno AND Essn=Ssn;
25.6 Overview of Transaction Management in Distributed Databases 907
Suppose that the query is submitted at site 2, which is where the query result will be needed. The DDBMS can determine from the guard condition on PROJS5 and WORKS_ON5 that all tuples satisfying the conditions (Dnum = 5 AND Pnumber = Pno) reside at site 2. Hence, it may decompose the query into the following rela- tional algebra subqueries:
T1 ← πEssn(PROJS5 Pnumber=PnoWORKS_ON5) T2 ← πEssn, Fname, Lname(T1 Essn=SsnEMPLOYEE) RESULT ← πFname, Lname, Hours(T2 * WORKS_ON5)
This decomposition can be used to execute the query by using a semijoin strategy. The DDBMS knows from the guard conditions that PROJS5 contains exactly those tuples satisfying (Dnum = 5) and that WORKS_ON5 contains all tuples to be joined with PROJS5; hence, subquery T1 can be executed at site 2, and the projected column Essn can be sent to site 1. Subquery T2 can then be executed at site 1, and the result can be sent back to site 2, where the final query result is calculated and displayed to the user. An alternative strategy would be to send the query Q itself to site 1, which includes all the database tuples, where it would be executed locally and from which the result would be sent back to site 2. The query optimizer would estimate the costs of both strategies and would choose the one with the lower cost estimate.
25.6 Overview of Transaction Management in Distributed Databases
The global and local transaction management software modules, along with the concurrency control and recovery manager of a DDBMS, collectively guarantee the ACID properties of transactions (see Chapter 21). We discuss distributed transac- tion management in this section and explore concurrency control in Section 25.7.
As can be seen in Figure 25.5, an additional component called the global transac- tion manager is introduced for supporting distributed transactions. The site where the transaction originated can temporarily assume the role of global transaction manager and coordinate the execution of database operations with transaction managers across multiple sites. Transaction managers export their functionality as an interface to the application programs. The operations exported by this interface are similar to those covered in Section 21.2.1, namely BEGIN_TRANSACTION, READ or WRITE, END_TRANSACTION, COMMIT_TRANSACTION, and ROLLBACK (or ABORT). The manager stores bookkeeping information related to each transaction, such as a unique identifier, originating site, name, and so on. For READ operations, it returns a local copy if valid and available. For WRITE operations, it ensures that updates are visible across all sites containing copies (replicas) of the data item. For ABORT operations, the manager ensures that no effects of the transaction are reflected in any site of the distributed database. For COMMIT operations, it ensures that the effects of a write are persistently recorded on all databases containing copies of the data item. Atomic termination (COMMIT/ ABORT) of distributed transactions is commonly implemented using the two-phase commit protocol. We give more details of this protocol in the following section.
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The transaction manager passes to the concurrency controller the database opera- tion and associated information. The controller is responsible for acquisition and release of associated locks. If the transaction requires access to a locked resource, it is delayed until the lock is acquired. Once the lock is acquired, the operation is sent to the runtime processor, which handles the actual execution of the database opera- tion. Once the operation is completed, locks are released and the transaction man- ager is updated with the result of the operation. We discuss commonly used distributed concurrency methods in Section 25.7.
25.6.1 Two-Phase Commit Protocol In Section 23.6, we described the two-phase commit protocol (2PC), which requires a global recovery manager, or coordinator, to maintain information needed for recovery, in addition to the local recovery managers and the information they main- tain (log, tables) The two-phase commit protocol has certain drawbacks that led to the development of the three-phase commit protocol, which we discuss next.
25.6.2 Three-Phase Commit Protocol The biggest drawback of 2PC is that it is a blocking protocol. Failure of the coordi- nator blocks all participating sites, causing them to wait until the coordinator recov- ers. This can cause performance degradation, especially if participants are holding locks to shared resources. Another problematic scenario is when both the coordina- tor and a participant that has committed crash together. In the two-phase commit protocol, a participant has no way to ensure that all participants got the commit message in the second phase. Hence once a decision to commit has been made by the coordinator in the first phase, participants will commit their transactions in the second phase independent of receipt of a global commit message by other partici- pants. Thus, in the situation that both the coordinator and a committed participant crash together, the result of the transaction becomes uncertain or nondeterministic. Since the transaction has already been committed by one participant, it cannot be aborted on recovery by the coordinator. Also, the transaction cannot be optimisti- cally committed on recovery since the original vote of the coordinator may have been to abort.
These problems are solved by the three-phase commit (3PC) protocol, which essen- tially divides the second commit phase into two subphases called prepare-to- commit and commit. The prepare-to-commit phase is used to communicate the result of the vote phase to all participants. If all participants vote yes, then the coordi- nator instructs them to move into the prepare-to-commit state. The commit subphase is identical to its two-phase counterpart. Now, if the coordinator crashes during this subphase, another participant can see the transaction through to completion. It can simply ask a crashed participant if it received a prepare-to-commit message. If it did not, then it safely assumes to abort. Thus the state of the protocol can be recovered irrespective of which participant crashes. Also, by limiting the time required for a transaction to commit or abort to a maximum time-out period, the protocol ensures that a transaction attempting to commit via 3PC releases locks on time-out.
25.7 Overview of Concurrency Control and Recovery in Distributed Databases 909
The main idea is to limit the wait time for participants who have committed and are waiting for a global commit or abort from the coordinator. When a participant receives a precommit message, it knows that the rest of the participants have voted to commit. If a precommit message has not been received, then the participant will abort and release all locks.
25.6.3 Operating System Support for Transaction Management
The following are the main benefits of operating system (OS)-supported transac- tion management:
■ Typically, DBMSs use their own semaphores9 to guarantee mutually exclu- sive access to shared resources. Since these semaphores are implemented in userspace at the level of the DBMS application software, the OS has no knowledge about them. Hence if the OS deactivates a DBMS process holding a lock, other DBMS processes wanting this lock resource get queued. Such a situation can cause serious performance degradation. OS-level knowledge of semaphores can help eliminate such situations.
■ Specialized hardware support for locking can be exploited to reduce associ- ated costs. This can be of great importance, since locking is one of the most common DBMS operations.
■ Providing a set of common transaction support operations though the ker- nel allows application developers to focus on adding new features to their products as opposed to reimplementing the common functionality for each application. For example, if different DDBMSs are to coexist on the same machine and they chose the two-phase commit protocol, then it is more beneficial to have this protocol implemented as part of the kernel so that the DDBMS developers can focus more on adding new features to their products.
25.7 Overview of Concurrency Control and Recovery in Distributed Databases
For concurrency control and recovery purposes, numerous problems arise in a dis- tributed DBMS environment that are not encountered in a centralized DBMS envi- ronment. These include the following:
■ Dealing with multiple copies of the data items. The concurrency control method is responsible for maintaining consistency among these copies. The recovery method is responsible for making a copy consistent with other copies if the site on which the copy is stored fails and recovers later.
9Semaphores are data structures used for synchronized and exclusive access to shared resources for preventing race conditions in a parallel computing system.
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■ Failure of individual sites. The DDBMS should continue to operate with its running sites, if possible, when one or more individual sites fail. When a site recovers, its local database must be brought up-to-date with the rest of the sites before it rejoins the system.
■ Failure of communication links. The system must be able to deal with the failure of one or more of the communication links that connect the sites. An extreme case of this problem is that network partitioning may occur. This breaks up the sites into two or more partitions, where the sites within each partition can communicate only with one another and not with sites in other partitions.
■ Distributed commit. Problems can arise with committing a transaction that is accessing databases stored on multiple sites if some sites fail during the commit process. The two-phase commit protocol (see Section 23.6) is often used to deal with this problem.
■ Distributed deadlock. Deadlock may occur among several sites, so tech- niques for dealing with deadlocks must be extended to take this into account.
Distributed concurrency control and recovery techniques must deal with these and other problems. In the following subsections, we review some of the techniques that have been suggested to deal with recovery and concurrency control in DDBMSs.
25.7.1 Distributed Concurrency Control Based on a Distinguished Copy of a Data Item
To deal with replicated data items in a distributed database, a number of concur- rency control methods have been proposed that extend the concurrency control techniques for centralized databases. We discuss these techniques in the context of extending centralized locking. Similar extensions apply to other concurrency control techniques. The idea is to designate a particular copy of each data item as a distinguished copy. The locks for this data item are associated with the distin- guished copy, and all locking and unlocking requests are sent to the site that contains that copy.
A number of different methods are based on this idea, but they differ in their method of choosing the distinguished copies. In the primary site technique, all dis- tinguished copies are kept at the same site. A modification of this approach is the primary site with a backup site. Another approach is the primary copy method, where the distinguished copies of the various data items can be stored in different sites. A site that includes a distinguished copy of a data item basically acts as the coordinator site for concurrency control on that item. We discuss these techniques next.
Primary Site Technique. In this method a single primary site is designated to be the coordinator site for all database items. Hence, all locks are kept at that site, and all requests for locking or unlocking are sent there. This method is thus an extension
25.7 Overview of Concurrency Control and Recovery in Distributed Databases 911
of the centralized locking approach. For example, if all transactions follow the two- phase locking protocol, serializability is guaranteed. The advantage of this approach is that it is a simple extension of the centralized approach and thus is not overly complex. However, it has certain inherent disadvantages. One is that all locking requests are sent to a single site, possibly overloading that site and causing a system bottleneck. A second disadvantage is that failure of the primary site paralyzes the system, since all locking information is kept at that site. This can limit system relia- bility and availability.
Although all locks are accessed at the primary site, the items themselves can be accessed at any site at which they reside. For example, once a transaction obtains a Read_lock on a data item from the primary site, it can access any copy of that data item. However, once a transaction obtains a Write_lock and updates a data item, the DDBMS is responsible for updating all copies of the data item before releasing the lock.
Primary Site with Backup Site. This approach addresses the second disadvantage of the primary site method by designating a second site to be a backup site. All lock- ing information is maintained at both the primary and the backup sites. In case of primary site failure, the backup site takes over as the primary site, and a new backup site is chosen. This simplifies the process of recovery from failure of the primary site, since the backup site takes over and processing can resume after a new backup site is chosen and the lock status information is copied to that site. It slows down the process of acquiring locks, however, because all lock requests and granting of locks must be recorded at both the primary and the backup sites before a response is sent to the requesting transaction. The problem of the primary and backup sites becoming overloaded with requests and slowing down the system remains undiminished.
Primary Copy Technique. This method attempts to distribute the load of lock coordination among various sites by having the distinguished copies of different data items stored at different sites. Failure of one site affects any transactions that are accessing locks on items whose primary copies reside at that site, but other transac- tions are not affected. This method can also use backup sites to enhance reliability and availability.
Choosing a New Coordinator Site in Case of Failure. Whenever a coordina- tor site fails in any of the preceding techniques, the sites that are still running must choose a new coordinator. In the case of the primary site approach with no backup site, all executing transactions must be aborted and restarted in a tedious recovery process. Part of the recovery process involves choosing a new primary site and creat- ing a lock manager process and a record of all lock information at that site. For methods that use backup sites, transaction processing is suspended while the backup site is designated as the new primary site and a new backup site is chosen and is sent copies of all the locking information from the new primary site.
If a backup site X is about to become the new primary site, X can choose the new backup site from among the system’s running sites. However, if no backup site
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existed, or if both the primary and the backup sites are down, a process called election can be used to choose the new coordinator site. In this process, any site Y that attempts to communicate with the coordinator site repeatedly and fails to do so can assume that the coordinator is down and can start the election process by send- ing a message to all running sites proposing that Y become the new coordinator. As soon as Y receives a majority of yes votes, Y can declare that it is the new coordina- tor. The election algorithm itself is quite complex, but this is the main idea behind the election method. The algorithm also resolves any attempt by two or more sites to become coordinator at the same time. The references in the Selected Bibliography at the end of this chapter discuss the process in detail.
25.7.2 Distributed Concurrency Control Based on Voting The concurrency control methods for replicated items discussed earlier all use the idea of a distinguished copy that maintains the locks for that item. In the voting method, there is no distinguished copy; rather, a lock request is sent to all sites that includes a copy of the data item. Each copy maintains its own lock and can grant or deny the request for it. If a transaction that requests a lock is granted that lock by a majority of the copies, it holds the lock and informs all copies that it has been granted the lock. If a transaction does not receive a majority of votes granting it a lock within a certain time-out period, it cancels its request and informs all sites of the cancellation.
The voting method is considered a truly distributed concurrency control method, since the responsibility for a decision resides with all the sites involved. Simulation studies have shown that voting has higher message traffic among sites than do the distinguished copy methods. If the algorithm takes into account possible site fail- ures during the voting process, it becomes extremely complex.
25.7.3 Distributed Recovery The recovery process in distributed databases is quite involved. We give only a very brief idea of some of the issues here. In some cases it is quite difficult even to deter- mine whether a site is down without exchanging numerous messages with other sites. For example, suppose that site X sends a message to site Y and expects a response from Y but does not receive it. There are several possible explanations:
■ The message was not delivered to Y because of communication failure.
■ Site Y is down and could not respond.
■ Site Y is running and sent a response, but the response was not delivered.
Without additional information or the sending of additional messages, it is difficult to determine what actually happened.
Another problem with distributed recovery is distributed commit. When a transac- tion is updating data at several sites, it cannot commit until it is sure that the effect of the transaction on every site cannot be lost. This means that every site must first
25.8 Distributed Catalog Management 913
have recorded the local effects of the transactions permanently in the local site log on disk. The two-phase commit protocol is often used to ensure the correctness of distributed commit (see Section 23.6).
25.8 Distributed Catalog Management Efficient catalog management in distributed databases is critical to ensure satisfac- tory performance related to site autonomy, view management, and data distribution and replication. Catalogs are databases themselves containing metadata about the distributed database system.
Three popular management schemes for distributed catalogs are centralized cata- logs, fully replicated catalogs, and partitioned catalogs. The choice of the scheme depends on the database itself as well as the access patterns of the applications to the underlying data.
Centralized Catalogs. In this scheme, the entire catalog is stored in one single site. Owing to its central nature, it is easy to implement. On the other hand, the advantages of reliability, availability, autonomy, and distribution of processing load are adversely impacted. For read operations from noncentral sites, the requested catalog data is locked at the central site and is then sent to the requesting site. On completion of the read operation, an acknowledgement is sent to the central site, which in turn unlocks this data. All update operations must be processed through the central site. This can quickly become a performance bottleneck for write- intensive applications.
Fully Replicated Catalogs. In this scheme, identical copies of the complete cata- log are present at each site. This scheme facilitates faster reads by allowing them to be answered locally. However, all updates must be broadcast to all sites. Updates are treated as transactions and a centralized two-phase commit scheme is employed to ensure catalog consitency. As with the centralized scheme, write-intensive applica- tions may cause increased network traffic due to the broadcast associated with the writes.
Partially Replicated Catalogs. The centralized and fully replicated schemes restrict site autonomy since they must ensure a consistent global view of the catalog. Under the partially replicated scheme, each site maintains complete catalog infor- mation on data stored locally at that site. Each site is also permitted to cache entries retrieved from remote sites. However, there are no guarantees that these cached copies will be the most recent and updated. The system tracks catalog entries for sites where the object was created and for sites that contain copies of this object. Any changes to copies are propagated immediately to the original (birth) site. Retrieving updated copies to replace stale data may be delayed until an access to this data occurs. In general, fragments of relations across sites should be uniquely accessible. Also, to ensure data distribution transparency, users should be allowed to create synonyms for remote objects and use these synonyms for subsequent referrals.
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25.9 Current Trends in Distributed Databases Current trends in distributed data management are centered on the Internet, in which petabytes of data can be managed in a scalable, dynamic, and reliable fashion. Two important areas in this direction are cloud computing and peer-to-peer data- bases.
25.9.1 Cloud Computing Cloud computing is the paradigm of offering computer infrastructure, platforms, and software as services over the Internet. It offers significant economic advantages by limiting both up-front capital investments toward computer infrastructure as well as total cost of ownership. It has introduced a new challenge of managing petabytes of data in a scalable fashion. Traditional database systems for managing enterprise data proved to be inadequate in handling this challenge, which has resulted in a major architectural revision. The Claremont report10 by a group of senior database researchers envisions that future research in cloud computing will result in the emergence of new data management architectures and the interplay of structured and unstructured data as well as other developments.
Performance costs associated with partial failures and global synchronization were key performance bottlenecks of traditional database solutions. The key insight is that the hash-value nature of the underlying datasets used by these organizations lends itself naturally to partitioning. For instance, search queries essentially involve a recursive process of mapping keywords to a set of related documents, which can benefit from such a partitioning. Also, the partitions can be treated independently, thereby eliminating the need for a coordinated commit. Another problem with tra- ditional DDBMSs is the lack of support for efficient dynamic partitioning of data, which limited scalability and resource utilization. Traditional systems treated sys- tem metadata and application data alike, with the system data requiring strict con- sistency and availability guarantees. But application data has variable requirements on these characteristics, depending on its nature. For example, while a search engine can afford weaker consistency guarantees, an online text editor like Google Docs, which allows concurrent users, has strict consistency requirements.
The metadata of a distributed database system should be decoupled from its actual data in order to ensure scalability. This decoupling can be used to develop innova- tive solutions to manage the actual data by exploiting their inherent suitability to partitioning and using traditional database solutions to manage critical system metadata. Since metadata is only a fraction of the total data set, it does not prove to be a performance bottleneck. Single object semantics of these implementations enables higher tolerance to nonavailability of certain sections of data. Access to data is typically by a single object in an atomic fashion. Hence, transaction support to such data is not as stringent as for traditional databases.11 There is a varied set of
10“The Claremont Report on Database Research” is available at http://db.cs.berkeley.edu/claremont/ claremontreport08.pdf. 11Readers may refer to the work done by Das et al. (2008) for further details.
25.10 Distributed Databases in Oracle 915
cloud services available today, including application services (salesforce.com), stor- age services (Amazon Simple Storage Service, or Amazon S3), compute services (Google App Engine, Amazon Elastic Compute Cloud—Amazon EC2), and data services (Amazon SimpleDB, Microsoft SQL Server Data Services, Google’s Datastore). More and more data-centric applications are expected to leverage data services in the cloud. While most current cloud services are data-analysis intensive, it is expected that business logic will eventually be migrated to the cloud. The key challenge in this migration would be to ensure the scalability advantages for multi- ple object semantics inherent to business logic. For a detailed treatment of cloud computing, refer to the relevant bibliographic references in this chapter’s Selected Bibliography.
25.9.2 Peer-to-Peer Database Systems A peer-to-peer database system (PDBS) aims to integrate advantages of P2P (peer- to-peer) computing, such as scalability, attack resilience, and self-organization, with the features of decentralized data management. Nodes are autonomous and are linked only to a small number of peers individually. It is permissible for a node to behave purely as a collection of files without offering a complete set of traditional DBMS functionality. While FDBS and MDBS mandate the existence of mappings between local and global federated schemas, PDBSs attempt to avoid a global schema by providing mappings between pairs of information sources. In PDBS, each peer potentially models semantically related data in a manner different from other peers, and hence the task of constructing a central mediated schema can be very challenging. PDBSs aim to decentralize data sharing. Each peer has a schema associated with its domain-specific stored data. The PDBS constructs a semantic path12 of mappings between peer schemas. Using this path, a peer to which a query has been submitted can obtain information from any relevant peer connected through this path. In multidatabase systems, a separate global query processor is used, whereas in a P2P system a query is shipped from one peer to another until it is processed completely. A query submitted to a node may be forwarded to others based on the mapping graph of semantic paths. Edutella and Piazza are examples of PDBSs. Details of these systems can be found from the sources mentioned in this chapter’s Selected Bibliography.
25.10 Distributed Databases in Oracle13
Oracle provides support for homogeneous, heterogeneous, and client server archi- tectures of distributed databases. In a homogeneous architecture, a minimum of two Oracle databases reside on at least one machine. Although the location and platform of the databases are transparent to client applications, they would need to
12A semantic path describes the higher-level relationship between two domains that are dissimilar but not unrelated. 13The discussion is based on available documentation at http://docs.oracle.com.
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distinguish between local and remote objects semantically. Using synonyms, this need can be overcome wherein users can access the remote objects with the same syntax as local objects. Different versions of DBMSs can be used, although it must be noted that Oracle offers backward compatibility but not forward compatibility between its versions. For example, it is possible that some of the SQL extensions that were incorporated into Oracle 11i may not be understood by Oracle 9.
In a heterogeneous architecture, at least one of the databases in the network is a non-Oracle system. The Oracle database local to the application hides the underly- ing heterogeneity and offers the view of a single local, underlying Oracle database. Connectivity is handled by use of an ODBC- or OLE-DB-compliant protocol or by Oracle’s Heterogeneous Services and Transparent Gateway agent components. A discussion of the Heterogeneous Services and Transparent Gateway agents is beyond the scope of this book, and the reader is advised to consult the online Oracle documentation.
In the client-server architecture, the Oracle database system is divided into two parts: a front end as the client portion, and a back end as the server portion. The client portion is the front-end database application that interacts with the user. The client has no data access responsibility and merely handles the requesting, process- ing, and presentation of data managed by the server. The server portion runs Oracle and handles the functions related to concurrent shared access. It accepts SQL and PL/SQL statements originating from client applications, processes them, and sends the results back to the client. Oracle client-server applications provide location transparency by making the location of data transparent to users; several features like views, synonyms, and procedures contribute to this. Global naming is achieved by using <TABLE_NAME@DATABASE_NAME> to refer to tables uniquely.
Oracle uses a two-phase commit protocol to deal with concurrent distributed trans- actions. The COMMIT statement triggers the two-phase commit mechanism. The RECO (recoverer) background process automatically resolves the outcome of those distributed transactions in which the commit was interrupted. The RECO of each local Oracle server automatically commits or rolls back any in-doubt distributed transactions consistently on all involved nodes. For long-term failures, Oracle allows each local DBA to manually commit or roll back any in-doubt transactions and free up resources. Global consistency can be maintained by restoring the data- base at each site to a predetermined fixed point in the past.
Oracle’s distributed database architecture is shown in Figure 25.12. A node in a dis- tributed database system can act as a client, as a server, or both, depending on the sit- uation. The figure shows two sites where databases called HQ (headquarters) and Sales are kept. For example, in the application shown running at the headquarters, for an SQL statement issued against local data (for example, DELETE FROM DEPT ...), the HQ computer acts as a server, whereas for a statement against remote data (for example, INSERT INTO EMP@SALES), the HQ computer acts as a client.
Communication in such a distributed heterogeneous environment is facilitated through Oracle Net Services, which supports standard network protocols and APIs. Under Oracle’s client-server implementation of distributed databases, Net Services
25.10 Distributed Databases in Oracle 917
Server
DEPT Table
Application
HQ Database
Connect to . . . Identified by . . .
Oracle Net
EMP Table
Sales Database
Transaction
.
.
.
Network
INSERT INTO EMP@SALES . . . ;
DELETE FROM DEPT . . . ;
SELECT . . . FROM EMP@SALES . . . ;
COMMIT;
Server
Oracle Net
Database Link
Figure 25.12 Oracle distributed database system.
Source: From Oracle (2008). Copyright © Oracle Corporation 2008. All rights reserved.
is responsible for establishing and managing connections between a client applica- tion and database server. It is present in each node on the network running an Oracle client application, database server, or both. It packages SQL statements into one of the many communication protocols to facilitate client-to-server communi- cation and then packages the results back similarly to the client. The support offered by Net Services to heterogeneity refers to platform specifications only and not the database software. Support for DBMSs other than Oracle is through Oracle’s Heterogeneous Services and Transparent Gateway. Each database has a unique global name provided by a hierarchical arrangement of network domain names that is prefixed to the database name to make it unique.
Oracle supports database links that define a one-way communication path from one Oracle database to another. For example,
CREATE DATABASE LINK sales.us.americas;
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establishes a connection to the sales database in Figure 25.12 under the network domain us that comes under domain americas. Using links, a user can access a remote object on another database subject to ownership rights without the need for being a user on the remote database.
Data in an Oracle DDBS can be replicated using snapshots or replicated master tables. Replication is provided at the following levels:
■ Basic replication. Replicas of tables are managed for read-only access. For updates, data must be accessed at a single primary site.
■ Advanced (symmetric) replication. This extends beyond basic replication by allowing applications to update table replicas throughout a replicated DDBS. Data can be read and updated at any site. This requires additional software called Oracle’s advanced replication option. A snapshot generates a copy of a part of the table by means of a query called the snapshot defining query. A simple snapshot definition looks like this:
CREATE SNAPSHOT SALES_ORDERS AS SELECT * FROM [email protected];
Oracle groups snapshots into refresh groups. By specifying a refresh interval, the snapshot is automatically refreshed periodically at that interval by up to ten Snapshot Refresh Processes (SNPs). If the defining query of a snapshot contains a distinct or aggregate function, a GROUP BY or CONNECT BY clause, or join or set operations, the snapshot is termed a complex snapshot and requires additional processing. Oracle (up to version 7.3) also supports ROWID snapshots that are based on physical row identifiers of rows in the master table.
Heterogeneous Databases in Oracle. In a heterogeneous DDBS, at least one database is a non-Oracle system. Oracle Open Gateways provides access to a non- Oracle database from an Oracle server, which uses a database link to access data or to execute remote procedures in the non-Oracle system. The Open Gateways feature includes the following:
■ Distributed transactions. Under the two-phase commit mechanism, trans- actions may span Oracle and non-Oracle systems.
■ Transparent SQL access. SQL statements issued by an application are trans- parently transformed into SQL statements understood by the non-Oracle system.
■ Pass-through SQL and stored procedures. An application can directly access a non-Oracle system using that system’s version of SQL. Stored proce- dures in a non-Oracle SQL-based system are treated as if they were PL/SQL remote procedures.
■ Global query optimization. Cardinality information, indexes, and so on at the non-Oracle system are accounted for by the Oracle server query opti- mizer to perform global query optimization.
■ Procedural access. Procedural systems like messaging or queuing systems are accessed by the Oracle server using PL/SQL remote procedure calls.
25.11 Summary 919
In addition to the above, data dictionary references are translated to make the non- Oracle data dictionary appear as a part of the Oracle server’s dictionary. Character set translations are done between national language character sets to connect multi- lingual databases.
From a security perspective, Oracle recommends that if a query originates at site A and accesses sites B, C, and D, then the auditing of links should be done in the data- base at site A only. This is because the remote databases cannot distinguish whether a successful connection request and following SQL statements are coming from another server or a locally connected client.
25.10.1 Directory Services A concept closely related with distributed enterprise systems is online directories. Online directories are essentially a structured organization of metadata needed for management functions. They can represent information about a variety of sources ranging from security credentials, shared network resources, and database catalog. Lightweight Directory Access Protocol (LDAP) is an industry standard protocol for directory services. LDAP enables the use of a partitioned Directory Information Tree (DIT) across multiple LDAP servers, which in turn can return references to other servers as a result of a directory query. Online directories and LDAP are particularly important in distributed databases, wherein access of meta- data related to transparencies discussed in Section 25.1 must be scalable, secure, and highly available.
Oracle supports LDAP Version 3 and online directories through Oracle Internet Directory, a general-purpose directory service for fast access and centralized man- agement of metadata pertaining to distributed network resources and users. It runs as an application on an Oracle database and communicates with the database through Oracle Net Services. It also provides password-based, anonymous, and certificate-based user authentication using SSL Version 3.
Figure 25.13 illustrates the architecture of the Oracle Internet Directory. The main components are:
■ Oracle directory server. Handles client requests and updates for informa- tion pertaining to people and resources.
■ Oracle directory replication server. Stores a copy of the LDAP data from Oracle directory servers as a backup.
■ Directory administrator: Supports both GUI-based and command line- based interfaces for directory administration.
25.11 Summary In this chapter we provided an introduction to distributed databases. This is a very broad topic, and we discussed only some of the basic techniques used with distrib- uted databases. First we discussed the reasons for distribution and the potential advantages of distributed databases over centralized systems. Then the concept of
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Oracle Application
Server Database
Oracle Net Connections
Oracle Directory Replication Server
Oracle Directory ServerLDAP over SSL
LDAP Clients
Directory Administration
Figure 25.13 Oracle Internet Directory overview.
Source: From Oracle (2005). Copyright © Oracle Corporation 2005. All rights reserved.
distribution transparency and the related concepts of fragmentation transparency and replication transparency were defined. We categorized DDBMSs by using crite- ria such as the degree of homogeneity of software modules and the degree of local autonomy. We distinguished between parallel and distributed system architectures and then introduced the generic architecture of distributed databases from both a component as well as a schematic architectural perspective. The issues of federated database management were then discussed in some detail, focusing on the needs of supporting various types of autonomies and dealing with semantic heterogeneity. We also reviewed the client-server architecture concepts and related them to distrib- uted databases. We discussed the design issues related to data fragmentation, repli- cation, and distribution, and we distinguished between horizontal and vertical fragments of relations. The use of data replication to improve system reliability and availability was then discussed. We illustrated some of the techniques used in dis- tributed query processing and discussed the cost of communication among sites, which is considered a major factor in distributed query optimization. The different techniques for executing joins were compared and we then presented the semijoin technique for joining relations that reside on different sites. Then we discussed transaction management, including different commit protocols and operating sys- tem support for transaction management. We briefly discussed the concurrency
Review Questions 921
control and recovery techniques used in DDBMSs, and then reviewed some of the additional problems that must be dealt with in a distributed environment that do not appear in a centralized environment. We reviewed catalog management in dis- tributed databases and summarized their relative advantages and disadvantages. We then introduced Cloud Computing and Peer to Peer Database Systems as new focus areas in DDBs in response to the need of managing petabytes of information acces- sible over the Internet today.
We described some of the facilities in Oracle to support distributed databases. We also discussed online directories and the LDAP protocol in brief.
Review Questions 25.1. What are the main reasons for and potential advantages of distributed data-
bases?
25.2. What additional functions does a DDBMS have over a centralized DBMS?
25.3. Discuss what is meant by the following terms: degree of homogeneity of a DDBMS, degree of local autonomy of a DDBMS, federated DBMS, distribution transparency, fragmentation transparency, replication transparency, multidatabase system.
25.4. Discuss the architecture of a DDBMS. Within the context of a centralized DBMS, briefly explain new components introduced by the distribution of data.
25.5. What are the main software modules of a DDBMS? Discuss the main func- tions of each of these modules in the context of the client-server architec- ture.
25.6. Compare the two-tier and three-tier client-server architectures.
25.7. What is a fragment of a relation? What are the main types of fragments? Why is fragmentation a useful concept in distributed database design?
25.8. Why is data replication useful in DDBMSs? What typical units of data are replicated?
25.9. What is meant by data allocation in distributed database design? What typi- cal units of data are distributed over sites?
25.10. How is a horizontal partitioning of a relation specified? How can a relation be put back together from a complete horizontal partitioning?
25.11. How is a vertical partitioning of a relation specified? How can a relation be put back together from a complete vertical partitioning?
25.12. Discuss the naming problem in distributed databases.
25.13. What are the different stages of processing a query in a DDBMS?
25.14. Discuss the different techniques for executing an equijoin of two files located at different sites. What main factors affect the cost of data transfer?
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25.15. Discuss the semijoin method for executing an equijoin of two files located at different sites. Under what conditions is an equijoin strategy efficient?
25.16. Discuss the factors that affect query decomposition. How are guard condi- tions and attribute lists of fragments used during the query decomposition process?
25.17. How is the decomposition of an update request different from the decompo- sition of a query? How are guard conditions and attribute lists of fragments used during the decomposition of an update request?
25.18. List the support offered by operating systems to a DDBMS and also their benefits.
25.19. Discuss the factors that do not appear in centralized systems that affect con- currency control and recovery in distributed systems.
25.20. Discuss the two-phase commit protocol used for transaction management in a DDBMS. List its limitations and explain how they are overcome using the three-phase commit protocol.
25.21. Compare the primary site method with the primary copy method for dis- tributed concurrency control. How does the use of backup sites affect each?
25.22. When are voting and elections used in distributed databases?
25.23. Discuss catalog management in distributed databases.
25.24. What are the main challenges facing a traditional DDBMS in the context of today’s Internet applications? How does cloud computing attempt to address them?
25.25. Discuss briefly the support offered by Oracle for homogeneous, heteroge- neous, and client-server based distributed database architectures.
25.26. Discuss briefly online directories, their management, and their role in dis- tributed databases.
Exercises 25.27. Consider the data distribution of the COMPANY database, where the frag-
ments at sites 2 and 3 are as shown in Figure 25.9 and the fragments at site 1 are as shown in Figure 3.6. For each of the following queries, show at least two strategies of decomposing and executing the query. Under what condi- tions would each of your strategies work well?
a. For each employee in department 5, retrieve the employee name and the names of the employee’s dependents.
b. Print the names of all employees who work in department 5 but who work on some project not controlled by department 5.
Exercises 923
25.28. Consider the following relations:
BOOKS(Book#, Primary_author, Topic, Total_stock, $price) BOOKSTORE(Store#, City, State, Zip, Inventory_value) STOCK(Store#, Book#, Qty)
Total_stock is the total number of books in stock and Inventory_value is the total inventory value for the store in dollars.
a. Give an example of two simple predicates that would be meaningful for the BOOKSTORE relation for horizontal partitioning.
b. How would a derived horizontal partitioning of STOCK be defined based on the partitioning of BOOKSTORE?
c. Show predicates by which BOOKS may be horizontally partitioned by topic.
d. Show how the STOCK may be further partitioned from the partitions in (b) by adding the predicates in (c).
25.29. Consider a distributed database for a bookstore chain called National Books with three sites called EAST, MIDDLE, and WEST. The relation schemas are given in Exercise 25.28. Consider that BOOKS are fragmented by $price amounts into:
B1: BOOK1: $price up to $20 B2: BOOK2: $price from $20.01 to $50 B3: BOOK3: $price from $50.01 to $100 B4: BOOK4: $price $100.01 and above
Similarly, BOOK_STORES are divided by ZIP Codes into:
S1: EAST: Zip up to 35000 S2: MIDDLE: Zip 35001 to 70000 S3: WEST: Zip 70001 to 99999
Assume that STOCK is a derived fragment based on BOOKSTORE only.
a. Consider the query:
SELECT Book#, Total_stock FROM Books WHERE $price > 15 AND $price < 55;
Assume that fragments of BOOKSTORE are nonreplicated and assigned based on region. Assume further that BOOKS are allocated as:
EAST: B1, B4 MIDDLE: B1, B2 WEST: B1, B2, B3, B4 Assuming the query was submitted in EAST, what remote subqueries does it generate? (Write in SQL.)
b. If the price of Book#= 1234 is updated from $45 to $55 at site MIDDLE, what updates does that generate? Write in English and then in SQL.
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c. Give a sample query issued at WEST that will generate a subquery for MIDDLE.
d. Write a query involving selection and projection on the above relations and show two possible query trees that denote different ways of execu- tion.
25.30. Consider that you have been asked to propose a database architecture in a large organization (General Motors, for example) to consolidate all data including legacy databases (from hierarchical and network models, which are explained in the Web Appendices D and E; no specific knowledge of these models is needed) as well as relational databases, which are geographically distributed so that global applications can be supported. Assume that alter- native one is to keep all databases as they are, while alternative two is to first convert them to relational and then support the applications over a distrib- uted integrated database.
a. Draw two schematic diagrams for the above alternatives showing the link- ages among appropriate schemas. For alternative one, choose the approach of providing export schemas for each database and construct- ing unified schemas for each application.
b. List the steps that you would have to go through under each alternative from the present situation until global applications are viable.
c. Compare these from the issues of: i. design time considerations
ii. runtime considerations
Selected Bibliography The textbooks by Ceri and Pelagatti (1984a) and Ozsu and Valduriez (1999) are devoted to distributed databases. Peterson and Davie (2008), Tannenbaum (2003), and Stallings (2007) cover data communications and computer networks. Comer (2008) discusses networks and internets. Ozsu et al. (1994) has a collection of papers on distributed object management.
Most of the research on distributed database design, query processing, and opti- mization occurred in the 1980s and 1990s; we quickly review the important refer- ences here. Distributed database design has been addressed in terms of horizontal and vertical fragmentation, allocation, and replication. Ceri et al. (1982) defined the concept of minterm horizontal fragments. Ceri et al. (1983) developed an integer programming-based optimization model for horizontal fragmentation and alloca- tion. Navathe et al. (1984) developed algorithms for vertical fragmentation based on attribute affinity and showed a variety of contexts for vertical fragment allocation. Wilson and Navathe (1986) present an analytical model for optimal allocation of fragments. Elmasri et al. (1987) discuss fragmentation for the ECR model; Karlapalem et al. (1996) discuss issues for distributed design of object databases. Navathe et al. (1996) discuss mixed fragmentation by combining horizontal and
Selected Bibliography 925
vertical fragmentation; Karlapalem et al. (1996) present a model for redesign of dis- tributed databases.
Distributed query processing, optimization, and decomposition are discussed in Hevner and Yao (1979), Kerschberg et al. (1982), Apers et al. (1983), Ceri and Pelagatti (1984), and Bodorick et al. (1992). Bernstein and Goodman (1981) discuss the theory behind semijoin processing. Wong (1983) discusses the use of relation- ships in relation fragmentation. Concurrency control and recovery schemes are dis- cussed in Bernstein and Goodman (1981a). Kumar and Hsu (1998) compiles some articles related to recovery in distributed databases. Elections in distributed systems are discussed in Garcia-Molina (1982). Lamport (1978) discusses problems with generating unique timestamps in a distributed system. Rahimi and Haug (2007) discuss a more flexible way to construct query critical metadata for P2P databases. Ouzzani and Bouguettaya (2004) outline fundamental problems in distributed query processing over Web-based data sources.
A concurrency control technique for replicated data that is based on voting is pre- sented by Thomas (1979). Gifford (1979) proposes the use of weighted voting, and Paris (1986) describes a method called voting with witnesses. Jajodia and Mutchler (1990) discuss dynamic voting. A technique called available copy is proposed by Bernstein and Goodman (1984), and one that uses the idea of a group is presented in ElAbbadi and Toueg (1988). Other work that discusses replicated data includes Gladney (1989), Agrawal and ElAbbadi (1990), ElAbbadi and Toueg (1989), Kumar and Segev (1993), Mukkamala (1989), and Wolfson and Milo (1991). Bassiouni (1988) discusses optimistic protocols for DDB concurrency control. Garcia-Molina (1983) and Kumar and Stonebraker (1987) discuss techniques that use the seman- tics of the transactions. Distributed concurrency control techniques based on lock- ing and distinguished copies are presented by Menasce et al. (1980) and Minoura and Wiederhold (1982). Obermark (1982) presents algorithms for distributed deadlock detection. In more recent work, Vadivelu et al. (2008) propose using backup mechanism and multilevel security to develop algorithms for improving concurrency. Madria et al. (2007) propose a mechanism based on a multiversion two-phase locking scheme and timestamping to address concurrency issues specific to mobile database systems. Boukerche and Tuck (2001) propose a technique that allows transactions to be out of order to a limited extent. They attempt to ease the load on the application developer by exploiting the network environment and pro- ducing a schedule equivalent to a temporally ordered serial schedule. Han et al. (2004) propose a deadlock-free and serializable extended Petri net model for Web- based distributed real-time databases.
A survey of recovery techniques in distributed systems is given by Kohler (1981). Reed (1983) discusses atomic actions on distributed data. Bhargava (1987) presents an edited compilation of various approaches and techniques for concurrency and reliability in distributed systems.
Federated database systems were first defined in McLeod and Heimbigner (1985). Techniques for schema integration in federated databases are presented by Elmasri et al. (1986), Batini et al. (1987), Hayne and Ram (1990), and Motro (1987).
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Elmagarmid and Helal (1988) and Gamal-Eldin et al. (1988) discuss the update problem in heterogeneous DDBSs. Heterogeneous distributed database issues are discussed in Hsiao and Kamel (1989). Sheth and Larson (1990) present an exhaus- tive survey of federated database management.
Since late 1980s multidatabase systems and interoperability have become important topics. Techniques for dealing with semantic incompatibilities among multiple databases are examined in DeMichiel (1989), Siegel and Madnick (1991), Krishnamurthy et al. (1991), and Wang and Madnick (1989). Castano et al. (1998) present an excellent survey of techniques for analysis of schemas. Pitoura et al. (1995) discuss object orientation in multidatabase systems. Xiao et al. (2003) pro- pose an XML-based model for a common data model for multidatabase systems and present a new approach for schema mapping based on this model. Lakshmanan et al. (2001) propose extending SQL for interoperability and describe the architec- ture and algorithms for achieving the same.
Transaction processing in multidatabases is discussed in Mehrotra et al. (1992), Georgakopoulos et al. (1991), Elmagarmid et al. (1990), and Brietbart et al. (1990), among others. Elmagarmid (1992) discuss transaction processing for advanced applications, including engineering applications discussed in Heiler et al. (1992).
The workflow systems, which are becoming popular to manage information in com- plex organizations, use multilevel and nested transactions in conjunction with dis- tributed databases. Weikum (1991) discusses multilevel transaction management. Alonso et al. (1997) discuss limitations of current workflow systems. Lopes et al. (2009) propose that users define and execute their own workflows using a client- side Web browser. They attempt to leverage Web 2.0 trends to simplify the user’s work for workflow management. Jung and Yeom (2008) exploit data workflow to develop an improved transaction management system that provides simultaneous, transparent access to the heterogeneous storages that constitute the HVEM DataGrid. Deelman and Chervanak (2008) list the challenges in data-intensive sci- entific workflows. Specifically, they look at automated management of data, effi- cient mapping techniques, and user feedback issues in workflow mapping. They also argue for data reuse as an efficient means to manage data and present the chal- lenges therein.
A number of experimental distributed DBMSs have been implemented. These include distributed INGRES by Epstein et al., (1978), DDTS by Devor and Weeldreyer, (1980), SDD-1 by Rothnie et al., (1980), System R* by Lindsay et al., (1984), SIRIUS-DELTA by Ferrier and Stangret, (1982), and MULTIBASE by Smith et al., (1981). The OMNIBASE system by Rusinkiewicz et al. (1988) and the Federated Information Base developed using the Candide data model by Navathe et al. (1994) are examples of federated DDBMSs. Pitoura et al. (1995) present a com- parative survey of the federated database system prototypes. Most commercial DBMS vendors have products using the client-server approach and offer distributed versions of their systems. Some system issues concerning client-server DBMS archi- tectures are discussed in Carey et al. (1991), DeWitt et al. (1990), and Wang and Rowe (1991). Khoshafian et al. (1992) discuss design issues for relational DBMSs in
the client-server environment. Client-server management issues are discussed in many books, such as Zantinge and Adriaans (1996). Di Stefano (2005) discusses data distribution issues specific to grid computing. A major part of this discussion may also apply to cloud computing.
Selected Bibliography 927
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part 11 Advanced Database Models,
Systems, and Applications
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931
Enhanced Data Models for Advanced Applications
As the use of database systems has grown, users havedemanded additional functionality from these software packages, with the purpose of making it easier to implement more advanced and complex user applications. Object-oriented databases and object- relational systems do provide features that allow users to extend their systems by specifying additional abstract data types for each application. However, it is quite useful to identify certain common features for some of these advanced applications and to create models that can represent them. Additionally, specialized storage structures and indexing methods can be implemented to improve the performance of these common features. Then the features can be implemented as abstract data types or class libraries and purchased separately from the basic DBMS software package. The term data blade has been used in Informix and cartridge in Oracle to refer to such optional submodules that can be included in a DBMS package. Users can utilize these features directly if they are suitable for their applications, without having to reinvent, reimplement, and reprogram such common features.
This chapter introduces database concepts for some of the common features that are needed by advanced applications and are being used widely. We will cover active rules that are used in active database applications, temporal concepts that are used in temporal database applications, and, briefly, some of the issues involving spatial databases and multimedia databases. We will also discuss deductive databases. It is important to note that each of these topics is very broad, and we give only a brief introduction to each. In fact, each of these areas can serve as the sole topic of a com- plete book.
In Section 26.1 we introduce the topic of active databases, which provide additional functionality for specifying active rules. These rules can be automatically triggered
26chapter 26
932 Chapter 26 Enhanced Data Models for Advanced Applications
by events that occur, such as database updates or certain times being reached, and can initiate certain actions that have been specified in the rule declaration to occur if certain conditions are met. Many commercial packages include some of the func- tionality provided by active databases in the form of triggers. Triggers are now part of the SQL-99 and later standards.
In Section 26.2 we introduce the concepts of temporal databases, which permit the database system to store a history of changes, and allow users to query both current and past states of the database. Some temporal database models also allow users to store future expected information, such as planned schedules. It is important to note that many database applications are temporal, but they are often implemented without having much temporal support from the DBMS package—that is, the tem- poral concepts are implemented in the application programs that access the data- base.
Section 26.3 gives a brief overview of spatial database concepts. We discuss types of spatial data, different kinds of spatial analyses, operations on spatial data, types of spatial queries, spatial data indexing, spatial data mining, and applications of spatial databases.
Section 26.4 is devoted to multimedia database concepts. Multimedia databases provide features that allow users to store and query different types of multimedia information, which includes images (such as pictures and drawings), video clips (such as movies, newsreels, and home videos), audio clips (such as songs, phone messages, and speeches), and documents (such as books and articles). We discuss automatic analysis of images, object recognition in images, and semantic tagging of images,
In Section 26.5 we discuss deductive databases,1 an area that is at the intersection of databases, logic, and artificial intelligence or knowledge bases. A deductive data- base system includes capabilities to define (deductive) rules, which can deduce or infer additional information from the facts that are stored in a database. Because part of the theoretical foundation for some deductive database systems is mathe- matical logic, such rules are often referred to as logic databases. Other types of sys- tems, referred to as expert database systems or knowledge-based systems, also incorporate reasoning and inferencing capabilities; such systems use techniques that were developed in the field of artificial intelligence, including semantic networks, frames, production systems, or rules for capturing domain-specific knowledge. Section 26.6 summarizes the chapter.
Readers may choose to peruse the particular topics they are interested in, as the sec- tions in this chapter are practically independent of one another.
1Section 26.5 is a summary of Deductive Databases. The full chapter from the third edition, which pro- vides a more comprehensive introduction, is available on the book’s Web site.
26.1 Active Database Concepts and Triggers 933
26.1 Active Database Concepts and Triggers Rules that specify actions that are automatically triggered by certain events have been considered important enhancements to database systems for quite some time. In fact, the concept of triggers—a technique for specifying certain types of active rules—has existed in early versions of the SQL specification for relational databases and triggers are now part of the SQL-99 and later standards. Commercial relational DBMSs—such as Oracle, DB2, and Microsoft SQLServer—have various versions of triggers available. However, much research into what a general model for active databases should look like has been done since the early models of triggers were proposed. In Section 26.1.1 we will present the general concepts that have been pro- posed for specifying rules for active databases. We will use the syntax of the Oracle commercial relational DBMS to illustrate these concepts with specific examples, since Oracle triggers are close to the way rules are specified in the SQL standard. Section 26.1.2 will discuss some general design and implementation issues for active databases. We give examples of how active databases are implemented in the STAR- BURST experimental DBMS in Section 26.1.3, since STARBURST provides for many of the concepts of generalized active databases within its framework. Section 26.1.4 discusses possible applications of active databases. Finally, Section 26.1.5 describes how triggers are declared in the SQL-99 standard.
26.1.1 Generalized Model for Active Databases and Oracle Triggers
The model that has been used to specify active database rules is referred to as the Event-Condition-Action (ECA) model. A rule in the ECA model has three compo- nents:
1. The event(s) that triggers the rule: These events are usually database update operations that are explicitly applied to the database. However, in the general model, they could also be temporal events2 or other kinds of external events.
2. The condition that determines whether the rule action should be executed: Once the triggering event has occurred, an optional condition may be evalu- ated. If no condition is specified, the action will be executed once the event occurs. If a condition is specified, it is first evaluated, and only if it evaluates to true will the rule action be executed.
3. The action to be taken: The action is usually a sequence of SQL statements, but it could also be a database transaction or an external program that will be automatically executed.
Let us consider some examples to illustrate these concepts. The examples are based on a much simplified variation of the COMPANY database application from Figure 3.5 and is shown in Figure 26.1, with each employee having a name (Name), Social
2An example would be a temporal event specified as a periodic time, such as: Trigger this rule every day at 5:30 A.M.
934 Chapter 26 Enhanced Data Models for Advanced Applications
Name Ssn Salary Dno Supervisor_ssn
EMPLOYEE
Dname Dno Total_sal Manager_ssn
DEPARTMENT
Figure 26.1 A simplified COMPANY database used for active rule examples.
Security number (Ssn), salary (Salary), department to which they are currently assigned (Dno, a foreign key to DEPARTMENT), and a direct supervisor (Supervisor_ssn, a (recursive) foreign key to EMPLOYEE). For this example, we assume that NULL is allowed for Dno, indicating that an employee may be temporar- ily unassigned to any department. Each department has a name (Dname), number (Dno), the total salary of all employees assigned to the department (Total_sal), and a manager (Manager_ssn, which is a foreign key to EMPLOYEE).
Notice that the Total_sal attribute is really a derived attribute, whose value should be the sum of the salaries of all employees who are assigned to the particular depart- ment. Maintaining the correct value of such a derived attribute can be done via an active rule. First we have to determine the events that may cause a change in the value of Total_sal, which are as follows:
1. Inserting (one or more) new employee tuples
2. Changing the salary of (one or more) existing employees
3. Changing the assignment of existing employees from one department to another
4. Deleting (one or more) employee tuples
In the case of event 1, we only need to recompute Total_sal if the new employee is immediately assigned to a department—that is, if the value of the Dno attribute for the new employee tuple is not NULL (assuming NULL is allowed for Dno). Hence, this would be the condition to be checked. A similar condition could be checked for event 2 (and 4) to determine whether the employee whose salary is changed (or who is being deleted) is currently assigned to a department. For event 3, we will always execute an action to maintain the value of Total_sal correctly, so no condition is needed (the action is always executed).
The action for events 1, 2, and 4 is to automatically update the value of Total_sal for the employee’s department to reflect the newly inserted, updated, or deleted employee’s salary. In the case of event 3, a twofold action is needed: one to update the Total_sal of the employee’s old department and the other to update the Total_sal of the employee’s new department.
The four active rules (or triggers) R1, R2, R3, and R4—corresponding to the above situation—can be specified in the notation of the Oracle DBMS as shown in Figure 26.2(a). Let us consider rule R1 to illustrate the syntax of creating triggers in Oracle.
26.1 Active Database Concepts and Triggers 935
(a) R1: CREATE TRIGGER Total_sal1 AFTER INSERT ON EMPLOYEE FOR EACH ROW WHEN ( NEW.Dno IS NOT NULL )
UPDATE DEPARTMENT SET Total_sal = Total_sal + NEW.Salary WHERE Dno = NEW.Dno;
R2: CREATE TRIGGER Total_sal2 AFTER UPDATE OF Salary ON EMPLOYEE FOR EACH ROW WHEN ( NEW.Dno IS NOT NULL )
UPDATE DEPARTMENT SET Total_sal = Total_sal + NEW.Salary – OLD.Salary WHERE Dno = NEW.Dno;
R3: CREATE TRIGGER Total_sal3 AFTER UPDATE OF Dno ON EMPLOYEE FOR EACH ROW
BEGIN UPDATE DEPARTMENT SET Total_sal = Total_sal + NEW.Salary WHERE Dno = NEW.Dno; UPDATE DEPARTMENT SET Total_sal = Total_sal – OLD.Salary WHERE Dno = OLD.Dno; END;
R4: CREATE TRIGGER Total_sal4 AFTER DELETE ON EMPLOYEE FOR EACH ROW WHEN ( OLD.Dno IS NOT NULL )
UPDATE DEPARTMENT SET Total_sal = Total_sal – OLD.Salary WHERE Dno = OLD.Dno;
(b) R5: CREATE TRIGGER Inform_supervisor1 BEFORE INSERT OR UPDATE OF Salary, Supervisor_ssn
ON EMPLOYEE FOR EACH ROW WHEN ( NEW.Salary > ( SELECT Salary FROM EMPLOYEE
WHERE Ssn = NEW.Supervisor_ssn ) ) inform_supervisor(NEW.Supervisor_ssn, NEW.Ssn );
Figure 26.2 Specifying active rules as triggers in Oracle notation. (a) Triggers for automatically main- taining the consistency of Total_sal of DEPARTMENT. (b) Trigger for comparing an employee’s salary with that of his or her supervisor.
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The CREATE TRIGGER statement specifies a trigger (or active rule) name— Total_sal1 for R1. The AFTER clause specifies that the rule will be triggered after the events that trigger the rule occur. The triggering events—an insert of a new employee in this example—are specified following the AFTER keyword.3
The ON clause specifies the relation on which the rule is specified—EMPLOYEE for R1. The optional keywords FOR EACH ROW specify that the rule will be triggered once for each row that is affected by the triggering event.4
The optional WHEN clause is used to specify any conditions that need to be checked after the rule is triggered, but before the action is executed. Finally, the action(s) to be taken is (are) specified as a PL/SQL block, which typically contains one or more SQL statements or calls to execute external procedures.
The four triggers (active rules) R1, R2, R3, and R4 illustrate a number of features of active rules. First, the basic events that can be specified for triggering the rules are the standard SQL update commands: INSERT, DELETE, and UPDATE. They are spec- ified by the keywords INSERT, DELETE, and UPDATE in Oracle notation. In the case of UPDATE, one may specify the attributes to be updated—for example, by writing UPDATE OF Salary, Dno. Second, the rule designer needs to have a way to refer to the tuples that have been inserted, deleted, or modified by the triggering event. The key- words NEW and OLD are used in Oracle notation; NEW is used to refer to a newly inserted or newly updated tuple, whereas OLD is used to refer to a deleted tuple or to a tuple before it was updated.
Thus, rule R1 is triggered after an INSERT operation is applied to the EMPLOYEE relation. In R1, the condition (NEW.Dno IS NOT NULL) is checked, and if it evaluates to true, meaning that the newly inserted employee tuple is related to a department, then the action is executed. The action updates the DEPARTMENT tuple(s) related to the newly inserted employee by adding their salary (NEW.Salary) to the Total_sal attribute of their related department.
Rule R2 is similar to R1, but it is triggered by an UPDATE operation that updates the SALARY of an employee rather than by an INSERT. Rule R3 is triggered by an update to the Dno attribute of EMPLOYEE, which signifies changing an employee’s assign- ment from one department to another. There is no condition to check in R3, so the action is executed whenever the triggering event occurs. The action updates both the old department and new department of the reassigned employees by adding their salary to Total_sal of their new department and subtracting their salary from Total_sal of their old department. Note that this should work even if the value of Dno is NULL, because in this case no department will be selected for the rule action.5
3As we will see, it is also possible to specify BEFORE instead of AFTER, which indicates that the rule is triggered before the triggering event is executed. 4Again, we will see that an alternative is to trigger the rule only once even if multiple rows (tuples) are affected by the triggering event. 5R1, R2, and R4 can also be written without a condition. However, it may be more efficient to execute them with the condition since the action is not invoked unless it is required.
26.1 Active Database Concepts and Triggers 937
<trigger> ::= CREATE TRIGGER <trigger name> ( AFTER I BEFORE ) <triggering events> ON <table name> [ FOR EACH ROW ] [ WHEN <condition> ] <trigger actions> ;
<triggering events> ::= <trigger event> {OR <trigger event> } <trigger event> ::= INSERT I DELETE I UPDATE [ OF <column name> { , <column name> } ] <trigger action> ::= <PL/SQL block>
Figure 26.3 A syntax summary for specifying triggers in the Oracle system (main options only).
It is important to note the effect of the optional FOR EACH ROW clause, which sig- nifies that the rule is triggered separately for each tuple. This is known as a row-level trigger. If this clause was left out, the trigger would be known as a statement-level trigger and would be triggered once for each triggering statement. To see the differ- ence, consider the following update operation, which gives a 10 percent raise to all employees assigned to department 5. This operation would be an event that triggers rule R2:
UPDATE EMPLOYEE SET Salary = 1.1 * Salary WHERE Dno = 5;
Because the above statement could update multiple records, a rule using row-level semantics, such as R2 in Figure 26.2, would be triggered once for each row, whereas a rule using statement-level semantics is triggered only once. The Oracle system allows the user to choose which of the above options is to be used for each rule. Including the optional FOR EACH ROW clause creates a row-level trigger, and leaving it out creates a statement-level trigger. Note that the keywords NEW and OLD can only be used with row-level triggers.
As a second example, suppose we want to check whenever an employee’s salary is greater than the salary of his or her direct supervisor. Several events can trigger this rule: inserting a new employee, changing an employee’s salary, or changing an employee’s supervisor. Suppose that the action to take would be to call an external procedure inform_supervisor,6 which will notify the supervisor. The rule could then be written as in R5 (see Figure 26.2(b)).
Figure 26.3 shows the syntax for specifying some of the main options available in Oracle triggers. We will describe the syntax for triggers in the SQL-99 standard in Section 26.1.5.
6Assuming that an appropriate external procedure has been declared. This is a feature that is available in SQL-99 and later standards.
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26.1.2 Design and Implementation Issues for Active Databases
The previous section gave an overview of some of the main concepts for specifying active rules. In this section, we discuss some additional issues concerning how rules are designed and implemented. The first issue concerns activation, deactivation, and grouping of rules. In addition to creating rules, an active database system should allow users to activate, deactivate, and drop rules by referring to their rule names. A deactivated rule will not be triggered by the triggering event. This feature allows users to selectively deactivate rules for certain periods of time when they are not needed. The activate command will make the rule active again. The drop com- mand deletes the rule from the system. Another option is to group rules into named rule sets, so the whole set of rules can be activated, deactivated, or dropped. It is also useful to have a command that can trigger a rule or rule set via an explicit PROCESS RULES command issued by the user.
The second issue concerns whether the triggered action should be executed before, after, instead of, or concurrently with the triggering event. A before trigger executes the trigger before executing the event that caused the trigger. It can be used in appli- cations such as checking for constraint violations. An after trigger executes the trig- ger after executing the event, and it can be used in applications such as maintaining derived data and monitoring for specific events and conditions. An instead of trig- ger executes the trigger instead of executing the event, and it can be used in applica- tions such as executing corresponding updates on base relations in response to an event that is an update of a view.
A related issue is whether the action being executed should be considered as a separate transaction or whether it should be part of the same transaction that triggered the rule. We will try to categorize the various options. It is important to note that not all options may be available for a particular active database system. In fact, most com- mercial systems are limited to one or two of the options that we will now discuss.
Let us assume that the triggering event occurs as part of a transaction execution. We should first consider the various options for how the triggering event is related to the evaluation of the rule’s condition. The rule condition evaluation is also known as rule consideration, since the action is to be executed only after considering whether the condition evaluates to true or false. There are three main possibilities for rule consideration:
1. Immediate consideration. The condition is evaluated as part of the same transaction as the triggering event, and is evaluated immediately. This case can be further categorized into three options: ■ Evaluate the condition before executing the triggering event. ■ Evaluate the condition after executing the triggering event. ■ Evaluate the condition instead of executing the triggering event.
2. Deferred consideration. The condition is evaluated at the end of the trans- action that included the triggering event. In this case, there could be many triggered rules waiting to have their conditions evaluated.
26.1 Active Database Concepts and Triggers 939
3. Detached consideration. The condition is evaluated as a separate transac- tion, spawned from the triggering transaction.
The next set of options concerns the relationship between evaluating the rule condi- tion and executing the rule action. Here, again, three options are possible: immediate, deferred, or detached execution. Most active systems use the first option. That is, as soon as the condition is evaluated, if it returns true, the action is immediately executed.
The Oracle system (see Section 26.1.1) uses the immediate consideration model, but it allows the user to specify for each rule whether the before or after option is to be used with immediate condition evaluation. It also uses the immediate execution model. The STARBURST system (see Section 26.1.3) uses the deferred consideration option, meaning that all rules triggered by a transaction wait until the triggering transaction reaches its end and issues its COMMIT WORK command before the rule conditions are evaluated.7
Another issue concerning active database rules is the distinction between row-level rules and statement-level rules. Because SQL update statements (which act as trig- gering events) can specify a set of tuples, one has to distinguish between whether the rule should be considered once for the whole statement or whether it should be con- sidered separately for each row (that is, tuple) affected by the statement. The SQL-99 standard (see Section 26.1.5) and the Oracle system (see Section 26.1.1) allow the user to choose which of the options is to be used for each rule, whereas STAR- BURST uses statement-level semantics only. We will give examples of how statement-level triggers can be specified in Section 26.1.3.
One of the difficulties that may have limited the widespread use of active rules, in spite of their potential to simplify database and software development, is that there are no easy-to-use techniques for designing, writing, and verifying rules. For exam- ple, it is quite difficult to verify that a set of rules is consistent, meaning that two or more rules in the set do not contradict one another. It is also difficult to guarantee termination of a set of rules under all circumstances. To illustrate the termination
R1: CREATE TRIGGER T1 AFTER INSERT ON TABLE1 FOR EACH ROW
UPDATE TABLE2 SET Attribute1 = ... ;
R2: CREATE TRIGGER T2 AFTER UPDATE OF Attribute1 ON TABLE2 FOR EACH ROW
INSERT INTO TABLE1 VALUES ( ... );
Figure 26.4 An example to illus- trate the termination problem for active rules.
7STARBURST also allows the user to start rule consideration explicitly via a PROCESS RULES com- mand.
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problem briefly, consider the rules in Figure 26.4. Here, rule R1 is triggered by an INSERT event on TABLE1 and its action includes an update event on Attribute1 of TABLE2. However, rule R2’s triggering event is an UPDATE event on Attribute1 of TABLE2, and its action includes an INSERT event on TABLE1. In this example, it is easy to see that these two rules can trigger one another indefinitely, leading to non- termination. However, if dozens of rules are written, it is very difficult to determine whether termination is guaranteed or not.
If active rules are to reach their potential, it is necessary to develop tools for the design, debugging, and monitoring of active rules that can help users design and debug their rules.
26.1.3 Examples of Statement-Level Active Rules in STARBURST
We now give some examples to illustrate how rules can be specified in the STAR- BURST experimental DBMS. This will allow us to demonstrate how statement-level rules can be written, since these are the only types of rules allowed in STARBURST.
The three active rules R1S, R2S, and R3S in Figure 26.5 correspond to the first three rules in Figure 26.2, but they use STARBURST notation and statement-level seman- tics. We can explain the rule structure using rule R1S. The CREATE RULE statement specifies a rule name—Total_sal1 for R1S. The ON clause specifies the relation on which the rule is specified—EMPLOYEE for R1S. The WHEN clause is used to spec- ify the events that trigger the rule.8 The optional IF clause is used to specify any conditions that need to be checked. Finally, the THEN clause is used to specify the actions to be taken, which are typically one or more SQL statements.
In STARBURST, the basic events that can be specified for triggering the rules are the standard SQL update commands: INSERT, DELETE, and UPDATE. These are speci- fied by the keywords INSERTED, DELETED, and UPDATED in STARBURST nota- tion. Second, the rule designer needs to have a way to refer to the tuples that have been modified. The keywords INSERTED, DELETED, NEW-UPDATED, and OLD- UPDATED are used in STARBURST notation to refer to four transition tables (rela- tions) that include the newly inserted tuples, the deleted tuples, the updated tuples before they were updated, and the updated tuples after they were updated, respec- tively. Obviously, depending on the triggering events, only some of these transition tables may be available. The rule writer can refer to these tables when writing the condition and action parts of the rule. Transition tables contain tuples of the same type as those in the relation specified in the ON clause of the rule—for R1S, R2S, and R3S, this is the EMPLOYEE relation.
In statement-level semantics, the rule designer can only refer to the transition tables as a whole and the rule is triggered only once, so the rules must be written differ- ently than for row-level semantics. Because multiple employee tuples may be
8Note that the WHEN keyword specifies events in STARBURST but is used to specify the rule condition in SQL and Oracle triggers.
26.1 Active Database Concepts and Triggers 941
R1S: CREATE RULE Total_sal1 ON EMPLOYEE WHEN INSERTED IF EXISTS ( SELECT * FROM INSERTED WHERE Dno IS NOT NULL ) THEN UPDATE DEPARTMENT AS D
SET D.Total_sal = D.Total_sal + ( SELECT SUM (I.Salary) FROM INSERTED AS I WHERE D.Dno = I.Dno )
WHERE D.Dno IN ( SELECT Dno FROM INSERTED );
R2S: CREATE RULE Total_sal2 ON EMPLOYEE WHEN UPDATED ( Salary ) IF EXISTS ( SELECT * FROM NEW-UPDATED WHERE Dno IS NOT NULL )
OR EXISTS ( SELECT * FROM OLD-UPDATED WHERE Dno IS NOT NULL ) THEN UPDATE DEPARTMENT AS D
SET D.Total_sal = D.Total_sal + ( SELECT SUM (N.Salary) FROM NEW-UPDATED AS N
WHERE D.Dno = N.Dno ) – ( SELECT SUM (O.Salary) FROM OLD-UPDATED AS O
WHERE D.Dno = O.Dno ) WHERE D.Dno IN ( SELECT Dno FROM NEW-UPDATED ) OR
D.Dno IN ( SELECT Dno FROM OLD-UPDATED );
R3S: CREATE RULE Total_sal3 ON EMPLOYEE WHEN UPDATED ( Dno ) THEN UPDATE DEPARTMENT AS D
SET D.Total_sal = D.Total_sal + ( SELECT SUM (N.Salary) FROM NEW-UPDATED AS N
WHERE D.Dno = N.Dno ) WHERE D.Dno IN ( SELECT Dno FROM NEW-UPDATED ); UPDATE DEPARTMENT AS D SET D.Total_sal = Total_sal –
( SELECT SUM (O.Salary) FROM OLD-UPDATED AS O WHERE D.Dno = O.Dno )
WHERE D.Dno IN ( SELECT Dno FROM OLD-UPDATED );
Figure 26.5 Active rules using statement-level semantics in STARBURST notation.
inserted in a single insert statement, we have to check if at least one of the newly inserted employee tuples is related to a department. In R1S, the condition
EXISTS (SELECT * FROM INSERTED WHERE Dno IS NOT NULL )
is checked, and if it evaluates to true, then the action is executed. The action updates in a single statement the DEPARTMENT tuple(s) related to the newly inserted employee(s) by adding their salaries to the Total_sal attribute of each related depart- ment. Because more than one newly inserted employee may belong to the same
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department, we use the SUM aggregate function to ensure that all their salaries are added.
Rule R2S is similar to R1S, but is triggered by an UPDATE operation that updates the salary of one or more employees rather than by an INSERT. Rule R3S is triggered by an update to the Dno attribute of EMPLOYEE, which signifies changing one or more employees’ assignment from one department to another. There is no condition in R3S, so the action is executed whenever the triggering event occurs.9 The action updates both the old department(s) and new department(s) of the reassigned employees by adding their salary to Total_sal of each new department and subtract- ing their salary from Total_sal of each old department.
In our example, it is more complex to write the statement-level rules than the row- level rules, as can be illustrated by comparing Figures 26.2 and 26.5. However, this is not a general rule, and other types of active rules may be easier to specify when using statement-level notation than when using row-level notation.
The execution model for active rules in STARBURST uses deferred consideration. That is, all the rules that are triggered within a transaction are placed in a set— called the conflict set—which is not considered for evaluation of conditions and execution until the transaction ends (by issuing its COMMIT WORK command). STARBURST also allows the user to explicitly start rule consideration in the middle of a transaction via an explicit PROCESS RULES command. Because multiple rules must be evaluated, it is necessary to specify an order among the rules. The syntax for rule declaration in STARBURST allows the specification of ordering among the rules to instruct the system about the order in which a set of rules should be consid- ered.10 Additionally, the transition tables—INSERTED, DELETED, NEW-UPDATED, and OLD-UPDATED—contain the net effect of all the operations within the transac- tion that affected each table, since multiple operations may have been applied to each table during the transaction.
26.1.4 Potential Applications for Active Databases We now briefly discuss some of the potential applications of active rules. Obviously, one important application is to allow notification of certain conditions that occur. For example, an active database may be used to monitor, say, the temperature of an industrial furnace. The application can periodically insert in the database the tem- perature reading records directly from temperature sensors, and active rules can be written that are triggered whenever a temperature record is inserted, with a condi- tion that checks if the temperature exceeds the danger level, and results in the action to raise an alarm.
9As in the Oracle examples, rules R1S and R2S can be written without a condition. However, it may be more efficient to execute them with the condition since the action is not invoked unless it is required. 10If no order is specified between a pair of rules, the system default order is based on placing the rule declared first ahead of the other rule.
26.2 Temporal Database Concepts 943
Active rules can also be used to enforce integrity constraints by specifying the types of events that may cause the constraints to be violated and then evaluating appro- priate conditions that check whether the constraints are actually violated by the event or not. Hence, complex application constraints, often known as business rules, may be enforced that way. For example, in the UNIVERSITY database applica- tion, one rule may monitor the GPA of students whenever a new grade is entered, and it may alert the advisor if the GPA of a student falls below a certain threshold; another rule may check that course prerequisites are satisfied before allowing a stu- dent to enroll in a course; and so on.
Other applications include the automatic maintenance of derived data, such as the examples of rules R1 through R4 that maintain the derived attribute Total_sal when- ever individual employee tuples are changed. A similar application is to use active rules to maintain the consistency of materialized views (see Section 5.3) whenever the base relations are modified. Alternately, an update operation specified on a view can be a triggering event, which can be converted to updates on the base relations by using an instead of trigger. These applications are also relevant to the new data ware- housing technologies (see Chapter 29). A related application maintains that replicated tables are consistent by specifying rules that modify the replicas when- ever the master table is modified.
26.1.5 Triggers in SQL-99 Triggers in the SQL-99 and later standards are quite similar to the examples we dis- cussed in Section 26.1.1, with some minor syntactic differences. The basic events that can be specified for triggering the rules are the standard SQL update com- mands: INSERT, DELETE, and UPDATE. In the case of UPDATE, one may specify the attributes to be updated. Both row-level and statement-level triggers are allowed, indicated in the trigger by the clauses FOR EACH ROW and FOR EACH STATEMENT, respectively. One syntactic difference is that the trigger may specify particular tuple variable names for the old and new tuples instead of using the keywords NEW and OLD, as shown in Figure 26.1. Trigger T1 in Figure 26.6 shows how the row-level trigger R2 from Figure 26.1(a) may be specified in SQL-99. Inside the REFERENCING clause, we named tuple variables (aliases) O and N to refer to the OLD tuple (before modification) and NEW tuple (after modification), respectively. Trigger T2 in Figure 26.6 shows how the statement-level trigger R2S from Figure 26.5 may be specified in SQL-99. For a statement-level trigger, the REFERENCING clause is used to refer to the table of all new tuples (newly inserted or newly updated) as N, whereas the table of all old tuples (deleted tuples or tuples before they were updated) is referred to as O.
26.2 Temporal Database Concepts Temporal databases, in the broadest sense, encompass all database applications that require some aspect of time when organizing their information. Hence, they provide a good example to illustrate the need for developing a set of unifying con- cepts for application developers to use. Temporal database applications have been
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developed since the early days of database usage. However, in creating these applica- tions, it is mainly left to the application designers and developers to discover, design, program, and implement the temporal concepts they need. There are many exam- ples of applications where some aspect of time is needed to maintain the informa- tion in a database. These include healthcare, where patient histories need to be maintained; insurance, where claims and accident histories are required as well as information about the times when insurance policies are in effect; reservation sys- tems in general (hotel, airline, car rental, train, and so on), where information on the dates and times when reservations are in effect are required; scientific databases, where data collected from experiments includes the time when each data is meas- ured; and so on. Even the two examples used in this book may be easily expanded into temporal applications. In the COMPANY database, we may wish to keep SALARY, JOB, and PROJECT histories on each employee. In the UNIVERSITY data- base, time is already included in the SEMESTER and YEAR of each SECTION of a COURSE, the grade history of a STUDENT, and the information on research grants. In fact, it is realistic to conclude that the majority of database applications have some temporal information. However, users often attempt to simplify or ignore temporal aspects because of the complexity that they add to their applications.
In this section, we will introduce some of the concepts that have been developed to deal with the complexity of temporal database applications. Section 26.2.1 gives an overview of how time is represented in databases, the different types of temporal
T1: CREATE TRIGGER Total_sal1 AFTER UPDATE OF Salary ON EMPLOYEE REFERENCING OLD ROW AS O, NEW ROW AS N FOR EACH ROW WHEN ( N.Dno IS NOT NULL ) UPDATE DEPARTMENT SET Total_sal = Total_sal + N.salary – O.salary WHERE Dno = N.Dno;
T2: CREATE TRIGGER Total_sal2 AFTER UPDATE OF Salary ON EMPLOYEE REFERENCING OLD TABLE AS O, NEW TABLE AS N FOR EACH STATEMENT WHEN EXISTS ( SELECT *FROM N WHERE N.Dno IS NOT NULL ) OR
EXISTS ( SELECT * FROM O WHERE O.Dno IS NOT NULL ) UPDATE DEPARTMENT AS D SET D.Total_sal = D.Total_sal + ( SELECT SUM (N.Salary) FROM N WHERE D.Dno=N.Dno ) – ( SELECT SUM (O.Salary) FROM O WHERE D.Dno=O.Dno ) WHERE Dno IN ( ( SELECT Dno FROM N ) UNION ( SELECT Dno FROM O ) );
Figure 26.6 Trigger T1 illustrating the syntax for defining triggers in SQL-99.
26.2 Temporal Database Concepts 945
information, and some of the different dimensions of time that may be needed. Section 26.2.2 discusses how time can be incorporated into relational databases. Section 26.2.3 gives some additional options for representing time that are possible in database models that allow complex-structured objects, such as object databases. Section 26.2.4 introduces operations for querying temporal databases, and gives a brief overview of the TSQL2 language, which extends SQL with temporal concepts. Section 26.2.5 focuses on time series data, which is a type of temporal data that is very important in practice.
26.2.1 Time Representation, Calendars, and Time Dimensions
For temporal databases, time is considered to be an ordered sequence of points in some granularity that is determined by the application. For example, suppose that some temporal application never requires time units that are less than one second. Then, each time point represents one second using this granularity. In reality, each second is a (short) time duration, not a point, since it may be further divided into milliseconds, microseconds, and so on. Temporal database researchers have used the term chronon instead of point to describe this minimal granularity for a particular application. The main consequence of choosing a minimum granularity—say, one second—is that events occurring within the same second will be considered to be simultaneous events, even though in reality they may not be.
Because there is no known beginning or ending of time, one needs a reference point from which to measure specific time points. Various calendars are used by various cultures (such as Gregorian (western), Chinese, Islamic, Hindu, Jewish, Coptic, and so on) with different reference points. A calendar organizes time into different time units for convenience. Most calendars group 60 seconds into a minute, 60 minutes into an hour, 24 hours into a day (based on the physical time of earth’s rotation around its axis), and 7 days into a week. Further grouping of days into months and months into years either follow solar or lunar natural phenomena, and are generally irregular. In the Gregorian calendar, which is used in most western countries, days are grouped into months that are 28, 29, 30, or 31 days, and 12 months are grouped into a year. Complex formulas are used to map the different time units to one another.
In SQL2, the temporal data types (see Chapter 4) include DATE (specifying Year, Month, and Day as YYYY-MM-DD), TIME (specifying Hour, Minute, and Second as HH:MM:SS), TIMESTAMP (specifying a Date/Time combination, with options for including subsecond divisions if they are needed), INTERVAL (a relative time dura- tion, such as 10 days or 250 minutes), and PERIOD (an anchored time duration with a fixed starting point, such as the 10-day period from January 1, 2009, to January 10, 2009, inclusive).11
11Unfortunately, the terminology has not been used consistently. For example, the term interval is often used to denote an anchored duration. For consistency, we will use the SQL terminology.
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Event Information versus Duration (or State) Information. A temporal data- base will store information concerning when certain events occur, or when certain facts are considered to be true. There are several different types of temporal infor- mation. Point events or facts are typically associated in the database with a single time point in some granularity. For example, a bank deposit event may be associ- ated with the timestamp when the deposit was made, or the total monthly sales of a product (fact) may be associated with a particular month (say, February 2010). Note that even though such events or facts may have different granularities, each is still associated with a single time value in the database. This type of information is often represented as time series data as we will discuss in Section 26.2.5. Duration events or facts, on the other hand, are associated with a specific time period in the data- base.12 For example, an employee may have worked in a company from August 15, 2003 until November 20, 2008.
A time period is represented by its start and end time points [START-TIME, END- TIME]. For example, the above period is represented as [2003-08-15, 2008-11-20]. Such a time period is often interpreted to mean the set of all time points from start- time to end-time, inclusive, in the specified granularity. Hence, assuming day gran- ularity, the period [2003-08-15, 2008-11-20] represents the set of all days from August 15, 2003, until November 20, 2008, inclusive.13
Valid Time and Transaction Time Dimensions. Given a particular event or fact that is associated with a particular time point or time period in the database, the association may be interpreted to mean different things. The most natural interpre- tation is that the associated time is the time that the event occurred, or the period during which the fact was considered to be true in the real world. If this interpreta- tion is used, the associated time is often referred to as the valid time. A temporal database using this interpretation is called a valid time database.
However, a different interpretation can be used, where the associated time refers to the time when the information was actually stored in the database; that is, it is the value of the system time clock when the information is valid in the system.14 In this case, the associated time is called the transaction time. A temporal database using this interpretation is called a transaction time database.
Other interpretations can also be intended, but these are considered to be the most common ones, and they are referred to as time dimensions. In some applications, only one of the dimensions is needed and in other cases both time dimensions are required, in which case the temporal database is called a bitemporal database. If
12This is the same as an anchored duration. It has also been frequently called a time interval, but to avoid confusion we will use period to be consistent with SQL terminology. 13The representation [2003-08-15, 2008-11-20] is called a closed interval representation. One can also use an open interval, denoted [2003-08-15, 2008-11-21), where the set of points does not include the end point. Although the latter representation is sometimes more convenient, we shall use closed intervals except where indicated. 14The explanation is more involved, as we will see in Section 26.2.3.
26.2 Temporal Database Concepts 947
(a)
Name
EMP_VT
Salary DnoSsn Supervisor_ssn Vst Vet
Name Salary Supervisor_ssnSsn Tst Tet
(b)
(c)
Dname
DEPT_VT
EMP_TT
Dname Total_sal Manager_ssnDno
Dno
Tst Tet
DEPT_TT
Total_salDno Manager_ssn Vst Vet
Name Salary Supervisor_ssnSsn Dno Tst Tet
EMP_BT
Dname Total_sal Manager_ssnDno Tst Tet
DEPT_BT
Vst Vet
Vst Vet
Figure 26.7 Different types of temporal relational databases. (a) Valid time database schema. (b) Transaction time database schema. (c) Bitemporal data- base schema.
other interpretations are intended for time, the user can define the semantics and program the applications appropriately, and it is called a user-defined time.
The next section shows how these concepts can be incorporated into relational databases, and Section 26.2.3 shows an approach to incorporate temporal concepts into object databases.
26.2.2 Incorporating Time in Relational Databases Using Tuple Versioning
Valid Time Relations. Let us now see how the different types of temporal data- bases may be represented in the relational model. First, suppose that we would like to include the history of changes as they occur in the real world. Consider again the database in Figure 26.1, and let us assume that, for this application, the granularity is day. Then, we could convert the two relations EMPLOYEE and DEPARTMENT into valid time relations by adding the attributes Vst (Valid Start Time) and Vet (Valid End Time), whose data type is DATE in order to provide day granularity. This is shown in Figure 26.7(a), where the relations have been renamed EMP_VT and DEPT_VT, respectively.
Consider how the EMP_VT relation differs from the nontemporal EMPLOYEE rela- tion (Figure 26.1).15 In EMP_VT, each tuple V represents a version of an employee’s
15A nontemporal relation is also called a snapshot relation because it shows only the current snapshot or current state of the database.
948 Chapter 26 Enhanced Data Models for Advanced Applications
Name
Smith 123456789 25000 5 333445555 2002-06-15 2003-05-31
Smith 123456789 30000 5 333445555 2003-06-01 Now
333445555 25000 4 999887777 1999-08-20 2001-01-31
333445555 30000 5 999887777 2001-02-01 2002-03-31
333445555 40000 5 888665555 2002-04-01 Now
222447777 28000 4 999887777 2001-05-01 2002-08-10
666884444 38000 5 333445555 2003-08-01 Now
Wong
Wong
Wong
Brown
Narayan
. . .
. . .
EMP_VT
Ssn Salary Dno Supervisor_ssn Vst Vet
Dname
Research
Research
DEPT_VT
5 888665555 2002-03-312001-09-20
333445555 2002-04-015 Now
Dno Manager_ssn Vst Vet
Figure 26.8 Some tuple versions in the valid time relations EMP_VT and DEPT_VT.
information that is valid (in the real world) only during the time period [V.Vst, V.Vet], whereas in EMPLOYEE each tuple represents only the current state or current version of each employee. In EMP_VT, the current version of each employee typi- cally has a special value, now, as its valid end time. This special value, now, is a temporal variable that implicitly represents the current time as time progresses. The nontemporal EMPLOYEE relation would only include those tuples from the EMP_VT relation whose Vet is now.
Figure 26.8 shows a few tuple versions in the valid-time relations EMP_VT and DEPT_VT. There are two versions of Smith, three versions of Wong, one version of Brown, and one version of Narayan. We can now see how a valid time relation should behave when information is changed. Whenever one or more attributes of an employee are updated, rather than actually overwriting the old values, as would happen in a nontemporal relation, the system should create a new version and close the current version by changing its Vet to the end time. Hence, when the user issued the command to update the salary of Smith effective on June 1, 2003, to $30000, the second version of Smith was created (see Figure 26.8). At the time of this update, the first version of Smith was the current version, with now as its Vet, but after the update now was changed to May 31, 2003 (one less than June 1, 2003, in day granu- larity), to indicate that the version has become a closed or history version and that the new (second) version of Smith is now the current one.
26.2 Temporal Database Concepts 949
It is important to note that in a valid time relation, the user must generally provide the valid time of an update. For example, the salary update of Smith may have been entered in the database on May 15, 2003, at 8:52:12 A.M., say, even though the salary change in the real world is effective on June 1, 2003. This is called a proactive update, since it is applied to the database before it becomes effective in the real world. If the update is applied to the database after it becomes effective in the real world, it is called a retroactive update. An update that is applied at the same time as it becomes effective is called a simultaneous update.
The action that corresponds to deleting an employee in a nontemporal database would typically be applied to a valid time database by closing the current version of the employee being deleted. For example, if Smith leaves the company effective January 19, 2004, then this would be applied by changing Vet of the current version of Smith from now to 2004-01-19. In Figure 26.8, there is no current version for Brown, because he presumably left the company on 2002-08-10 and was logically deleted. However, because the database is temporal, the old information on Brown is still there.
The operation to insert a new employee would correspond to creating the first tuple version for that employee, and making it the current version, with the Vst being the effective (real world) time when the employee starts work. In Figure 26.7, the tuple on Narayan illustrates this, since the first version has not been updated yet.
Notice that in a valid time relation, the nontemporal key, such as Ssn in EMPLOYEE, is no longer unique in each tuple (version). The new relation key for EMP_VT is a combination of the nontemporal key and the valid start time attribute Vst,16 so we use (Ssn, Vst) as primary key. This is because, at any point in time, there should be at most one valid version of each entity. Hence, the constraint that any two tuple ver- sions representing the same entity should have nonintersecting valid time periods should hold on valid time relations. Notice that if the nontemporal primary key value may change over time, it is important to have a unique surrogate key attrib- ute, whose value never changes for each real-world entity, in order to relate all ver- sions of the same real-world entity.
Valid time relations basically keep track of the history of changes as they become effective in the real world. Hence, if all real-world changes are applied, the database keeps a history of the real-world states that are represented. However, because updates, insertions, and deletions may be applied retroactively or proactively, there is no record of the actual database state at any point in time. If the actual database states are important to an application, then one should use transaction time relations.
Transaction Time Relations. In a transaction time database, whenever a change is applied to the database, the actual timestamp of the transaction that applied the change (insert, delete, or update) is recorded. Such a database is most useful when changes are applied simultaneously in the majority of cases—for example, real-time stock trading or banking transactions. If we convert the nontemporal database in
16A combination of the nontemporal key and the valid end time attribute Vet could also be used.
950 Chapter 26 Enhanced Data Models for Advanced Applications
Figure 26.1 into a transaction time database, then the two relations EMPLOYEE and DEPARTMENT are converted into transaction time relations by adding the attrib- utes Tst (Transaction Start Time) and Tet (Transaction End Time), whose data type is typically TIMESTAMP. This is shown in Figure 26.7(b), where the relations have been renamed EMP_TT and DEPT_TT, respectively.
In EMP_TT, each tuple V represents a version of an employee’s information that was created at actual time V.Tst and was (logically) removed at actual time V.Tet (because the information was no longer correct). In EMP_TT, the current version of each employee typically has a special value, uc (Until Changed), as its transaction end time, which indicates that the tuple represents correct information until it is changed by some other transaction.17 A transaction time database has also been called a rollback database,18 because a user can logically roll back to the actual database state at any past point in time T by retrieving all tuple versions V whose transaction time period [V.Tst, V.Tet] includes time point T.
Bitemporal Relations. Some applications require both valid time and transaction time, leading to bitemporal relations. In our example, Figure 26.7(c) shows how the EMPLOYEE and DEPARTMENT nontemporal relations in Figure 26.1 would appear as bitemporal relations EMP_BT and DEPT_BT, respectively. Figure 26.9 shows a few tuples in these relations. In these tables, tuples whose transaction end time Tet is uc are the ones representing currently valid information, whereas tuples whose Tet is an absolute timestamp are tuples that were valid until (just before) that timestamp. Hence, the tuples with uc in Figure 26.9 correspond to the valid time tuples in Figure 26.7. The transaction start time attribute Tst in each tuple is the timestamp of the transaction that created that tuple.
Now consider how an update operation would be implemented on a bitemporal relation. In this model of bitemporal databases,19 no attributes are physically changed in any tuple except for the transaction end time attribute Tet with a value of uc.20 To illustrate how tuples are created, consider the EMP_BT relation. The current version V of an employee has uc in its Tet attribute and now in its Vet attribute. If some attribute—say, Salary—is updated, then the transaction T that performs the update should have two parameters: the new value of Salary and the valid time VT when the new salary becomes effective (in the real world). Assume that VT− is the
17The uc variable in transaction time relations corresponds to the now variable in valid time relations. The semantics are slightly different though. 18Here, the term rollback does not have the same meaning as transaction rollback (see Chapter 23) dur- ing recovery, where the transaction updates are physically undone. Rather, here the updates can be logically undone, allowing the user to examine the database as it appeared at a previous time point. 19There have been many proposed temporal database models. We describe specific models here as examples to illustrate the concepts. 20Some bitemporal models allow the Vet attribute to be changed also, but the interpretations of the tuples are different in those models.
26.2 Temporal Database Concepts 951
Name
Smith 123456789 25000 5 333445555 2002-06-15
Smith 123456789 25000 5 333445555 2002-06-15
123456789 30000 5 333445555 2003-06-01
333445555 25000 4 999887777 1999-08-20
333445555 25000 4 999887777 1999-08-20
333445555 30000 5 999887777 2001-02-01
333445555 30000 5
5
4
4
5
999887777
888667777
999887777
999887777
333445555
2001-02-01
2002-04-01
2001-05-01
2001-05-01
2003-08-01
2002-06-08, 13:05:58
2003-06-04, 08:56:12
2003-06-04, 08:56:12
1999-08-20, 11:18:23
2001-01-07, 14:33:02
2001-01-07, 14:33:02
2002-03-28, 09:23:57
2002-03-28, 09:23:57
2001-04-27, 16:22:05
2002-08-12, 10:11:07
2003-07-28, 09:25:37
2003-06-04,08:56:12
uc
uc
2001-01-07,14:33:02
uc
2002-03-28,09:23:57
uc
uc
2002-08-12,10:11:07
uc
uc
Now
2003-05-31
Now
Now
2001-01-31
Now
2002-03-31
Now
Now
2002-08-10
Now
Smith
Wong
Wong
Wong
Wong
Wong 333445555
Brown 222447777
Brown 222447777
Narayan
. . .
40000
28000
28000
38000666884444
EMP_BT
Ssn Salary Dno Supervisor_ssn Vst Vet Tst Tet
Dname
Research
Research
DEPT_VT
5 888665555 Now2001-09-20
888665555 2001-09-205 1997-03-31
Dno Manager_ssn Vst Vet
2001-09-15,14:52:12
2002-03-28,09:23:57
Tst
2001-03-28,09:23:57
uc
Research 333445555 2002-04-015 Now 2002-03-28,09:23:57 uc
Tet
Figure 26.9 Some tuple versions in the bitemporal relations EMP_BT and DEPT_BT.
time point before VT in the given valid time granularity and that transaction T has a timestamp TS(T). Then, the following physical changes would be applied to the EMP_BT table:
1. Make a copy V2 of the current version V; set V2.Vet to VT−, V2.Tst to TS(T), V2.Tet to uc, and insert V2 in EMP_BT; V2 is a copy of the previous current version V after it is closed at valid time VT−.
2. Make a copy V3 of the current version V; set V3.Vst to VT, V3.Vet to now, V3.Salary to the new salary value, V3.Tst to TS(T), V3.Tet to uc, and insert V3 in EMP_BT; V3 represents the new current version.
3. Set V.Tet to TS(T) since the current version is no longer representing correct information.
As an illustration, consider the first three tuples V1, V2, and V3 in EMP_BT in Figure 26.9. Before the update of Smith’s salary from 25000 to 30000, only V1 was in EMP_BT and it was the current version and its Tet was uc. Then, a transaction T whose timestamp TS(T) is ‘2003-06-04,08:56:12’ updates the salary to 30000 with the effective valid time of ‘2003-06-01’. The tuple V2 is created, which is a copy of V1 except that its Vet is set to ‘2003-05-31’, one day less than the new valid time and its Tst is the timestamp of the updating transaction. The tuple V3 is also created, which has the new salary, its Vst is set to ‘2003-06-01’, and its Tst is also the time- stamp of the updating transaction. Finally, the Tet of V1 is set to the timestamp of
952 Chapter 26 Enhanced Data Models for Advanced Applications
the updating transaction, ‘2003-06-04,08:56:12’. Note that this is a retroactive update, since the updating transaction ran on June 4, 2003, but the salary change is effective on June 1, 2003.
Similarly, when Wong’s salary and department are updated (at the same time) to 30000 and 5, the updating transaction’s timestamp is ‘2001-01-07,14:33:02’ and the effective valid time for the update is ‘2001-02-01’. Hence, this is a proactive update because the transaction ran on January 7, 2001, but the effective date was February 1, 2001. In this case, tuple V4 is logically replaced by V5 and V6.
Next, let us illustrate how a delete operation would be implemented on a bitempo- ral relation by considering the tuples V9 and V10 in the EMP_BT relation of Figure 26.9. Here, employee Brown left the company effective August 10, 2002, and the log- ical delete is carried out by a transaction T with TS(T) = 2002-08-12,10:11:07. Before this, V9 was the current version of Brown, and its Tet was uc. The logical delete is implemented by setting V9.Tet to 2002-08-12,10:11:07 to invalidate it, and creating the final version V10 for Brown, with its Vet = 2002-08-10 (see Figure 26.9). Finally, an insert operation is implemented by creating the first version as illustrated by V11 in the EMP_BT table.
Implementation Considerations. There are various options for storing the tuples in a temporal relation. One is to store all the tuples in the same table, as shown in Figures 26.8 and 26.9. Another option is to create two tables: one for the currently valid information and the other for the rest of the tuples. For example, in the bitemporal EMP_BT relation, tuples with uc for their Tet and now for their Vet would be in one relation, the current table, since they are the ones currently valid (that is, represent the current snapshot), and all other tuples would be in another relation. This allows the database administrator to have different access paths, such as indexes for each relation, and keeps the size of the current table reasonable. Another possibility is to create a third table for corrected tuples whose Tet is not uc.
Another option that is available is to vertically partition the attributes of the tempo- ral relation into separate relations so that if a relation has many attributes, a whole new tuple version is created whenever any one of the attributes is updated. If the attributes are updated asynchronously, each new version may differ in only one of the attributes, thus needlessly repeating the other attribute values. If a separate rela- tion is created to contain only the attributes that always change synchronously, with the primary key replicated in each relation, the database is said to be in temporal normal form. However, to combine the information, a variation of join known as temporal intersection join would be needed, which is generally expensive to implement.
It is important to note that bitemporal databases allow a complete record of changes. Even a record of corrections is possible. For example, it is possible that two tuple versions of the same employee may have the same valid time but different attribute values as long as their transaction times are disjoint. In this case, the tuple with the later transaction time is a correction of the other tuple version. Even incor- rectly entered valid times may be corrected this way. The incorrect state of the data-
26.2 Temporal Database Concepts 953
base will still be available as a previous database state for querying purposes. A data- base that keeps such a complete record of changes and corrections is sometimes called an append-only database.
26.2.3 Incorporating Time in Object-Oriented Databases Using Attribute Versioning
The previous section discussed the tuple versioning approach to implementing temporal databases. In this approach, whenever one attribute value is changed, a whole new tuple version is created, even though all the other attribute values will be identical to the previous tuple version. An alternative approach can be used in database systems that support complex structured objects, such as object data- bases (see Chapter 11) or object-relational systems. This approach is called attribute versioning.
In attribute versioning, a single complex object is used to store all the temporal changes of the object. Each attribute that changes over time is called a time-varying attribute, and it has its values versioned over time by adding temporal periods to the attribute. The temporal periods may represent valid time, transaction time, or bitemporal, depending on the application requirements. Attributes that do not change over time are called nontime-varying and are not associated with the tem- poral periods. To illustrate this, consider the example in Figure 26.10, which is an attribute-versioned valid time representation of EMPLOYEE using the object defini- tion language (ODL) notation for object databases (see Chapter 11). Here, we assumed that name and Social Security number are nontime-varying attributes, whereas salary, department, and supervisor are time-varying attributes (they may change over time). Each time-varying attribute is represented as a list of tuples <Valid_start_time, Valid_end_time, Value>, ordered by valid start time.
Whenever an attribute is changed in this model, the current attribute version is closed and a new attribute version for this attribute only is appended to the list. This allows attributes to change asynchronously. The current value for each attrib- ute has now for its Valid_end_time. When using attribute versioning, it is useful to include a lifespan temporal attribute associated with the whole object whose value is one or more valid time periods that indicate the valid time of existence for the whole object. Logical deletion of the object is implemented by closing the lifespan. The constraint that any time period of an attribute within an object should be a subset of the object’s lifespan should be enforced.
For bitemporal databases, each attribute version would have a tuple with five com- ponents:
<Valid_start_time, Valid_end_time, Trans_start_time, Trans_end_time, Value>
The object lifespan would also include both valid and transaction time dimensions. Therefore, the full capabilities of bitemporal databases can be available with attrib- ute versioning. Mechanisms similar to those discussed earlier for updating tuple versions can be applied to updating attribute versions.
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class TEMPORAL_SALARY { attribute Date Valid_start_time;
attribute Date Valid_end_time; attribute float Salary;
};
class TEMPORAL_DEPT { attribute Date Valid_start_time;
attribute Date Valid_end_time; attribute DEPARTMENT_VT Dept;
};
class TEMPORAL_SUPERVISOR { attribute Date Valid_start_time;
attribute Date Valid_end_time; attribute EMPLOYEE_VT Supervisor;
};
class TEMPORAL_LIFESPAN { attribute Date Valid_ start time;
attribute Date Valid end time; };
class EMPLOYEE_VT ( extent EMPLOYEES ) { attribute list<TEMPORAL_LIFESPAN> lifespan;
attribute string Name; attribute string Ssn; attribute list<TEMPORAL_SALARY> Sal_history; attribute list<TEMPORAL_DEPT> Dept_history; attribute list <TEMPORAL_SUPERVISOR> Supervisor_history;
};
Figure 26.10 Possible ODL schema for a temporal valid time EMPLOYEE_VT object class using attribute versioning.
26.2.4 Temporal Querying Constructs and the TSQL2 Language
So far, we have discussed how data models may be extended with temporal con- structs. Now we give a brief overview of how query operations need to be extended for temporal querying. We will briefly discuss the TSQL2 language, which extends SQL for querying valid time, transaction time, and bitemporal relational databases.
In nontemporal relational databases, the typical selection conditions involve attrib- ute conditions, and tuples that satisfy these conditions are selected from the set of
26.2 Temporal Database Concepts 955
current tuples. Following that, the attributes of interest to the query are specified by a projection operation (see Chapter 6). For example, in the query to retrieve the names of all employees working in department 5 whose salary is greater than 30000, the selection condition would be as follows:
((Salary > 30000) AND (Dno = 5))
The projected attribute would be Name. In a temporal database, the conditions may involve time in addition to attributes. A pure time condition involves only time— for example, to select all employee tuple versions that were valid on a certain time point T or that were valid during a certain time period [T1, T2]. In this case, the spec- ified time period is compared with the valid time period of each tuple version [T.Vst, T.Vet], and only those tuples that satisfy the condition are selected. In these opera- tions, a period is considered to be equivalent to the set of time points from T1 to T2 inclusive, so the standard set comparison operations can be used. Additional opera- tions, such as whether one time period ends before another starts are also needed.21
Some of the more common operations used in queries are as follows:
[T.Vst, T.Vet] INCLUDES [T1, T2] Equivalent to T1 ≥ T.Vst AND T2 ≤ T.Vet [T.Vst, T.Vet] INCLUDED_IN [T1, T2] Equivalent to T1 ≤ T.Vst AND T2 ≥ T.Vet [T.Vst, T.Vet] OVERLAPS [T1, T2] Equivalent to (T1 ≤ T.Vet AND T2 ≥ T.Vst)
22
[T.Vst, T.Vet] BEFORE [T1, T2] Equivalent to T1 ≥ T.Vet [T.Vst, T.Vet] AFTER [T1, T2] Equivalent to T2 ≤ T.Vst [T.Vst, T.Vet] MEETS_BEFORE [T1, T2] Equivalent to T1 = T.Vet + 1
23
[T.Vst, T.Vet] MEETS_AFTER [T1, T2] Equivalent to T2 + 1 = T.Vst
Additionally, operations are needed to manipulate time periods, such as computing the union or intersection of two time periods. The results of these operations may not themselves be periods, but rather temporal elements—a collection of one or more disjoint time periods such that no two time periods in a temporal element are directly adjacent. That is, for any two time periods [T1, T2] and [T3, T4] in a tempo- ral element, the following three conditions must hold:
■ [T1, T2] intersection [T3, T4] is empty.
■ T3 is not the time point following T2 in the given granularity.
■ T1 is not the time point following T4 in the given granularity.
The latter conditions are necessary to ensure unique representations of temporal elements. If two time periods [T1, T2] and [T3, T4] are adjacent, they are combined
21A complete set of operations, known as Allen’s algebra (Allen, 1983), has been defined for compar- ing time periods. 22This operation returns true if the intersection of the two periods is not empty; it has also been called INTERSECTS_WITH. 23Here, 1 refers to one time point in the specified granularity. The MEETS operations basically specify if one period starts immediately after another period ends.
956 Chapter 26 Enhanced Data Models for Advanced Applications
into a single time period [T1, T4]. This is called coalescing of time periods. Coalescing also combines intersecting time periods.
To illustrate how pure time conditions can be used, suppose a user wants to select all employee versions that were valid at any point during 2002. The appropriate selec- tion condition applied to the relation in Figure 26.8 would be
[T.Vst, T.Vet] OVERLAPS [2002-01-01, 2002-12-31]
Typically, most temporal selections are applied to the valid time dimension. For a bitemporal database, one usually applies the conditions to the currently correct tuples with uc as their transaction end times. However, if the query needs to be applied to a previous database state, an AS_OF T clause is appended to the query, which means that the query is applied to the valid time tuples that were correct in the database at time T.
In addition to pure time conditions, other selections involve attribute and time conditions. For example, suppose we wish to retrieve all EMP_VT tuple versions T for employees who worked in department 5 at any time during 2002. In this case, the condition is
[T.Vst, T.Vet]OVERLAPS [2002-01-01, 2002-12-31] AND (T.Dno = 5)
Finally, we give a brief overview of the TSQL2 query language, which extends SQL with constructs for temporal databases. The main idea behind TSQL2 is to allow users to specify whether a relation is nontemporal (that is, a standard SQL relation) or temporal. The CREATE TABLE statement is extended with an optional AS clause to allow users to declare different temporal options. The following options are avail- able:
■ <AS VALID STATE <GRANULARITY> (valid time relation with valid time period)
■ <AS VALID EVENT <GRANULARITY> (valid time relation with valid time point)
■ <AS TRANSACTION (transaction time relation with transaction time period)
■ <AS VALID STATE <GRANULARITY> AND TRANSACTION (bitemporal rela- tion, valid time period)
■ <AS VALID EVENT <GRANULARITY> AND TRANSACTION (bitemporal rela- tion, valid time point)
The keywords STATE and EVENT are used to specify whether a time period or time point is associated with the valid time dimension. In TSQL2, rather than have the user actually see how the temporal tables are implemented (as we discussed in the previous sections), the TSQL2 language adds query language constructs to specify various types of temporal selections, temporal projections, temporal aggregations, transformation among granularities, and many other concepts. The book by Snodgrass et al. (1995) describes the language.
26.3 Spatial Database Concepts 957
26.2.5 Time Series Data Time series data is used very often in financial, sales, and economics applications. They involve data values that are recorded according to a specific predefined sequence of time points. Therefore, they are a special type of valid event data, where the event time points are predetermined according to a fixed calendar. Consider the example of closing daily stock prices of a particular company on the New York Stock Exchange. The granularity here is day, but the days that the stock market is open are known (nonholiday weekdays). Hence, it has been common to specify a computa- tional procedure that calculates the particular calendar associated with a time series. Typical queries on time series involve temporal aggregation over higher granularity intervals—for example, finding the average or maximum weekly closing stock price or the maximum and minimum monthly closing stock price from the daily information.
As another example, consider the daily sales dollar amount at each store of a chain of stores owned by a particular company. Again, typical temporal aggregates would be retrieving the weekly, monthly, or yearly sales from the daily sales information (using the sum aggregate function), or comparing same store monthly sales with previous monthly sales, and so on.
Because of the specialized nature of time series data and the lack of support for it in older DBMSs, it has been common to use specialized time series management sys- tems rather than general-purpose DBMSs for managing such information. In such systems, it has been common to store time series values in sequential order in a file, and apply specialized time series procedures to analyze the information. The prob- lem with this approach is that the full power of high-level querying in languages such as SQL will not be available in such systems.
More recently, some commercial DBMS packages are offering time series exten- sions, such as the Oracle time cartridge and the time series data blade of Informix Universal Server. In addition, the TSQL2 language provides some support for time series in the form of event tables.
26.3 Spatial Database Concepts24
26.3.1 Introduction to Spatial Databases Spatial databases incorporate functionality that provides support for databases that keep track of objects in a multidimensional space. For example, cartographic data- bases that store maps include two-dimensional spatial descriptions of their objects—from countries and states to rivers, cities, roads, seas, and so on. The sys- tems that manage geographic data and related applications are known as
24The contribution of Pranesh Parimala Ranganathan to this section is appreciated.
958 Chapter 26 Enhanced Data Models for Advanced Applications
Table 26.1 Common Types of Analysis for Spatial Data
Analysis Type Type of Operations and Measurements
Measurements Distance, perimeter, shape, adjacency, and direction
Spatial analysis/statistics Pattern, autocorrelation, and indexes of similarity and topology using spatial and nonspatial data
Flow analysis Connectivity and shortest path
Location analysis Analysis of points and lines within a polygon
Terrain analysis Slope/aspect, catchment area, drainage network
Search Thematic search, search by region
Geographical Information Systems (GIS), and they are used in areas such as envi- ronmental applications, transportation systems, emergency response systems, and battle management. Other databases, such as meteorological databases for weather information, are three-dimensional, since temperatures and other meteorological information are related to three-dimensional spatial points. In general, a spatial database stores objects that have spatial characteristics that describe them and that have spatial relationships among them. The spatial relationships among the objects are important, and they are often needed when querying the database. Although a spatial database can in general refer to an n-dimensional space for any n, we will limit our discussion to two dimensions as an illustration.
A spatial database is optimized to store and query data related to objects in space, including points, lines and polygons. Satellite images are a prominent example of spatial data. Queries posed on these spatial data, where predicates for selection deal with spatial parameters, are called spatial queries. For example, “What are the names of all bookstores within five miles of the College of Computing building at Georgia Tech?” is a spatial query. Whereas typical databases process numeric and character data, additional functionality needs to be added for databases to process spatial data types. A query such as “List all the customers located within twenty miles of company headquarters” will require the processing of spatial data types typically outside the scope of standard relational algebra and may involve consult- ing an external geographic database that maps the company headquarters and each customer to a 2-D map based on their address. Effectively, each customer will be associated to a <latitude, longitude> position. A traditional B+-tree index based on customers’ zip codes or other nonspatial attributes cannot be used to process this query since traditional indexes are not capable of ordering multidimensional coor- dinate data. Therefore, there is a special need for databases tailored for handling spatial data and spatial queries.
Table 26.1 shows the common analytical operations involved in processing geo- graphic or spatial data.25 Measurement operations are used to measure some
25List of GIS analysis operations as proposed in Albrecht (1996).
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global properties of single objects (such as the area, the relative size of an object’s parts, compactness, or symmetry), and to measure the relative position of different objects in terms of distance and direction. Spatial analysis operations, which often use statistical techniques, are used to uncover spatial relationships within and among mapped data layers. An example would be to create a map—known as a prediction map—that identifies the locations of likely customers for particular products based on the historical sales and demographic information. Flow analysis operations help in determining the shortest path between two points and also the connectivity among nodes or regions in a graph. Location analysis aims to find if the given set of points and lines lie within a given polygon (location). The process involves generating a buffer around existing geographic features and then identify- ing or selecting features based on whether they fall inside or outside the boundary of the buffer. Digital terrain analysis is used to build three-dimensional models, where the topography of a geographical location can be represented with an x, y, z data model known as Digital Terrain (or Elevation) Model (DTM/DEM). The x and y dimensions of a DTM represent the horizontal plane, and z represents spot heights for the respective x, y coordinates. Such models can be used for analysis of environmental data or during the design of engineering projects that require terrain information. Spatial search allows a user to search for objects within a particular spatial region. For example, thematic search allows us to search for objects related to a particular theme or class, such as “Find all water bodies within 25 miles of Atlanta” where the class is water.
There are also topological relationships among spatial objects. These are often used in Boolean predicates to select objects based on their spatial relationships. For example, if a city boundary is represented as a polygon and freeways are represented as multilines, a condition such as “Find all freeways that go through Arlington, Texas” would involve an intersects operation, to determine which freeways (lines) intersect the city boundary (polygon).
26.3.2 Spatial Data Types and Models This section briefly describes the common data types and models for storing spatial data. Spatial data comes in three basic forms. These forms have become a de facto standard due to their wide use in commercial systems.
■ Map Data26 includes various geographic or spatial features of objects in a map, such as an object’s shape and the location of the object within the map. The three basic types of features are points, lines, and polygons (or areas). Points are used to represent spatial characteristics of objects whose locations correspond to a single 2-d coordinate (x, y, or longitude/latitude) in the scale of a particular application. Depending on the scale, some examples of point objects could be buildings, cellular towers, or stationary vehicles. Moving
26These types of geographic data are based on ESRI’s guide to GIS. See www.gis.com/implementing_gis/data/data_types.html
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vehicles and other moving objects can be represented by a sequence of point locations that change over time. Lines represent objects having length, such as roads or rivers, whose spatial characteristics can be approximated by a sequence of connected lines. Polygons are used to represent spatial charac- teristics of objects that have a boundary, such as countries, states, lakes, or cities. Notice that some objects, such as buildings or cities, can be repre- sented as either points or polygons, depending on the scale of detail.
■ Attribute data is the descriptive data that GIS systems associate with map features. For example, suppose that a map contains features that represent counties within a US state (such as Texas or Oregon). Attributes for each county feature (object) could include population, largest city/town, area in square miles, and so on. Other attribute data could be included for other fea- tures in the map, such as states, cities, congressional districts, census tracts, and so on.
■ Image data includes data such as satellite images and aerial photographs, which are typically created by cameras. Objects of interest, such as buildings and roads, can be identified and overlaid on these images. Images can also be attributes of map features. One can add images to other map features so that clicking on the feature would display the image. Aerial and satellite images are typical examples of raster data.
Models of spatial information are sometimes grouped into two broad categories: field and object. A spatial application (such as remote sensing or highway traffic con- trol) is modeled using either a field- or an object-based model, depending on the requirements and the traditional choice of model for the application. Field models are often used to model spatial data that is continuous in nature, such as terrain ele- vation, temperature data, and soil variation characteristics, whereas object models have traditionally been used for applications such as transportation networks, land parcels, buildings, and other objects that possess both spatial and non-spatial attrib- utes.
26.3.3 Spatial Operators Spatial operators are used to capture all the relevant geometric properties of objects embedded in the physical space and the relations between them, as well as to perform spatial analysis. Operators are classified into three broad categories.
■ Topological operators. Topological properties are invariant when topologi- cal transformations are applied. These properties do not change after trans- formations like rotation, translation, or scaling. Topological operators are hierarchically structured in several levels, where the base level offers opera- tors the ability to check for detailed topological relations between regions with a broad boundary, and the higher levels offer more abstract operators that allow users to query uncertain spatial data independent of the underly- ing geometric data model. Examples include open (region), close (region), and inside (point, loop).
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■ Projective operators. Projective operators, such as convex hull, are used to express predicates about the concavity/convexity of objects as well as other spatial relations (for example, being inside the concavity of a given object).
■ Metric operators. Metric operators provide a more specific description of the object’s geometry. They are used to measure some global properties of single objects (such as the area, relative size of an object’s parts, compactness, and symmetry), and to measure the relative position of different objects in terms of distance and direction. Examples include length (arc) and distance (point, point).
Dynamic Spatial Operators. The operations performed by the operators men- tioned above are static, in the sense that the operands are not affected by the appli- cation of the operation. For example, calculating the length of the curve has no effect on the curve itself. Dynamic operations alter the objects upon which the operations act. The three fundamental dynamic operations are create, destroy, and update. A representative example of dynamic operations would be updating a spa- tial object that can be subdivided into translate (shift position), rotate (change ori- entation), scale up or down, reflect (produce a mirror image), and shear (deform).
Spatial Queries. Spatial queries are requests for spatial data that require the use of spatial operations. The following categories illustrate three typical types of spatial queries:
■ Range query. Finds the objects of a particular type that are within a given spatial area or within a particular distance from a given location. (For exam- ple, find all hospitals within the Metropolitan Atlanta city area, or find all ambulances within five miles of an accident location.)
■ Nearest neighbor query. Finds an object of a particular type that is closest to a given location. (For example, find the police car that is closest to the loca- tion of crime.)
■ Spatial joins or overlays. Typically joins the objects of two types based on some spatial condition, such as the objects intersecting or overlapping spa- tially or being within a certain distance of one another. (For example, find all townships located on a major highway between two cities or find all homes that are within two miles of a lake.)
26.3.4 Spatial Data Indexing A spatial index is used to organize objects into a set of buckets (which correspond
to pages of secondary memory), so that objects in a particular spatial region can be easily located. Each bucket has a bucket region, a part of space containing all objects stored in the bucket. The bucket regions are usually rectangles; for point data struc- tures, these regions are disjoint and they partition the space so that each point belongs to precisely one bucket. There are essentially two ways of providing a spatial index.
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1. Specialized indexing structures that allow efficient search for data objects based on spatial search operations are included in the database system. These indexing structures would play a similar role to that performed by B+-tree indexes in traditional database systems. Examples of these indexing struc- tures are grid files and R-trees. Special types of spatial indexes, known as spatial join indexes, can be used to speed up spatial join operations.
2. Instead of creating brand new indexing structures, the two-dimensional (2-d) spatial data is converted to single-dimensional (1-d) data, so that tra- ditional indexing techniques (B+-tree) can be used. The algorithms for converting from 2-d to 1-d are known as space filling curves. We will not discuss these methods in detail (see the Selected Bibliography for further references).
We give an overview of some of the spatial indexing techniques next.
Grid Files. We introduced grid files for indexing of data on multiple attributes in Chapter 18. They can also be used for indexing 2-dimensional and higher n- dimensional spatial data. The fixed-grid method divides an n-dimensional hyper- space into equal size buckets. The data structure that implements the fixed grid is an n-dimensional array. The objects whose spatial locations lie within a cell (totally or partially) can be stored in a dynamic structure to handle overflows. This structure is useful for uniformly distributed data like satellite imagery. However, the fixed-grid structure is rigid, and its directory can be sparse and large.
R-Trees. The R-tree is a height-balanced tree, which is an extension of the B+-tree for k-dimensions, where k > 1. For two dimensions (2-d), spatial objects are approx- imated in the R-tree by their minimum bounding rectangle (MBR), which is the smallest rectangle, with sides parallel to the coordinate system (x and y) axis, that contains the object. R-trees are characterized by the following properties, which are similar to the properties for B+-trees (see Section 18.3) but are adapted to 2-d spa- tial objects. As in Section 18.3, we use M to indicate the maximum number of entries that can fit in an R-tree node.
1. The structure of each index entry (or index record) in a leaf node is (I, object-identifier), where I is the MBR for the spatial object whose identifier is object-identifier.
2. Every node except the root node must be at least half full. Thus, a leaf node that is not the root should contain m entries (I, object-identifier) where M/2 <= m <= M. Similarly, a non-leaf node that is not the root should contain m entries (I, child-pointer) where M/2 <= m <= M, and I is the MBR that con- tains the union of all the rectangles in the node pointed at by child-pointer.
3. All leaf nodes are at the same level, and the root node should have at least two pointers unless it is a leaf node.
4. All MBRs have their sides parallel to the axes of the global coordinate system.
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Other spatial storage structures include quadtrees and their variations. Quadtrees generally divide each space or subspace into equally sized areas, and proceed with the subdivisions of each subspace to identify the positions of various objects. Recently, many newer spatial access structures have been proposed, and this area remains an active research area.
Spatial Join Index. A spatial join index precomputes a spatial join operation and stores the pointers to the related object in an index structure. Join indexes improve the performance of recurring join queries over tables that have low update rates. Spatial join conditions are used to answer queries such as “Create a list of highway- river combinations that cross.” The spatial join is used to identify and retrieve these pairs of objects that satisfy the cross spatial relationship. Because computing the results of spatial relationships is generally time consuming, the result can be com- puted once and stored in a table that has the pairs of object identifiers (or tuple ids) that satisfy the spatial relationship, which is essentially the join index.
A join index can be described by a bipartite graph G = (V1,V2,E), where V1 con- tains the tuple ids of relation R, and V2 contains the tuple ids of relation S. Edge set contains an edge (vr,vs) for vr in R and vs in S, if there is a tuple corresponding to (vr,vs) in the join index. The bipartite graph models all of the related tuples as con- nected vertices in the graphs. Spatial join indexes are used in operations (see Section 26.3.3) that involve computation of relationships among spatial objects.
26.3.5 Spatial Data Mining Spatial data tends to be highly correlated. For example, people with similar charac- teristics, occupations, and backgrounds tend to cluster together in the same neigh- borhoods.
The three major spatial data mining techniques are spatial classification, spatial association, and spatial clustering.
■ Spatial classification. The goal of classification is to estimate the value of an attribute of a relation based on the value of the relation’s other attributes. An example of the spatial classification problem is determining the locations of nests in a wetland based on the value of other attributes (for example, vege- tation durability and water depth); it is also called the location prediction problem. Similarly, where to expect hotspots in crime activity is also a loca- tion prediction problem.
■ Spatial association. Spatial association rules are defined in terms of spatial predicates rather than items. A spatial association rule is of the form
P1 ^ P2 ^ ... ^ Pn ⇒ Q1 ^ Q2 ^ ... ^ Qm, where at least one of the Pi’s or Q j’s is a spatial predicate. For example, the rule
is_a(x, country) ^ touches(x, Mediterranean) ⇒ is_a (x, wine-exporter)
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(that is, a country that is adjacent to the Mediterranean Sea is typically a wine exporter) is an example of an association rule, which will have a certain support s and confidence c.27
Spatial colocation rules attempt to generalize association rules to point to collec- tion data sets that are indexed by space. There are several crucial differences between spatial and nonspatial associations including:
1. The notion of a transaction is absent in spatial situations, since data is embedded in continuous space. Partitioning space into transactions would lead to an overestimate or an underestimate of interest measures, for exam- ple, support or confidence.
2. Size of item sets in spatial databases is small, that is, there are many fewer items in the item set in a spatial situation than in a nonspatial situation.
In most instances, spatial items are a discrete version of continuous variables. For example, in the United States income regions may be defined as regions where the mean yearly income is within certain ranges, such as, below $40,000, from $40,000 to $100,000, and above $100,000.
■ Spatial Clustering attempts to group database objects so that the most sim- ilar objects are in the same cluster, and objects in different clusters are as dis- similar as possible. One application of spatial clustering is to group together seismic events in order to determine earthquake faults. An example of a spa- tial clustering algorithm is density-based clustering, which tries to find clusters based on the density of data points in a region. These algorithms treat clusters as dense regions of objects in the data space. Two variations of these algorithms are density-based spatial clustering of applications with noise (DBSCAN)28 and density-based clustering (DENCLUE).29 DBSCAN is a density-based clustering algorithm because it finds a number of clusters starting from the estimated density distribution of corresponding nodes.
26.3.6 Applications of Spatial Data Spatial data management is useful in many disciplines, including geography, remote sensing, urban planning, and natural resource management. Spatial database man- agement is playing an important role in the solution of challenging scientific prob- lems such as global climate change and genomics. Due to the spatial nature of genome data, GIS and spatial database management systems have a large role to play in the area of bioinformatics. Some of the typical applications include pattern recognition (for example, to check if the topology of a particular gene in the genome is found in any other sequence feature map in the database), genome
27Concepts of support and confidence for association rules are discussed as part of data mining in Section 28.2. 28DBSCAN was proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu (1996). 29DENCLUE was proposed by Hinnenberg and Gabriel (2007).
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browser development, and visualization maps. Another important application area of spatial data mining is the spatial outlier detection. A spatial outlier is a spatially referenced object whose nonspatial attribute values are significantly different from those of other spatially referenced objects in its spatial neighborhood. For example, if a neighborhood of older houses has just one brand-new house, that house would be an outlier based on the nonspatial attribute ‘house_age’. Detecting spatial outliers is useful in many applications of geographic information systems and spatial data- bases. These application domains include transportation, ecology, public safety, public health, climatology, and location-based services.
26.4 Multimedia Database Concepts Multimedia databases provide features that allow users to store and query different types of multimedia information, which includes images (such as photos or draw- ings), video clips (such as movies, newsreels, or home videos), audio clips (such as songs, phone messages, or speeches), and documents (such as books or articles). The main types of database queries that are needed involve locating multimedia sources that contain certain objects of interest. For example, one may want to locate all video clips in a video database that include a certain person, say Michael Jackson. One may also want to retrieve video clips based on certain activities included in them, such as video clips where a soccer goal is scored by a certain player or team.
The above types of queries are referred to as content-based retrieval, because the multimedia source is being retrieved based on its containing certain objects or activities. Hence, a multimedia database must use some model to organize and index the multimedia sources based on their contents. Identifying the contents of multimedia sources is a difficult and time-consuming task. There are two main approaches. The first is based on automatic analysis of the multimedia sources to identify certain mathematical characteristics of their contents. This approach uses different techniques depending on the type of multimedia source (image, video, audio, or text). The second approach depends on manual identification of the objects and activities of interest in each multimedia source and on using this infor- mation to index the sources. This approach can be applied to all multimedia sources, but it requires a manual preprocessing phase where a person has to scan each multimedia source to identify and catalog the objects and activities it contains so that they can be used to index the sources.
In the first part of this section, we will briefly discuss some of the characteristics of each type of multimedia source—images, video, audio, and text/documents. Then we will discuss approaches for automatic analysis of images followed by the prob- lem of object recognition in images. We end this section with some remarks on ana- lyzing audio sources.
An image is typically stored either in raw form as a set of pixel or cell values, or in compressed form to save space. The image shape descriptor describes the geometric shape of the raw image, which is typically a rectangle of cells of a certain width and height. Hence, each image can be represented by an m by n grid of cells. Each cell
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contains a pixel value that describes the cell content. In black-and-white images, pixels can be one bit. In gray scale or color images, a pixel is multiple bits. Because images may require large amounts of space, they are often stored in compressed form. Compression standards, such as GIF, JPEG, or MPEG, use various mathemat- ical transformations to reduce the number of cells stored but still maintain the main image characteristics. Applicable mathematical transforms include Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT), and wavelet transforms.
To identify objects of interest in an image, the image is typically divided into homo- geneous segments using a homogeneity predicate. For example, in a color image, adja- cent cells that have similar pixel values are grouped into a segment. The homogeneity predicate defines conditions for automatically grouping those cells. Segmentation and compression can hence identify the main characteristics of an image.
A typical image database query would be to find images in the database that are similar to a given image. The given image could be an isolated segment that con- tains, say, a pattern of interest, and the query is to locate other images that contain that same pattern. There are two main techniques for this type of search. The first approach uses a distance function to compare the given image with the stored images and their segments. If the distance value returned is small, the probability of a match is high. Indexes can be created to group stored images that are close in the distance metric so as to limit the search space. The second approach, called the transformation approach, measures image similarity by having a small number of transformations that can change one image’s cells to match the other image. Transformations include rotations, translations, and scaling. Although the transfor- mation approach is more general, it is also more time-consuming and difficult.
A video source is typically represented as a sequence of frames, where each frame is a still image. However, rather than identifying the objects and activities in every individual frame, the video is divided into video segments, where each segment comprises a sequence of contiguous frames that includes the same objects/activities. Each segment is identified by its starting and ending frames. The objects and activi- ties identified in each video segment can be used to index the segments. An index- ing technique called frame segment trees has been proposed for video indexing. The index includes both objects, such as persons, houses, and cars, as well as activities, such as a person delivering a speech or two people talking. Videos are also often compressed using standards such as MPEG.
Audio sources include stored recorded messages, such as speeches, class presenta- tions, or even surveillance recordings of phone messages or conversations by law enforcement. Here, discrete transforms can be used to identify the main character- istics of a certain person’s voice in order to have similarity-based indexing and retrieval. We will briefly comment on their analysis in Section 26.4.4.
A text/document source is basically the full text of some article, book, or magazine. These sources are typically indexed by identifying the keywords that appear in the text and their relative frequencies. However, filler words or common words called stopwords are eliminated from the process. Because there can be many keywords
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when attempting to index a collection of documents, techniques have been devel- oped to reduce the number of keywords to those that are most relevant to the col- lection. A dimensionality reduction technique called singular value decompositions (SVD), which is based on matrix transformations, can be used for this purpose. An indexing technique called telescoping vector trees (TV-trees), can then be used to group similar documents. Chapter 27 discusses document processing in detail.
26.4.1 Automatic Analysis of Images Analysis of multimedia sources is critical to support any type of query or search interface. We need to represent multimedia source data such as images in terms of features that would enable us to define similarity. The work done so far in this area uses low-level visual features such as color, texture, and shape, which are directly related to the perceptual aspects of image content. These features are easy to extract and represent, and it is convenient to design similarity measures based on their sta- tistical properties.
Color is one of the most widely used visual features in content-based image retrieval since it does not depend upon image size or orientation. Retrieval based on color similarity is mainly done by computing a color histogram for each image that identifies the proportion of pixels within an image for the three color channels (red, green, blue—RGB). However, RGB representation is affected by the orientation of the object with respect to illumination and camera direction. Therefore, current image retrieval techniques compute color histograms using competing invariant representations such as HSV (hue, saturation, value). HSV describes colors as points in a cylinder whose central axis ranges from black at the bottom to white at the top with neutral colors between them. The angle around the axis corresponds to the hue, the distance from the axis corresponds to the saturation, and the distance along the axis corresponds to the value (brightness).
Texture refers to the patterns in an image that present the properties of homogene- ity that do not result from the presence of a single color or intensity value. Examples of texture classes are rough and silky. Examples of textures that can be identified include pressed calf leather, straw matting, cotton canvas, and so on. Just as pictures are represented by arrays of pixels (picture elements), textures are repre- sented by arrays of texels (texture elements). These textures are then placed into a number of sets, depending on how many textures are identified in the image. These sets not only contain the texture definition but also indicate where in the image the texture is located. Texture identification is primarily done by modeling it as a two- dimensional, gray-level variation. The relative brightness of pairs of pixels is com- puted to estimate the degree of contrast, regularity, coarseness, and directionality.
Shape refers to the shape of a region within an image. It is generally determined by applying segmentation or edge detection to an image. Segmentation is a region- based approach that uses an entire region (sets of pixels), whereas edge detection is a boundary-based approach that uses only the outer boundary characteristics of entities. Shape representation is typically required to be invariant to translation,
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rotation, and scaling. Some well-known methods for shape representation include Fourier descriptors and moment invariants.
26.4.2 Object Recognition in Images Object recognition is the task of identifying real-world objects in an image or a video sequence. The system must be able to identify the object even when the images of the object vary in viewpoints, size, scale, or even when they are rotated or translated. Some approaches have been developed to divide the original image into regions based on similarity of contiguous pixels. Thus, in a given image showing a tiger in the jungle, a tiger subimage may be detected against the background of the jungle, and when compared with a set of training images, it may be tagged as a tiger.
The representation of the multimedia object in an object model is extremely impor- tant. One approach is to divide the image into homogeneous segments using a homogeneous predicate. For example, in a colored image, adjacent cells that have similar pixel values are grouped into a segment. The homogeneity predicate defines conditions for automatically grouping those cells. Segmentation and compression can hence identify the main characteristics of an image. Another approach finds measurements of the object that are invariant to transformations. It is impossible to keep a database of examples of all the different transformations of an image. To deal with this, object recognition approaches find interesting points (or features) in an image that are invariant to transformations.
An important contribution to this field was made by Lowe,30 who used scale- invariant features from images to perform reliable object recognition. This approach is called scale-invariant feature transform (SIFT). The SIFT features are invariant to image scaling and rotation, and partially invariant to change in illumi- nation and 3D camera viewpoint. They are well localized in both the spatial and frequency domains, reducing the probability of disruption by occlusion, clutter, or noise. In addition, the features are highly distinctive, which allows a single feature to be correctly matched with high probability against a large database of features, providing a basis for object and scene recognition.
For image matching and recognition, SIFT features (also known as keypoint features) are first extracted from a set of reference images and stored in a database. Object recognition is then performed by comparing each feature from the new image with the features stored in the database and finding candidate matching fea- tures based on the Euclidean distance of their feature vectors. Since the keypoint features are highly distinctive, a single feature can be correctly matched with good probability in a large database of features.
In addition to SIFT, there are a number of competing methods available for object recognition under clutter or partial occlusion. For example, RIFT, a rotation invari- ant generalization of SIFT, identifies groups of local affine regions (image features
30See Lowe (2004), “Distinctive Image Features from Scale-Invariant Keypoints.”
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having a characteristic appearance and elliptical shape) that remain approximately affinely rigid across a range of views of an object, and across multiple instances of the same object class.
26.4.3 Semantic Tagging of Images The notion of implicit tagging is an important one for image recognition and com- parison. Multiple tags may attach to an image or a subimage: for instance, in the example we referred to above, tags such as “tiger,” “jungle,” “green,” and “stripes” may be associated with that image. Most image search techniques retrieve images based on user-supplied tags that are often not very accurate or comprehensive. To improve search quality, a number of recent systems aim at automated generation of these image tags. In case of multimedia data, most of its semantics is present in its content. These systems use image-processing and statistical-modeling techniques to analyze image content to generate accurate annotation tags that can then be used to retrieve images by content. Since different annotation schemes will use different vocabularies to annotate images, the quality of image retrieval will be poor. To solve this problem, recent research techniques have proposed the use of concept hierar- chies, taxonomies, or ontologies using OWL (Web Ontology Language), in which terms and their relationships are clearly defined. These can be used to infer higher- level concepts based on tags. Concepts like “sky” and “grass” may be further divided into “clear sky” and “cloudy sky” or “dry grass” and “green grass” in such a taxon- omy. These approaches generally come under semantic tagging and can be used in conjunction with the above feature-analysis and object-identification strategies.
26.4.4 Analysis of Audio Data Sources Audio sources are broadly classified into speech, music, and other audio data. Each of these are significantly different from the other, hence different types of audio data are treated differently. Audio data must be digitized before it can be processed and stored. Indexing and retrieval of audio data is arguably the toughest among all types of media, because like video, it is continuous in time and does not have easily mea- surable characteristics such as text. Clarity of sound recordings is easy to perceive humanly but is hard to quantify for machine learning. Interestingly, speech data often uses speech recognition techniques to aid the actual audio content, as this can make indexing this data a lot easier and more accurate. This is sometimes referred to as text-based indexing of audio data. The speech metadata is typically content dependent, in that the metadata is generated from the audio content, for example, the length of the speech, the number of speakers, and so on. However, some of the metadata might be independent of the actual content, such as the length of the speech and the format in which the data is stored. Music indexing, on the other hand, is done based on the statistical analysis of the audio signal, also known as content-based indexing. Content-based indexing often makes use of the key features of sound: intensity, pitch, timbre, and rhythm. It is possible to compare different pieces of audio data and retrieve information from them based on the calculation of certain features, as well as application of certain transforms.
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26.5 Introduction to Deductive Databases
26.5.1 Overview of Deductive Databases In a deductive database system we typically specify rules through a declarative lan- guage—a language in which we specify what to achieve rather than how to achieve it. An inference engine (or deduction mechanism) within the system can deduce new facts from the database by interpreting these rules. The model used for deduc- tive databases is closely related to the relational data model, and particularly to the domain relational calculus formalism (see Section 6.6). It is also related to the field of logic programming and the Prolog language. The deductive database work based on logic has used Prolog as a starting point. A variation of Prolog called Datalog is used to define rules declaratively in conjunction with an existing set of relations, which are themselves treated as literals in the language. Although the lan- guage structure of Datalog resembles that of Prolog, its operational semantics—that is, how a Datalog program is executed—is still different.
A deductive database uses two main types of specifications: facts and rules. Facts are specified in a manner similar to the way relations are specified, except that it is not necessary to include the attribute names. Recall that a tuple in a relation describes some real-world fact whose meaning is partly determined by the attribute names. In a deductive database, the meaning of an attribute value in a tuple is determined solely by its position within the tuple. Rules are somewhat similar to relational views. They specify virtual relations that are not actually stored but that can be formed from the facts by applying inference mechanisms based on the rule specifi- cations. The main difference between rules and views is that rules may involve recursion and hence may yield virtual relations that cannot be defined in terms of basic relational views.
The evaluation of Prolog programs is based on a technique called backward chain- ing, which involves a top-down evaluation of goals. In the deductive databases that use Datalog, attention has been devoted to handling large volumes of data stored in a relational database. Hence, evaluation techniques have been devised that resemble those for a bottom-up evaluation. Prolog suffers from the limitation that the order of specification of facts and rules is significant in evaluation; moreover, the order of literals (defined in Section 26.5.3) within a rule is significant. The execution tech- niques for Datalog programs attempt to circumvent these problems.
26.5.2 Prolog/Datalog Notation The notation used in Prolog/Datalog is based on providing predicates with unique names. A predicate has an implicit meaning, which is suggested by the predicate name, and a fixed number of arguments. If the arguments are all constant values, the predicate simply states that a certain fact is true. If, on the other hand, the pred- icate has variables as arguments, it is either considered as a query or as part of a rule or constraint. In our discussion, we adopt the Prolog convention that all constant
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Facts SUPERVISE(franklin, john). SUPERVISE(franklin, ramesh). SUPERVISE(franklin, joyce). SUPERVISE(jennifer, alicia). SUPERVISE(jennifer, ahmad). SUPERVISE(james, franklin). SUPERVISE(james, jennifer). . . .
Rules SUPERIOR(X, Y ) :– SUPERVISE(X, Y ). SUPERIOR(X, Y ) :– SUPERVISE(X, Z ), SUPERIOR(Z, Y ). SUBORDINATE(X, Y ) :– SUPERIOR(Y, X ).
Queries SUPERIOR(james, Y )? SUPERIOR(james, joyce)?
joyceramesh
franklin
james(b)(a)
john ahmad
jennifer
alicia
Figure 26.11 (a) Prolog notation. (b) The supervisory tree.
values in a predicate are either numeric or character strings; they are represented as identifiers (or names) that start with a lowercase letter, whereas variable names always start with an uppercase letter.
Consider the example shown in Figure 26.11, which is based on the relational data- base in Figure 3.6, but in a much simplified form. There are three predicate names: supervise, superior, and subordinate. The SUPERVISE predicate is defined via a set of facts, each of which has two arguments: a supervisor name, followed by the name of a direct supervisee (subordinate) of that supervisor. These facts correspond to the actual data that is stored in the database, and they can be considered as constituting a set of tuples in a relation SUPERVISE with two attributes whose schema is
SUPERVISE(Supervisor, Supervisee)
Thus, SUPERVISE(X, Y ) states the fact that X supervises Y. Notice the omission of the attribute names in the Prolog notation. Attribute names are only represented by virtue of the position of each argument in a predicate: the first argument represents the supervisor, and the second argument represents a direct subordinate.
The other two predicate names are defined by rules. The main contributions of deductive databases are the ability to specify recursive rules and to provide a frame- work for inferring new information based on the specified rules. A rule is of the form head :– body, where :– is read as if and only if. A rule usually has a single pred- icate to the left of the :– symbol—called the head or left-hand side (LHS) or conclusion of the rule—and one or more predicates to the right of the :– symbol— called the body or right-hand side (RHS) or premise(s) of the rule. A predicate with constants as arguments is said to be ground; we also refer to it as an instantiated predicate. The arguments of the predicates that appear in a rule typi- cally include a number of variable symbols, although predicates can also contain
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constants as arguments. A rule specifies that, if a particular assignment or binding of constant values to the variables in the body (RHS predicates) makes all the RHS predicates true, it also makes the head (LHS predicate) true by using the same assignment of constant values to variables. Hence, a rule provides us with a way of generating new facts that are instantiations of the head of the rule. These new facts are based on facts that already exist, corresponding to the instantiations (or bind- ings) of predicates in the body of the rule. Notice that by listing multiple predicates in the body of a rule we implicitly apply the logical AND operator to these predi- cates. Hence, the commas between the RHS predicates may be read as meaning and.
Consider the definition of the predicate SUPERIOR in Figure 26.11, whose first argument is an employee name and whose second argument is an employee who is either a direct or an indirect subordinate of the first employee. By indirect subordi- nate, we mean the subordinate of some subordinate down to any number of levels. Thus SUPERIOR(X, Y ) stands for the fact that X is a superior of Y through direct or indirect supervision. We can write two rules that together specify the meaning of the new predicate. The first rule under Rules in the figure states that for every value of X and Y, if SUPERVISE(X, Y)—the rule body—is true, then SUPERIOR(X, Y )—the rule head—is also true, since Y would be a direct subordinate of X (at one level down). This rule can be used to generate all direct superior/subordinate relation- ships from the facts that define the SUPERVISE predicate. The second recursive rule states that if SUPERVISE(X, Z) and SUPERIOR(Z, Y ) are both true, then SUPERIOR(X, Y) is also true. This is an example of a recursive rule, where one of the rule body predicates in the RHS is the same as the rule head predicate in the LHS. In general, the rule body defines a number of premises such that if they are all true, we can deduce that the conclusion in the rule head is also true. Notice that if we have two (or more) rules with the same head (LHS predicate), it is equivalent to saying that the predicate is true (that is, that it can be instantiated) if either one of the bodies is true; hence, it is equivalent to a logical OR operation. For example, if we have two rules X :– Y and X :– Z, they are equivalent to a rule X :– Y OR Z. The latter form is not used in deductive systems, however, because it is not in the stan- dard form of rule, called a Horn clause, as we discuss in Section 26.5.4.
A Prolog system contains a number of built-in predicates that the system can inter- pret directly. These typically include the equality comparison operator =(X, Y), which returns true if X and Y are identical and can also be written as X=Y by using the standard infix notation.31 Other comparison operators for numbers, such as <, <=, >, and >=, can be treated as binary predicates. Arithmetic functions such as +, –, *, and / can be used as arguments in predicates in Prolog. In contrast, Datalog (in its basic form) does not allow functions such as arithmetic operations as arguments; indeed, this is one of the main differences between Prolog and Datalog. However, extensions to Datalog have been proposed that do include functions.
31A Prolog system typically has a number of different equality predicates that have different interpreta- tions.
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A query typically involves a predicate symbol with some variable arguments, and its meaning (or answer) is to deduce all the different constant combinations that, when bound (assigned) to the variables, can make the predicate true. For example, the first query in Figure 26.11 requests the names of all subordinates of james at any level. A different type of query, which has only constant symbols as arguments, returns either a true or a false result, depending on whether the arguments provided can be deduced from the facts and rules. For example, the second query in Figure 26.11 returns true, since SUPERIOR(james, joyce) can be deduced.
26.5.3 Datalog Notation In Datalog, as in other logic-based languages, a program is built from basic objects called atomic formulas. It is customary to define the syntax of logic-based lan- guages by describing the syntax of atomic formulas and identifying how they can be combined to form a program. In Datalog, atomic formulas are literals of the form p(a1, a2, ..., an), where p is the predicate name and n is the number of arguments for predicate p. Different predicate symbols can have different numbers of arguments, and the number of arguments n of predicate p is sometimes called the arity or degree of p. The arguments can be either constant values or variable names. As mentioned earlier, we use the convention that constant values either are numeric or start with a lowercase character, whereas variable names always start with an uppercase character.
A number of built-in predicates are included in Datalog, which can also be used to construct atomic formulas. The built-in predicates are of two main types: the binary comparison predicates < (less), <= (less_or_equal), > (greater), and >= (greater_or_equal) over ordered domains; and the comparison predicates = (equal) and /= (not_equal) over ordered or unordered domains. These can be used as binary predicates with the same functional syntax as other predicates—for example, by writing less(X, 3)—or they can be specified by using the customary infix notation X<3. Note that because the domains of these predicates are potentially infinite, they should be used with care in rule definitions. For example, the predicate greater(X, 3), if used alone, generates an infinite set of values for X that satisfy the predicate (all integer numbers greater than 3).
A literal is either an atomic formula as defined earlier—called a positive literal—or an atomic formula preceded by not. The latter is a negated atomic formula, called a negative literal. Datalog programs can be considered to be a subset of the predicate calculus formulas, which are somewhat similar to the formulas of the domain rela- tional calculus (see Section 6.7). In Datalog, however, these formulas are first con- verted into what is known as clausal form before they are expressed in Datalog, and only formulas given in a restricted clausal form, called Horn clauses,32 can be used in Datalog.
32Named after the mathematician Alfred Horn.
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26.5.4 Clausal Form and Horn Clauses Recall from Section 6.6 that a formula in the relational calculus is a condition that includes predicates called atoms (based on relation names). Additionally, a formula can have quantifiers—namely, the universal quantifier (for all) and the existential quantifier (there exists). In clausal form, a formula must be transformed into another formula with the following characteristics:
■ All variables in the formula are universally quantified. Hence, it is not neces- sary to include the universal quantifiers (for all) explicitly; the quantifiers are removed, and all variables in the formula are implicitly quantified by the uni- versal quantifier.
■ In clausal form, the formula is made up of a number of clauses, where each clause is composed of a number of literals connected by OR logical connec- tives only. Hence, each clause is a disjunction of literals.
■ The clauses themselves are connected by AND logical connectives only, to form a formula. Hence, the clausal form of a formula is a conjunction of clauses.
It can be shown that any formula can be converted into clausal form. For our pur- poses, we are mainly interested in the form of the individual clauses, each of which is a disjunction of literals. Recall that literals can be positive literals or negative liter- als. Consider a clause of the form:
NOT(P1) OR NOT(P2) OR ... OR NOT(Pn) OR Q1 OR Q2 OR ... OR Qm (1)
This clause has n negative literals and m positive literals. Such a clause can be trans- formed into the following equivalent logical formula:
P1 AND P2 AND ... AND Pn ⇒ Q1 OR Q2 OR ... OR Qm (2)
where ⇒ is the implies symbol. The formulas (1) and (2) are equivalent, meaning that their truth values are always the same. This is the case because if all the Pi liter- als (i = 1, 2, ..., n) are true, the formula (2) is true only if at least one of the Qi’s is true, which is the meaning of the ⇒ (implies) symbol. For formula (1), if all the Pi literals (i = 1, 2, ..., n) are true, their negations are all false; so in this case formula (1) is true only if at least one of the Qi’s is true. In Datalog, rules are expressed as a restricted form of clauses called Horn clauses, in which a clause can contain at most one positive literal. Hence, a Horn clause is either of the form
NOT (P1) OR NOT(P2) OR ... OR NOT(Pn) OR Q (3)
or of the form
NOT (P1) OR NOT(P2) OR ... OR NOT(Pn) (4)
The Horn clause in (3) can be transformed into the clause
P1 AND P2 AND ... AND Pn ⇒ Q (5)
which is written in Datalog as the following rule:
Q :– P1, P2, ..., Pn. (6)
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1. SUPERIOR(X, Y ) :– SUPERVISE(X, Y ). (rule 1) 2. SUPERIOR(X, Y ) :– SUPERVISE(X, Z ), SUPERIOR(Z, Y ). (rule 2) 3. SUPERVISE(jennifer, ahmad). (ground axiom, given) 4. SUPERVISE(james, jennifer). (ground axiom, given) 5. SUPERIOR(jennifer, ahmad). (apply rule 1 on 3) 6. SUPERIOR(james, ahmad). (apply rule 2 on 4 and 5)
Figure 26.12 Proving a new fact.
The Horn clause in (4) can be transformed into
P1 AND P2 AND ... AND Pn ⇒ (7)
which is written in Datalog as follows:
P1, P2, ..., Pn. (8)
A Datalog rule, as in (6), is hence a Horn clause, and its meaning, based on formula (5), is that if the predicates P1 AND P2 AND ... AND Pn are all true for a particular binding to their variable arguments, then Q is also true and can hence be inferred. The Datalog expression (8) can be considered as an integrity constraint, where all the predicates must be true to satisfy the query.
In general, a query in Datalog consists of two components:
■ A Datalog program, which is a finite set of rules
■ A literal P(X1, X2, ..., Xn), where each Xi is a variable or a constant
A Prolog or Datalog system has an internal inference engine that can be used to process and compute the results of such queries. Prolog inference engines typically return one result to the query (that is, one set of values for the variables in the query) at a time and must be prompted to return additional results. On the con- trary, Datalog returns results set-at-a-time.
26.5.5 Interpretations of Rules There are two main alternatives for interpreting the theoretical meaning of rules: proof-theoretic and model-theoretic. In practical systems, the inference mechanism within a system defines the exact interpretation, which may not coincide with either of the two theoretical interpretations. The inference mechanism is a computational procedure and hence provides a computational interpretation of the meaning of rules. In this section, first we discuss the two theoretical interpretations. Then we briefly discuss inference mechanisms as a way of defining the meaning of rules.
In the proof-theoretic interpretation of rules, we consider the facts and rules to be true statements, or axioms. Ground axioms contain no variables. The facts are ground axioms that are given to be true. Rules are called deductive axioms, since they can be used to deduce new facts. The deductive axioms can be used to con- struct proofs that derive new facts from existing facts. For example, Figure 26.12 shows how to prove the fact SUPERIOR(james, ahmad) from the rules and facts
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given in Figure 26.11. The proof-theoretic interpretation gives us a procedural or computational approach for computing an answer to the Datalog query. The process of proving whether a certain fact (theorem) holds is known as theorem proving.
The second type of interpretation is called the model-theoretic interpretation. Here, given a finite or an infinite domain of constant values,33 we assign to a predi- cate every possible combination of values as arguments. We must then determine whether the predicate is true or false. In general, it is sufficient to specify the combi- nations of arguments that make the predicate true, and to state that all other combi- nations make the predicate false. If this is done for every predicate, it is called an interpretation of the set of predicates. For example, consider the interpretation shown in Figure 26.13 for the predicates SUPERVISE and SUPERIOR. This interpre- tation assigns a truth value (true or false) to every possible combination of argu- ment values (from a finite domain) for the two predicates.
An interpretation is called a model for a specific set of rules if those rules are always true under that interpretation; that is, for any values assigned to the variables in the rules, the head of the rules is true when we substitute the truth values assigned to the predicates in the body of the rule by that interpretation. Hence, whenever a par- ticular substitution (binding) to the variables in the rules is applied, if all the predi- cates in the body of a rule are true under the interpretation, the predicate in the head of the rule must also be true. The interpretation shown in Figure 26.13 is a model for the two rules shown, since it can never cause the rules to be violated. Notice that a rule is violated if a particular binding of constants to the variables makes all the predicates in the rule body true but makes the predicate in the rule head false. For example, if SUPERVISE(a, b) and SUPERIOR(b, c) are both true under some interpretation, but SUPERIOR(a, c) is not true, the interpretation can- not be a model for the recursive rule:
SUPERIOR(X, Y) :– SUPERVISE(X, Z), SUPERIOR(Z, Y)
In the model-theoretic approach, the meaning of the rules is established by provid- ing a model for these rules. A model is called a minimal model for a set of rules if we cannot change any fact from true to false and still get a model for these rules. For example, consider the interpretation in Figure 26.13, and assume that the SUPERVISE predicate is defined by a set of known facts, whereas the SUPERIOR predicate is defined as an interpretation (model) for the rules. Suppose that we add the predicate SUPERIOR(james, bob) to the true predicates. This remains a model for the rules shown, but it is not a minimal model, since changing the truth value of SUPERIOR(james,bob) from true to false still provides us with a model for the rules. The model shown in Figure 26.13 is the minimal model for the set of facts that are defined by the SUPERVISE predicate.
In general, the minimal model that corresponds to a given set of facts in the model- theoretic interpretation should be the same as the facts generated by the proof-
33The most commonly chosen domain is finite and is called the Herbrand Universe.
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Rules SUPERIOR(X, Y ) :– SUPERVISE(X, Y ). SUPERIOR(X, Y ) :– SUPERVISE(X, Z ), SUPERIOR(Z, Y ).
Interpretation
Known Facts: SUPERVISE(franklin, john) is true. SUPERVISE(franklin, ramesh) is true. SUPERVISE(franklin, joyce) is true. SUPERVISE(jennifer, alicia) is true. SUPERVISE(jennifer, ahmad) is true. SUPERVISE(james, franklin) is true. SUPERVISE(james, jennifer) is true. SUPERVISE(X, Y ) is false for all other possible (X, Y ) combinations
Derived Facts: SUPERIOR(franklin, john) is true. SUPERIOR(franklin, ramesh) is true. SUPERIOR(franklin, joyce) is true. SUPERIOR(jennifer, alicia) is true. SUPERIOR(jennifer, ahmad) is true. SUPERIOR(james, franklin) is true. SUPERIOR(james, jennifer) is true. SUPERIOR(james, john) is true. SUPERIOR(james, ramesh) is true. SUPERIOR(james, joyce) is true. SUPERIOR(james, alicia) is true. SUPERIOR(james, ahmad) is true. SUPERIOR(X, Y ) is false for all other possible (X, Y ) combinations
Figure 26.13 An interpretation that is a minimal model.
theoretic interpretation for the same original set of ground and deductive axioms. However, this is generally true only for rules with a simple structure. Once we allow negation in the specification of rules, the correspondence between interpretations does not hold. In fact, with negation, numerous minimal models are possible for a given set of facts.
A third approach to interpreting the meaning of rules involves defining an inference mechanism that is used by the system to deduce facts from the rules. This inference mechanism would define a computational interpretation to the meaning of the rules. The Prolog logic programming language uses its inference mechanism to define the meaning of the rules and facts in a Prolog program. Not all Prolog pro- grams correspond to the proof-theoretic or model-theoretic interpretations; it depends on the type of rules in the program. However, for many simple Prolog pro- grams, the Prolog inference mechanism infers the facts that correspond either to the proof-theoretic interpretation or to a minimal model under the model-theoretic interpretation.
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EMPLOYEE(john). MALE(john). EMPLOYEE(franklin). MALE(franklin). EMPLOYEE(aIicia). MALE(ramesh). EMPLOYEE(jennifer). MALE(ahmad). EMPLOYEE(ramesh). MALE(james). EMPLOYEE(joyce). EMPLOYEE(ahmad). FEMALE(alicia). EMPLOYEE(james). FEMALE(jennifer).
FEMALE(joyce). SALARY(john, 30000). SALARY(franklin, 40000). PROJECT(productx). SALARY(alicia, 25000). PROJECT(producty). SALARY(jennifer, 43000). PROJECT(productz). SALARY(ramesh, 38000). PROJECT(computerization). SALARY(joyce, 25000). PROJECT(reorganization). SALARY(ahmad, 25000). PROJECT(newbenefits). SALARY(james, 55000).
WORKS_ON(john, productx, 32). DEPARTMENT(john, research). WORKS_ON(john, producty, 8). DEPARTMENT(franklin, research). WORKS_ON(ramesh, productz, 40). DEPARTMENT(alicia, administration). WORKS_ON(joyce, productx, 20). DEPARTMENT(jennifer, administration). WORKS_ON(joyce, producty, 20). DEPARTMENT(ramesh, research). WORKS_ON(franklin, producty, 10). DEPARTMENT(joyce, research). WORKS_ON(franklin, productz, 10). DEPARTMENT(ahmad, administration). WORKS_ON(franklin, computerization, 10). DEPARTMENT(james, headquarters). WORKS_ON(franklin, reorganization, 10).
WORKS_ON(alicia, newbenefits, 30). SUPERVISE(franklln, john). WORKS_ON(alicia, computerization, 10). SUPERVISE(franklln, ramesh) WORKS_ON(ahmad, computerization, 35). SUPERVISE(frankin , joyce). WORKS_ON(ahmad, newbenefits, 5). SUPERVISE(jennifer, aIicia). WORKS_ON(jennifer, newbenefits, 20). SUPERVISE(jennifer, ahmad). WORKS_ON(jennifer, reorganization, 15). SUPERVISE(james, franklin). WORKS_ON(james, reorganization, 10). SUPERVISE(james, jennifer).
Figure 26.14 Fact predicates for part of the database from Figure 3.6.
26.5.6 Datalog Programs and Their Safety There are two main methods of defining the truth values of predicates in actual Datalog programs. Fact-defined predicates (or relations) are defined by listing all the combinations of values (the tuples) that make the predicate true. These corre- spond to base relations whose contents are stored in a database system. Figure 26.14 shows the fact-defined predicates EMPLOYEE, MALE, FEMALE, DEPARTMENT, SUPERVISE, PROJECT, and WORKS_ON, which correspond to part of the relational database shown in Figure 3.6. Rule-defined predicates (or views) are defined by being the head (LHS) of one or more Datalog rules; they correspond to virtual rela-
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SUPERIOR(X, Y ) :– SUPERVISE(X, Y ). SUPERIOR(X, Y ) :– SUPERVISE(X, Z ), SUPERIOR(Z, Y ).
SUBORDINATE(X, Y ) :– SUPERIOR(Y, X ).
SUPERVISOR(X ) :– EMPLOYEE(X ), SUPERVISE(X, Y ). OVER_40K_EMP(X ) :– EMPLOYEE(X ), SALARY(X, Y ), Y >= 40000. UNDER_40K_SUPERVISOR(X ) :– SUPERVISOR(X ), NOT(OVER_40_K_EMP(X )). MAIN_PRODUCTX_EMP(X ) :– EMPLOYEE(X ), WORKS_ON(X, productx, Y ), Y >=20. PRESIDENT(X ) :– EMPLOYEE(X), NOT(SUPERVISE(Y, X ) ).
Figure 26.15 Rule-defined predicates.
tions whose contents can be inferred by the inference engine. Figure 26.15 shows a number of rule-defined predicates.
A program or a rule is said to be safe if it generates a finite set of facts. The general theoretical problem of determining whether a set of rules is safe is undecidable. However, one can determine the safety of restricted forms of rules. For example, the rules shown in Figure 26.16 are safe. One situation where we get unsafe rules that can generate an infinite number of facts arises when one of the variables in the rule can range over an infinite domain of values, and that variable is not limited to rang- ing over a finite relation. For example, consider the following rule:
BIG_SALARY(Y ) :– Y>60000
Here, we can get an infinite result if Y ranges over all possible integers. But suppose that we change the rule as follows:
BIG_SALARY(Y ) :– EMPLOYEE(X), Salary(X, Y ), Y>60000
In the second rule, the result is not infinite, since the values that Y can be bound to are now restricted to values that are the salary of some employee in the database— presumably, a finite set of values. We can also rewrite the rule as follows:
BIG_SALARY(Y ) :– Y>60000, EMPLOYEE(X ), Salary(X, Y )
In this case, the rule is still theoretically safe. However, in Prolog or any other system that uses a top-down, depth-first inference mechanism, the rule creates an infinite loop, since we first search for a value for Y and then check whether it is a salary of an employee. The result is generation of an infinite number of Y values, even though these, after a certain point, cannot lead to a set of true RHS predicates. One defini- tion of Datalog considers both rules to be safe, since it does not depend on a partic- ular inference mechanism. Nonetheless, it is generally advisable to write such a rule in the safest form, with the predicates that restrict possible bindings of variables placed first. As another example of an unsafe rule, consider the following rule:
HAS_SOMETHING(X, Y ) :– EMPLOYEE(X )
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REL_ONE(A, B, C ). REL_TWO(D, E, F ). REL_THREE(G, H, I, J ).
SELECT_ONE_A_EQ_C(X, Y, Z ) :– REL_ONE(C, Y, Z ). SELECT_ONE_B_LESS_5(X, Y, Z ) :– REL_ONE(X, Y, Z ), Y< 5. SELECT_ONE_A_EQ_C_AND_B_LESS_5(X, Y, Z ) :– REL_ONE(C, Y, Z ), Y<5
SELECT_ONE_A_EQ_C_OR_B_LESS_5(X, Y, Z ) :– REL_ONE(C, Y, Z ). SELECT_ONE_A_EQ_C_OR_B_LESS_5(X, Y, Z ) :– REL_ONE(X, Y, Z ), Y<5.
PROJECT_THREE_ON_G_H(W, X ) :– REL_THREE(W, X, Y, Z ).
UNION_ONE_TWO(X, Y, Z ) :– REL_ONE(X, Y, Z ). UNION_ONE_TWO(X, Y, Z ) :– REL_TWO(X, Y, Z ).
INTERSECT_ONE_TWO(X, Y, Z ) :– REL_ONE(X, Y, Z ), REL_TWO(X, Y, Z ).
DIFFERENCE_TWO_ONE(X, Y, Z ) :– REL_TWO(X, Y, Z ) NOT(REL_ONE(X, Y, Z ).
CART PROD _ONE_THREE(T, U, V, W, X, Y, Z ) :– REL_ONE(T, U, V), REL_THREE(W, X, Y, Z ).
NATURAL_JOIN_ONE_THREE_C_EQ_G(U, V, W, X, Y, Z ) :– REL_ONE(U, V, W ), REL_THREE(W, X, Y, Z ).
Figure 26.16 Predicates for illustrating relational operations.
Here, an infinite number of Y values can again be generated, since the variable Y appears only in the head of the rule and hence is not limited to a finite set of values. To define safe rules more formally, we use the concept of a limited variable. A vari- able X is limited in a rule if (1) it appears in a regular (not built-in) predicate in the body of the rule; (2) it appears in a predicate of the form X=c or c=X or (c1<<=X and X<=c2) in the rule body, where c, c1, and c2 are constant values; or (3) it appears in a predicate of the form X=Y or Y=X in the rule body, where Y is a limited vari- able. A rule is said to be safe if all its variables are limited.
26.5.7 Use of Relational Operations It is straightforward to specify many operations of the relational algebra in the form of Datalog rules that define the result of applying these operations on the database relations (fact predicates). This means that relational queries and views can easily be specified in Datalog. The additional power that Datalog provides is in the specifica- tion of recursive queries, and views based on recursive queries. In this section, we
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show how some of the standard relational operations can be specified as Datalog rules. Our examples will use the base relations (fact-defined predicates) REL_ONE, REL_TWO, and REL_THREE, whose schemas are shown in Figure 26.16. In Datalog, we do not need to specify the attribute names as in Figure 26.16; rather, the arity (degree) of each predicate is the important aspect. In a practical system, the domain (data type) of each attribute is also important for operations such as UNION, INTERSECTION, and JOIN, and we assume that the attribute types are compatible for the various operations, as discussed in Chapter 3.
Figure 26.16 illustrates a number of basic relational operations. Notice that if the Datalog model is based on the relational model and hence assumes that predicates (fact relations and query results) specify sets of tuples, duplicate tuples in the same predicate are automatically eliminated. This may or may not be true, depending on the Datalog inference engine. However, it is definitely not the case in Prolog, so any of the rules in Figure 26.16 that involve duplicate elimination are not correct for Prolog. For example, if we want to specify Prolog rules for the UNION operation with duplicate elimination, we must rewrite them as follows:
UNION_ONE_TWO(X, Y, Z) :– REL_ONE(X, Y, Z).
UNION_ONE_TWO(X, Y, Z) :– REL_TWO(X, Y, Z), NOT(REL_ONE(X, Y, Z)).
However, the rules shown in Figure 26.16 should work for Datalog, if duplicates are automatically eliminated. Similarly, the rules for the PROJECT operation shown in Figure 26.16 should work for Datalog in this case, but they are not correct for Prolog, since duplicates would appear in the latter case.
26.5.8 Evaluation of Nonrecursive Datalog Queries In order to use Datalog as a deductive database system, it is appropriate to define an inference mechanism based on relational database query processing concepts. The inherent strategy involves a bottom-up evaluation, starting with base relations; the order of operations is kept flexible and subject to query optimization. In this section we discuss an inference mechanism based on relational operations that can be applied to nonrecursive Datalog queries. We use the fact and rule base shown in Figures 26.14 and 26.15 to illustrate our discussion.
If a query involves only fact-defined predicates, the inference becomes one of searching among the facts for the query result. For example, a query such as
DEPARTMENT(X, Research)?
is a selection of all employee names X who work for the Research department. In relational algebra, it is the query:
π$1 (σ$2 = “Research” (DEPARTMENT))
which can be answered by searching through the fact-defined predicate department(X,Y ). The query involves relational SELECT and PROJECT operations on a base relation, and it can be handled by the database query processing and opti- mization techniques discussed in Chapter 19.
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SUPERVISOR UNDER_40K_SUPERVISOR
OVER_40K_EMP
PRESIDENT
MAIN_PRODUCT_EMP
WORKS_ON EMPLOYEE SALARY SUPERVISE
DEPARTMENT PROJECT FEMALE MALE
SUBORDINATE
SUPERIOR
Figure 26.17 Predicate dependency graph for Figures 26.15 and 26.16.
When a query involves rule-defined predicates, the inference mechanism must compute the result based on the rule definitions. If a query is nonrecursive and involves a predicate p that appears as the head of a rule p :– p1, p2, ..., pn, the strategy is first to compute the relations corresponding to p1, p2, ..., pn and then to compute the relation corresponding to p. It is useful to keep track of the dependency among the predicates of a deductive database in a predicate dependency graph. Figure 26.17 shows the graph for the fact and rule predicates shown in Figures 26.14 and 26.15. The dependency graph contains a node for each predicate. Whenever a pred- icate A is specified in the body (RHS) of a rule, and the head (LHS) of that rule is the predicate B, we say that B depends on A, and we draw a directed edge from A to B. This indicates that in order to compute the facts for the predicate B (the rule head), we must first compute the facts for all the predicates A in the rule body. If the dependency graph has no cycles, we call the rule set nonrecursive. If there is at least one cycle, we call the rule set recursive. In Figure 26.17, there is one recursively defined predicate—namely, SUPERIOR—which has a recursive edge pointing back to itself. Additionally, because the predicate subordinate depends on SUPERIOR, it also requires recursion in computing its result.
A query that includes only nonrecursive predicates is called a nonrecursive query. In this section we discuss only inference mechanisms for nonrecursive queries. In Figure 26.17, any query that does not involve the predicates SUBORDINATE or SUPERIOR is nonrecursive. In the predicate dependency graph, the nodes corre- sponding to fact-defined predicates do not have any incoming edges, since all fact- defined predicates have their facts stored in a database relation. The contents of a fact-defined predicate can be computed by directly retrieving the tuples in the cor- responding database relation.
26.6 Summary 983
The main function of an inference mechanism is to compute the facts that corre- spond to query predicates. This can be accomplished by generating a relational expression involving relational operators as SELECT, PROJECT, JOIN, UNION, and SET DIFFERENCE (with appropriate provision for dealing with safety issues) that, when executed, provides the query result. The query can then be executed by utiliz- ing the internal query processing and optimization operations of a relational data- base management system. Whenever the inference mechanism needs to compute the fact set corresponding to a nonrecursive rule-defined predicate p, it first locates all the rules that have p as their head. The idea is to compute the fact set for each such rule and then to apply the UNION operation to the results, since UNION corre- sponds to a logical OR operation. The dependency graph indicates all predicates q on which each p depends, and since we assume that the predicate is nonrecursive, we can always determine a partial order among such predicates q. Before computing the fact set for p, first we compute the fact sets for all predicates q on which p depends, based on their partial order. For example, if a query involves the predicate UNDER_40K_SUPERVISOR, we must first compute both SUPERVISOR and OVER_40K_EMP. Since the latter two depend only on the fact-defined predicates EMPLOYEE, SALARY, and SUPERVISE, they can be computed directly from the stored database relations.
This concludes our introduction to deductive databases. Additional material may be found at the book’s Website, where the complete Chapter 25 from the third edition is available. This includes a discussion on algorithms for recursive query processing. We have included an extensive bibliography of work in deductive databases, recur- sive query processing, magic sets, combination of relational databases with deduc- tive rules, and GLUE-NAIL! System at the end of this chapter.
26.6 Summary In this chapter we introduced database concepts for some of the common features that are needed by advanced applications: active databases, temporal databases, spa- tial databases, multimedia databases, and deductive databases. It is important to note that each of these is a broad topic and warrants a complete textbook.
First we introduced the topic of active databases, which provide additional func- tionality for specifying active rules. We introduced the Event-Condition-Action (ECA) model for active databases. The rules can be automatically triggered by events that occur—such as a database update—and they can initiate certain actions that have been specified in the rule declaration if certain conditions are true. Many commercial packages have some of the functionality provided by active databases in the form of triggers. We discussed the different options for specifying rules, such as row-level versus statement-level, before versus after, and immediate versus deferred. We gave examples of row-level triggers in the Oracle commercial system, and statement-level rules in the STARBURST experimental system. The syntax for trig- gers in the SQL-99 standard was also discussed. We briefly discussed some design issues and some possible applications for active databases.
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Next we introduced some of the concepts of temporal databases, which permit the database system to store a history of changes and allow users to query both current and past states of the database. We discussed how time is represented and distin- guished between the valid time and transaction time dimensions. We discussed how valid time, transaction time, and bitemporal relations can be implemented using tuple versioning in the relational model, with examples to illustrate how updates, inserts, and deletes are implemented. We also showed how complex objects can be used to implement temporal databases using attribute versioning. We looked at some of the querying operations for temporal relational databases and gave a brief introduction to the TSQL2 language.
Then we turned to spatial databases. Spatial databases provide concepts for data- bases that keep track of objects that have spatial characteristics. We discussed the types of spatial data, types of operators for processing spatial data, types of spatial queries, and spatial indexing techniques, including the popular R-trees. Then we discussed some spatial data mining techniques and applications of spatial data.
We discussed some basic types of multimedia databases and their important char- acteristics. Multimedia databases provide features that allow users to store and query different types of multimedia information, which includes images (such as pictures and drawings), video clips (such as movies, newsreels, and home videos), audio clips (such as songs, phone messages, and speeches), and documents (such as books and articles). We provided a brief overview of the various types of media sources and how multimedia sources may be indexed. Images are an extremely com- mon type of data among databases today and are likely to occupy a large proportion of stored data in databases. We therefore provided a more detailed treatment of images: their automatic analysis, recognition of objects within images, and their semantic tagging—all of which contribute to developing better systems to retrieve images by content, which still remains a challenging problem. We also commented on the analysis of audio data sources.
We concluded the chapter with an introduction to deductive databases. We gave an overview of Prolog and Datalog notation. We discussed the clausal form of formu- las. Datalog rules are restricted to Horn clauses, which contain at most one positive literal. We discussed the proof-theoretic and model-theoretic interpretation of rules. We briefly discussed Datalog rules and their safety and the ways of expressing relational operators using Datalog rules. Finally, we discussed an inference mecha- nism based on relational operations that can be used to evaluate nonrecursive Datalog queries using relational query optimization techniques. While Datalog has been a popular language with many applications, unfortunately, implementations of deductive database systems such as LDL or VALIDITY have not become widely commercially available.
Review Questions 985
Review Questions 26.1. What are the differences between row-level and statement-level active rules?
26.2. What are the differences among immediate, deferred, and detached consideration of active rule conditions?
26.3. What are the differences among immediate, deferred, and detached execution of active rule actions?
26.4. Briefly discuss the consistency and termination problems when designing a set of active rules.
26.5. Discuss some applications of active databases.
26.6. Discuss how time is represented in temporal databases and compare the dif- ferent time dimensions.
26.7. What are the differences between valid time, transaction time, and bitempo- ral relations?
26.8. Describe how the insert, delete, and update commands should be imple- mented on a valid time relation.
26.9. Describe how the insert, delete, and update commands should be imple- mented on a bitemporal relation.
26.10. Describe how the insert, delete, and update commands should be imple- mented on a transaction time relation.
26.11. What are the main differences between tuple versioning and attribute ver- sioning?
26.12. How do spatial databases differ from regular databases?
26.13. What are the different types of spatial data?
26.14. Name the main types of spatial operators and different classes of spatial queries.
26.15. What are the properties of R-trees that act as an index for spatial data?
26.16. Describe how a spatial join index between spatial objects can be constructed.
26.17. What are the different types of spatial data mining?
26.18. State the general form of a spatial association rule. Give an example of a spa- tial association rule.
26.19. What are the different types of multimedia sources?
26.20. How are multimedia sources indexed for content-based retrieval?
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26.21. What important features of images are used to compare them?
26.22. What are the different approaches to recognizing objects in images?
26.23. How is semantic tagging of images used?
26.24. What are the difficulties in analyzing audio sources?
26.25. What are deductive databases?
26.26. Write sample rules in Prolog to define that courses with course number above CS5000 are graduate courses and that DBgrads are those graduate stu- dents who enroll in CS6400 and CS8803.
26.27. Define clausal form of formulas and Horn clauses.
26.28. What is theorem proving and what is proof-theoretic interpretation of rules?
26.29. What is model-theoretic interpretation and how does it differ from proof- theoretic interpretation?
26.30. What are fact-defined predicates and rule-defined predicates?
26.31. What is a safe rule?
26.32. Give examples of rules that can define relational operations SELECT, PROJECT, JOIN, and SET operations.
26.33. Discuss the inference mechanism based on relational operations that can be applied to evaluate nonrecursive Datalog queries.
Exercises 26.34. Consider the COMPANY database described in Figure 3.6. Using the syntax
of Oracle triggers, write active rules to do the following:
a. Whenever an employee’s project assignments are changed, check if the total hours per week spent on the employee’s projects are less than 30 or greater than 40; if so, notify the employee’s direct supervisor.
b. Whenever an employee is deleted, delete the PROJECT tuples and DEPENDENT tuples related to that employee, and if the employee man- ages a department or supervises employees, set the Mgr_ssn for that department to NULL and set the Super_ssn for those employees to NULL.
26.35. Repeat 26.34 but use the syntax of STARBURST active rules.
26.36. Consider the relational schema shown in Figure 26.18. Write active rules for keeping the Sum_commissions attribute of SALES_PERSON equal to the sum of the Commission attribute in SALES for each sales person. Your rules should also check if the Sum_commissions exceeds 100000; if it does, call a procedure Notify_manager(S_id). Write both statement-level rules in STARBURST nota- tion and row-level rules in Oracle.
Exercises 987
S_id V_id Commission
SALES
Salesperson_id Name Title Phone Sum_commissions
SALES_PERSON
Figure 26.18 Database schema for sales and salesperson commissions in Exercise 26.36.
26.37. Consider the UNIVERSITY EER schema in Figure 8.10. Write some rules (in English) that could be implemented via active rules to enforce some com- mon integrity constraints that you think are relevant to this application.
26.38. Discuss which of the updates that created each of the tuples shown in Figure 26.9 were applied retroactively and which were applied proactively.
26.39. Show how the following updates, if applied in sequence, would change the contents of the bitemporal EMP_BT relation in Figure 26.9. For each update, state whether it is a retroactive or proactive update.
a. On 2004-03-10,17:30:00, the salary of Narayan is updated to 40000, effec- tive on 2004-03-01.
b. On 2003-07-30,08:31:00, the salary of Smith was corrected to show that it should have been entered as 31000 (instead of 30000 as shown), effective on 2003-06-01.
c. On 2004-03-18,08:31:00, the database was changed to indicate that Narayan was leaving the company (that is, logically deleted) effective on 2004-03-31.
d. On 2004-04-20,14:07:33, the database was changed to indicate the hiring of a new employee called Johnson, with the tuple <‘Johnson’, ‘334455667’, 1, NULL > effective on 2004-04-20.
e. On 2004-04-28,12:54:02, the database was changed to indicate that Wong was leaving the company (that is, logically deleted) effective on 2004-06- 01.
f. On 2004-05-05,13:07:33, the database was changed to indicate the rehir- ing of Brown, with the same department and supervisor but with salary 35000 effective on 2004-05-01.
26.40. Show how the updates given in Exercise 26.39, if applied in sequence, would change the contents of the valid time EMP_VT relation in Figure 26.8.
26.41. Add the following facts to the sample database in Figure 26.11:
SUPERVISE(ahmad, bob), SUPERVISE(franklin, gwen).
First modify the supervisory tree in Figure 26.11(b) to reflect this change. Then construct a diagram showing the top-down evaluation of the query SUPERIOR(james, Y) using rules 1 and 2 from Figure 26.12.
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26.42. Consider the following set of facts for the relation PARENT(X, Y), where Y is the parent of X:
PARENT(a, aa), PARENT(a, ab), PARENT(aa, aaa), PARENT(aa, aab), PARENT(aaa, aaaa), PARENT(aaa, aaab).
Consider the rules
r1: ANCESTOR(X, Y ) :– PARENT(X, Y ) r2: ANCESTOR(X, Y ) :– PARENT(X, Z ), ANCESTOR(Z, Y )
which define ancestor Y of X as above.
a. Show how to solve the Datalog query
ANCESTOR(aa, X)?
and show your work at each step.
b. Show the same query by computing only the changes in the ancestor rela- tion and using that in rule 2 each time.
[This question is derived from Bancilhon and Ramakrishnan (1986).]
26.43. Consider a deductive database with the following rules:
ANCESTOR(X, Y ) :– FATHER(X, Y ) ANCESTOR(X, Y ) :– FATHER(X, Z ), ANCESTOR(Z, Y )
Notice that FATHER(X, Y ) means that Y is the father of X; ANCESTOR(X, Y ) means that Y is the ancestor of X.
Consider the following fact base:
FATHER(Harry, Issac), FATHER(Issac, John), FATHER(John, Kurt).
a. Construct a model-theoretic interpretation of the above rules using the given facts.
b. Consider that a database contains the above relations FATHER(X, Y ), another relation BROTHER(X, Y ), and a third relation BIRTH(X, B ), where B is the birth date of person X. State a rule that computes the first cousins of the following variety: their fathers must be brothers.
c. Show a complete Datalog program with fact-based and rule-based literals that computes the following relation: list of pairs of cousins, where the first person is born after 1960 and the second after 1970. You may use greater than as a built-in predicate. (Note: Sample facts for brother, birth, and person must also be shown.)
26.44. Consider the following rules:
REACHABLE(X, Y ) :– FLIGHT(X, Y ) REACHABLE(X, Y ) :– FLIGHT(X, Z ), REACHABLE(Z, Y )
where REACHABLE(X, Y ) means that city Y can be reached from city X, and FLIGHT(X, Y ) means that there is a flight to city Y from city X.
Selected Bibliography 989
a. Construct fact predicates that describe the following:
i. Los Angeles, New York, Chicago, Atlanta, Frankfurt, Paris, Singapore, Sydney are cities.
ii. The following flights exist: LA to NY, NY to Atlanta, Atlanta to Frankfurt, Frankfurt to Atlanta, Frankfurt to Singapore, and Singapore to Sydney. (Note: No flight in reverse direction can be auto- matically assumed.)
b. Is the given data cyclic? If so, in what sense?
c. Construct a model-theoretic interpretation (that is, an interpretation similar to the one shown in Figure 26.13) of the above facts and rules.
d. Consider the query
REACHABLE(Atlanta, Sydney)?
How will this query be executed? List the series of steps it will go through.
e. Consider the following rule-defined predicates:
ROUND-TRIP-REACHABLE(X, Y ) :– REACHABLE(X, Y ), REACHABLE(Y, X )
DURATION(X, Y, Z )
Draw a predicate dependency graph for the above predicates. (Note: DURATION(X, Y, Z ) means that you can take a flight from X to Y in Z hours.)
f. Consider the following query: What cities are reachable in 12 hours from Atlanta? Show how to express it in Datalog. Assume built-in predicates like greater-than(X, Y ). Can this be converted into a relational algebra statement in a straightforward way? Why or why not?
g. Consider the predicate population(X, Y ), where Y is the population of city X. Consider the following query: List all possible bindings of the predicate pair (X, Y ), where Y is a city that can be reached in two flights from city X, which has over 1 million people. Show this query in Datalog. Draw a corresponding query tree in relational algebraic terms.
Selected Bibliography The book by Zaniolo et al. (1997) consists of several parts, each describing an advanced database concept such as active, temporal, and spatial/text/multimedia databases. Widom and Ceri (1996) and Ceri and Fraternali (1997) focus on active database concepts and systems. Snodgrass (1995) describes the TSQL2 language and data model. Khoshafian and Baker (1996), Faloutsos (1996), and Subrahmanian (1998) describe multimedia database concepts. Tansel et al. (1993) is a collection of chapters on temporal databases.
STARBURST rules are described in Widom and Finkelstein (1990). Early work on active databases includes the HiPAC project, discussed in Chakravarthy et al. (1989)
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and Chakravarthy (1990). A glossary for temporal databases is given in Jensen et al. (1994). Snodgrass (1987) focuses on TQuel, an early temporal query language.
Temporal normalization is defined in Navathe and Ahmed (1989). Paton (1999) and Paton and Diaz (1999) survey active databases. Chakravarthy et al. (1994) describe SENTINEL and object-based active systems. Lee et al. (1998) discuss time series management.
The book by Shekhar and Chawla (2003) consists of all aspects of spatial databases including spatial data models, spatial storage and indexing, and spatial data mining. Scholl et al. (2001) is another textbook on spatial data management. Albrecht (1996) describes in detail the various GIS analysis operations. Clementini and Di Felice (1993) give a detailed description of the spatial operators. Güting (1994) describes the spatial data structures and querying languages for spatial database sys- tems. Guttman (1984) proposed R-trees for spatial data indexing. Manolopoulos et al. (2005) is a book on the theory and applications of R-trees. Papadias et al. (2003) discuss query processing using R-trees for spatial networks. Ester et al. (2001) pro- vide a comprehensive discussion on the algorithms and applications of spatial data mining. Koperski and Han (1995) discuss association rule discovery from geo- graphic databases. Brinkhoff et al. (1993) provide a comprehensive overview of the usage of R-trees for efficient processing of spatial joins. Rotem (1991) describes spa- tial join indexes comprehensively. Shekhar and Xiong (2008) is a compilation of various sources that discuss different aspects of spatial database management sys- tems and GIS. The density-based clustering algorithms DBSCAN and DENCLUE are proposed by Ester et al. (1996) and Hinnenberg and Gabriel (2007) respectively.
Multimedia database modeling has a vast amount of literature—it is difficult to point to all important references here. IBM’s QBIC (Query By Image Content) sys- tem described in Niblack et al. (1998) was one of the first comprehensive approaches for querying images based on content. It is now available as a part of IBM’s DB2 database image extender. Zhao and Grosky (2002) discuss content-based image retrieval. Carneiro and Vasconselos (2005) present a database-centric view of seman- tic image annotation and retrieval. Content-based retrieval of subimages is discussed by Luo and Nascimento (2004). Tuceryan and Jain (1998) discuss various aspects of texture analysis. Object recognition using SIFT is discussed in Lowe (2004). Lazebnik et al. (2004) describe the use of local affine regions to model 3D objects (RIFT). Among other object recognition approaches, G-RIF is described in Kim et al. (2006), Bay et al. (2006) discuss SURF, Ke and Sukthankar (2004) present PCA-SIFT, and Mikolajczyk and Schmid (2005) describe GLOH. Fan et al. (2004) present a tech- nique for automatic image annotation by using concept-sensitive objects. Fotouhi et al. (2007) was the first international workshop on many faces of multimedia seman- tics, which is continuing annually. Thuraisingham (2001) classifies audio data into different categories, and by treating each of these categories differently, elaborates on the use of metadata for audio. Prabhakaran (1996) has also discussed how speech processing techniques can add valuable metadata information to the audio piece.
The early developments of the logic and database approach are surveyed by Gallaire et al. (1984). Reiter (1984) provides a reconstruction of relational database theory,
while Levesque (1984) provides a discussion of incomplete knowledge in light of logic. Gallaire and Minker (1978) provide an early book on this topic. A detailed treatment of logic and databases appears in Ullman (1989, Volume 2), and there is a related chapter in Volume 1 (1988). Ceri, Gottlob, and Tanca (1990) present a com- prehensive yet concise treatment of logic and databases. Das (1992) is a comprehen- sive book on deductive databases and logic programming. The early history of Datalog is covered in Maier and Warren (1988). Clocksin and Mellish (2003) is an excellent reference on Prolog language.
Aho and Ullman (1979) provide an early algorithm for dealing with recursive queries, using the least fixed-point operator. Bancilhon and Ramakrishnan (1986) give an excellent and detailed description of the approaches to recursive query pro- cessing, with detailed examples of the naive and seminaive approaches. Excellent survey articles on deductive databases and recursive query processing include Warren (1992) and Ramakrishnan and Ullman (1995). A complete description of the seminaive approach based on relational algebra is given in Bancilhon (1985). Other approaches to recursive query processing include the recursive query/sub- query strategy of Vieille (1986), which is a top-down interpreted strategy, and the Henschen-Naqvi (1984) top-down compiled iterative strategy. Balbin and Ramamohanrao (1987) discuss an extension of the seminaive differential approach for multiple predicates.
The original paper on magic sets is by Bancilhon et al. (1986). Beeri and Ramakrishnan (1987) extend it. Mumick et al. (1990a) show the applicability of magic sets to nonrecursive nested SQL queries. Other approaches to optimizing rules without rewriting them appear in Vieille (1986, 1987). Kifer and Lozinskii (1986) propose a different technique. Bry (1990) discusses how the top-down and bottom- up approaches can be reconciled. Whang and Navathe (1992) describe an extended disjunctive normal form technique to deal with recursion in relational algebra expressions for providing an expert system interface over a relational DBMS.
Chang (1981) describes an early system for combining deductive rules with rela- tional databases. The LDL system prototype is described in Chimenti et al. (1990). Krishnamurthy and Naqvi (1989) introduce the choice notion in LDL. Zaniolo (1988) discusses the language issues for the LDL system. A language overview of CORAL is provided in Ramakrishnan et al. (1992), and the implementation is described in Ramakrishnan et al. (1993). An extension to support object-oriented features, called CORAL++, is described in Srivastava et al. (1993). Ullman (1985) provides the basis for the NAIL! system, which is described in Morris et al. (1987). Phipps et al. (1991) describe the GLUE-NAIL! deductive database system.
Zaniolo (1990) reviews the theoretical background and the practical importance of deductive databases. Nicolas (1997) gives an excellent history of the developments leading up to Deductive Object-Oriented Database (DOOD) systems. Falcone et al. (1997) survey the DOOD landscape. References on the VALIDITY system include Friesen et al. (1995), Vieille (1998), and Dietrich et al. (1999).
Selected Bibliography 991
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Introduction to Information Retrieval and Web Search1
In most of the chapters in this book so far, we have dis-cussed techniques for modeling, designing, querying, transaction processing of, and managing structured data. In Section 12.1 we dis- cussed the difference between structured, semistructured, and unstructured data. Information retrieval deals mainly with unstructured data, and the techniques for indexing, searching, and retrieving information from large collections of unstruc- tured documents. In this chapter we will provide an introduction to information retrieval. This is a very broad topic, so we will focus on the similarities and differ- ences between information retrieval and database technologies, and on the indexing techniques that form the basis of many information retrieval systems.
This chapter is organized as follows. In Section 27.1 we introduce information retrieval (IR) concepts and discuss how IR differs from traditional databases. Section 27.2 is devoted to a discussion of retrieval models, which form the basis for IR search. Section 27.3 covers different types of queries in IR systems. Section 27.4 discusses text preprocessing, and Section 27.5 provides an overview of IR indexing, which is at the heart of any IR system. In Section 27.6 we describe the various eval- uation metrics for IR systems performance. Section 27.7 details Web analysis and its relationship to information retrieval, and Section 27.8 briefly introduces the current trends in IR. Section 27.9 summarizes the chapter. For a limited overview of IR, we suggest that students read Sections 27.1 through 27.6.
27chapter 27
1This chapter is coauthored with Saurav Sahay of the Georgia Institute of Technology.
994 Chapter 27 Introduction to Information Retrieval and Web Search
27.1 Information Retrieval (IR) Concepts Information retrieval is the process of retrieving documents from a collection in response to a query (or a search request) by a user. This section provides an overview of information retrieval (IR) concepts. In Section 27.1.1, we introduce information retrieval in general and then discuss the different kinds and levels of search that IR encompasses. In Section 27.1.2, we compare IR and database tech- nologies. Section 27.1.3 gives a brief history of IR. We then present the different modes of user interaction with IR systems in Section 27.1.4. In Section 27.1.5, we describe the typical IR process with a detailed set of tasks and then with a simplified process flow, and end with a brief discussion of digital libraries and the Web.
27.1.1 Introduction to Information Retrieval We first review the distinction between structured and unstructured data (see Section 12.1) to see how information retrieval differs from structured data manage- ment. Consider a relation (or table) called HOUSES with the attributes:
HOUSES(Lot#, Address, Square_footage, Listed_price)
This is an example of structured data. We can compare this relation with home- buying contract documents, which are examples of unstructured data. These types of documents can vary from city to city, and even county to county, within a given state in the United States. Typically, a contract document in a particular state will have a standard list of clauses described in paragraphs within sections of the docu- ment, with some predetermined (fixed) text and some variable areas whose content is to be supplied by the specific buyer and seller. Other variable information would include interest rate for financing, down-payment amount, closing dates, and so on. The documents could also possibly include some pictures taken during a home inspection. The information content in such documents can be considered unstructured data that can be stored in a variety of possible arrangements and for- mats. By unstructured information, we generally mean information that does not have a well-defined formal model and corresponding formal language for represen- tation and reasoning, but rather is based on understanding of natural language.
With the advent of the World Wide Web (or Web, for short), the volume of unstruc- tured information stored in messages and documents that contain textual and mul- timedia information has exploded. These documents are stored in a variety of standard formats, including HTML, XML (see Chapter 12), and several audio and video formatting standards. Information retrieval deals with the problems of stor- ing, indexing, and retrieving (searching) such information to satisfy the needs of users. The problems that IR deals with are exacerbated by the fact that the number of Web pages and the number of social interaction events is already in the billions, and is growing at a phenomenal rate. All forms of unstructured data described above are being added at the rates of millions per day, expanding the searchable space on the Web at rapidly increasing rates.
27.1 Information Retrieval (IR) Concepts 995
Historically, information retrieval is “the discipline that deals with the structure, analysis, organization, storage, searching, and retrieval of information” as defined by Gerald Salton, an IR pioneer.2 We can enhance the definition slightly to say that it applies in the context of unstructured documents to satisfy a user’s information needs. This field has existed even longer than the database field, and was originally concerned with retrieval of cataloged information in libraries based on titles, authors, topics, and keywords. In academic programs, the field of IR has long been a part of Library and Information Science programs. Information in the context of IR does not require machine-understandable structures, such as in relational database systems. Examples of such information include written texts, abstracts, documents, books, Web pages, e-mails, instant messages, and collections from digital libraries. Therefore, all loosely represented (unstructured) or semistructured information is also part of the IR discipline.
We introduced XML modeling and retrieval in Chapter 12 and discussed advanced data types, including spatial, temporal, and multimedia data, in Chapter 26. RDBMS vendors are providing modules to support many of these data types, as well as XML data, in the newer versions of their products, sometimes referred to as extended RDBMSs, or object-relational database management systems (ORDBMSs, see Chapter 11). The challenge of dealing with unstructured data is largely an infor- mation retrieval problem, although database researchers have been applying data- base indexing and search techniques to some of these problems.
IR systems go beyond database systems in that they do not limit the user to a spe- cific query language, nor do they expect the user to know the structure (schema) or content of a particular database. IR systems use a user’s information need expressed as a free-form search request (sometimes called a keyword search query, or just query) for interpretation by the system. Whereas the IR field historically dealt with cataloging, processing, and accessing text in the form of documents for decades, in today’s world the use of Web search engines is becoming the dominant way to find information. The traditional problems of text indexing and making collections of documents searchable have been transformed by making the Web itself into a quickly accessible repository of human knowledge.
An IR system can be characterized at different levels: by types of users, types of data, and the types of the information need, along with the size and scale of the informa- tion repository it addresses. Different IR systems are designed to address specific problems that require a combination of different characteristics. These characteris- tics can be briefly described as follows:
Types of Users. The user may be an expert user (for example, a curator or a librarian), who is searching for specific information that is clear in his/her mind and forms relevant queries for the task, or a layperson user with a generic infor- mation need. The latter cannot create highly relevant queries for search (for
2See Salton’s 1968 book entitled Automatic Information Organization and Retrieval.
996 Chapter 27 Introduction to Information Retrieval and Web Search
example, students trying to find information about a new topic, researchers try- ing to assimilate different points of view about a historical issue, a scientist ver- ifying a claim by another scientist, or a person trying to shop for clothing).
Types of Data. Search systems can be tailored to specific types of data. For example, the problem of retrieving information about a specific topic may be handled more efficiently by customized search systems that are built to collect and retrieve only information related to that specific topic. The information repository could be hierarchically organized based on a concept or topic hierar- chy. These topical domain-specific or vertical IR systems are not as large as or as diverse as the generic World Wide Web, which contains information on all kinds of topics. Given that these domain-specific collections exist and may have been acquired through a specific process, they can be exploited much more effi- ciently by a specialized system.
Types of Information Need. In the context of Web search, users’ information needs may be defined as navigational, informational, or transactional.3
Navigational search refers to finding a particular piece of information (such as the Georgia Tech University Website) that a user needs quickly. The purpose of informational search is to find current information about a topic (such as research activities in the college of computing at Georgia Tech—this is the clas- sic IR system task). The goal of transactional search is to reach a site where fur- ther interaction happens (such as joining a social network, product shopping, online reservations, accessing databases, and so on).
Levels of Scale. In the words of Nobel Laureate Herbert Simon,
What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it. 4
This overabundance of information sources in effect creates a high noise-to-signal ratio in IR systems. Especially on the Web, where billions of pages are indexed, IR interfaces are built with efficient scalable algorithms for distributed searching, indexing, caching, merging, and fault tolerance. IR search engines can be limited in level to more specific collections of documents. Enterprise search systems offer IR solutions for searching different entities in an enterprise’s intranet, which consists of the network of computers within that enterprise. The searchable entities include e-mails, corporate documents, manuals, charts, and presentations, as well as reports related to people, meetings, and projects. They still typically deal with hundreds of millions of entities in large global enterprises. On a smaller scale, there are personal information systems such as those on desktops and laptops, called desktop search engines (for example, Google Desktop), for retrieving files, folders, and different kinds of entities stored on the computer. There are peer-to-peer systems, such as
3See Broder (2002) for details. 4From Simon (1971), “Designing Organizations for an Information-Rich World.”
27.1 Information Retrieval (IR) Concepts 997
BitTorrent, which allows sharing of music in the form of audio files, as well as spe- cialized search engines for audio, such as Lycos and Yahoo! audio search.
27.1.2 Databases and IR Systems: A Comparison Within the computer science discipline, databases and IR systems are closely related fields. Databases deal with structured information retrieval through well-defined formal languages for representation and manipulation based on the theoretically founded data models. Efficient algorithms have been developed for operators that allow rapid execution of complex queries. IR, on the other hand, deals with unstruc- tured search with possibly vague query or search semantics and without a well- defined logical schematic representation. Some of the key differences between databases and IR systems are listed in Table 27.1.
Whereas databases have fixed schemas defined in some data model such as the rela- tional model, an IR system has no fixed data model; it views data or documents according to some scheme, such as the vector space model, to aid in query process- ing (see Section 27.2). Databases using the relational model employ SQL for queries and transactions. The queries are mapped into relational algebra operations and search algorithms (see Chapter 19) and return a new relation (table) as the query result, providing an exact answer to the query for the current state of the database. In IR systems, there is no fixed language for defining the structure (schema) of the document or for operating on the document—queries tend to be a set of query terms (keywords) or a free-form natural language phrase. An IR query result is a list of document ids, or some pieces of text or multimedia objects (images, videos, and so on), or a list of links to Web pages.
The result of a database query is an exact answer; if no matching records (tuples) are found in the relation, the result is empty (null). On the other hand, the answer to a user request in an IR query represents the IR system’s best attempt at retrieving the
Table 27.1 A Comparison of Databases and IR Systems
Databases
■ Structured data ■ Schema driven ■ Relational (or object, hierarchical, and
network) model is predominant ■ Structured query model ■ Rich metadata operations ■ Query returns data ■ Results are based on exact matching (always
correct)
IR Systems ■ Unstructured data ■ No fixed schema; various data models
(e.g., vector space model) ■ Free-form query models ■ Rich data operations ■ Search request returns list or pointers to
documents ■ Results are based on approximate matching
and measures of effectiveness (may be imprecise and ranked)
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information most relevant to that query. Whereas database systems maintain a large amount of metadata and allow their use in query optimization, the operations in IR systems rely on the data values themselves and their occurrence frequencies. Complex statistical analysis is sometimes performed to determine the relevance of each document or parts of a document to the user request.
27.1.3 A Brief History of IR Information retrieval has been a common task since the times of ancient civiliza- tions, which devised ways to organize, store, and catalog documents and records. Media such as papyrus scrolls and stone tablets were used to record documented information in ancient times. These efforts allowed knowledge to be retained and transferred among generations. With the emergence of public libraries and the printing press, large-scale methods for producing, collecting, archiving, and distrib- uting documents and books evolved. As computers and automatic storage systems emerged, the need to apply these methods to computerized systems arose. Several techniques emerged in the 1950s, such as the seminal work of H. P. Luhn,5 who pro- posed using words and their frequency counts as indexing units for documents, and using measures of word overlap between queries and documents as the retrieval cri- terion. It was soon realized that storing large amounts of text was not difficult. The harder task was to search for and retrieve that information selectively for users with specific information needs. Methods that explored word distribution statistics gave rise to the choice of keywords based on their distribution properties6 and keyword- based weighting schemes.
The earlier experiments with document retrieval systems such as SMART7 in the 1960s adopted the inverted file organization based on keywords and their weights as the method of indexing (see Section 27.5). Serial (or sequential) organization proved inadequate if queries required fast, near real-time response times. Proper organiza- tion of these files became an important area of study; document classification and clustering schemes ensued. The scale of retrieval experiments remained a challenge due to lack of availability of large text collections. This soon changed with the World Wide Web. Also, the Text Retrieval Conference (TREC) was launched by NIST (National Institute of Standards and Technology) in 1992 as a part of the TIPSTER program8 with the goal of providing a platform for evaluating information retrieval methodologies and facilitating technology transfer to develop IR products.
A search engine is a practical application of information retrieval to large-scale document collections. With significant advances in computers and communica- tions technologies, people today have interactive access to enormous amounts of user-generated distributed content on the Web. This has spurred the rapid growth
5See Luhn (1957) “A statistical approach to mechanized encoding and searching of literary information.” 6See Salton, Yang, and Yu (1975). 7For details, see Buckley et al. (1993). 8For details, see Harman (1992).
in search engine technology, where search engines are trying to discover different kinds of real-time content found on the Web. The part of a search engine responsi- ble for discovering, analyzing, and indexing these new documents is known as a crawler. Other types of search engines exist for specific domains of knowledge. For example, the biomedical literature search database was started in the 1970s and is now supported by the PubMed search engine,9 which gives access to over 20 million abstracts.
While continuous progress is being made to tailor search results to the needs of an end user, the challenge remains in providing high-quality, pertinent, and timely information that is precisely aligned to the information needs of individual users.
27.1.4 Modes of Interaction in IR Systems In the beginning of Section 27.1, we defined information retrieval as the process of retrieving documents from a collection in response to a query (or a search request) by a user. Typically the collection is made up of documents containing unstructured data. Other kinds of documents include images, audio recordings, video strips, and maps. Data may be scattered nonuniformly in these documents with no definitive structure. A query is a set of terms (also referred to as keywords) used by the searcher to specify an information need (for example, the terms ‘databases’ and ‘operating systems’ may be regarded as a query to a computer science bibliographic database). An informational request or a search query may also be a natural lan- guage phrase or a question (for example, “What is the currency of China?” or “Find Italian restaurants in Sarasota, Florida.”).
There are two main modes of interaction with IR systems—retrieval and brows- ing—which, although similar in goal, are accomplished through different interac- tion tasks. Retrieval is concerned with the extraction of relevant information from a repository of documents through an IR query, while browsing signifies the activ- ity of a user visiting or navigating through similar or related documents based on the user’s assessment of relevance. During browsing, a user’s information need may not be defined a priori and is flexible. Consider the following browsing scenario: A user specifies ‘Atlanta’ as a keyword. The information retrieval system retrieves links to relevant result documents containing various aspects of Atlanta for the user. The user comes across the term ‘Georgia Tech’ in one of the returned documents, and uses some access technique (such as clicking on the phrase ‘Georgia Tech’ in a docu- ment, which has a built-in link) and visits documents about Georgia Tech in the same or a different Website (repository). There the user finds an entry for ‘Athletics’ that leads the user to information about various athletic programs at Georgia Tech. Eventually, the user ends his search at the Fall schedule for the Yellow Jackets foot- ball team, which he finds to be of great interest. This user activity is known as browsing. Hyperlinks are used to interconnect Web pages and are mainly used for browsing. Anchor texts are text phrases within documents used to label hyperlinks and are very relevant to browsing.
27.1 Information Retrieval (IR) Concepts 999
9See www.ncbi.nlm.nih.gov/pubmed/.
1000 Chapter 27 Introduction to Information Retrieval and Web Search
Web search combines both aspects—browsing and retrieval—and is one of the main applications of information retrieval today. Web pages are analogous to docu- ments. Web search engines maintain an indexed repository of Web pages, usually using the technique of inverted indexing (see Section 27.5). They retrieve the most relevant Web pages for the user in response to the user’s search request with a possi- ble ranking in descending order of relevance. The rank of a Webpage in a retrieved set is the measure of its relevance to the query that generated the result set.
27.1.5 Generic IR Pipeline As we mentioned earlier, documents are made up of unstructured natural language text composed of character strings from English and other languages. Common examples of documents include newswire services (such as AP or Reuters), corpo- rate manuals and reports, government notices, Web page articles, blogs, tweets, books, and journal papers. There are two main approaches to IR: statistical and semantic.
In a statistical approach, documents are analyzed and broken down into chunks of text (words, phrases, or n-grams, which are all subsequences of length n characters in a text or document) and each word or phrase is counted, weighted, and measured for relevance or importance. These words and their properties are then compared with the query terms for potential degree of match to produce a ranked list of resulting documents that contain the words. Statistical approaches are further clas- sified based on the method employed. The three main statistical approaches are Boolean, vector space, and probabilistic (see Section 27.2).
Semantic approaches to IR use knowledge-based techniques of retrieval that broadly rely on the syntactic, lexical, sentential, discourse-based, and pragmatic lev- els of knowledge understanding. In practice, semantic approaches also apply some form of statistical analysis to improve the retrieval process.
Figure 27.1 shows the various stages involved in an IR processing system. The steps shown on the left in Figure 27.1 are typically offline processes, which prepare a set of documents for efficient retrieval; these are document preprocessing, document modeling, and indexing. The steps involved in query formation, query processing, searching mechanism, document retrieval, and relevance feedback are shown on the right in Figure 27.1. In each box, we highlight the important concepts and issues. The rest of this chapter describes some of the concepts involved in the various tasks within the IR process shown in Figure 27.1.
Figure 27.2 shows a simplified IR processing pipeline. In order to perform retrieval on documents, the documents are first represented in a form suitable for retrieval. The significant terms and their properties are extracted from the documents and are represented in a document index where the words/terms and their properties are stored in a matrix that contains these terms and the references to the documents that contain them. This index is then converted into an inverted index (see Figure 27.4) of a word/term vs. document matrix. Given the query words, the documents
27.2 Retrieval Models 1001
Document 3 Document 2
Document 1 Document Corpus
Preprocessing
Modeling
Indexing
Stopword removal Stemming Thesaurus Digits, hyphens, punctuation marks, cases Information extraction
Retrieval models Type of queries
Inverted index construction Index vocabulary Document statistics Index maintenance
SEARCH INTENT Information
Need/Search
Query Formation
Query Processing
Searching Mechanism
Relevance Feedback
Legend Dashed line indicates
next iteration
Choice of search strategy (approximate vs. exact matches, exhaustive vs. top K) Type of similarity measure
Keywords, Boolean, phrase, proximity, wildcard queries, etc.
Conversion from humanly understandable to internal format Situation assessment Query expansion heuristics (users’s profile, related metadata, etc.)
Storing user’s feedback Personalization Pattern analysis of relevant
results
Metadata Integration
Ranking results Showing useful
metadata
External data ontologies
Document Retrieval
Figure 27.1 Generic IR framework.
containing these words—and the document properties, such as date of creation, author, and type of document—are fetched from the inverted index and compared with the query. This comparison results in a ranked list shown to the user. The user can then provide feedback on the results that triggers implicit or explicit query expansion to fetch results that are more relevant for the user. Most IR systems allow for an interactive search where the query and the results are successively refined.
27.2 Retrieval Models In this section we briefly describe the important models of IR. These are the three main statistical models—Boolean, vector space, and probabilistic—and the seman- tic model.
1002 Chapter 27 Introduction to Information Retrieval and Web Search
Documents
EXTRACT
FEEDBACK QUERY
FETCH
PROCESS
Inverted Index
COMPARE Query x
Documents
RANK
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Document #3 Two tickets tickled slightly angst-riden
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Document #1
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Result #1
Index
D1 1 1 0 1 1 0... D2 1 1 1 0 1 1... D3 1 1 0 1 1 1... D4 0 1 0 0 1 0... D5 0 0 0 1 0 1... D6 1 0 1 0 0 0...
W1 1 1 1 0 0 1... W2 1 1 1 1 0 0... W3 0 1 0 0 0 1... W4 1 0 1 0 1 0... W5 1 1 1 1 0 0... W6 0 1 1 0 1 0...
SEARCH INTENT
Figure 27.2 Simplified IR process pipeline.
27.2.1 Boolean Model In this model, documents are represented as a set of terms. Queries are formulated as a combination of terms using the standard Boolean logic set-theoretic operators such as AND, OR and NOT. Retrieval and relevance are considered as binary concepts in this model, so the retrieved elements are an “exact match” retrieval of relevant documents. There is no notion of ranking of resulting documents. All retrieved documents are considered equally important—a major simplification that does not consider frequencies of document terms or their proximity to other terms com- pared against the query terms.
Boolean retrieval models lack sophisticated ranking algorithms and are among the earliest and simplest information retrieval models. These models make it easy to associate metadata information and write queries that match the contents of the
27.2 Retrieval Models 1003
documents as well as other properties of documents, such as date of creation, author, and type of document.
27.2.2 Vector Space Model The vector space model provides a framework in which term weighting, ranking of retrieved documents, and relevance feedback are possible. Documents are repre- sented as features and weights of term features in an n-dimensional vector space of terms. Features are a subset of the terms in a set of documents that are deemed most relevant to an IR search for this particular set of documents. The process of select- ing these important terms (features) and their properties as a sparse (limited) list out of the very large number of available terms (the vocabulary can contain hun- dreds of thousands of terms) is independent of the model specification. The query is also specified as a terms vector (vector of features), and this is compared to the document vectors for similarity/relevance assessment.
The similarity assessment function that compares two vectors is not inherent to the model—different similarity functions can be used. However, the cosine of the angle between the query and document vector is a commonly used function for similarity assessment. As the angle between the vectors decreases, the cosine of the angle approaches one, meaning that the similarity of the query with a document vector increases. Terms (features) are weighted proportional to their frequency counts to reflect the importance of terms in the calculation of relevance measure. This is dif- ferent from the Boolean model, which does not take into account the frequency of words in the document for relevance match.
In the vector model, the document term weight wij (for term i in document j) is repre- sented based on some variation of the TF (term frequency) or TF-IDF (term frequency-inverse document frequency) scheme (as we will describe below). TF-IDF is a statistical weight measure that is used to evaluate the importance of a document word in a collection of documents. The following formula is typically used:
In the formula given above, we use the following symbols:
■ dj is the document vector.
■ q is the query vector.
■ wij is the weight of term i in document j.
■ wiq is the weight of term i in query vector q.
■ |V| is the number of dimensions in the vector that is the total number of important keywords (or features).
TF-IDF uses the product of normalized frequency of a term i (TFij) in document Dj and the inverse document frequency of the term i (IDFi) to weight a term in a
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1004 Chapter 27 Introduction to Information Retrieval and Web Search
document. The idea is that terms that capture the essence of a document occur fre- quently in the document (that is, their TF is high), but if such a term were to be a good term that discriminates the document from others, it must occur in only a few documents in the general population (that is, its IDF should be high as well).
IDF values can be easily computed for a fixed collection of documents. In case of Web search engines, taking a representative sample of documents approximates IDF computation. The following formulas can be used:
In these formulas, the meaning of the symbols is:
■ TFij is the normalized term frequency of term i in document Dj. ■ fij is the number of occurrences of term i in document Dj. ■ IDFi is the inverse document frequency weight for term i. ■ N is the number of documents in the collection. ■ ni is the number of documents in which term i occurs.
Note that if a term i occurs in all documents, then ni = N and hence IDFi = log (1) becomes zero, nullifying its importance and creating a situation where division by zero can occur. The weight of term i in document j, wij is computed based on its TF- IDF value in some techniques. To prevent division by zero, it is common to add a 1 to the denominator in the formulae such as the cosine formula above.
Sometimes, the relevance of the document with respect to a query (rel(Dj,Q)) is directly measured as the sum of the TF-IDF values of the terms in the Query Q:
The normalization factor (similar to the denominator of the cosine formula) is incorporated into the TF-IDF formula itself, thereby measuring relevance of a doc- ument to the query by the computation of the dot product of the query and docu- ment vectors.
The Rocchio10 algorithm is a well-known relevance feedback algorithm based on the vector space model that modifies the initial query vector and its weights in response to user-identified relevant documents. It expands the original query vector q to a new vector qe as follows:
q q D
d D
de r
r ird D
ir d D
r r ir ir
= + − ∈ ∈
∑ ∑α β γ | | | |
,
rel( , )D Q TF IDFj i Q ij i= ×∈∑
TF f f
IDF N n
ij ij ij i V
i i
=
= ( ) =
∑ 1to | |
log /
10See Rocchio (1971).
27.2 Retrieval Models 1005
Here, Dr and Dir are relevant and nonrelevant document sets and α, β, and γ are parameters of the equation. The values of these parameters determine how the feed- back affects the original query, and these may be determined after a number of trial- and-error experiments.
27.2.3 Probabilistic Model The similarity measures in the vector space model are somewhat ad hoc. For exam- ple, the model assumes that those documents closer to the query in cosine space are more relevant to the query vector. In the probabilistic model, a more concrete and definitive approach is taken: ranking documents by their estimated probability of relevance with respect to the query and the document. This is the basis of the Probability Ranking Principle developed by Robertson:11
In the probabilistic framework, the IR system has to decide whether the documents belong to the relevant set or the nonrelevant set for a query. To make this decision, it is assumed that a predefined relevant set and nonrelevant set exist for the query, and the task is to calculate the probability that the document belongs to the relevant set and compare that with the probability that the document belongs to the nonrel- evant set.
Given the document representation D of a document, estimating the relevance R and nonrelevance NR of that document involves computation of conditional prob- ability P(R|D) and P(NR|D). These conditional probabilities can be calculated using Bayes’ Rule:12
P(R|D) = P(D|R) × P(R)/P(D) P(NR|D) = P(D|NR) × P(NR)/P(D)
A document D is classified as relevant if P(R|D) > P(NR|D). Discarding the constant P(D), this is equivalent to saying that a document is relevant if:
P(D|R) × P(R) > P(D|NR) × P(NR)
The likelihood ratio P(D|R)/P(D|NR) is used as a score to determine the likelihood of the document with representation D belonging to the relevant set.
The term independence or Naïve Bayes assumption is used to estimate P(D|R) using computation of P(ti|R) for term ti. The likelihood ratios P(D|R)/P(D|NR) of docu- ments are used as a proxy for ranking based on the assumption that highly ranked documents will have a high likelihood of belonging to the relevant set.13
11For a description of the Cheshire II system, see Robertson (1997). 12Bayes’ theorem is a standard technique for measuring likelihood; see Howson and Urbach (1993), for example. 13Readers should refer to Croft et al. (2009) pages 246–247 for a detailed description.
1006 Chapter 27 Introduction to Information Retrieval and Web Search
With some reasonable assumptions and estimates about the probabilistic model along with extensions for incorporating query term weights and document term weights in the model, a probabilistic ranking algorithm called BM25 (Best Match 25) is quite popular. This weighting scheme has evolved from several versions of the Okapi14 system.
The Okapi weight for Document dj and query q is computed by the formula below. Additional notations are as follows:
■ ti is a term.
■ fij is the raw frequency count of term ti in document dj.
■ fiq is the raw frequency count of term ti in query q.
■ N is the total number of documents in the collection.
■ dfi is the number of documents that contain the term ti.
■ dlj is the document length (in bytes) of dj.
■ avdl is the average document length of the collection.
The Okapi relevance score of a document dj for a query q is given by the equation below, where k1 (between 1.0–2.0), b (usually 0.75) ,and k2 (between 1–1000) are parameters:
27.2.4 Semantic Model However sophisticated the above statistical models become, they can miss many rel- evant documents because those models do not capture the complete meaning or information need conveyed by a user’s query. In semantic models, the process of matching documents to a given query is based on concept level and semantic matching instead of index term (keyword) matching. This allows retrieval of rele- vant documents that share meaningful associations with other documents in the query result, even when these associations are not inherently observed or statisti- cally captured.
Semantic approaches include different levels of analysis, such as morphological, syntactic, and semantic analysis, to retrieve documents more effectively. In morphological analysis, roots and affixes are analyzed to determine the parts of speech (nouns, verbs, adjectives, and so on) of the words. Following morphological analysis, syntactic analysis follows to parse and analyze complete phrases in docu- ments. Finally, the semantic methods have to resolve word ambiguities and/or gen- erate relevant synonyms based on the semantic relationships between levels of structural entities in documents (words, paragraphs, pages, or entire documents).
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14City University of London Okapi System by Robertson, Walker, and Hancock-Beaulieu (1995).
27.3 Types of Queries in IR Systems 1007
The development of a sophisticated semantic system requires complex knowledge bases of semantic information as well as retrieval heuristics. These systems often require techniques from artificial intelligence and expert systems. Knowledge bases like Cyc15 and WordNet16 have been developed for use in knowledge-based IR sys- tems based on semantic models. The Cyc knowledge base, for example, is a represen- tation of a vast quantity of commonsense knowledge about assertions (over 2.5 million facts and rules) interrelating more than 155,000 concepts for reasoning about the objects and events of everyday life. WordNet is an extensive thesaurus (over 115,000 concepts) that is very popular and is used by many systems and is under continuous development (see Section 27.4.3).
27.3 Types of Queries in IR Systems Different keywords are associated with the document set during the process of indexing. These keywords generally consist of words, phrases, and other characteri- zations of documents such as date created, author names, and type of document. They are used by an IR system to build an inverted index (see Section 27.5), which is then consulted during the search. The queries formulated by users are compared to the set of index keywords. Most IR systems also allow the use of Boolean and other operators to build a complex query. The query language with these operators enriches the expressiveness of a user’s information need.
27.3.1 Keyword Queries Keyword-based queries are the simplest and most commonly used forms of IR queries: the user just enters keyword combinations to retrieve documents. The query keyword terms are implicitly connected by a logical AND operator. A query such as ‘database concepts’ retrieves documents that contain both the words ‘data- base’ and ‘concepts’ at the top of the retrieved results. In addition, most systems also retrieve documents that contain only ‘database’ or only ‘concepts’ in their text. Some systems remove most commonly occurring words (such as a, the, of, and so on, called stopwords) as a preprocessing step before sending the filtered query key- words to the IR engine. Most IR systems do not pay attention to the ordering of these words in the query. All retrieval models provide support for keyword queries.
27.3.2 Boolean Queries Some IR systems allow using the AND, OR, NOT, ( ), + , and – Boolean operators in combinations of keyword formulations. AND requires that both terms be found. OR lets either term be found. NOT means any record containing the second term will be excluded. ‘( )’ means the Boolean operators can be nested using parentheses. ‘+’ is equivalent to AND, requiring the term; the ‘+’ should be placed directly in front
15See Lenat (1995). 16See Miller (1990) for a detailed description of WordNet.
1008 Chapter 27 Introduction to Information Retrieval and Web Search
of the search term. ‘–’ is equivalent to AND NOT and means to exclude the term; the ‘–’ should be placed directly in front of the search term not wanted. Complex Boolean queries can be built out of these operators and their combinations, and they are evaluated according to the classical rules of Boolean algebra. No ranking is possible, because a document either satisfies such a query (is “relevant”) or does not satisfy it (is “nonrelevant”). A document is retrieved for a Boolean query if the query is logically true as an exact match in the document. Users generally do not use combinations of these complex Boolean operators, and IR systems support a restricted version of these set operators. Boolean retrieval models can directly sup- port different Boolean operator implementations for these kinds of queries.
27.3.3 Phrase Queries When documents are represented using an inverted keyword index for searching, the relative order of the terms in the document is lost. In order to perform exact phrase retrieval, these phrases should be encoded in the inverted index or imple- mented differently (with relative positions of word occurrences in documents). A phrase query consists of a sequence of words that makes up a phrase. The phrase is generally enclosed within double quotes. Each retrieved document must contain at least one instance of the exact phrase. Phrase searching is a more restricted and spe- cific version of proximity searching that we mention below. For example, a phrase searching query could be ‘conceptual database design’. If phrases are indexed by the retrieval model, any retrieval model can be used for these query types. A phrase the- saurus may also be used in semantic models for fast dictionary searching for phrases.
27.3.4 Proximity Queries Proximity search refers to a search that accounts for how close within a record mul- tiple terms should be to each other. The most commonly used proximity search option is a phrase search that requires terms to be in the exact order. Other proxim- ity operators can specify how close terms should be to each other. Some will also specify the order of the search terms. Each search engine can define proximity oper- ators differently, and the search engines use various operator names such as NEAR, ADJ(adjacent), or AFTER. In some cases, a sequence of single words is given, together with a maximum allowed distance between them. Vector space models that also maintain information about positions and offsets of tokens (words) have robust implementations for this query type. However, providing support for com- plex proximity operators becomes computationally expensive because it requires the time-consuming preprocessing of documents, and is thus suitable for smaller document collections rather than for the Web.
27.3.5 Wildcard Queries Wildcard searching is generally meant to support regular expressions and pattern matching-based searching in text. In IR systems, certain kinds of wildcard search support may be implemented—usually words with any trailing characters (for
27.4 Text Preprocessing 1009
example, ‘data*’ would retrieve data, database, datapoint, dataset, and so on). Providing support for wildcard searches in IR systems involves preprocessing over- head and is not considered worth the cost by many Web search engines today. Retrieval models do not directly provide support for this query type.
27.3.6 Natural Language Queries There are a few natural language search engines that aim to understand the struc- ture and meaning of queries written in natural language text, generally as a question or narrative. This is an active area of research that employs techniques like shallow semantic parsing of text, or query reformulations based on natural language under- standing. The system tries to formulate answers for such queries from retrieved results. Some search systems are starting to provide natural language interfaces to provide answers to specific types of questions, such as definition and factoid ques- tions, which ask for definitions of technical terms or common facts that can be retrieved from specialized databases. Such questions are usually easier to answer because there are strong linguistic patterns giving clues to specific types of sen- tences—for example, ‘defined as’ or ‘refers to’. Semantic models can provide support for this query type.
27.4 Text Preprocessing In this section we review the commonly used text preprocessing techniques that are part of the text processing task in Figure 27.1.
27.4.1 Stopword Removal Stopwords are very commonly used words in a language that play a major role in the formation of a sentence but which seldom contribute to the meaning of that sentence. Words that are expected to occur in 80 percent or more of the documents in a collection are typically referred to as stopwords, and they are rendered poten- tially useless. Because of the commonness and function of these words, they do not contribute much to the relevance of a document for a query search. Examples include words such as the, of, to, a, and, in, said, for, that, was, on, he, is, with, at, by, and it. These words are presented here with decreasing frequency of occurrence from a large corpus of documents called AP89.17 The fist six of these words account for 20 percent of all words in the listing, and the most frequent 50 words account for 40 percent of all text.
Removal of stopwords from a document must be performed before indexing. Articles, prepositions, conjunctions, and some pronouns are generally classified as stopwords. Queries must also be preprocessed for stopword removal before the actual retrieval process. Removal of stopwords results in elimination of possible spurious indexes, thereby reducing the size of an index structure by about 40
17For details, see Croft et al. (2009), pages 75–90.
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percent or more. However, doing so could impact the recall if the stopword is an integral part of a query (for example, a search for the phrase ‘To be or not to be,’ where removal of stopwords makes the query inappropriate, as all the words in the phrase are stopwords). Many search engines do not employ query stopword removal for this reason.
27.4.2 Stemming A stem of a word is defined as the word obtained after trimming the suffix and pre- fix of an original word. For example, ‘comput’ is the stem word for computer, com- puting, and computation. These suffixes and prefixes are very common in the English language for supporting the notion of verbs, tenses, and plural forms. Stemming reduces the different forms of the word formed by inflection (due to plu- rals or tenses) and derivation to a common stem.
A stemming algorithm can be applied to reduce any word to its stem. In English, the most famous stemming algorithm is Martin Porter’s stemming algorithm. The Porter stemmer18 is a simplified version of Lovin’s technique that uses a reduced set of about 60 rules (from 260 suffix patterns in Lovin’s technique) and organizes them into sets; conflicts within one subset of rules are resolved before going on to the next. Using stemming for preprocessing data results in a decrease in the size of the indexing structure and an increase in recall, possibly at the cost of precision.
27.4.3 Utilizing a Thesaurus A thesaurus comprises a precompiled list of important concepts and the main word that describes each concept for a particular domain of knowledge. For each concept in this list, a set of synonyms and related words is also compiled.19 Thus, a synonym can be converted to its matching concept during preprocessing. This preprocessing step assists in providing a standard vocabulary for indexing and searching. Usage of a thesaurus, also known as a collection of synonyms, has a substantial impact on the recall of information systems. This process can be complicated because many words have different meanings in different contexts.
UMLS20 is a large biomedical thesaurus of millions of concepts (called the Metathesaurus) and a semantic network of meta concepts and relationships that organize the Metathesaurus (see Figure 27.3). The concepts are assigned labels from the semantic network. This thesaurus of concepts contains synonyms of medical terms, hierarchies of broader and narrower terms, and other relationships among words and concepts that make it a very extensive resource for information retrieval of documents in the medical domain. Figure 27.3 illustrates part of the UMLS Semantic Network.
18See Porter (1980). 19See Baeza-Yates and Ribeiro-Neto (1999). 20Unified Medical Language System from the National Library of Medicine.
27.4 Text Preprocessing 1011
Organ or Tissue
Function
Physiologic Function
Biologic Function
Pathologic Function
Organism Function
Cell Function
Molecular Function
Cell or Molecular
Dysfunction
Disease or
Syndrome
Experimental Model of Disease
Mental or Behavioral
Dysfunction
Neoplastic Process
Mental Process
Genetic Function
Figure 27.3 A Portion of the UMLS Semantic Network: “Biologic Function” Hierarchy
Source: UMLS Reference Manual, National Library of Medicine.
WordNet21 is a manually constructed thesaurus that groups words into strict syn- onym sets called synsets. These synsets are divided into noun, verb, adjective, and adverb categories. Within each category, these synsets are linked together by appro- priate relationships such as class/subclass or “is-a” relationships for nouns.
WordNet is based on the idea of using a controlled vocabulary for indexing, thereby eliminating redundancies. It is also useful in providing assistance to users with locating terms for proper query formulation.
27.4.4 Other Preprocessing Steps: Digits, Hyphens, Punctuation Marks, Cases
Digits, dates, phone numbers, e-mail addresses, URLs, and other standard types of text may or may not be removed during preprocessing. Web search engines, however, index them in order to to use this type of information in the document
21See Fellbaum (1998) for a detailed description of WordNet.
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metadata to improve precision and recall (see Section 27.6 for detailed definitions of precision and recall).
Hyphens and punctuation marks may be handled in different ways. Either the entire phrase with the hyphens/punctuation marks may be used, or they may be elimi- nated. In some systems, the character representing the hyphen/punctuation mark may be removed, or may be replaced with a space. Different information retrieval systems follow different rules of processing. Handling hyphens automatically can be complex: it can either be done as a classification problem, or more commonly by some heuristic rules.
Most information retrieval systems perform case-insensitive search, converting all the letters of the text to uppercase or lowercase. It is also worth noting that many of these text preprocessing steps are language specific, such as involving accents and diacritics and the idiosyncrasies that are associated with a particular language.
27.4.5 Information Extraction Information extraction (IE) is a generic term used for extracting structured con- tent from text. Text analytic tasks such as identifying noun phrases, facts, events, people, places, and relationships are examples of IE tasks. These tasks are also called named entity recognition tasks and use rule-based approaches with either a the- saurus, regular expressions and grammars, or probabilistic approaches. For IR and search applications, IE technologies are mostly used to identify contextually rele- vant features that involve text analysis, matching, and categorization for improving the relevance of search systems. Language technologies using part-of-speech tagging are applied to semantically annotate the documents with extracted features to aid search relevance.
27.5 Inverted Indexing The simplest way to search for occurrences of query terms in text collections can be performed by sequentially scanning the text. This kind of online searching is only appropriate when text collections are quite small. Most information retrieval sys- tems process the text collections to create indexes and operate upon the inverted index data structure (refer to the indexing task in Figure 27.1). An inverted index structure comprises vocabulary and document information. Vocabulary is a set of distinct query terms in the document set. Each term in a vocabulary set has an asso- ciated collection of information about the documents that contain the term, such as document id, occurrence count, and offsets within the document where the term occurs. The simplest form of vocabulary terms consists of words or individual tokens of the documents. In some cases, these vocabulary terms also consist of phrases, n-grams, entities, links, names, dates, or manually assigned descriptor terms from documents and/or Web pages. For each term in the vocabulary, the cor- responding document ids, occurrence locations of the term in each document, number of occurrences of the term in each document, and other relevant informa- tion may be stored in the document information section.
27.5 Inverted Indexing 1013
Weights are assigned to document terms to represent an estimate of the usefulness of the given term as a descriptor for distinguishing the given document from other documents in the same collection. A term may be a better descriptor of one docu- ment than of another by the weighting process (see Section 27.2).
An inverted index of a document collection is a data structure that attaches distinct terms with a list of all documents that contains the term. The process of inverted index construction involves the extraction and processing steps shown in Figure 27.2. Acquired text is first preprocessed and the documents are represented with the vocabulary terms. Documents’ statistics are collected in document lookup tables. Statistics generally include counts of vocabulary terms in individual documents as well as different collections, their positions of occurrence within the documents, and the lengths of the documents. The vocabulary terms are weighted at indexing time according to different criteria for collections. For example, in some cases terms in the titles of the documents may be weighted more heavily than terms that occur in other parts of the documents.
One of the most popular weighting schemes is the TF-IDF (term frequency-inverse document frequency) metric that we described in Section 27.2. For a given term this weighting scheme distinguishes to some extent the documents in which the term occurs more often from those in which the term occurs very little or never. These weights are normalized to account for varying document lengths, further ensuring that longer documents with proportionately more occurrences of a word are not favored for retrieval over shorter documents with proportionately fewer occur- rences. These processed document-term streams (matrices) are then inverted into term-document streams (matrices) for further IR steps.
Figure 27.4 shows an illustration of term-document-position vectors for the four illustrative terms—example, inverted, index, and market—which refer to the three documents and the position where they occur in those documents.
The different steps involved in inverted index construction can be summarized as follows:
1. Break the documents into vocabulary terms by tokenizing, cleansing, stopword removal, stemming, and/or use of an additional thesaurus as vocabulary.
2. Collect document statistics and store the statistics in a document lookup table.
3. Invert the document-term stream into a term-document stream along with additional information such as term frequencies, term positions, and term weights.
Searching for relevant documents from the inverted index, given a set of query terms, is generally a three-step process.
1. Vocabulary search. If the query comprises multiple terms, they are sepa- rated and treated as independent terms. Each term is searched in the vocab- ulary. Various data structures, like variations of B+-tree or hashing, may be
This example shows an example of an inverted index.
Inverted index is a data structure for associating terms to documents.
Stock market index is used for capturing the sentiments of the financial market.
Stock market index is used for capturing the sentiments of the financial market.
ID
1.
2.
3.
4.
Term
example
inverted
index
market
Document: position
1:2, 1:5
1:8, 2:1
1:9, 2:2, 3:3
3:2, 3:13
Document 1
Document 2
Document 2
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used to optimize the search process. Query terms may also be ordered in lex- icographic order to improve space efficiency.
2. Document information retrieval. The document information for each term is retrieved.
3. Manipulation of retrieved information. The document information vector for each term obtained in step 2 is now processed further to incorporate var- ious forms of query logic. Various kinds of queries like prefix, range, context, and proximity queries are processed in this step to construct the final result based on the document collections returned in step 2.
27.6 Evaluation Measures of Search Relevance
Without proper evaluation techniques, one cannot compare and measure the rele- vance of different retrieval models and IR systems in order to make improvements.
Figure 27.4 Example of an inverted index.
27.6 Evaluation Measures of Search Relevance 1015
Evaluation techniques of IR systems measure the topical relevance and user relevance. Topical relevance measures the extent to which the topic of a result matches the topic of the query. Mapping one’s information need with “perfect” queries is a cognitive task, and many users are not able to effectively form queries that would retrieve results more suited to their information need. Also, since a major chunk of user queries are informational in nature, there is no fixed set of right answers to show to the user. User relevance is a term used to describe the “goodness” of a retrieved result with regard to the user’s information need. User rel- evance includes other implicit factors, such as user perception, context, timeliness, the user’s environment, and current task needs. Evaluating user relevance may also involve subjective analysis and study of user retrieval tasks to capture some of the properties of implicit factors involved in accounting for users’ bias for judging performance.
In Web information retrieval, no binary classification decision is made on whether a document is relevant or nonrelevant to a query (whereas the Boolean (or binary) retrieval model uses this scheme, as we discussed in Section 27.2.1). Instead, a rank- ing of the documents is produced for the user. Therefore, some evaluation measures focus on comparing different rankings produced by IR systems. We discuss some of these measures next.
27.6.1 Recall and Precision Recall and precision metrics are based on the binary relevance assumption (whether each document is relevant or nonrelevant to the query). Recall is defined as the number of relevant documents retrieved by a search divided by the total number of existing relevant documents. Precision is defined as the number of relevant docu- ments retrieved by a search divided by the total number of documents retrieved by that search. Figure 27.5 is a pictorial representation of the terms retrieved vs. relevant and shows how search results relate to four different sets of documents.
Relevant?
Yes No
Hits
TP
False Alarms
FP
Misses
FN
Correct Rejections
TN
Retrieved?
Yes
No
☺
☺
�
�
Figure 27.5 Retrieved vs. relevant search results.
1016 Chapter 27 Introduction to Information Retrieval and Web Search
Table 27.2 Precision and Recall for Ranked Retrieval
Doc. No. Rank Position i Relevant Precision(i) Recall(i)
10 1 Yes 1/1 = 100% 1/10 = 10% 2 2 Yes 2/2 = 100% 2/10 = 20% 3 3 Yes 3/3 = 100% 3/10 = 30% 5 4 No 3/4 = 75% 3/10 = 30%
17 5 No 3/5 = 60% 3/10 = 30% 34 6 No 3/6 = 50% 3/10 = 30%
215 7 Yes 4/7 = 57.1% 4/10 = 40% 33 8 Yes 5/8 = 62.5% 5/10 = 50% 45 9 No 5/9 = 55.5% 5/10 = 50% 16 10 Yes 6/10 = 60% 6/10 = 60%
The notation for Figure 27.5 is as follows:
■ TP: true positive
■ FP: false positive
■ FN: false negative
■ TN: true negative
The terms true positive, false positive, false negative, and true negative are generally used in any type of classification tasks to compare the given classification of an item with the desired correct classification. Using the term hits for the documents that truly or “correctly” match the user request, we can define:
Recall = |Hits|/|Relevant|
Precision = |Hits|/|Retrieved|
Recall and precision can also be defined in a ranked retrieval setting. The Recall at rank position i for document di
q (denoted by r(i)) (di q is the retrieved document at
position i for query q) is the fraction of relevant documents from d1 q to di
q in the result set for the query. Let the set of relevant documents from d1
q to di q in that set
be Si with cardinality | Si |. Let (|Dq| be the size of relevant documents for the query. In this case,|Si | ≤ |Dq|). Then:
Recall r(i) = |Si |/|Dq|
The Precision at rank position i or document di q (denoted by p(i)) is the fraction of
documents from d1 q to di
q in the result set that are relevant:
Precision p(i) = |Si |/i
Table 27.2 illustrates the p(i), r(i), and average precision (discussed in the next section) metrics. It can be seen that recall can be increased by presenting more results to the user, but this approach runs the risk of decreasing the precision. In the
27.6 Evaluation Measures of Search Relevance 1017
example, the number of relevant documents for some query = 10. The rank posi- tion and the relevance of an individual document are shown. The precision and recall value can be computed at each position within the ranked list as shown in the last two columns.
27.6.2 Average Precision Average precision is computed based on the precision at each relevant document in the ranking. This measure is useful for computing a single precision value to com- pare different retrieval algorithms on a query q.
Consider the sample precision values of relevant documents in Table 27.2. The aver- age precision (Pavg value) for the example in Table 27.2 is P(1) + P(2) + P(3) + P(7) + P(8) + P(10)/6 = 79.93 percent (only relevant documents are considered in this calculation). Many good algorithms tend to have high top-k average precision for small values of k, with correspondingly low values of recall.
27.6.3 Recall/Precision Curve A recall/precision curve can be drawn based on the recall and precision values at each rank position, where the x-axis is the recall and the y-axis is the precision. Instead of using the precision and recall at each rank position, the curve is com- monly plotted using recall levels r(i) at 0 percent, 10 percent, 20 percent...100 per- cent. The curve usually has a negative slope, reflecting the inverse relationship between precision and recall.
27.6.4 F-Score F-score (F) is the harmonic mean of the precision (p) and recall (r) values. High precision is achieved almost always at the expense of recall and vice versa. It is a matter of the application’s context whether to tune the system for high precision or high recall. F-score is a single measure that combines precision and recall to com- pare different result sets:
One of the properties of harmonic mean is that the harmonic mean of two numbers tends to be closer to the smaller of the two. Thus F is automatically biased toward the smaller of the precision and recall values. Therefore, for a high F-score, both precision and recall must be high.
F
p r
= +
2 1 1
F pr
p r =
+ 2
P p i D d D q
i
q
q avg = ∈∑ ( ) | |
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27.7 Web Search and Analysis22
The emergence of the Web has brought millions of users to search for information, which is stored in a very large number of active sites. To make this information acces- sible, search engines such as Google and Yahoo! have to crawl and index these sites and document collections in their index databases. Moreover, search engines have to regularly update their indexes given the dynamic nature of the Web as new Web sites are created and current ones are updated or deleted. Since there are many millions of pages available on the Web on different topics, search engines have to apply many sophisticated techniques such as link analysis to identify the importance of pages.
There are other types of search engines besides the ones that regularly crawl the Web and create automatic indexes: these are human-powered, vertical search engines or metasearch engines. These search engines are developed with the help of computer- assisted systems to aid the curators with the process of assigning indexes. They con- sist of manually created specialized Web directories that are hierarchically organized indexes to guide user navigation to different resources on the Web. Vertical search engines are customized topic-specific search engines that crawl and index a specific collection of documents on the Web and provide search results from that specific collection. Metasearch engines are built on top of search engines: they query differ- ent search engines simultaneously and aggregate and provide search results from these sources.
Another source of searchable Web documents is digital libraries. Digital libraries can be broadly defined as collections of electronic resources and services for the delivery of materials in a variety of formats. These collections may include a univer- sity’s library catalog, catalogs from a group of participating universities as in the State of Florida University System, or a compilation of multiple external resources on the World Wide Web such as Google Scholar or the IEEE/ACM index. These interfaces provide universal access to different types of content—such as books, articles, audio, and video—situated in different database systems and remote repos- itories. Similar to real libraries, these digital collections are maintained via a catalog and organized in categories for online reference. Digital libraries “include personal, distributed, and centralized collections such as online public access catalogs (OPACs) and bibliographic databases, distributed document databases, scholarly and professional discussion lists and electronic journals, other online databases, forums, and bulletin boards.” 23
27.7.1 Web Analysis and Its Relationship to Information Retrieval
In addition to browsing and searching the Web, another important activity closely related to information retrieval is to analyze or mine information on the Web for
22The contributions of Pranesh P. Ranganathan and Hari P. Kumar to this section is appreciated. 23Covi and Kling (1996), page 672.
27.7 Web Search and Analysis 1019
new information of interest. (We discuss mining of data from files and databases in Chapter 28.) Application of data analysis techniques for discovery and analysis of useful information from the Web is known as Web analysis. Over the past few years the World Wide Web has emerged as an important repository of information for many day-to-day applications for individual consumers, as well as a significant plat- form for e-commerce and for social networking. These properties make it an inter- esting target for data analysis applications. The Web mining and analysis field is an integration of a wide range of fields spanning information retrieval, text analysis, natural language processing, data mining, machine learning, and statistical analysis.
The goals of Web analysis are to improve and personalize search results relevance and to identify trends that may be of value to various businesses and organizations. We elaborate on these goals next.
■ Finding relevant information. People usually search for specific informa- tion on the Web by entering keywords in a search engine or browsing infor- mation portals and using services. Search services are constrained by search relevance problems since they have to map and approximate the information need of millions of users as an a priori task. Low precision (see Section 27.6) ensues due to results that are nonrelevant to the user. In the case of the Web, high recall (see section 27.6) is impossible to determine due to the inability to index all the pages on the Web. Also, measuring recall does not make sense since the user is concerned with only the top few documents. The most rele- vant feedback for the user is typically from only the top few results.
■ Personalization of the information. Different people have different content and presentation preferences. By collecting personal information and then generating user-specific dynamic Web pages, the pages are personalized for the user. The customization tools used in various Web-based applications and services, such as click-through monitoring, eyeball tracking, explicit or implicit user profile learning, and dynamic service composition using Web APIs, are used for service adaptation and personalization. A personalization engine typically has algorithms that make use of the user’s personalization information—collected by various tools—to generate user-specific search results.
■ Finding information of commercial value. This problem deals with finding interesting patterns in users’ interests, behaviors, and their use of products and services, which may be of commercial value. For example, businesses such as the automobile industry, clothing, shoes, and cosmetics may improve their services by identifying patterns such as usage trends and user prefer- ences using various Web analysis techniques.
Based on the above goals, we can classify Web analysis into three categories: Web content analysis, which deals with extracting useful information/knowledge from Web page contents; Web structure analysis, which discovers knowledge from hyperlinks representing the structure of the Web; and Web usage analysis, which mines user access patterns from usage logs that record the activity of every user.
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27.7.2 Searching the Web The World Wide Web is a huge corpus of information, but locating resources that are both high quality and relevant to the needs of the user is very difficult. The set of Web pages taken as a whole has almost no unifying structure, with variability in authoring style and content, thereby making it more difficult to precisely locate needed information. Index-based search engines have been one of the prime tools by which users search for information on the Web. Web search engines crawl the Web and create an index to the Web for searching purposes. When a user specifies his need for information by supplying keywords, these Web search engines query their repository of indexes and produce links or URLs with abbreviated content as search results. There may be thousands of pages relevant to a particular query. A problem arises when only a few most relevant results are to be returned to the user. The discussion we had about querying and relevance-based ranking in IR systems in Sections 27.2 and 27.3 is applicable to Web search engines. These ranking algo- rithms explore the link structure of the Web.
Web pages, unlike standard text collections, contain connections to other Web pages or documents (via the use of hyperlinks), allowing users to browse from page to page. A hyperlink has two components: a destination page and an anchor text describing the link. For example, a person can link to the Yahoo! Website on his Web page with anchor text such as “My favorite Website.” Anchor texts can be thought of as being implicit endorsements. They provide very important latent human annota- tion. A person linking to other Web pages from his Web page is assumed to have some relation to those Web pages. Web search engines aim to distill results per their relevance and authority. There are many redundant hyperlinks, like the links to the homepage on every Web page of the Web site. Such hyperlinks must be eliminated from the search results by the search engines.
A hub is a Web page or a Website that links to a collection of prominent sites (authorities) on a common topic. A good authority is a page that is pointed to by many good hubs, while a good hub is a page that points to many good authorities. These ideas are used by the HITS ranking algorithm, which is described in Section 27.7.3. It is often found that authoritative pages are not very self-descriptive, and authorities on broad topics seldom link directly to one another. These properties of hyperlinks are being actively used to improve Web search engine result ranking and organize the results as hubs and authorities. We briefly discuss a couple of ranking algorithms below.
27.7.3 Analyzing the Link Structure of Web Pages The goal of Web structure analysis is to generate structural summary about the Website and Web pages. It focuses on the inner structure of documents and deals with the link structure using hyperlinks at the interdocument level. The structure and content of Web pages are often combined for information retrieval by Web search engines. Given a collection of interconnected Web documents, interesting and informative facts describing their connectivity in the Web subset can be discov- ered. Web structure analysis is also used to reveal the structure of Web pages, which
27.7 Web Search and Analysis 1021
helps with navigation and makes it possible to compare/integrate Web page schemes. This aspect of Web structure analysis facilitates Web document classifica- tion and clustering on the basis of structure.
The PageRank Ranking Algorithm. As discussed earlier, ranking algorithms are used to order search results based on relevance and authority. Google uses the well- known PageRank algorithm,24 which is based on the “importance” of each page. Every Web page has a number of forward links (out-edges) and backlinks (in- edges). It is very difficult to determine all the backlinks of a Web page, while it is rel- atively straightforward to determine its forward links. According to the PageRank algorithm, highly linked pages are more important (have greater authority) than pages with fewer links. However, not all backlinks are important. A backlink to a page from a credible source is more important than a link from some arbitrary page. Thus a page has a high rank if the sum of the ranks of its backlinks is high. PageRank was an attempt to see how good an approximation to the “importance” of a page can be obtained from the link structure.
The computation of page ranking follows an iterative approach. PageRank of a Web page is calculated as a sum of the PageRanks of all its backlinks. PageRank treats the Web like a Markov model. An imaginary Web surfer visits an infinite string of pages by clicking randomly. The PageRank of a page is an estimate of how often the surfer winds up at a particular page. PageRank is a measure of query-independent impor- tance of a page/node. For example, let P(X) be the PageRank of any page X and C(X) be the number of outgoing links from page X, and let d be the damping factor in the range 0 < d < 1. Usually d is set to 0.85. Then PageRank for a page A can be calcu- lated as:
P(A) = (1 – d) + d (P(T1)/C(T1) + ... + P(Tn)/C(Tn))
Here T1, T2, ..., Tn are the pages that point to Page A (that is, are citations to page A). PageRank forms a probability distribution over Web pages, so the sum of all Web pages’ PageRanks is one.
The HITS Ranking Algorithm. The HITS25 algorithm proposed by Jon Kleinberg is another type of ranking algorithm exploiting the link structure of the Web. The algorithm presumes that a good hub is a document that points to many hubs, and a good authority is a document that is pointed at by many other author- ities. The algorithm contains two main steps: a sampling component and a weight- propagation component. The sampling component constructs a focused collection S of pages with the following properties:
1. S is relatively small.
2. S is rich in relevant pages.
3. S contains most (or a majority) of the strongest authorities.
24The PageRank algorithm was proposed by Lawrence Page (1998) and Sergey Brin, founders of Google. For more information, see http://en.wikipedia.org/wiki/PageRank. 25See Kleinberg (1999).
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The weight component recursively calculates the hub and authority values for each document as follows:
1. Initialize hub and authority values for all pages in S by setting them to 1.
2. While (hub and authority values do not converge):
a. For each page in S, calculate authority value = Sum of hub values of all pages pointing to the current page.
b. For each page in S, calculate hub value = Sum of authority values of all pages pointed at by the current page.
c. Normalize hub and authority values such that sum of all hub values in S equals 1 and the sum of all authority values in S equals 1.
27.7.4 Web Content Analysis As mentioned earlier, Web content analysis refers to the process of discovering use- ful information from Web content/data/documents. The Web content data consists of unstructured data such as free text from electronically stored documents, semi- structured data typically found as HTML documents with embedded image data, and more structured data such as tabular data, and pages in HTML, XML, or other markup languages generated as output from databases. More generally, the term Web content refers to any real data in the Web page that is intended for the user accessing that page. This usually consists of but is not limited to text and graphics.
We will first discuss some preliminary Web content analysis tasks and then look at the traditional analysis tasks of Web page classification and clustering later.
Structured Data Extraction. Structured data on the Web is often very important as it represents essential information, such as a structured table showing the airline flight schedule between two cities. There are several approaches to structured data extraction. One includes writing a wrapper, or a program that looks for different structural characteristics of the information on the page and extracts the right con- tent. Another approach is to manually write an extraction program for each Website based on observed format patterns of the site, which is very labor intensive and time consuming. It does not scale to a large number of sites. A third approach is wrapper induction or wrapper learning, where the user first manually labels a set of train- ing set pages, and the learning system generates rules—based on the learning pages—that are applied to extract target items from other Web pages. A fourth approach is the automatic approach, which aims to find patterns/grammars from the Web pages and then uses wrapper generation to produce a wrapper to extract data automatically.
Web Information Integration. The Web is immense and has millions of docu- ments, authored by many different persons and organizations. Because of this, Web pages that contain similar information may have different syntax and different words that describe the same concepts. This creates the need for integrating
27.7 Web Search and Analysis 1023
information from diverse Web pages. Two popular approaches for Web information integration are:
1. Web query interface integration, to enable querying multiple Web data- bases that are not visible in external interfaces and are hidden in the “deep Web.” The deep Web26 consists of those pages that do not exist until they are created dynamically as the result of a specific database search, which pro- duces some of the information in the page (see Chapter 14). Since tradi- tional search engine crawlers cannot probe and collect information from such pages, the deep Web has heretofore been hidden from crawlers.
2. Schema matching, such as integrating directories and catalogs to come up with a global schema for applications. An example of such an application would be to combine a personal health record of an individual by matching and collecting data from various sources dynamically by cross-linking health records from multiple systems.
These approaches remain an area of active research and a detailed discussion of them is beyond the scope of this book. Consult the Selected Bibliography at the end of this chapter for further details.
Ontology-Based Information Integration. This task involves using ontologies to effectively combine information from multiple heterogeneous sources. Ontologies—formal models of representation with explicitly defined concepts and named relationships linking them—are used to address the issues of semantic het- erogeneity in data sources. Different classes of approaches are used for information integration using ontologies.
■ Single ontology approaches use one global ontology that provides a shared vocabulary for the specification of the semantics. They work if all informa- tion sources to be integrated provide nearly the same view on a domain of knowledge. For example, UMLS (described in Section 27.4.3) can serve as a common ontology for biomedical applications.
■ In a multiple ontology approach, each information source is described by its own ontology. In principle, the “source ontology” can be a combination of several other ontologies but it cannot be assumed that the different “source ontologies” share the same vocabulary. Dealing with multiple, partially over- lapping, and potentially conflicting ontologies is a very difficult problem faced by many applications, including those in bioinformatics and other complex area of knowledge.
■ Hybrid ontology approaches are similar to multiple ontology approaches: the semantics of each source is described by its own ontology. But in order to make the source ontologies comparable to each other, they are built upon one global shared vocabulary. The shared vocabulary contains basic terms (the primitives) of a domain of knowledge. Because each term of source
26The deep Web as defined by Bergman (2001).
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ontology is based on the primitives, the terms become more easily compara- ble than in multiple ontology approaches. The advantage of a hybrid approach is that new sources can be easily added without the need to modify the mappings or the shared vocabulary. In multiple and hybrid approaches, several research issues, such as ontology mapping, alignment, and merging, need to be addressed.
Building Concept Hierarchies. One common way of organizing search results is via a linear ranked list of documents. But for some users and applications, a better way to display results would be to create groupings of related documents in the search result. One way of organizing documents in a search result, and for organiz- ing information in general, is by creating a concept hierarchy. The documents in a search result are organized into groups in a hierarchical fashion. Other related tech- niques to organize docments are through classification and clustering (see Chapter 28). Clustering creates groups of documents, where the documents in each group share many common concepts.
Segmenting Web Pages and Detecting Noise. There are many superfluous parts in a Web document, such as advertisements and navigation panels. The infor- mation and text in these superfluous parts should be eliminated as noise before classifying the documents based on their content. Hence, before applying classifica- tion or clustering algorithms to a set of documents, the areas or blocks of the docu- ments that contain noise should be removed.
27.7.5 Approaches to Web Content Analysis The two main approaches to Web content analysis are (1) agent based (IR view) and (2) database based (DB view).
The agent-based approach involves the development of sophisticated artificial intelligence systems that can act autonomously or semi-autonomously on behalf of a particular user, to discover and process Web-based information. Generally, the agent-based Web analysis systems can be placed into the following three categories:
■ Intelligent Web agents are software agents that search for relevant informa- tion using characteristics of a particular application domain (and possibly a user profile) to organize and interpret the discovered information. For example, an intelligent agent that retrieves product information from a vari- ety of vendor sites using only general information about the product domain.
■ Information Filtering/Categorization is another technique that utilizes Web agents for categorizing Web documents. These Web agents use methods from information retrieval, and semantic information based on the links among various documents to organize documents into a concept hierarchy.
■ Personalized Web agents are another type of Web agents that utilize the per- sonal preferences of users to organize search results, or to discover informa- tion and documents that could be of value for a particular user. User
27.7 Web Search and Analysis 1025
preferences could be learned from previous user choices, or from other indi- viduals who are considered to have similar preferences to the user.
The database-based approach aims to infer the structure of the Website or to trans- form a Web site to organize it as a database so that better information management and querying on the Web become possible. This approach of Web content analysis primarily tries to model the data on the Web and integrate it so that more sophisti- cated queries than keyword-based search can be performed. These could be achieved by finding the schema of Web documents, building a Web document ware- house, a Web knowledge base, or a virtual database. The database-based approach may use a model such as the Object Exchange Model (OEM)27 that represents semi- structured data by a labeled graph. The data in the OEM is viewed as a graph, with objects as the vertices and labels on the edges. Each object is identified by an object identifier and a value that is either atomic—such as integer, string, GIF image, or HTML document—or complex in the form of a set of object references.
The main focus of the database-based approach has been with the use of multilevel databases and Web query systems. A multilevel database at its lowest level is a data- base containing primitive semistructured information stored in various Web repos- itories, such as hypertext documents. At the higher levels, metadata or generalizations are extracted from lower levels and organized in structured collec- tions such as relational or object-oriented databases. In a Web query system, infor- mation about the content and structure of Web documents is extracted and organized using database-like techniques. Query languages similar to SQL can then be used to search and query Web documents. They combine structural queries, based on the organization of hypertext documents, and content-based queries.
27.7.6 Web Usage Analysis Web usage analysis is the application of data analysis techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web- based applications. This activity does not directly contribute to information retrieval; but it is important to improve or enhance the users’ search experience. Web usage data describes the pattern of usage of Web pages, such as IP addresses, page references, and the date and time of accesses for a user, user group, or an appli- cation. Web usage analysis typically consists of three main phases: preprocessing, pattern discovery, and pattern analysis.
1. Preprocessing. Preprocessing converts the information collected about usage statistics and patterns into a form that can be utilized by the pattern discovery methods. We use the term “page view” to refer to pages viewed or visited by a user. There are several different types of preprocessing tech- niques available:
■ Usage preprocessing analyzes the available collected data about usage pat- terns of users, applications, and groups of users. Because this data is often incomplete, the process is difficult. Data cleaning techniques are necessary to
27See Kosala and Blockeel (2000).
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eliminate the impact of irrelevant items in the analysis result. Frequently, usage data is identified by an IP address, and consists of clicking streams that are collected at the server. Better data is available if a usage tracking process is installed at the client site.
■ Content preprocessing is the process of converting text, image, scripts and other content into a form that can be used by the usage analysis. Often, this consists of performing content analysis such as classification or clustering. The clustering or classification techniques can group usage information for similar types of Web pages, so that usage patterns can be discovered for spe- cific classes of Web pages that describe particular topics. Page views can also be classified according to their intended use, such as for sales or for discovery or for other uses.
■ Structure preprocessing: The structure preprocessing can be done by pars- ing and reformatting the information about hyperlinks and structure between viewed pages. One difficulty is that the site structure may be dynamic and may have to be constructed for each server session.
2. Pattern Discovery
The techniques that are used in pattern discovery are based on methods from the fields of statistics, machine learning, pattern recognition, data analysis, data mining, and other similar areas. These techniques are adapted so they take into consideration the specific knowledge and characteristics for Web Analysis. For example, in association rule discovery (See Section 28.2), the notion of a transaction for market-basket analysis considers the items to be unordered. But the order of accessing of Web pages is important, and so it should be considered in Web usage analysis. Hence, pattern discovery involves mining sequences of page views. In general, using Web usage data, the following types of data mining activities may be performed for pattern discovery.
■ Statistical analysis. Statistical techniques are the most common method to extract knowledge about visitors to a Website. By analyzing the session log, it is possible to apply statistical measures such as mean, median, and frequency count to parameters such as pages viewed, viewing time per page, length of navigation paths between pages, and other parameters that are relevant to Web usage analysis.
■ Association rules. In the context of Web usage analysis, association rules refer to sets of pages that are accessed together with a support value exceed- ing some specified threshold. (See Section 28.2 on association rules.) These pages may not be directly connected to one another via hyperlinks. For example, association rule discovery may reveal a correlation between users who visited a page containing electronic products to those who visit a page about sporting equipment.
■ Clustering. In the Web usage domain, there are two kinds of interesting clusters to be discovered: usage clusters and page clusters. Clustering of users tends to establish groups of users exhibiting similar browsing patterns.
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Such knowledge is especially useful for inferring user demographics in order to perform market segmentation in E-commerce applications or provide personalized Web content to the users. Clustering of pages is based on the content of the pages, and pages with similar contents are grouped together. This type of clustering can be utilized in Internet search engines, and in tools that provide assistance to Web browsing.
■ Classification. In the Web domain, one goal is to develop a profile of users belonging to a particular class or category. This requires extraction and selection of features that best describe the properties of a given class or cate- gory of users. As an example, an interesting pattern that may be discovered would be: 60% of users who placed an online order in /Product/Books are in the 18-25 age group and live in rented apartments.
■ Sequential patterns. These kinds of patterns identify sequences of Web accesses, which may be used to predict the next set of Web pages to be accessed by a certain class of users. These patterns can be used by marketers to produce targeted advertisements on Web pages. Another type of sequen- tial pattern pertains to which items are typically purchased following the purchase of a particular item. For example, after purchasing a computer, a printer is often purchased
■ Dependency modeling. Dependency modeling aims to determine and model significant dependencies among the various variables in the Web domain. As an example, one may be interested to build a model representing the different stages a visitor undergoes while shopping in an online store based on the actions chosen (e.g., from a casual visitor to a serious potential buyer).
3. Pattern Analysis
The final step is to filter out those rules or patterns that are considered to be not of interest from the discovered patterns. The particular analysis method- ology based on the application. One common technique for pattern analysis is to use a query language such as SQL to detect various patterns and rela- tionships. Another technique involves loading of usage data into a data ware- house with ETL tools and performing OLAP operations to view it along multiple dimensions (see Section 29.3). It is common to use visualization techniques, such as graphing patterns or assigning colors to different values, to highlight patterns or trends in the data.
27.7.7 Practical Applications of Web Analysis
Web Analytics. The goal of web analytics is to understand and optimize the per- formance of Web usage. This requires collecting, analyzing, and performance mon- itoring of Internet usage data. On-site Web analytics measures the performance of a Website in a commercial context. This data is typically compared against key per- formance indicators to measure effectiveness or performance of the Website as a whole, and can be used to improve a Website or improve the marketing strategies.
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Web Spamming. It has become increasingly important for companies and indi- viduals to have their Websites/Web pages appear in the top search results. To achieve this, it is essential to understand search engine ranking algorithms and to present the information in one’s page in such a way that the page is ranked high when the respective keywords are queried. There is a thin line separating legitimate page opti- mization for business purposes and spamming. Web Spamming is thus defined as a deliberate activity to promote one’s page by manipulating the results returned by the search engines. Web analysis may be used to detect such pages and discard them from search results.
Web Security. Web analysis can be used to find interesting usage patterns of Websites. If any flaw in a Website has been exploited, it can be inferred using Web analysis thereby allowing the design of more robust Websites. For example, the backdoor or information leak of Web servers can be detected by using Web analysis techniques on some abnormal Web application log data. Security analysis tech- niques such as intrusion detection and denial of service attacks are based on Web access pattern analysis.
Web Crawlers. Web crawlers are programs that visit Web pages and create copies of all the visited pages so they can be processed by a search engine for indexing the downloaded pages to provide fast searches. Another use of crawlers is to automati- cally check and maintain the Websites. For example, the HTML code and the links in a Website can be checked and validated by the crawler. Another unfortunate use of crawlers is to collect e-mail addresses from Web pages, so they can be used for spam e-mails later.
27.8 Trends in Information Retrieval In this section we review a few concepts that are being considered in more recent research work in information retrieval.
27.8.1 Faceted Search Faceted Search is a technique that allows for integrated search and navigation expe- rience by allowing users to explore by filtering available information. This search technique is used often in ecommerce Websites and applications enabling users to navigate a multi-dimensional information space. Facets are generally used for han- dling three or more dimensions of classification. This allows the faceted classifica- tion scheme to classify an object in various ways based on different taxonomical criteria. For example, a Web page may be classified in various ways: by content (air- lines, music, news, ...); by use (sales, information, registration, ...); by location; by language used (HTML, XML, ...) and in other ways or facets. Hence, the object can be classified in multiple ways based on multiple taxonomies.
A facet defines properties or characteristics of a class of objects. The properties should be mutually exclusive and exhaustive. For example, a collection of art objects might be classified using an artist facet (name of artist), an era facet (when the art
27.8 Trends in Information Retrieval 1029
was created), a type facet (painting, sculpture, mural, ...), a country of origin facet, a media facet (oil, watercolor, stone, metal, mixed media, ...), a collection facet (where the art resides), and so on.
Faceted search uses faceted classification that enables a user to navigate information along multiple paths corresponding to different orderings of the facets. This con- trasts with traditional taxonomies in which the hierarchy of categories is fixed and unchanging. University of California, Berkeley’s Flamenco project28 is one of the earlier examples of a faceted search system.
27.8.2 Social Search The traditional view of Web navigation and browsing assumes that a single user is searching for information. This view contrasts with previous research by library sci- entists who studied users’ information seeking habits. This research demonstrated that additional individuals may be valuable information resources during informa- tion search by a single user. More recently, research indicates that there is often direct user cooperation during Web-based information search. Some studies report that significant segments of the user population are engaged in explicit collabora- tion on joint search tasks on the Web. Active collaboration by multiple parties also occur in certain cases (for example, enterprise settings); at other times, and perhaps for a majority of searches, users often interact with others remotely, asynchronously, and even involuntarily and implicitly.
Socially enabled online information search (social search) is a new phenomenon facilitated by recent Web technologies. Collaborative social search involves different ways for active involvement in search-related activities such as co-located search, remote collaboration on search tasks, use of social network for search, use of exper- tise networks, involving social data mining or collective intelligence to improve the search process and even social interactions to facilitate information seeking and sense making. This social search activity may be done synchronously, asynchronously, co- located or in remote shared workspaces. Social psychologists have experimentally val- idated that the act of social discussions has facilitated cognitive performance. People in social groups can provide solutions (answers to questions), pointers to databases or to other people (meta-knowledge), validation and legitimization of ideas, and can serve as memory aids and help with problem reformulation. Guided participation is a process in which people co-construct knowledge in concert with peers in their com- munity. Information seeking is mostly a solitary activity on the Web today. Some recent work on collaborative search reports several interesting findings and the potential of this technology for better information access.
27.8.3 Conversational Search Conversational Search (CS) is an interactive and collaborative information finding interaction. The participants engage in a conversation and perform a social search activity that is aided by intelligent agents. The collaborative search activity helps the
28Yee (2003) describes faceted metadata for image search.
1030 Chapter 27 Introduction to Information Retrieval and Web Search
agent learn about conversations with interactions and feedback from participants. It uses the semantic retrieval model with natural language understanding to provide the users with faster and relevant search results. It moves search from being a soli- tary activity to being a more participatory activity for the user. The search agent performs multiple tasks of finding relevant information and connecting the users together; participants provide feedback to the agent during the conversations that allows the agent to perform better.
27.9 Summary In this chapter we covered an important area called information retrieval (IR) that is closely related to databases. With the advent of the Web, unstructured data with text, images, audio, and video is proliferating at phenomenal rates. While database management systems have a very good handle on structured data, the unstructured data containing a variety of data types is being stored mainly on ad hoc information repositories on the Web that are available for consumption primarily via IR systems. Google, Yahoo, and similar search engines are IR systems that make the advances in this field readily available for the average end-user, giving them a richer search expe- rience with continuous improvement.
We started by defining the basic terminology of IR, presented the query and brows- ing modes of interaction in IR systems, and provided a comparison of the IR and database technologies. We presented schematics of the IR process at a detailed and an overview level, and then discussed digital libraries, which are repositories of tar- geted content on the Web for academic institutions as well as professional commu- nities, and gave a brief history of IR.
We presented the various retrieval models including Boolean, vector space, proba- bilistic, and semantic models. They allow for a measurement of whether a docu- ment is relevant to a user query and provide similarity measurement heuristics. We then discussed various evaluation metrics such as recall and precision and F-score to measure the goodness of the results of IR queries. Then we presented different types of queries—besides keyword-based queries, which dominate, there are other types including Boolean, phrase, proximity, natural language, and others for which explicit support needs to be provided by the retrieval model. Text preprocessing is important in IR systems, and various activities like stopword removal, stemming, and the use of thesauruses were discussed. We then discussed the construction and use of inverted indexes, which are at the core of IR systems and contribute to factors involving search efficiency. Relevance feedback was briefly addressed—it is impor- tant to modify and improve the retrieval of pertinent information for the user through his interaction and engagement in the search process.
We did a somewhat detailed introduction to analysis of the Web as it relates to information retrieval. We divided this treatment into the analysis of content, struc- ture, and usage of the Web. Web search was discussed, including an analysis of the Web link structure, followed by an introduction to algorithms for ranking the results from a Web search such as PageRank and HITS. Finally, we briefly discussed
Review Questions 1031
current trends, including faceted search, social search, and conversational search. This is an introductory treatment of a vast field and the reader is referred to special- ized textbooks on information retrieval and search engines.
Review Questions 27.1. What is structured data and unstructured data? Give an example of each
from your experience with data that you may have used.
27.2. Give a general definition of information retrieval (IR). What does informa- tion retrieval involve when we consider information on the Web?
27.3. Discuss the types of data and the types of users in today’s information retrieval systems.
27.4. What is meant by navigational, informational, and transformational search?
27.5. What are the two main modes of interaction with an IR system? Describe with examples.
27.6. Explain the main differences between database and IR systems mentioned in Table 27.1.
27.7. Describe the main components of the IR system as shown in Figure 27.1.
27.8. What are digital libraries? What types of data are typically found in them?
27.9. Name some digital libraries that you have accessed. What do they contain and how far back does the data go?
27.10. Give a brief history of IR and mention the landmark developments.
27.11. What is the Boolean model of IR? What are its limitations?
27.12. What is the vector space model of IR? How does a vector get constructed to represent a document?
27.13. Define the TF-IDF scheme of determining the weight of a keyword in a document. What is the necessity of including IDF in the weight of a term?
27.14. What are probabilistic and semantic models of IR?
27.15. Define recall and precision in IR systems.
27.16. Give the definition of precision and recall in a ranked list of results at position i.
27.17. How is F-score defined as a metric of information retrieval? In what way does it account for both precision and recall?
27.18. What are the different types of queries in an IR system? Describe each with an example.
27.19. What are the approaches to processing phrase and proximity queries?
1032 Chapter 27 Introduction to Information Retrieval and Web Search
27.20. Describe the detailed IR process shown in Figure 27.2.
27.21. What is stopword removal and stemming? Why are these processes necessary for better information retrieval?
27.22. What is a thesaurus? How is it beneficial to IR?
27.23. What is information extraction? What are the different types of information extraction from structured text?
27.24. What are vocabularies in IR systems? What role do they play in the indexing of documents?
27.25. Take five documents with about three sentences each with some related con- tent. Construct an inverted index of all important stems (keywords) from these documents.
27.26. Describe the process of constructing the result of a search request using an inverted index.
27.27. Define relevance feedback.
27.28. Describe the three types of Web analyses discussed in this chapter.
27.29. List the important tasks mentioned that are involved in analyzing Web con- tent. Describe each in a couple of sentences.
27.30. What are the three categories of agent-based Web content analyses men- tioned in this chapter?
27.31. What is the database-based approach to analyzing Web content? What are Web query systems?
27.32. What algorithms are popular in ranking or determining the importance of Web pages? Which algorithm was proposed by the founders of Google?
27.33. What is the basic idea behind the PageRank algorithm?
27.34. What are hubs and authority pages? How does the HITS algorithm use these concepts?
27.35. What can you learn from Web usage analysis? What data does it generate?
27.36. What mining operations are commonly performed on Web usage data? Give an example of each.
27.37. What are the applications of Web usage mining?
27.38. What is search relevance? How is it determined?
27.39. Define faceted search. Make up a set of facets for a database containing all types of buildings. For example, two facets could be “building value or price” and “building type (residential, office, warehouse, factory, and so on)”.
27.40. What is social search? What does collaborative social search involve?
27.41. Define and explain conversational search.
Selected Bibliography 1033
Selected Bibliography Information retrieval and search technologies are active areas of research and devel- opment in industry and academia. There are many IR textbooks that provide detailed discussion on the materials that we have briefly introduced in this chapter. A recent book entitled Search Engines: Information Retrieval in Practice by Croft, Metzler, and Strohman (2009) gives a practical overview of search engine concepts and principles. Introduction to Information Retrieval by Manning, Raghavan, and Schutze (2008) is an authoritative book on information retrieval. Another introduc- tory textbook in IR is Modern Information Retrieval by Ricardo Baeza-Yates and Berthier Ribeiro-Neto (1999), which provides detailed coverage of various aspects of IR technology. Gerald Salton’s (1968) and van Rijsbergen’s (1979) classic books on information retrieval provide excellent descriptions of the foundational research done in the IR field until the late 1960s. Salton also introduced the vector space model as a model of IR. Manning and Schutze (1999) provide a good summary of natural language technologies and text preprocessing. “Interactive Information Retrieval in Digital Environments” by Xie (2008) provides a good human-centered approach to information retrieval. The book Managing Gigabytes by Witten, Moffat, and Bell (1999) provides detailed discussions for indexing techniques. The TREC book by Voorhees and Harman (2005) provides a description of test collection and evaluation procedures in the context of TREC competitions.
Broder (2002) classifies Web queries into three distinct classes—navigational, infor- mational, and transactional—and presents a detailed taxonomy of Web search. Covi and Kling (1996) give a broad definition for digital libraries in their paper and dis- cuss organizational dimensions of effective digital library use. Luhn (1957) did some seminal work in IR at IBM in the 1950s on autoindexing and business intelligence that received a lot of attention at that time. The SMART system (Salton et al. (1993)), developed at Cornell, was one of the earliest advanced IR systems that used fully automatic term indexing, hierarchical clustering, and document ranking by degree of similarity to the query. The SMART system represented documents and queries as weighted term vectors according to the vector space model. Porter (1980) is credited with the weak and strong stemming algorithms that have become standards. Robertson (1997) developed a sophisticated weighting scheme in the City University of London Okapi system that became very popular in TREC competitions. Lenat (1995) started the Cyc project in the 1980s for incorporating formal logic and knowl- edge bases in information processing systems. Efforts toward creating the WordNet thesaurus continued in the 1990s, and are still ongoing. WordNet concepts and prin- ciples are described in the book by Fellbaum (1998). Rocchio (1971) describes the relevance feedback algorithm, which is described in Salton’s (1971) book on The SMART Retrieval System–Experiments in Automatic Document Processing.
Abiteboul, Buneman, and Suciu (1999) provide an extensive discussion of data on the Web in their book that emphasizes semistructured data. Atzeni and Mendelzon (2000) wrote an editorial in the VLDB journal on databases and the Web. Atzeni et al. (2002) propose models and transformations for Web-based data. Abiteboul et al. (1997) propose the Lord query language for managing semistructured data.
1034 Chapter 27 Introduction to Information Retrieval and Web Search
Chakrabarti (2002) is an excellent book on knowledge discovery from the Web. The book by Liu (2006) consists of several parts, each providing a comprehensive overview of the concepts involved with Web data analysis and its applications. Excellent survey articles on Web analysis include Kosala and Blockeel (2000) and Liu et al. (2004). Etzioni (1996) provides a good starting point for understanding Web mining and describes the tasks and issues related with the World Wide Web. An excellent overview of the research issues, techniques, and development efforts asso- ciated with Web content and usage analysis is presented by Cooley et al. (1997). Cooley (2003) focuses on mining Web usage patterns through the use of Web struc- ture. Spiliopoulou (2000) describes Web usage analysis in detail. Web mining based on page structure is described in Madria et al. (1999) and Chakraborti et al. (1999). Algorithms to compute the rank of a Web page are given by Page et al. (1999), who describe the famous PageRank algorithm, and Kleinberg (1998), who presents the HITS algorithm.
1035
Data Mining Concepts
Over the last three decades, many organizationshave generated a large amount of machine- readable data in the form of files and databases. To process this data, we have the database technology available that supports query languages like SQL. The problem with SQL is that it is a structured language that assumes the user is aware of the database schema. SQL supports operations of relational algebra that allow a user to select rows and columns of data from tables or join-related information from tables based on common fields. In the next chapter, we will see that data warehousing tech- nology affords several types of functionality: that of consolidation, aggregation, and summarization of data. Data warehouses let us view the same information along multiple dimensions. In this chapter, we will focus our attention on another very popular area of interest known as data mining. As the term connotes, data mining refers to the mining or discovery of new information in terms of patterns or rules from vast amounts of data. To be practically useful, data mining must be carried out efficiently on large files and databases. Although some data mining features are being provided in RDBMSs, data mining is not well-integrated with database man- agement systems.
We will briefly review the state of the art of this rather extensive field of data min- ing, which uses techniques from such areas as machine learning, statistics, neural networks, and genetic algorithms. We will highlight the nature of the information that is discovered, the types of problems faced when trying to mine databases, and the types of applications of data mining. We will also survey the state of the art of a large number of commercial tools available (see Section 28.7) and describe a num- ber of research advances that are needed to make this area viable.
28chapter 28
1036 Chapter 28 Data Mining Concepts
28.1 Overview of Data Mining Technology In reports such as the very popular Gartner Report,1 data mining has been hailed as one of the top technologies for the near future. In this section we relate data mining to the broader area called knowledge discovery and contrast the two by means of an illustrative example.
28.1.1 Data Mining versus Data Warehousing The goal of a data warehouse (see Chapter 29) is to support decision making with data. Data mining can be used in conjunction with a data warehouse to help with certain types of decisions. Data mining can be applied to operational databases with individual transactions. To make data mining more efficient, the data ware- house should have an aggregated or summarized collection of data. Data mining helps in extracting meaningful new patterns that cannot necessarily be found by merely querying or processing data or metadata in the data warehouse. Therefore, data mining applications should be strongly considered early, during the design of a data warehouse. Also, data mining tools should be designed to facilitate their use in conjunction with data warehouses. In fact, for very large databases running into ter- abytes and even petabytes of data, successful use of data mining applications will depend first on the construction of a data warehouse.
28.1.2 Data Mining as a Part of the Knowledge Discovery Process
Knowledge Discovery in Databases, frequently abbreviated as KDD, typically encompasses more than data mining. The knowledge discovery process comprises six phases:2 data selection, data cleansing, enrichment, data transformation or encoding, data mining, and the reporting and display of the discovered information.
As an example, consider a transaction database maintained by a specialty consumer goods retailer. Suppose the client data includes a customer name, ZIP Code, phone number, date of purchase, item code, price, quantity, and total amount. A variety of new knowledge can be discovered by KDD processing on this client database. During data selection, data about specific items or categories of items, or from stores in a specific region or area of the country, may be selected. The data cleansing process then may correct invalid ZIP Codes or eliminate records with incorrect phone prefixes. Enrichment typically enhances the data with additional sources of information. For example, given the client names and phone numbers, the store may purchase other data about age, income, and credit rating and append them to each record. Data transformation and encoding may be done to reduce the amount
1The Gartner Report is one example of the many technology survey publications that corporate man- agers rely on to make their technology selection discussions. 2This discussion is largely based on Adriaans and Zantinge (1996).
28.1 Overview of Data Mining Technology 1037
of data. For instance, item codes may be grouped in terms of product categories into audio, video, supplies, electronic gadgets, camera, accessories, and so on. ZIP Codes may be aggregated into geographic regions, incomes may be divided into ranges, and so on. In Figure 29.1, we will show a step called cleaning as a precursor to the data warehouse creation. If data mining is based on an existing warehouse for this retail store chain, we would expect that the cleaning has already been applied. It is only after such preprocessing that data mining techniques are used to mine different rules and patterns.
The result of mining may be to discover the following type of new information:
■ Association rules—for example, whenever a customer buys video equip- ment, he or she also buys another electronic gadget.
■ Sequential patterns—for example, suppose a customer buys a camera, and within three months he or she buys photographic supplies, then within six months he is likely to buy an accessory item. This defines a sequential pat- tern of transactions. A customer who buys more than twice in lean periods may be likely to buy at least once during the Christmas period.
■ Classification trees—for example, customers may be classified by frequency of visits, types of financing used, amount of purchase, or affinity for types of items; some revealing statistics may be generated for such classes.
We can see that many possibilities exist for discovering new knowledge about buy- ing patterns, relating factors such as age, income group, place of residence, to what and how much the customers purchase. This information can then be utilized to plan additional store locations based on demographics, run store promotions, com- bine items in advertisements, or plan seasonal marketing strategies. As this retail store example shows, data mining must be preceded by significant data preparation before it can yield useful information that can directly influence business decisions.
The results of data mining may be reported in a variety of formats, such as listings, graphic outputs, summary tables, or visualizations.
28.1.3 Goals of Data Mining and Knowledge Discovery Data mining is typically carried out with some end goals or applications. Broadly speaking, these goals fall into the following classes: prediction, identification, classi- fication, and optimization.
■ Prediction. Data mining can show how certain attributes within the data will behave in the future. Examples of predictive data mining include the analysis of buying transactions to predict what consumers will buy under certain discounts, how much sales volume a store will generate in a given period, and whether deleting a product line will yield more profits. In such applications, business logic is used coupled with data mining. In a scientific context, certain seismic wave patterns may predict an earthquake with high probability.
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■ Identification. Data patterns can be used to identify the existence of an item, an event, or an activity. For example, intruders trying to break a system may be identified by the programs executed, files accessed, and CPU time per ses- sion. In biological applications, existence of a gene may be identified by cer- tain sequences of nucleotide symbols in the DNA sequence. The area known as authentication is a form of identification. It ascertains whether a user is indeed a specific user or one from an authorized class, and involves a com- parison of parameters or images or signals against a database.
■ Classification. Data mining can partition the data so that different classes or categories can be identified based on combinations of parameters. For example, customers in a supermarket can be categorized into discount- seeking shoppers, shoppers in a rush, loyal regular shoppers, shoppers attached to name brands, and infrequent shoppers. This classification may be used in different analyses of customer buying transactions as a post- mining activity. Sometimes classification based on common domain knowledge is used as an input to decompose the mining problem and make it simpler. For instance, health foods, party foods, or school lunch foods are distinct categories in the supermarket business. It makes sense to analyze relationships within and across categories as separate problems. Such cate- gorization may be used to encode the data appropriately before subjecting it to further data mining.
■ Optimization. One eventual goal of data mining may be to optimize the use of limited resources such as time, space, money, or materials and to maxi- mize output variables such as sales or profits under a given set of constraints. As such, this goal of data mining resembles the objective function used in operations research problems that deals with optimization under constraints.
The term data mining is popularly used in a very broad sense. In some situations it includes statistical analysis and constrained optimization as well as machine learn- ing. There is no sharp line separating data mining from these disciplines. It is beyond our scope, therefore, to discuss in detail the entire range of applications that make up this vast body of work. For a detailed understanding of the topic, readers are referred to specialized books devoted to data mining.
28.1.4 Types of Knowledge Discovered during Data Mining
The term knowledge is broadly interpreted as involving some degree of intelligence. There is a progression from raw data to information to knowledge as we go through additional processing. Knowledge is often classified as inductive versus deductive. Deductive knowledge deduces new information based on applying prespecified log- ical rules of deduction on the given data. Data mining addresses inductive knowl- edge, which discovers new rules and patterns from the supplied data. Knowledge can be represented in many forms: In an unstructured sense, it can be represented by rules or propositional logic. In a structured form, it may be represented in deci-
28.2 Association Rules 1039
sion trees, semantic networks, neural networks, or hierarchies of classes or frames. It is common to describe the knowledge discovered during data mining as follows:
■ Association rules. These rules correlate the presence of a set of items with another range of values for another set of variables. Examples: (1) When a female retail shopper buys a handbag, she is likely to buy shoes. (2) An X-ray image containing characteristics a and b is likely to also exhibit characteristic c.
■ Classification hierarchies. The goal is to work from an existing set of events or transactions to create a hierarchy of classes. Examples: (1) A population may be divided into five ranges of credit worthiness based on a history of previous credit transactions. (2) A model may be developed for the factors that determine the desirability of a store location on a 1–10 scale. (3) Mutual funds may be classified based on performance data using characteristics such as growth, income, and stability.
■ Sequential patterns. A sequence of actions or events is sought. Example: If a patient underwent cardiac bypass surgery for blocked arteries and an aneurysm and later developed high blood urea within a year of surgery, he or she is likely to suffer from kidney failure within the next 18 months. Detection of sequential patterns is equivalent to detecting associations among events with certain temporal relationships.
■ Patterns within time series. Similarities can be detected within positions of a time series of data, which is a sequence of data taken at regular intervals, such as daily sales or daily closing stock prices. Examples: (1) Stocks of a util- ity company, ABC Power, and a financial company, XYZ Securities, showed the same pattern during 2009 in terms of closing stock prices. (2) Two prod- ucts show the same selling pattern in summer but a different one in winter. (3) A pattern in solar magnetic wind may be used to predict changes in Earth’s atmospheric conditions.
■ Clustering. A given population of events or items can be partitioned (seg- mented) into sets of “similar” elements. Examples: (1) An entire population of treatment data on a disease may be divided into groups based on the sim- ilarity of side effects produced. (2) The adult population in the United States may be categorized into five groups from most likely to buy to least likely to buy a new product. (3) The Web accesses made by a collection of users against a set of documents (say, in a digital library) may be analyzed in terms of the keywords of documents to reveal clusters or categories of users.
For most applications, the desired knowledge is a combination of the above types. We expand on each of the above knowledge types in the following sections.
28.2 Association Rules
28.2.1 Market-Basket Model, Support, and Confidence One of the major technologies in data mining involves the discovery of association rules. The database is regarded as a collection of transactions, each involving a set of
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items. A common example is that of market-basket data. Here the market basket corresponds to the sets of items a consumer buys in a supermarket during one visit. Consider four such transactions in a random sample shown in Figure 28.1.
An association rule is of the form X => Y, where X = {x1, x2, ..., xn}, and Y = {y1, y2, ..., ym} are sets of items, with xi and yj being distinct items for all i and all j. This association states that if a customer buys X, he or she is also likely to buy Y. In gen- eral, any association rule has the form LHS (left-hand side) => RHS (right-hand side), where LHS and RHS are sets of items. The set LHS ∪ RHS is called an itemset, the set of items purchased by customers. For an association rule to be of interest to a data miner, the rule should satisfy some interest measure. Two common interest measures are support and confidence.
The support for a rule LHS => RHS is with respect to the itemset; it refers to how frequently a specific itemset occurs in the database. That is, the support is the per- centage of transactions that contain all of the items in the itemset LHS ∪ RHS. If the support is low, it implies that there is no overwhelming evidence that items in LHS ∪ RHS occur together because the itemset occurs in only a small fraction of trans- actions. Another term for support is prevalence of the rule.
The confidence is with regard to the implication shown in the rule. The confidence of the rule LHS => RHS is computed as the support(LHS ∪ RHS)/support(LHS). We can think of it as the probability that the items in RHS will be purchased given that the items in LHS are purchased by a customer. Another term for confidence is strength of the rule.
As an example of support and confidence, consider the following two rules: milk => juice and bread => juice. Looking at our four sample transactions in Figure 28.1, we see that the support of {milk, juice} is 50 percent and the support of {bread, juice} is only 25 percent. The confidence of milk => juice is 66.7 percent (meaning that, of three transactions in which milk occurs, two contain juice) and the confidence of bread => juice is 50 percent (meaning that one of two transactions containing bread also contains juice).
As we can see, support and confidence do not necessarily go hand in hand. The goal of mining association rules, then, is to generate all possible rules that exceed some minimum user-specified support and confidence thresholds. The problem is thus decomposed into two subproblems:
1. Generate all itemsets that have a support that exceeds the threshold. These sets of items are called large (or frequent) itemsets. Note that large here means large support.
Transaction_id Time Items_bought 101 6:35 milk, bread, cookies, juice 792 7:38 milk, juice
1130 8:05 milk, eggs 1735 8:40 bread, cookies, coffee
Figure 28.1 Sample transactions in market-basket model.
28.2 Association Rules 1041
2. For each large itemset, all the rules that have a minimum confidence are gen- erated as follows: For a large itemset X and Y ⊂ X, let Z = X – Y; then if sup- port(X)/support(Z) > minimum confidence, the rule Z => Y (that is, X – Y => Y) is a valid rule.
Generating rules by using all large itemsets and their supports is relatively straight- forward. However, discovering all large itemsets together with the value for their support is a major problem if the cardinality of the set of items is very high. A typi- cal supermarket has thousands of items. The number of distinct itemsets is 2m, where m is the number of items, and counting support for all possible itemsets becomes very computation intensive. To reduce the combinatorial search space, algorithms for finding association rules utilize the following properties:
■ A subset of a large itemset must also be large (that is, each subset of a large itemset exceeds the minimum required support).
■ Conversely, a superset of a small itemset is also small (implying that it does not have enough support).
The first property is referred to as downward closure. The second property, called the antimonotonicity property, helps to reduce the search space of possible solu- tions. That is, once an itemset is found to be small (not a large itemset), then any extension to that itemset, formed by adding one or more items to the set, will also yield a small itemset.
28.2.2 Apriori Algorithm The first algorithm to use the downward closure and antimontonicity properties was the Apriori algorithm, shown as Algorithm 28.1.
We illustrate Algorithm 28.1 using the transaction data in Figure 28.1 using a mini- mum support of 0.5. The candidate 1-itemsets are {milk, bread, juice, cookies, eggs, coffee} and their respective supports are 0.75, 0.5, 0.5, 0.5, 0.25, and 0.25. The first four items qualify for L1 since each support is greater than or equal to 0.5. In the first iteration of the repeat-loop, we extend the frequent 1-itemsets to create the candi- date frequent 2-itemsets, C2. C2 contains {milk, bread}, {milk, juice}, {bread, juice}, {milk, cookies}, {bread, cookies}, and {juice, cookies}. Notice, for example, that {milk, eggs} does not appear in C2 since {eggs} is small (by the antimonotonicity property) and does not appear in L1. The supports for the six sets contained in C2 are 0.25, 0.5, 0.25, 0.25, 0.5, and 0.25 and are computed by scanning the set of trans- actions. Only the second 2-itemset {milk, juice} and the fifth 2-itemset {bread, cookies} have support greater than or equal to 0.5. These two 2-itemsets form the frequent 2-itemsets, L2.
Algorithm 28.1. Apriori Algorithm for Finding Frequent (Large) Itemsets
Input: Database of m transactions, D, and a minimum support, mins, represented as a fraction of m.
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Output: Frequent itemsets, L1, L2, ..., Lk
Begin /* steps or statements are numbered for better readability */
1. Compute support(ij) = count(ij)/m for each individual item, i1, i2, ..., in by scanning the database once and counting the number of transactions that item ij appears in (that is, count(ij));
2. The candidate frequent 1-itemset, C1, will be the set of items i1, i2, ..., in;
3. The subset of items containing ij from C1 where support(ij) >= mins becomes the frequent
1-itemset, L1;
4. k = 1;
termination = false;
repeat
1. Lk+1 = ;
2. Create the candidate frequent (k+1)-itemset, Ck+1, by combining members of Lk that have k–1 items in common (this forms candidate frequent (k+1)- itemsets by selectively extending frequent k-itemsets by one item);
3. In addition, only consider as elements of Ck+1 those k+1 items such that every subset of size k appears in Lk;
4. Scan the database once and compute the support for each member of Ck+1; if the support for a member of Ck+1 >= mins then add that member to Lk+1;
5. If Lk+1 is empty then termination = true
else k = k + 1;
until termination;
End;
In the next iteration of the repeat-loop, we construct candidate frequent 3-itemsets by adding additional items to sets in L2. However, for no extension of itemsets in L2 will all 2-item subsets be contained in L2. For example, consider {milk, juice, bread}; the 2-itemset {milk, bread} is not in L2, hence {milk, juice, bread} cannot be a fre- quent 3-itemset by the downward closure property. At this point the algorithm ter- minates with L1 equal to {{milk}, {bread}, {juice}, {cookies}} and L2 equal to {{milk, juice}, {bread, cookies}}.
Several other algorithms have been proposed to mine association rules. They vary mainly in terms of how the candidate itemsets are generated, and how the supports for the candidate itemsets are counted. Some algorithms use such data structures as bitmaps and hashtrees to keep information about itemsets. Several algorithms have been proposed that use multiple scans of the database because the potential number of itemsets, 2m, can be too large to set up counters during a single scan. We will examine three improved algorithms (compared to the Apriori algorithm) for asso- ciation rule mining: the Sampling algorithm, the Frequent-Pattern Tree algorithm, and the Partition algorithm.
28.2 Association Rules 1043
28.2.3 Sampling Algorithm The main idea for the Sampling algorithm is to select a small sample, one that fits in main memory, of the database of transactions and to determine the frequent itemsets from that sample. If those frequent itemsets form a superset of the frequent itemsets for the entire database, then we can determine the real frequent itemsets by scanning the remainder of the database in order to compute the exact support val- ues for the superset itemsets. A superset of the frequent itemsets can usually be found from the sample by using, for example, the Apriori algorithm, with a lowered minimum support.
In some rare cases, some frequent itemsets may be missed and a second scan of the database is needed. To decide whether any frequent itemsets have been missed, the concept of the negative border is used. The negative border with respect to a fre- quent itemset, S, and set of items, I, is the minimal itemsets contained in PowerSet(I) and not in S. The basic idea is that the negative border of a set of fre- quent itemsets contains the closest itemsets that could also be frequent. Consider the case where a set X is not contained in the frequent itemsets. If all subsets of X are contained in the set of frequent itemsets, then X would be in the negative border.
We illustrate this with the following example. Consider the set of items I = {A, B, C, D, E} and let the combined frequent itemsets of size 1 to 3 be S = {{A}, {B}, {C}, {D}, {AB}, {AC}, {BC}, {AD}, {CD}, {ABC}}. The negative border is {{E}, {BD}, {ACD}}. The set {E} is the only 1-itemset not contained in S, {BD} is the only 2-itemset not in S but whose 1-itemset subsets are, and {ACD} is the only 3-itemset whose 2-itemset subsets are all in S. The negative border is important since it is necessary to determine the support for those itemsets in the negative border to ensure that no large itemsets are missed from analyzing the sample data.
Support for the negative border is determined when the remainder of the database is scanned. If we find that an itemset, X, in the negative border belongs in the set of all frequent itemsets, then there is a potential for a superset of X to also be frequent. If this happens, then a second pass over the database is needed to make sure that all frequent itemsets are found.
28.2.4 Frequent-Pattern (FP) Tree and FP-Growth Algorithm The Frequent-Pattern Tree (FP-tree) is motivated by the fact that Apriori-based algorithms may generate and test a very large number of candidate itemsets. For example, with 1000 frequent 1-itemsets, the Apriori algorithm would have to generate
or 499,500 candidate 2-itemsets. The FP-Growth algorithm is one approach that eliminates the generation of a large number of candidate itemsets.
1000
2
⎛ ⎝⎜
⎞ ⎠⎟
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The algorithm first produces a compressed version of the database in terms of an FP-tree (frequent-pattern tree). The FP-tree stores relevant itemset information and allows for the efficient discovery of frequent itemsets. The actual mining process adopts a divide-and-conquer strategy where the mining process is decomposed into a set of smaller tasks that each operates on a conditional FP-tree, a subset (projec- tion) of the original tree. To start with, we examine how the FP-tree is constructed. The database is first scanned and the frequent 1-itemsets along with their support are computed. With this algorithm, the support is the count of transactions contain- ing the item rather than the fraction of transactions containing the item. The fre- quent 1-itemsets are then sorted in nonincreasing order of their support. Next, the root of the FP-tree is created with a NULL label. The database is scanned a second time and for each transaction T in the database, the frequent 1-itemsets in T are placed in order as was done with the frequent 1-itemsets. We can designate this sorted list for T as consisting of a first item, the head, and the remaining items, the tail. The itemset information (head, tail) is inserted into the FP-tree recursively, starting at the root node, as follows:
1. If the current node, N, of the FP-tree has a child with an item name = head, then increment the count associated with node N by 1, else create a new node, N, with a count of 1, link N to its parent and link N with the item header table (used for efficient tree traversal).
2. If the tail is nonempty, then repeat step (1) using as the sorted list only the tail, that is, the old head is removed and the new head is the first item from the tail and the remaining items become the new tail.
The item header table, created during the process of building the FP-tree, contains three fields per entry for each frequent item: item identifier, support count, and node link. The item identifier and support count are self-explanatory. The node link is a pointer to an occurrence of that item in the FP-tree. Since multiple occurrences of a single item may appear in the FP-tree, these items are linked together as a list where the start of the list is pointed to by the node link in the item header table. We illustrate the building of the FP-tree using the transaction data in Figure 28.1. Let us use a minimum support of 2. One pass over the four transactions yields the follow- ing frequent 1-itemsets with associated support: {{(milk, 3)}, {(bread, 2)}, {(cookies, 2)}, {(juice, 2)}}. The database is scanned a second time and each transaction will be processed again.
For the first transaction, we create the sorted list, T = {milk, bread, cookies, juice}. The items in T are the frequent 1-itemsets from the first transaction. The items are ordered based on the nonincreasing ordering of the count of the 1-itemsets found in pass 1 (that is, milk first, bread second, and so on). We create a NULL root node for the FP-tree and insert milk as a child of the root, bread as a child of milk, cookies as a child of bread, and juice as a child of cookies. We adjust the entries for the fre- quent items in the item header table.
For the second transaction, we have the sorted list {milk, juice}. Starting at the root, we see that a child node with label milk exists, so we move to that node and update
28.2 Association Rules 1045
its count (to account for the second transaction that contains milk). We see that there is no child of the current node with label juice, so we create a new node with label juice. The item header table is adjusted.
The third transaction only has 1-frequent item, {milk}. Again, starting at the root, we see that the node with label milk exists, so we move to that node, increment its count, and adjust the item header table. The final transaction contains frequent items, {bread, cookies}. At the root node, we see that a child with label bread does not exist. Thus, we create a new child of the root, initialize its counter, and then insert cookies as a child of this node and initialize its count. After the item header table is updated, we end up with the FP-tree and item header table as shown in Figure 28.2. If we examine this FP-tree, we see that it indeed represents the original transactions in a compressed format (that is, only showing the items from each transaction that are large 1-itemsets).
Algorithm 28.2 is used for mining the FP-tree for frequent patterns. With the FP- tree, it is possible to find all frequent patterns that contain a given frequent item by starting from the item header table for that item and traversing the node links in the FP-tree. The algorithm starts with a frequent 1-itemset (suffix pattern) and con- structs its conditional pattern base and then its conditional FP-tree. The conditional pattern base is made up of a set of prefix paths, that is, where the frequent item is a suffix. For example, if we consider the item juice, we see from Figure 28.2 that there are two paths in the FP-tree that end with juice: (milk, bread, cookies, juice) and (milk, juice). The two associated prefix paths are (milk, bread, cookies) and (milk). The conditional FP-tree is constructed from the patterns in the conditional pattern base. The mining is recursively performed on this FP-tree. The frequent patterns are formed by concatenating the suffix pattern with the frequent patterns produced from a conditional FP-tree.
Item Support Link
Milk 3
Bread 2
Cookies 2
Juice 2
Bread: 1Milk: 3
Bread: 1
Cookies: 1
Juice: 1
Juice: 1 Cookies: 1
NULL
Figure 28.2 FP-tree and item header table.
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Algorithm 28.2. FP-Growth Algorithm for Finding Frequent Itemsets
Input: FP-tree and a minimum support, mins
Output: frequent patterns (itemsets)
procedure FP-growth (tree, alpha);
Begin if tree contains a single path P then
for each combination, beta, of the nodes in the path generate pattern (beta ∪ alpha) with support = minimum support of nodes in beta
else for each item, i, in the header of the tree do
begin generate pattern beta = (i ∪ alpha) with support = i.support; construct beta’s conditional pattern base; construct beta’s conditional FP-tree, beta_tree; if beta_tree is not empty then
FP-growth(beta_tree, beta); end;
End;
We illustrate the algorithm using the data in Figure 28.1 and the tree in Figure 28.2. The procedure FP-growth is called with the two parameters: the original FP-tree and NULL for the variable alpha. Since the original FP-tree has more than a single path, we execute the else part of the first if statement. We start with the frequent item, juice. We will examine the frequent items in order of lowest support (that is, from the last entry in the table to the first). The variable beta is set to juice with support equal to 2.
Following the node link in the item header table, we construct the conditional pat- tern base consisting of two paths (with juice as suffix). These are (milk, bread, cook- ies: 1) and (milk: 1). The conditional FP-tree consists of only a single node, milk: 2. This is due to a support of only 1 for node bread and cookies, which is below the minimal support of 2. The algorithm is called recursively with an FP-tree of only a single node (that is, milk: 2) and a beta value of juice. Since this FP-tree only has one path, all combinations of beta and nodes in the path are generated—that is, {milk, juice}—with support of 2.
Next, the frequent item, cookies, is used. The variable beta is set to cookies with sup- port = 2. Following the node link in the item header table, we construct the condi- tional pattern base consisting of two paths. These are (milk, bread: 1) and (bread: 1). The conditional FP-tree is only a single node, bread: 2. The algorithm is called recursively with an FP-tree of only a single node (that is, bread: 2) and a beta value of cookies. Since this FP-tree only has one path, all combinations of beta and nodes in the path are generated, that is, {bread, cookies} with support of 2. The frequent item, bread, is considered next. The variable beta is set to bread with support = 2. Following the node link in the item header table, we construct the conditional
28.2 Association Rules 1047
pattern base consisting of one path, which is (milk: 1). The conditional FP-tree is empty since the count is less than the minimum support. Since the conditional FP- tree is empty, no frequent patterns will be generated.
The last frequent item to consider is milk. This is the top item in the item header table and as such has an empty conditional pattern base and empty conditional FP- tree. As a result, no frequent patterns are added. The result of executing the algo- rithm is the following frequent patterns (or itemsets) with their support: {{milk: 3}, {bread: 2}, {cookies: 2}, {juice: 2}, {milk, juice: 2}, {bread, cookies: 2}}.
28.2.5 Partition Algorithm Another algorithm, called the Partition algorithm,3 is summarized below. If we are given a database with a small number of potential large itemsets, say, a few thou- sand, then the support for all of them can be tested in one scan by using a partition- ing technique. Partitioning divides the database into nonoverlapping subsets; these are individually considered as separate databases and all large itemsets for that par- tition, called local frequent itemsets, are generated in one pass. The Apriori algorithm can then be used efficiently on each partition if it fits entirely in main memory. Partitions are chosen in such a way that each partition can be accommodated in main memory. As such, a partition is read only once in each pass. The only caveat with the partition method is that the minimum support used for each partition has a slightly different meaning from the original value. The minimum support is based on the size of the partition rather than the size of the database for determining local frequent (large) itemsets. The actual support threshold value is the same as given earlier, but the support is computed only for a partition.
At the end of pass one, we take the union of all frequent itemsets from each parti- tion. This forms the global candidate frequent itemsets for the entire database. When these lists are merged, they may contain some false positives. That is, some of the itemsets that are frequent (large) in one partition may not qualify in several other partitions and hence may not exceed the minimum support when the original database is considered. Note that there are no false negatives; no large itemsets will be missed. The global candidate large itemsets identified in pass one are verified in pass two; that is, their actual support is measured for the entire database. At the end of phase two, all global large itemsets are identified. The Partition algorithm lends itself naturally to a parallel or distributed implementation for better efficiency. Further improvements to this algorithm have been suggested.4
28.2.6 Other Types of Association Rules
Association Rules among Hierarchies. There are certain types of associations that are particularly interesting for a special reason. These associations occur among
3See Savasere et al. (1995) for details of the algorithm, the data structures used to implement it, and its performance comparisons. 4See Cheung et al. (1996) and Lin and Dunham (1998).
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Beverages
Carbonated Noncarbonated
Orange Apple Others Plain Clear
Colas Clear drinks
Mixed drinks
Bottled juices
Bottled water
Wine coolers
Desserts
Ice cream Baked Frozen yogurt
Rich cream
Reduce Healthy
Figure 28.3 Taxonomy of items in a supermarket.
hierarchies of items. Typically, it is possible to divide items among disjoint hierar- chies based on the nature of the domain. For example, foods in a supermarket, items in a department store, or articles in a sports shop can be categorized into classes and subclasses that give rise to hierarchies. Consider Figure 28.3, which shows the taxon- omy of items in a supermarket. The figure shows two hierarchies—beverages and desserts, respectively. The entire groups may not produce associations of the form beverages => desserts, or desserts => beverages. However, associations of the type Healthy-brand frozen yogurt => bottled water, or Rich cream-brand ice cream => wine cooler may produce enough confidence and support to be valid association rules of interest.
Therefore, if the application area has a natural classification of the itemsets into hierarchies, discovering associations within the hierarchies is of no particular inter- est. The ones of specific interest are associations across hierarchies. They may occur among item groupings at different levels.
Multidimensional Associations. Discovering association rules involves search- ing for patterns in a file. In Figure 28.1, we have an example of a file of customer transactions with three dimensions: Transaction_id, Time, and Items_bought. However, our data mining tasks and algorithms introduced up to this point only involve one dimension: Items_bought. The following rule is an example of includ- ing the label of the single dimension: Items_bought(milk) => Items_bought(juice). It may be of interest to find association rules that involve multiple dimensions, for
28.2 Association Rules 1049
example, Time(6:30...8:00) => Items_bought(milk). Rules like these are called multidimensional association rules. The dimensions represent attributes of records of a file or, in terms of relations, columns of rows of a relation, and can be categori- cal or quantitative. Categorical attributes have a finite set of values that display no ordering relationship. Quantitative attributes are numeric and their values display an ordering relationship, for example, <. Items_bought is an example of a categori- cal attribute and Transaction_id and Time are quantitative.
One approach to handling a quantitative attribute is to partition its values into nonoverlapping intervals that are assigned labels. This can be done in a static man- ner based on domain-specific knowledge. For example, a concept hierarchy may group values for Salary into three distinct classes: low income (0 < Salary < 29,999), middle income (30,000 < Salary < 74,999), and high income (Salary > 75,000). From here, the typical Apriori-type algorithm or one of its variants can be used for the rule mining since the quantitative attributes now look like categorical attributes. Another approach to partitioning is to group attribute values based on data distri- bution, for example, equi-depth partitioning, and to assign integer values to each partition. The partitioning at this stage may be relatively fine, that is, a larger num- ber of intervals. Then during the mining process, these partitions may combine with other adjacent partitions if their support is less than some predefined maxi- mum value. An Apriori-type algorithm can be used here as well for the data mining.
Negative Associations. The problem of discovering a negative association is harder than that of discovering a positive association. A negative association is of the following type: 60 percent of customers who buy potato chips do not buy bottled water. (Here, the 60 percent refers to the confidence for the negative association rule.) In a database with 10,000 items, there are 210,000 possible combinations of items, a majority of which do not appear even once in the database. If the absence of a certain item combination is taken to mean a negative association, then we poten- tially have millions and millions of negative association rules with RHSs that are of no interest at all. The problem, then, is to find only interesting negative rules. In gen- eral, we are interested in cases in which two specific sets of items appear very rarely in the same transaction. This poses two problems.
1. For a total item inventory of 10,000 items, the probability of any two being bought together is (1/10,000) * (1/10,000) = 10
–8. If we find the actual sup- port for these two occurring together to be zero, that does not represent a significant departure from expectation and hence is not an interesting (neg- ative) association.
2. The other problem is more serious. We are looking for item combinations with very low support, and there are millions and millions with low or even zero support. For example, a data set of 10 million transactions has most of the 2.5 billion pairwise combinations of 10,000 items missing. This would generate billions of useless rules.
Therefore, to make negative association rules interesting, we must use prior knowl- edge about the itemsets. One approach is to use hierarchies. Suppose we use the hierarchies of soft drinks and chips shown in Figure 28.4.
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Soft drinks
Joke Wakeup Topsy
Chips
Days Nightos Party’Os
Figure 28.4 Simple hierarchy of soft drinks and chips.
A strong positive association has been shown between soft drinks and chips. If we find a large support for the fact that when customers buy Days chips they predomi- nantly buy Topsy and not Joke and not Wakeup, that would be interesting because we would normally expect that if there is a strong association between Days and Topsy, there should also be such a strong association between Days and Joke or Days and Wakeup.5
In the frozen yogurt and bottled water groupings shown in Figure 28.3, suppose the Reduce versus Healthy-brand division is 80–20 and the Plain and Clear brands divi- sion is 60–40 among respective categories. This would give a joint probability of Reduce frozen yogurt being purchased with Plain bottled water as 48 percent among the transactions containing a frozen yogurt and bottled water. If this sup- port, however, is found to be only 20 percent, it would indicate a significant negative association among Reduce yogurt and Plain bottled water; again, that would be interesting.
The problem of finding negative association is important in the above situations given the domain knowledge in the form of item generalization hierarchies (that is, the beverage given and desserts hierarchies shown in Figure 28.3), the existing posi- tive associations (such as between the frozen yogurt and bottled water groups), and the distribution of items (such as the name brands within related groups). The scope of discovery of negative associations is limited in terms of knowing the item hierarchies and distributions. Exponential growth of negative associations remains a challenge.
28.2.7 Additional Considerations for Association Rules Mining association rules in real-life databases is complicated by the following fac- tors:
■ The cardinality of itemsets in most situations is extremely large, and the vol- ume of transactions is very high as well. Some operational databases in retailing and communication industries collect tens of millions of transac- tions per day.
■ Transactions show variability in such factors as geographic location and sea- sons, making sampling difficult.
■ Item classifications exist along multiple dimensions. Hence, driving the dis- covery process with domain knowledge, particularly for negative rules, is extremely difficult.
5For simplicity we are assuming a uniform distribution of transactions among members of a hierarchy.
28.3 Classification 1051
■ Quality of data is variable; significant problems exist with missing, erro- neous, conflicting, as well as redundant data in many industries.
28.3 Classification Classification is the process of learning a model that describes different classes of data. The classes are predetermined. For example, in a banking application, cus- tomers who apply for a credit card may be classified as a poor risk, fair risk, or good risk. Hence this type of activity is also called supervised learning. Once the model is built, it can be used to classify new data. The first step—learning the model—is accomplished by using a training set of data that has already been classified. Each record in the training data contains an attribute, called the class label, which indi- cates which class the record belongs to. The model that is produced is usually in the form of a decision tree or a set of rules. Some of the important issues with regard to the model and the algorithm that produces the model include the model’s ability to predict the correct class of new data, the computational cost associated with the algorithm, and the scalability of the algorithm.
We will examine the approach where our model is in the form of a decision tree. A decision tree is simply a graphical representation of the description of each class or, in other words, a representation of the classification rules. A sample decision tree is pictured in Figure 28.5. We see from Figure 28.5 that if a customer is married and if salary >= 50K, then they are a good risk for a bank credit card. This is one of the rules that describe the class good risk. Traversing the decision tree from the root to each leaf node forms other rules for this class and the two other classes. Algorithm 28.3 shows the procedure for constructing a decision tree from a training data set. Initially, all training samples are at the root of the tree. The samples are partitioned
Married
Salary Acct_balance
Fair risk Good risk
Poor risk
< 20K >= 20K >= 50K < 5K >= 5K
< 25 >= 25
< 50K
NoYes
Fair risk Good risk Poor risk Age
Figure 28.5 Sample decision tree for credit card applications.
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recursively based on selected attributes. The attribute used at a node to partition the samples is the one with the best splitting criterion, for example, the one that maxi- mizes the information gain measure.
Algorithm 28.3. Algorithm for Decision Tree Induction
Input: Set of training data records: R1, R2, ..., Rm and set of attributes: A1, A2, ..., An
Output: Decision tree
procedure Build_tree (records, attributes); Begin
create a node N; if all records belong to the same class, C then
return N as a leaf node with class label C; if attributes is empty then
return N as a leaf node with class label C, such that the majority of records belong to it;
select attribute Ai (with the highest information gain) from attributes; label node N with Ai; for each known value, vj, of Ai do
begin add a branch from node N for the condition Ai = vj; Sj = subset of records where Ai = vj; if Sj is empty then
add a leaf, L, with class label C, such that the majority of records belong to it and return L
else add the node returned by Build_tree(Sj, attributes – Ai); end;
End;
Before we illustrate Algorithm 28.3, we will explain the information gain measure in more detail. The use of entropy as the information gain measure is motivated by the goal of minimizing the information needed to classify the sample data in the resulting partitions and thus minimizing the expected number of conditional tests needed to classify a new record. The expected information needed to classify train- ing data of s samples, where the Class attribute has n values (v1, ..., vn) and si is the number of samples belonging to class label vi, is given by
where pi is the probability that a random sample belongs to the class with label vi. An estimate for pi is si /s. Consider an attribute A with values {v1, ..., vm} used as the test attribute for splitting in the decision tree. Attribute A partitions the samples into the subsets S1, ..., Sm where samples in each Sj have a value of vj for attribute A. Each Sj may contain samples that belong to any of the classes. The number of
I S S S p pn i i i
n
1 2 2 1
, , ..., log( ) = − =
∑
28.3 Classification 1053
samples in Sj that belong to class i can be denoted as sij. The entropy associated with using attribute A as the test attribute is defined as
I(s1j, ..., snj) can be defined using the formulation for I(s1, ..., sn) with pi being replaced by pij where pij = sij /sj. Now the information gain by partitioning on attrib- ute A, Gain(A), is defined as I(s1, ..., sn) – E(A). We can use the sample training data from Figure 28.6 to illustrate the algorithm.
The attribute RID represents the record identifier used for identifying an individual record and is an internal attribute. We use it to identify a particular record in our example. First, we compute the expected information needed to classify the training data of 6 records as I(s1, s2) where there are two classes: the first class label value cor- responds to yes and the second to no. So,
I(3,3) = − 0.5log2 0.5 − 0.5log2 0.5 = 1.
Now, we compute the entropy for each of the four attributes as shown below. For Married = yes, we have s11 = 2, s21 = 1 and I(s11, s21) = 0.92. For Married = no, we have s12 = 1, s22 = 2 and I(s12, s22) = 0.92. So, the expected information needed to classify a sample using attribute Married as the partitioning attribute is
E(Married) = 3/6 I(s11, s21) + 3/6 I(s12, s22) = 0.92.
The gain in information, Gain(Married), would be 1 – 0.92 = 0.08. If we follow simi- lar steps for computing the gain with respect to the other three attributes we end up with
E(Salary) = 0.33 and Gain(Salary) = 0.67
E(Acct_balance) = 0.92 and Gain(Acct_balance) = 0.08
E(Age) = 0.54 and Gain(Age) = 0.46
Since the greatest gain occurs for attribute Salary, it is chosen as the partitioning attribute. The root of the tree is created with label Salary and has three branches, one for each value of Salary. For two of the three values, that is, <20K and >=50K, all the samples that are partitioned accordingly (records with RIDs 4 and 5 for <20K
E A S S
S I S S
j nj
j
m
j nj( ) ...
, ...,= + +
× ( ) =
∑ 1 1
1
RID Married Salary Acct_balance Age Loanworthy
1 no >=50K <5K >=25 yes
2 yes >=50K >=5K >=25 yes
3 yes 20K. . .50K <5K <25 no
4 no <20K >=5K <25 no
5 no <20K <5K >=25 no
6 yes 20K. . .50K >=5K >=25 yes
Figure 28.6 Sample training data for classification algorithm.
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Salary
{3} {6}
{4,5}
Class is “no”
Class is “no”
Class is “yes”
Class is “yes”
< 20K 20K . . . 50K
>= 50K
< 25 >= 25
{1,2}Age
Figure 28.7 Decision tree based on sample training data where the leaf nodes are represented by a set of RIDs of the partitioned records.
and records with RIDs 1 and 2 for >=50K) fall within the same class loanworthy no and loanworthy yes respectively for those two values. So we create a leaf node for each. The only branch that needs to be expanded is for the value 20K...50K with two samples, records with RIDs 3 and 6 in the training data. Continuing the process using these two records, we find that Gain(Married) is 0, Gain(Acct_balance) is 1, and Gain(Age) is 1.
We can choose either Age or Acct_balance since they both have the largest gain. Let us choose Age as the partitioning attribute. We add a node with label Age that has two branches, less than 25, and greater or equal to 25. Each branch partitions the remaining sample data such that one sample record belongs to each branch and hence one class. Two leaf nodes are created and we are finished. The final decision tree is pictured in Figure 28.7.
28.4 Clustering The previous data mining task of classification deals with partitioning data based on using a preclassified training sample. However, it is often useful to partition data without having a training sample; this is also known as unsupervised learning. For example, in business, it may be important to determine groups of customers who have similar buying patterns, or in medicine, it may be important to determine groups of patients who show similar reactions to prescribed drugs. The goal of clus- tering is to place records into groups, such that records in a group are similar to each other and dissimilar to records in other groups. The groups are usually disjoint.
An important facet of clustering is the similarity function that is used. When the data is numeric, a similarity function based on distance is typically used. For exam- ple, the Euclidean distance can be used to measure similarity. Consider two n- dimensional data points (records) rj and rk. We can consider the value for the ith dimension as rji and rki for the two records. The Euclidean distance between points rj and rk in n-dimensional space is calculated as:
Distance( , ) ...r r r r r r r rj k j k j k jn kn= − + − + + −1 1 2
2 2
2 2
28.4 Clustering 1055
The smaller the distance between two points, the greater is the similarity as we think of them. A classic clustering algorithm is the k-Means algorithm, Algorithm 28.4.
Algorithm 28.4. k-Means Clustering Algorithm
Input: a database D, of m records, r1, ..., rm and a desired number of clusters k
Output: set of k clusters that minimizes the squared error criterion
Begin randomly choose k records as the centroids for the k clusters; repeat assign each record, ri, to a cluster such that the distance between ri
and the cluster centroid (mean) is the smallest among the k clusters; recalculate the centroid (mean) for each cluster based on the records
assigned to the cluster; until no change;
End;
The algorithm begins by randomly choosing k records to represent the centroids (means), m1, ..., mk, of the clusters, C1, ..., Ck. All the records are placed in a given cluster based on the distance between the record and the cluster mean. If the dis- tance between mi and record rj is the smallest among all cluster means, then record rj is placed in cluster Ci. Once all records have been initially placed in a cluster, the mean for each cluster is recomputed. Then the process repeats, by examining each record again and placing it in the cluster whose mean is closest. Several iterations may be needed, but the algorithm will converge, although it may terminate at a local optimum. The terminating condition is usually the squared-error criterion. For clusters C1, ..., Ck with means m1, ..., mk, the error is defined as:
We will examine how Algorithm 28.4 works with the (two-dimensional) records in Figure 28.8. Assume that the number of desired clusters k is 2. Let the algorithm choose records with RID 3 for cluster C1 and RID 6 for cluster C2 as the initial cluster centroids. The remaining records will be assigned to one of those clusters during the
Error Distance= ∀ ∈=
∑∑ ( , )r mj i r Ci
k
j i
2
1
RID Age Years_of_service
5031
52052
51053
5524
01035
52556
Figure 28.8 Sample 2-dimensional records for clustering example (the RID column is not considered).
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first iteration of the repeat loop. The record with RID 1 has a distance from C1 of 22.4 and a distance from C2 of 32.0, so it joins cluster C1. The record with RID 2 has a distance from C1 of 10.0 and a distance from C2 of 5.0, so it joins cluster C2. The record with RID 4 has a distance from C1 of 25.5 and a distance from C2 of 36.6, so it joins cluster C1. The record with RID 5 has a distance from C1 of 20.6 and a dis- tance from C2 of 29.2, so it joins cluster C1. Now, the new means (centroids) for the two clusters are computed. The mean for a cluster, Ci, with n records of m dimen- sions is the vector:
The new mean for C1 is (33.75, 8.75) and the new mean for C2 is (52.5, 25). A sec- ond iteration proceeds and the six records are placed into the two clusters as follows: records with RIDs 1, 4, 5 are placed in C1 and records with RIDs 2, 3, 6 are placed in C2. The mean for C1 and C2 is recomputed as (28.3, 6.7) and (51.7, 21.7), respec- tively. In the next iteration, all records stay in their previous clusters and the algo- rithm terminates.
Traditionally, clustering algorithms assume that the entire data set fits in main memory. More recently, researchers have developed algorithms that are efficient and are scalable for very large databases. One such algorithm is called BIRCH. BIRCH is a hybrid approach that uses both a hierarchical clustering approach, which builds a tree representation of the data, as well as additional clustering methods, which are applied to the leaf nodes of the tree. Two input parameters are used by the BIRCH algorithm. One specifies the amount of available main memory and the other is an initial threshold for the radius of any cluster. Main memory is used to store descrip- tive cluster information such as the center (mean) of a cluster and the radius of the cluster (clusters are assumed to be spherical in shape). The radius threshold affects the number of clusters that are produced. For example, if the radius threshold value is large, then few clusters of many records will be formed. The algorithm tries to maintain the number of clusters such that their radius is below the radius threshold. If available memory is insufficient, then the radius threshold is increased.
The BIRCH algorithm reads the data records sequentially and inserts them into an in-memory tree structure, which tries to preserve the clustering structure of the data. The records are inserted into the appropriate leaf nodes (potential clusters) based on the distance between the record and the cluster center. The leaf node where the insertion happens may have to split, depending upon the updated center and radius of the cluster and the radius threshold parameter. Additionally, when splitting, extra cluster information is stored, and if memory becomes insufficient, then the radius threshold will be increased. Increasing the radius threshold may actually produce a side effect of reducing the number of clusters since some nodes may be merged.
Overall, BIRCH is an efficient clustering method with a linear computational com- plexity in terms of the number of records to be clustered.
C n
r n
ri ji r C
jm r Cj i j i
= ⎛
⎝ ⎜ ⎜
⎞
⎠ ⎟ ⎟∀ ∈ ∀ ∈
∑ ∑1 1, ...,
28.5 Approaches to Other Data Mining Problems 1057
28.5 Approaches to Other Data Mining Problems
28.5.1 Discovery of Sequential Patterns The discovery of sequential patterns is based on the concept of a sequence of item- sets. We assume that transactions such as the supermarket-basket transactions we discussed previously are ordered by time of purchase. That ordering yields a sequence of itemsets. For example, {milk, bread, juice}, {bread, eggs}, {cookies, milk, coffee} may be such a sequence of itemsets based on three visits by the same cus- tomer to the store. The support for a sequence S of itemsets is the percentage of the given set U of sequences of which S is a subsequence. In this example, {milk, bread, juice} {bread, eggs} and {bread, eggs} {cookies, milk, coffee} are considered subsequences. The problem of identifying sequential patterns, then, is to find all subsequences from the given sets of sequences that have a user-defined minimum support. The sequence S1, S2, S3, ... is a predictor of the fact that a customer who buys itemset S1 is likely to buy itemset S2 and then S3, and so on. This prediction is based on the frequency (support) of this sequence in the past. Various algorithms have been investigated for sequence detection.
28.5.2 Discovery of Patterns in Time Series Time series are sequences of events; each event may be a given fixed type of a trans- action. For example, the closing price of a stock or a fund is an event that occurs every weekday for each stock and fund. The sequence of these values per stock or fund constitutes a time series. For a time series, one may look for a variety of pat- terns by analyzing sequences and subsequences as we did above. For example, we might find the period during which the stock rose or held steady for n days, or we might find the longest period over which the stock had a fluctuation of no more than 1 percent over the previous closing price, or we might find the quarter during which the stock had the most percentage gain or percentage loss. Time series may be compared by establishing measures of similarity to identify companies whose stocks behave in a similar fashion. Analysis and mining of time series is an extended func- tionality of temporal data management (see Chapter 26).
28.5.3 Regression Regression is a special application of the classification rule. If a classification rule is regarded as a function over the variables that maps these variables into a target class variable, the rule is called a regression rule. A general application of regression occurs when, instead of mapping a tuple of data from a relation to a specific class, the value of a variable is predicted based on that tuple. For example, consider a relation
LAB_TESTS (patient ID, test 1, test 2, ..., test n)
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which contains values that are results from a series of n tests for one patient. The target variable that we wish to predict is P, the probability of survival of the patient. Then the rule for regression takes the form:
(test 1 in range1) and (test 2 in range2) and ... (test n in rangen) ⇒ P = x, or x < P ≤ y
The choice depends on whether we can predict a unique value of P or a range of val- ues for P. If we regard P as a function:
P = f (test 1, test 2, ..., test n)
the function is called a regression function to predict P. In general, if the function appears as
Y = f (X1, X2, ..., Xn),
and f is linear in the domain variables xi, the process of deriving f from a given set of tuples for <X1, X2, ..., Xn, y> is called linear regression. Linear regression is a com- monly used statistical technique for fitting a set of observations or points in n dimensions with the target variable y.
Regression analysis is a very common tool for analysis of data in many research domains. The discovery of the function to predict the target variable is equivalent to a data mining operation.
28.5.4 Neural Networks A neural network is a technique derived from artificial intelligence research that uses generalized regression and provides an iterative method to carry it out. Neural networks use the curve-fitting approach to infer a function from a set of samples. This technique provides a learning approach; it is driven by a test sample that is used for the initial inference and learning. With this kind of learning method, responses to new inputs may be able to be interpolated from the known samples. This interpo- lation, however, depends on the world model (internal representation of the prob- lem domain) developed by the learning method.
Neural networks can be broadly classified into two categories: supervised and unsu- pervised networks. Adaptive methods that attempt to reduce the output error are supervised learning methods, whereas those that develop internal representations without sample outputs are called unsupervised learning methods.
Neural networks self-adapt; that is, they learn from information about a specific problem. They perform well on classification tasks and are therefore useful in data mining. Yet, they are not without problems. Although they learn, they do not pro- vide a good representation of what they have learned. Their outputs are highly quantitative and not easy to understand. As another limitation, the internal repre- sentations developed by neural networks are not unique. Also, in general, neural networks have trouble modeling time series data. Despite these shortcomings, they are popular and frequently used by several commercial vendors.
28.5 Approaches to Other Data Mining Problems 1059
28.5.5 Genetic Algorithms Genetic algorithms (GAs) are a class of randomized search procedures capable of adaptive and robust search over a wide range of search space topologies. Modeled after the adaptive emergence of biological species from evolutionary mechanisms, and introduced by Holland,6 GAs have been successfully applied in such diverse fields as image analysis, scheduling, and engineering design.
Genetic algorithms extend the idea from human genetics of the four-letter alphabet (based on the A,C,T,G nucleotides) of the human DNA code. The construction of a genetic algorithm involves devising an alphabet that encodes the solutions to the decision problem in terms of strings of that alphabet. Strings are equivalent to indi- viduals. A fitness function defines which solutions can survive and which cannot. The ways in which solutions can be combined are patterned after the cross-over operation of cutting and combining strings from a father and a mother. An initial population of a well-varied population is provided, and a game of evolution is played in which mutations occur among strings. They combine to produce a new generation of individuals; the fittest individuals survive and mutate until a family of successful solutions develops.
The solutions produced by GAs are distinguished from most other search tech- niques by the following characteristics:
■ A GA search uses a set of solutions during each generation rather than a sin- gle solution.
■ The search in the string-space represents a much larger parallel search in the space of encoded solutions.
■ The memory of the search done is represented solely by the set of solutions available for a generation.
■ A genetic algorithm is a randomized algorithm since search mechanisms use probabilistic operators.
■ While progressing from one generation to the next, a GA finds near-optimal balance between knowledge acquisition and exploitation by manipulating encoded solutions.
Genetic algorithms are used for problem solving and clustering problems. Their ability to solve problems in parallel provides a powerful tool for data mining. The drawbacks of GAs include the large overproduction of individual solutions, the ran- dom character of the searching process, and the high demand on computer process- ing. In general, substantial computing power is required to achieve anything of significance with genetic algorithms.
6Holland’s seminal work (1975) entitled Adaptation in Natural and Artificial Systems introduced the idea of genetic algorithms.
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28.6 Applications of Data Mining Data mining technologies can be applied to a large variety of decision-making con- texts in business. In particular, areas of significant payoffs are expected to include the following:
■ Marketing. Applications include analysis of consumer behavior based on buying patterns; determination of marketing strategies including advertis- ing, store location, and targeted mailing; segmentation of customers, stores, or products; and design of catalogs, store layouts, and advertising cam- paigns.
■ Finance. Applications include analysis of creditworthiness of clients, seg- mentation of account receivables, performance analysis of finance invest- ments like stocks, bonds, and mutual funds; evaluation of financing options; and fraud detection.
■ Manufacturing. Applications involve optimization of resources like machines, manpower, and materials; and optimal design of manufacturing processes, shop-floor layouts, and product design, such as for automobiles based on customer requirements.
■ Health Care. Applications include discovery of patterns in radiological images, analysis of microarray (gene-chip) experimental data to cluster genes and to relate to symptoms or diseases, analysis of side effects of drugs and effectiveness of certain treatments, optimization of processes within a hospi- tal, and the relationship of patient wellness data with doctor qualifications.
28.7 Commercial Data Mining Tools Currently, commercial data mining tools use several common techniques to extract knowledge. These include association rules, clustering, neural networks, sequenc- ing, and statistical analysis. We discussed these earlier. Also used are decision trees, which are a representation of the rules used in classification or clustering, and statis- tical analyses, which may include regression and many other techniques. Other commercial products use advanced techniques such as genetic algorithms, case- based reasoning, Bayesian networks, nonlinear regression, combinatorial optimiza- tion, pattern matching, and fuzzy logic. In this chapter we have already discussed some of these.
Most data mining tools use the ODBC (Open Database Connectivity) interface. ODBC is an industry standard that works with databases; it enables access to data in most of the popular database programs such as Access, dBASE, Informix, Oracle, and SQL Server. Some of these software packages provide interfaces to specific data- base programs; the most common are Oracle, Access, and SQL Server. Most of the tools work in the Microsoft Windows environment and a few work in the UNIX operating system. The trend is for all products to operate under the Microsoft Windows environment. One tool, Data Surveyor, mentions ODMG compliance; see Chapter 11 where we discuss the ODMG object-oriented standard.
28.7 Commercial Data Mining Tools 1061
In general, these programs perform sequential processing in a single machine. Many of these products work in the client-server mode. Some products incorporate paral- lel processing in parallel computer architectures and work as a part of online analyt- ical processing (OLAP) tools.
28.7.1 User Interface Most of the tools run in a graphical user interface (GUI) environment. Some prod- ucts include sophisticated visualization techniques to view data and rules (for example, SGI’s MineSet), and are even able to manipulate data this way interac- tively. Text interfaces are rare and are more common in tools available for UNIX, such as IBM’s Intelligent Miner.
28.7.2 Application Programming Interface Usually, the application programming interface (API) is an optional tool. Most products do not permit using their internal functions. However, some of them allow the application programmer to reuse their code. The most common interfaces are C libraries and Dynamic Link Libraries (DLLs). Some tools include proprietary data- base command languages.
In Table 28.1 we list 11 representative data mining tools. To date, there are almost one hundred commercial data mining products available worldwide. Non-U.S. products include Data Surveyor from the Netherlands and PolyAnalyst from Russia.
28.7.3 Future Directions Data mining tools are continually evolving, building on ideas from the latest scien- tific research. Many of these tools incorporate the latest algorithms taken from artificial intelligence (AI), statistics, and optimization.
Currently, fast processing is done using modern database techniques—such as dis- tributed processing—in client-server architectures, in parallel databases, and in data warehousing. For the future, the trend is toward developing Internet capabilities more fully. Additionally, hybrid approaches will become commonplace, and pro- cessing will be done using all resources available. Processing will take advantage of both parallel and distributed computing environments. This shift is especially important because modern databases contain very large amounts of information. Not only are multimedia databases growing, but also image storage and retrieval are slow operations. Also, the cost of secondary storage is decreasing, so massive infor- mation storage will be feasible, even for small companies. Thus, data mining pro- grams will have to deal with larger sets of data of more companies.
Most of data mining software will use the ODBC standard to extract data from busi- ness databases; proprietary input formats can be expected to disappear. There is a definite need to include nonstandard data, including images and other multimedia data, as source data for data mining.
Table 28.1 Some Representative Data Mining Tools
Company Product Technique Platform Interface*
AcknoSoft Kate Decision trees, Case-based reasoning
Windows UNIX Microsoft Access
Angoss Knowledge SEEKER
Decision trees, Statistics Windows ODBC
Business Objects Business Miner Neural nets, Machine learn- ing
Windows ODBC
CrossZ QueryObject Statistical analysis, Optimization algorithm
Windows MVS UNIX
ODBC
Data Distilleries Data Surveyor Comprehensive; can mix different types of data mining
UNIX ODBC ODMG- compliant
DBMiner Technology Inc.
DBMiner OLAP analysis, Associations, Classification, Clustering algorithms
Windows Microsoft 7.0 OLAP
IBM Intelligent Miner Classification, Association rules, Predictive models
UNIX (AIX) IBM DB2
Megaputer Intelligence
PolyAnalyst Symbolic knowledge acquisition, Evolutionary programming
Windows OS/2 ODBC Oracle DB2
NCR Management Discovery Tool (MDT)
Association rules Windows ODBC
Purple Insight MineSet Decision trees, Association rules
UNIX (Irix) Oracle Sybase Informix
SAS Enterprise Miner Decision trees, Association rules, Neural nets, Regression, Clustering
UNIX (Solaris) Windows Macintosh
ODBC Oracle AS/400
*ODBC: Open Data Base Connectivity
ODMG: Object Data Management Group
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28.8 Summary In this chapter we surveyed the important discipline of data mining, which uses database technology to discover additional knowledge or patterns in the data. We gave an illustrative example of knowledge discovery in databases, which has a wider scope than data mining. For data mining, among the various techniques, we focused on the details of association rule mining, classification, and clustering. We presented algorithms in each of these areas and illustrated with examples of how those algo- rithms work.
A variety of other techniques, including the AI-based neural networks and genetic algorithms, were also briefly discussed. Active research is ongoing in data mining and we have outlined some of the expected research directions. In the future data- base technology products market, a great deal of data mining activity is expected. We summarized 11 out of nearly one hundred data mining tools available; future research is expected to extend the number and functionality significantly.
Review Questions 28.1. What are the different phases of the knowledge discovery from databases?
Describe a complete application scenario in which new knowledge may be mined from an existing database of transactions.
28.2. What are the goals or tasks that data mining attempts to facilitate?
28.3. What are the five types of knowledge produced from data mining?
28.4. What are association rules as a type of knowledge? Give a definition of sup- port and confidence and use them to define an association rule.
28.5. What is the downward closure property? How does it aid in developing an efficient algorithm for finding association rules, that is, with regard to find- ing large itemsets?
28.6. What was the motivating factor for the development of the FP-tree algo- rithm for association rule mining?
28.7. Describe an association rule among hierarchies with an example.
28.8. What is a negative association rule in the context of the hierarchy in Figure 28.3?
28.9. What are the difficulties of mining association rules from large databases?
28.10. What are classification rules and how are decision trees related to them?
28.11. What is entropy and how is it used in building decision trees?
28.12. How does clustering differ from classification?
28.13. Describe neural networks and genetic algorithms as techniques for data mining. What are the main difficulties in using these techniques?
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Trans_id Items_purchased 101 milk, bread, eggs 102 milk, juice 103 juice, butter 104 milk, bread, eggs 105 coffee, eggs 106 coffee 107 coffee, juice 108 milk, bread, cookies, eggs 109 cookies, butter 110 milk, bread
RID Age City Gender Education Repeat_customer 101 20...30 NY F college YES 102 20...30 SF M graduate YES 103 31...40 NY F college YES 104 51...60 NY F college NO 105 31...40 LA M high school NO 106 41...50 NY F college YES 107 41...50 NY F graduate YES 108 20...30 LA M college YES 109 20...30 NY F high school NO 110 20...30 NY F college YES
Exercises 28.14. Apply the Apriori algorithm to the following data set.
The set of items is {milk, bread, cookies, eggs, butter, coffee, juice}. Use 0.2 for the minimum support value.
28.15. Show two rules that have a confidence of 0.7 or greater for an itemset con- taining three items from Exercise 28.14.
28.16. For the Partition algorithm, prove that any frequent itemset in the database must appear as a local frequent itemset in at least one partition.
28.17. Show the FP-tree that would be made for the data from Exercise 28.14.
28.18. Apply the FP-Growth algorithm to the FP-tree from Exercise 28.17 and show the frequent itemsets.
28.19. Apply the classification algorithm to the following set of data records. The class attribute is Repeat_customer.
28.20. Consider the following set of two-dimensional records:
Selected Bibliography 1065
RID Dimension1 Dimension2 1 8 4 2 5 4 3 2 4 4 2 6 5 2 8 6 8 6
Also consider two different clustering schemes: (1) where Cluster1 contains records {1,2,3} and Cluster2 contains records {4,5,6} and (2) where Cluster1 contains records {1,6} and Cluster2 contains records {2,3,4,5}. Which scheme is better and why?
28.21. Use the k-Means algorithm to cluster the data from Exercise 28.20. We can use a value of 3 for K and we can assume that the records with RIDs 1, 3, and 5 are used for the initial cluster centroids (means).
28.22. The k-Means algorithm uses a similarity metric of distance between a record and a cluster centroid. If the attributes of the records are not quantitative but categorical in nature, such as Income_level with values {low, medium, high} or Married with values {Yes, No} or State_of_residence with values {Alabama, Alaska, ..., Wyoming}, then the distance metric is not meaningful. Define a more suitable similarity metric that can be used for clustering data records that contain categorical data.
Selected Bibliography Literature on data mining comes from several fields, including statistics, mathemat- ical optimization, machine learning, and artificial intelligence. Chen et al. (1996) give a good summary of the database perspective on data mining. The book by Han and Kamber (2001) is an excellent text, describing in detail the different algorithms and techniques used in the data mining area. Work at IBM Almaden research has produced a large number of early concepts and algorithms as well as results from some performance studies. Agrawal et al. (1993) report the first major study on association rules. Their Apriori algorithm for market basket data in Agrawal and Srikant (1994) is improved by using partitioning in Savasere et al. (1995); Toivonen (1996) proposes sampling as a way to reduce the processing effort. Cheung et al. (1996) extends the partitioning to distributed environments; Lin and Dunham (1998) propose techniques to overcome problems with data skew. Agrawal et al. (1993b) discuss the performance perspective on association rules. Mannila et al. (1994), Park et al. (1995), and Amir et al. (1997) present additional efficient algo- rithms related to association rules. Han et al. (2000) present the FP-tree algorithm
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discussed in this chapter. Srikant and Agrawal(1995) proposes mining generalized rules. Savasere et al. (1998) present the first approach to mining negative associa- tions. Agrawal et al. (1996) describe the Quest system at IBM. Sarawagi et al. (1998) describe an implementation where association rules are integrated with a relational database management system. Piatesky-Shapiro and Frawley (1992) have con- tributed papers from a wide range of topics related to knowledge discovery. Zhang et al. (1996) present the BIRCH algorithm for clustering large databases. Information about decision tree learning and the classification algorithm presented in this chapter can be found in Mitchell (1997).
Adriaans and Zantinge (1996), Fayyad et al. (1997), and Weiss and Indurkhya (1998) are books devoted to the different aspects of data mining and its use in pre- diction. The idea of genetic algorithms was proposed by Holland (1975); a good survey of genetic algorithms appears in Srinivas and Patnaik (1994). Neural net- works have a vast literature; a comprehensive introduction is available in Lippman (1987).
Tan et al. (2006) provides a comprehensive introduction to data mining and has a detailed set of references. Readers are also advised to consult proceedings of two prominent annual conferences in data mining: the Knowledge Discovery and Data Mining Conference (KDD), which has been running since 1995, and the SIAM International Conference on Data Mining (SDM), which has been running since 2001. Links to past conferences may be found at http://dblp.uni-trier.de.
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Overview of Data Warehousing and OLAP
The increasing processing power and sophisticationof analytical tools and techniques have resulted in the development of what are known as data warehouses. These data warehouses provide storage, functionality, and responsiveness to queries beyond the capabilities of transaction-oriented databases. Accompanying this ever-increasing power is a great demand to improve the data access performance of databases. As we have seen throughout this book, traditional databases balance the requirement of data access with the need to ensure data integrity. In modern organizations, users of data are often completely removed from the data sources. Many people only need read- access to data, but still need fast access to a larger volume of data than can conve- niently be downloaded to the desktop. Often such data comes from multiple databases. Because many of the analyses performed are recurrent and predictable, software vendors and systems support staff are designing systems to support these functions. Presently there is a great need to provide decision makers from middle management upward with information at the correct level of detail to support deci- sion making. Data warehousing, online analytical processing (OLAP), and data min- ing provide this functionality. We gave an introduction to data mining techniques in Chapter 28. In this chapter we give a broad overview of data warehousing and OLAP technologies.
29.1 Introduction, Definitions, and Terminology In Chapter 1 we defined a database as a collection of related data and a database sys- tem as a database and database software together. A data warehouse is also a collec- tion of information as well as a supporting system. However, a clear distinction
29chapter 29
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exists. Traditional databases are transactional (relational, object-oriented, network, or hierarchical). Data warehouses have the distinguishing characteristic that they are mainly intended for decision-support applications. They are optimized for data retrieval, not routine transaction processing.
Because data warehouses have been developed in numerous organizations to meet particular needs, there is no single, canonical definition of the term data warehouse. Professional magazine articles and books in the popular press have elaborated on the meaning in a variety of ways. Vendors have capitalized on the popularity of the term to help market a variety of related products, and consultants have provided a large variety of services, all under the data warehousing banner. However, data warehouses are quite distinct from traditional databases in their structure, func- tioning, performance, and purpose.
W. H. Inmon1 characterized a data warehouse as a subject-oriented, integrated, non- volatile, time-variant collection of data in support of management’s decisions. Data warehouses provide access to data for complex analysis, knowledge discovery, and decision making. They support high-performance demands on an organization’s data and information. Several types of applications—OLAP, DSS, and data mining applications—are supported. We define each of these next.
OLAP (online analytical processing) is a term used to describe the analysis of com- plex data from the data warehouse. In the hands of skilled knowledge workers, OLAP tools use distributed computing capabilities for analyses that require more storage and processing power than can be economically and efficiently located on an individual desktop.
DSS (decision-support systems), also known as EIS—executive information sys- tems; not to be confused with enterprise integration systems—support an organiza- tion’s leading decision makers with higher-level data for complex and important decisions. Data mining (which we discussed in Chapter 28) is used for knowledge discovery, the process of searching data for unanticipated new knowledge.
Traditional databases support online transaction processing (OLTP), which includes insertions, updates, and deletions, while also supporting information query requirements. Traditional relational databases are optimized to process queries that may touch a small part of the database and transactions that deal with insertions or updates of a few tuples per relation to process. Thus, they cannot be optimized for OLAP, DSS, or data mining. By contrast, data warehouses are designed precisely to support efficient extraction, processing, and presentation for analytic and decision-making purposes. In comparison to traditional databases, data warehouses generally contain very large amounts of data from multiple sources that may include databases from different data models and sometimes files acquired from independent systems and platforms.
1Inmon (1992) is credited with initially using the term warehouse. The latest edition of his work is Inmon (2005).
29.2 Characteristics of Data Warehouses 1069
Databases Cleaning
Backflushing
Reformatting
Data mining
DSS EIS
OLAP
Other data inputs
Updates/new data
Metadata
Data
Data warehouse
Figure 29.1 Sample transactions in market-basket model.
29.2 Characteristics of Data Warehouses To discuss data warehouses and distinguish them from transactional databases calls for an appropriate data model. The multidimensional data model (explained in more detail in Section 29.3) is a good fit for OLAP and decision-support technolo- gies. In contrast to multidatabases, which provide access to disjoint and usually het- erogeneous databases, a data warehouse is frequently a store of integrated data from multiple sources, processed for storage in a multidimensional model. Unlike most transactional databases, data warehouses typically support time-series and trend analysis, both of which require more historical data than is generally maintained in transactional databases.
Compared with transactional databases, data warehouses are nonvolatile. This means that information in the data warehouse changes far less often and may be regarded as non–real-time with periodic updating. In transactional systems, transac- tions are the unit and are the agent of change to the database; by contrast, data ware- house information is much more coarse-grained and is refreshed according to a careful choice of refresh policy, usually incremental. Warehouse updates are handled by the warehouse’s acquisition component that provides all required preprocessing.
We can also describe data warehousing more generally as a collection of decision sup- port technologies, aimed at enabling the knowledge worker (executive, manager, ana- lyst) to make better and faster decisions.2 Figure 29.1 gives an overview of the conceptual structure of a data warehouse. It shows the entire data warehousing process, which includes possible cleaning and reformatting of data before loading it into the warehouse. This process is handled by tools known as ETL (extraction, transformation, and loading) tools. At the back end of the process, OLAP, data min- ing, and DSS may generate new relevant information such as rules; this information is shown in the figure going back into the warehouse. The figure also shows that data sources may include files.
2Chaudhuri and Dayal (1997) provide an excellent tutorial on the topic, with this as a starting definition.
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Data warehouses have the following distinctive characteristics:3
■ Multidimensional conceptual view
■ Generic dimensionality
■ Unlimited dimensions and aggregation levels
■ Unrestricted cross-dimensional operations
■ Dynamic sparse matrix handling
■ Client-server architecture
■ Multiuser support
■ Accessibility
■ Transparency
■ Intuitive data manipulation
■ Consistent reporting performance
■ Flexible reporting
Because they encompass large volumes of data, data warehouses are generally an order of magnitude (sometimes two orders of magnitude) larger than the source databases. The sheer volume of data (likely to be in terabytes or even petabytes) is an issue that has been dealt with through enterprise-wide data warehouses, virtual data warehouses, and data marts:
■ Enterprise-wide data warehouses are huge projects requiring massive investment of time and resources.
■ Virtual data warehouses provide views of operational databases that are materialized for efficient access.
■ Data marts generally are targeted to a subset of the organization, such as a department, and are more tightly focused.
29.3 Data Modeling for Data Warehouses Multidimensional models take advantage of inherent relationships in data to popu- late data in multidimensional matrices called data cubes. (These may be called hypercubes if they have more than three dimensions.) For data that lends itself to dimensional formatting, query performance in multidimensional matrices can be much better than in the relational data model. Three examples of dimensions in a corporate data warehouse are the corporation’s fiscal periods, products, and regions.
A standard spreadsheet is a two-dimensional matrix. One example would be a spreadsheet of regional sales by product for a particular time period. Products could be shown as rows, with sales revenues for each region comprising the columns. (Figure 29.2 shows this two-dimensional organization.) Adding a time dimension,
3Codd and Salley (1993) coined the term OLAP and mentioned these characteristics. We have reordered their original list.
29.3 Data Modeling for Data Warehouses 1071
Reg 1
P123
P124
P125
P126
Region P
ro d
u ct
Reg 2 Reg 3
Figure 29.2 A two-dimensional matrix model.
such as an organization’s fiscal quarters, would produce a three-dimensional matrix, which could be represented using a data cube.
Figure 29.3 shows a three-dimensional data cube that organizes product sales data by fiscal quarters and sales regions. Each cell could contain data for a specific product,
P126
P127
P ro
d uc
t
P125
P124
P123
Reg 1 Reg 2
Region Reg 3
Qtr 1
Qtr 2Fis
cal_ qua
rter Qtr
3 Qtr
4 Figure 29.3 A three-dimensional data cube model.
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Reg 4
R eg
io n
Reg 3
Reg 2
Reg 1 Qtr 1
Qtr 2
Fiscal quarter
Qtr 3 Qtr 4
P 1 23
P 1 24
Pro duc
t P 1
25 P 1
26 P 1
27Figure 29.4 Pivoted version of the data cube from Figure 29.3.
specific fiscal quarter, and specific region. By including additional dimensions, a data hypercube could be produced, although more than three dimensions cannot be eas- ily visualized or graphically presented. The data can be queried directly in any com- bination of dimensions, bypassing complex database queries. Tools exist for viewing data according to the user’s choice of dimensions.
Changing from one-dimensional hierarchy (orientation) to another is easily accom- plished in a data cube with a technique called pivoting (also called rotation). In this technique the data cube can be thought of as rotating to show a different orienta- tion of the axes. For example, you might pivot the data cube to show regional sales revenues as rows, the fiscal quarter revenue totals as columns, and the company’s products in the third dimension (Figure 29.4). Hence, this technique is equivalent to having a regional sales table for each product separately, where each table shows quarterly sales for that product region by region.
Multidimensional models lend themselves readily to hierarchical views in what is known as roll-up display and drill-down display. A roll-up display moves up the hierarchy, grouping into larger units along a dimension (for example, summing weekly data by quarter or by year). Figure 29.5 shows a roll-up display that moves from individual products to a coarser-grain of product categories. Shown in Figure 29.6, a drill-down display provides the opposite capability, furnishing a finer- grained view, perhaps disaggregating country sales by region and then regional sales by subregion and also breaking up products by styles.
29.3 Data Modeling for Data Warehouses 1073
Products 1XX
Products 2XX
Products 3XX
Products 4XX
Region P
ro d
uc t
ca te
g o
rie s
Region 1 Region 2 Region 3
Figure 29.5 The roll-up operation.
The multidimensional storage model involves two types of tables: dimension tables and fact tables. A dimension table consists of tuples of attributes of the dimension. A fact table can be thought of as having tuples, one per a recorded fact. This fact contains some measured or observed variable(s) and identifies it (them) with point- ers to dimension tables. The fact table contains the data, and the dimensions iden- tify each tuple in that data. Figure 29.7 contains an example of a fact table that can be viewed from the perspective of multiple dimension tables.
Two common multidimensional schemas are the star schema and the snowflake schema. The star schema consists of a fact table with a single table for each dimen- sion (Figure 29.7). The snowflake schema is a variation on the star schema in which
A B C D
P123 Styles
P124 Styles
P125 Styles
A B C
A B C D
Sub_reg 1 Sub_reg 2
Region 1 Region 2
Sub_reg 3 Sub_reg 4 Sub_reg 1
Figure 29.6 The drill-down operation.
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Dimension table
Product
Prod_no Prod_name Prod_descr Prod_style Prod_line
Fact table
Business results
Product Quarter Region Sales_revenue
Dimension table
Fiscal quarter
Qtr Year Beg_date End_date
Dimension table
Region Subregion
Figure 29.7 A star schema with fact and dimensional tables.
Dimension tables
Pname
Prod_name Prod_descr Product
Prod_no Prod_name Style Prod_line_no
Fact table
Business results
Product Quarter Region Revenue
Pline
Prod_line_no Prod_line_name
Dimension tables
Fiscal quarter
Qtr Year Beg_date
FQ dates
Beg_date End_date
Sales revenue
Region Subregion
Figure 29.8 A snowflake schema.
the dimensional tables from a star schema are organized into a hierarchy by normal- izing them (Figure 29.8). Some installations are normalizing data warehouses up to the third normal form so that they can access the data warehouse to the finest level of detail. A fact constellation is a set of fact tables that share some dimension tables. Figure 29.9 shows a fact constellation with two fact tables, business results and busi- ness forecast. These share the dimension table called product. Fact constellations limit the possible queries for the warehouse.
Data warehouse storage also utilizes indexing techniques to support high- performance access (see Chapter 18 for a discussion of indexing). A technique called bitmap indexing constructs a bit vector for each value in a domain (column)
29.4 Building a Data Warehouse 1075
Fact table I
Business results
Prod_no Prod_name Prod_descr Prod_style Prod_line
Dimension table
Product
Product Quarter Region Revenue
Fact table II
Business forecast
Product Future_qtr Region Projected_revenue
Figure 29.9 A fact constellation.
being indexed. It works very well for domains of low cardinality. There is a 1 bit placed in the jth position in the vector if the jth row contains the value being indexed. For example, imagine an inventory of 100,000 cars with a bitmap index on car size. If there are four car sizes—economy, compact, mid-size, and full-size— there will be four bit vectors, each containing 100,000 bits (12.5K) for a total index size of 50K. Bitmap indexing can provide considerable input/output and storage space advantages in low-cardinality domains. With bit vectors a bitmap index can provide dramatic improvements in comparison, aggregation, and join performance.
In a star schema, dimensional data can be indexed to tuples in the fact table by join indexing. Join indexes are traditional indexes to maintain relationships between primary key and foreign key values. They relate the values of a dimension of a star schema to rows in the fact table. For example, consider a sales fact table that has city and fiscal quarter as dimensions. If there is a join index on city, for each city the join index maintains the tuple IDs of tuples containing that city. Join indexes may involve multiple dimensions.
Data warehouse storage can facilitate access to summary data by taking further advantage of the nonvolatility of data warehouses and a degree of predictability of the analyses that will be performed using them. Two approaches have been used: (1) smaller tables including summary data such as quarterly sales or revenue by product line, and (2) encoding of level (for example, weekly, quarterly, annual) into existing tables. By comparison, the overhead of creating and maintaining such aggregations would likely be excessive in a volatile, transaction-oriented database.
29.4 Building a Data Warehouse In constructing a data warehouse, builders should take a broad view of the antici- pated use of the warehouse. There is no way to anticipate all possible queries or analyses during the design phase. However, the design should specifically support ad-hoc querying, that is, accessing data with any meaningful combination of values for the attributes in the dimension or fact tables. For example, a marketing- intensive consumer-products company would require different ways of organizing the data warehouse than would a nonprofit charity focused on fund raising. An appropriate schema should be chosen that reflects anticipated usage.
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Acquisition of data for the warehouse involves the following steps:
1. The data must be extracted from multiple, heterogeneous sources, for exam- ple, databases or other data feeds such as those containing financial market data or environmental data.
2. Data must be formatted for consistency within the warehouse. Names, meanings, and domains of data from unrelated sources must be reconciled. For instance, subsidiary companies of a large corporation may have different fiscal calendars with quarters ending on different dates, making it difficult to aggregate financial data by quarter. Various credit cards may report their transactions differently, making it difficult to compute all credit sales. These format inconsistencies must be resolved.
3. The data must be cleaned to ensure validity. Data cleaning is an involved and complex process that has been identified as the largest labor-demanding component of data warehouse construction. For input data, cleaning must occur before the data is loaded into the warehouse. There is nothing about cleaning data that is specific to data warehousing and that could not be applied to a host database. However, since input data must be examined and formatted consistently, data warehouse builders should take this opportu- nity to check for validity and quality. Recognizing erroneous and incomplete data is difficult to automate, and cleaning that requires automatic error cor- rection can be even tougher. Some aspects, such as domain checking, are eas- ily coded into data cleaning routines, but automatic recognition of other data problems can be more challenging. (For example, one might require that City = ‘San Francisco’ together with State = ‘CT’ be recognized as an incorrect combination.) After such problems have been taken care of, similar data from different sources must be coordinated for loading into the ware- house. As data managers in the organization discover that their data is being cleaned for input into the warehouse, they will likely want to upgrade their data with the cleaned data. The process of returning cleaned data to the source is called backflushing (see Figure 29.1).
4. The data must be fitted into the data model of the warehouse. Data from the various sources must be installed in the data model of the warehouse. Data may have to be converted from relational, object-oriented, or legacy data- bases (network and/or hierarchical) to a multidimensional model.
5. The data must be loaded into the warehouse. The sheer volume of data in the warehouse makes loading the data a significant task. Monitoring tools for loads as well as methods to recover from incomplete or incorrect loads are required. With the huge volume of data in the warehouse, incremental updating is usually the only feasible approach. The refresh policy will proba- bly emerge as a compromise that takes into account the answers to the fol- lowing questions:
■ How up-to-date must the data be?
■ Can the warehouse go offline, and for how long?
■ What are the data interdependencies?
29.4 Building a Data Warehouse 1077
■ What is the storage availability?
■ What are the distribution requirements (such as for replication and parti- tioning)?
■ What is the loading time (including cleaning, formatting, copying, trans- mitting, and overhead such as index rebuilding)?
As we have said, databases must strike a balance between efficiency in transaction processing and supporting query requirements (ad hoc user requests), but a data warehouse is typically optimized for access from a decision maker’s needs. Data storage in a data warehouse reflects this specialization and involves the following processes:
■ Storing the data according to the data model of the warehouse ■ Creating and maintaining required data structures ■ Creating and maintaining appropriate access paths ■ Providing for time-variant data as new data are added ■ Supporting the updating of warehouse data ■ Refreshing the data ■ Purging data
Although adequate time can be devoted initially to constructing the warehouse, the sheer volume of data in the warehouse generally makes it impossible to simply reload the warehouse in its entirety later on. Alternatives include selective (partial) refreshing of data and separate warehouse versions (requiring double storage capac- ity for the warehouse!). When the warehouse uses an incremental data refreshing mechanism, data may need to be periodically purged; for example, a warehouse that maintains data on the previous twelve business quarters may periodically purge its data each year.
Data warehouses must also be designed with full consideration of the environment in which they will reside. Important design considerations include the following:
■ Usage projections
■ The fit of the data model
■ Characteristics of available sources
■ Design of the metadata component
■ Modular component design
■ Design for manageability and change
■ Considerations of distributed and parallel architecture
We discuss each of these in turn. Warehouse design is initially driven by usage pro- jections; that is, by expectations about who will use the warehouse and how they will use it. Choice of a data model to support this usage is a key initial decision. Usage projections and the characteristics of the warehouse’s data sources are both taken into account. Modular design is a practical necessity to allow the warehouse to evolve with the organization and its information environment. Additionally, a well-
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built data warehouse must be designed for maintainability, enabling the warehouse managers to plan for and manage change effectively while providing optimal sup- port to users.
You may recall the term metadata from Chapter 1; metadata was defined as the description of a database including its schema definition. The metadata repository is a key data warehouse component. The metadata repository includes both technical and business metadata. The first, technical metadata, covers details of acquisition processing, storage structures, data descriptions, warehouse operations and mainte- nance, and access support functionality. The second, business metadata, includes the relevant business rules and organizational details supporting the warehouse.
The architecture of the organization’s distributed computing environment is a major determining characteristic for the design of the warehouse.
There are two basic distributed architectures: the distributed warehouse and the federated warehouse. For a distributed warehouse, all the issues of distributed databases are relevant, for example, replication, partitioning, communications, and consistency concerns. A distributed architecture can provide benefits particularly important to warehouse performance, such as improved load balancing, scalability of performance, and higher availability. A single replicated metadata repository would reside at each distribution site. The idea of the federated warehouse is like that of the federated database: a decentralized confederation of autonomous data warehouses, each with its own metadata repository. Given the magnitude of the challenge inherent to data warehouses, it is likely that such federations will consist of smaller scale components, such as data marts. Large organizations may choose to federate data marts rather than build huge data warehouses.
29.5 Typical Functionality of a Data Warehouse
Data warehouses exist to facilitate complex, data-intensive, and frequent ad hoc queries. Accordingly, data warehouses must provide far greater and more efficient query support than is demanded of transactional databases. The data warehouse access component supports enhanced spreadsheet functionality, efficient query processing, structured queries, ad hoc queries, data mining, and materialized views. In particular, enhanced spreadsheet functionality includes support for state-of-the- art spreadsheet applications (for example, MS Excel) as well as for OLAP applica- tions programs. These offer preprogrammed functionalities such as the following:
■ Roll-up. Data is summarized with increasing generalization (for example, weekly to quarterly to annually).
■ Drill-down. Increasing levels of detail are revealed (the complement of roll- up).
■ Pivot. Cross tabulation (also referred to as rotation) is performed. ■ Slice and dice. Projection operations are performed on the dimensions. ■ Sorting. Data is sorted by ordinal value.
29.6 Data Warehouse versus Views 1079
■ Selection. Data is available by value or range. ■ Derived (computed) attributes. Attributes are computed by operations on
stored and derived values.
Because data warehouses are free from the restrictions of the transactional environ- ment, there is an increased efficiency in query processing. Among the tools and techniques used are query transformation; index intersection and union; special ROLAP (relational OLAP) and MOLAP (multidimensional OLAP) functions; SQL extensions; advanced join methods; and intelligent scanning (as in piggy-backing multiple queries).
Improved performance has also been attained with parallel processing. Parallel server architectures include symmetric multiprocessor (SMP), cluster, and mas- sively parallel processing (MPP), and combinations of these.
Knowledge workers and decision makers use tools ranging from parametric queries to ad hoc queries to data mining. Thus, the access component of the data warehouse must provide support for structured queries (both parametric and ad hoc). Together, these make up a managed query environment. Data mining itself uses techniques from statistical analysis and artificial intelligence. Statistical analysis can be performed by advanced spreadsheets, by sophisticated statistical analysis soft- ware, or by custom-written programs. Techniques such as lagging, moving averages, and regression analysis are also commonly employed. Artificial intelligence tech- niques, which may include genetic algorithms and neural networks, are used for classification and are employed to discover knowledge from the data warehouse that may be unexpected or difficult to specify in queries. (We treat data mining in detail in Chapter 28.)
29.6 Data Warehouse versus Views Some people have considered data warehouses to be an extension of database views. Earlier we mentioned materialized views as one way of meeting requirements for improved access to data (see Section 5.3 for a discussion of views). Materialized views have been explored for their performance enhancement. Views, however, pro- vide only a subset of the functions and capabilities of data warehouses. Views and data warehouses are alike in that they both have read-only extracts from databases and subject orientation. However, data warehouses are different from views in the following ways:
■ Data warehouses exist as persistent storage instead of being materialized on demand.
■ Data warehouses are not usually relational, but rather multidimensional. Views of a relational database are relational.
■ Data warehouses can be indexed to optimize performance. Views cannot be indexed independent of the underlying databases.
■ Data warehouses characteristically provide specific support of functionality; views cannot.
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■ Data warehouses provide large amounts of integrated and often temporal data, generally more than is contained in one database, whereas views are an extract of a database.
29.7 Difficulties of Implementing Data Warehouses
Some significant operational issues arise with data warehousing: construction, administration, and quality control. Project management—the design, construc- tion, and implementation of the warehouse—is an important and challenging con- sideration that should not be underestimated. The building of an enterprise-wide warehouse in a large organization is a major undertaking, potentially taking years from conceptualization to implementation. Because of the difficulty and amount of lead time required for such an undertaking, the widespread development and deployment of data marts may provide an attractive alternative, especially to those organizations with urgent needs for OLAP, DSS, and/or data mining support.
The administration of a data warehouse is an intensive enterprise, proportional to the size and complexity of the warehouse. An organization that attempts to admin- ister a data warehouse must realistically understand the complex nature of its administration. Although designed for read access, a data warehouse is no more a static structure than any of its information sources. Source databases can be expected to evolve. The warehouse’s schema and acquisition component must be expected to be updated to handle these evolutions.
A significant issue in data warehousing is the quality control of data. Both quality and consistency of data are major concerns. Although the data passes through a cleaning function during acquisition, quality and consistency remain significant issues for the database administrator. Melding data from heterogeneous and dis- parate sources is a major challenge given differences in naming, domain definitions, identification numbers, and the like. Every time a source database changes, the data warehouse administrator must consider the possible interactions with other ele- ments of the warehouse.
Usage projections should be estimated conservatively prior to construction of the data warehouse and should be revised continually to reflect current requirements. As utilization patterns become clear and change over time, storage and access paths can be tuned to remain optimized for support of the organization’s use of its ware- house. This activity should continue throughout the life of the warehouse in order to remain ahead of demand. The warehouse should also be designed to accommo- date the addition and attrition of data sources without major redesign. Sources and source data will evolve, and the warehouse must accommodate such change. Fitting the available source data into the data model of the warehouse will be a continual challenge, a task that is as much art as science. Because there is continual rapid change in technologies, both the requirements and capabilities of the warehouse will change considerably over time. Additionally, data warehousing technology itself will continue to evolve for some time so that component structures and functional-
Review Questions 1081
ities will continually be upgraded. This certain change is excellent motivation for having fully modular design of components.
Administration of a data warehouse will require far broader skills than are needed for traditional database administration. A team of highly skilled technical experts with overlapping areas of expertise will likely be needed, rather than a single indi- vidual. Like database administration, data warehouse administration is only partly technical; a large part of the responsibility requires working effectively with all the members of the organization with an interest in the data warehouse. However diffi- cult that can be at times for database administrators, it is that much more challeng- ing for data warehouse administrators, as the scope of their responsibilities is considerably broader.
Design of the management function and selection of the management team for a database warehouse are crucial. Managing the data warehouse in a large organization will surely be a major task. Many commercial tools are available to support manage- ment functions. Effective data warehouse management will certainly be a team func- tion, requiring a wide set of technical skills, careful coordination, and effective leadership. Just as we must prepare for the evolution of the warehouse, we must also recognize that the skills of the management team will, of necessity, evolve with it.
29.8 Summary In this chapter we surveyed the field known as data warehousing. Data warehousing can be seen as a process that requires a variety of activities to precede it. In contrast, data mining (see Chapter 28) may be thought of as an activity that draws knowledge from an existing data warehouse. We introduced key concepts related to data ware- housing and we discussed the special functionality associated with a multidimen- sional view of data. We also discussed the ways in which data warehouses supply decision makers with information at the correct level of detail, based on an appro- priate organization and perspective.
Review Questions 29.1. What is a data warehouse? How does it differ from a database?
29.2. Define the terms: OLAP (online analytical processing), ROLAP (relational OLAP), MOLAP (multidimensional OLAP), and DSS (decision-support systems).
29.3. Describe the characteristics of a data warehouse. Divide them into function- ality of a warehouse and advantages users derive from it.
29.4. What is the multidimensional data model? How is it used in data ware- housing?
29.5. Define the following terms: star schema, snowflake schema, fact constella- tion, data marts.
1082 Chapter 29 Overview of Data Warehousing and OLAP
29.6. What types of indexes are built for a warehouse? Illustrate the uses for each with an example.
29.7. Describe the steps of building a warehouse.
29.8. What considerations play a major role in the design of a warehouse?
29.9. Describe the functions a user can perform on a data warehouse and illustrate the results of these functions on a sample multidimensional data warehouse.
29.10. How is the concept of a relational view related to a data warehouse and data marts? In what way are they different?
29.11. List the difficulties in implementing a data warehouse.
29.12. List the open issues and research problems in data warehousing.
Selected Bibliography Inmon (1992, 2005) is credited for giving the term wide acceptance. Codd and Salley (1993) popularized the term online analytical processing (OLAP) and defined a set of characteristics for data warehouses to support OLAP. Kimball (1996) is known for his contribution to the development of the data warehousing field. Mattison (1996) is one of the several books on data warehousing that gives a comprehensive analysis of techniques available in data warehouses and the strate- gies companies should use in deploying them. Ponniah (2002) gives a very good practical overview of the data warehouse building process from requirements collection to deployment maintenance. Bischoff and Alexander (1997) is a compila- tion of advice from experts. Chaudhuri and Dayal (1997) give an excellent tutorial on the topic, while Widom (1995) points to a number of outstanding research problems.
1083
Alternative Diagrammatic Notations for ER Models
Figure A.1 shows a number of different diagram-matic notations for representing ER and EER model concepts. Unfortunately, there is no standard notation: different database design practitioners prefer different notations. Similarly, various CASE (computer- aided software engineering) tools and OOA (object-oriented analysis) methodolo- gies use various notations. Some notations are associated with models that have additional concepts and constraints beyond those of the ER and EER models described in Chapters 7 through 9, while other models have fewer concepts and constraints. The notation we used in Chapter 7 is quite close to the original notation for ER diagrams, which is still widely used. We discuss some alternate notations here.
Figure A.1(a) shows different notations for displaying entity types/classes, attrib- utes, and relationships. In Chapters 7 through 9, we used the symbols marked (i) in Figure A.1(a)—namely, rectangle, oval, and diamond. Notice that symbol (ii) for entity types/classes, symbol (ii) for attributes, and symbol (ii) for relationships are similar, but they are used by different methodologies to represent three different concepts. The straight line symbol (iii) for representing relationships is used by sev- eral tools and methodologies.
Figure A.1(b) shows some notations for attaching attributes to entity types. We used notation (i). Notation (ii) uses the third notation (iii) for attributes from Figure A.1(a). The last two notations in Figure A.1(b)—(iii) and (iv)—are popular in OOA methodologies and in some CASE tools. In particular, the last notation displays both the attributes and the methods of a class, separated by a horizontal line.
Aappendix A
1084 Appendix A Alternative Diagrammatic Notations for ER Models
Entity type/class symbols E(i) E(ii)
Attribute symbols (i) (ii)
Relationship symbols (i) (ii)
(iii)
(iii)
(a)
A
R
A A
R R
(b) Ssn Name Address
.
.
.
EMPLOYEE(ii)
EMPLOYEE
Ssn(i) Name
Address . . .
.
.
.
(iii) Ssn
Name Address
EMPLOYEE
.
.
.
.
.
.
(iv)
Ssn
Name Address
Hire_emp
Fire_emp
EMPLOYEE
(c) (i)
(ii)
1 N
(iii)
(iv)
(v)
(vi) *
(d) (i)
(ii)
1 N
(0,n) (1,1)
(0,n)(1,1) (iii)
(iv)
(e) (i)
(iv) C
(ii) (iii)C
S2S1 S3
d o
S2S1 S3
G
Gs
C
S2S1 S3
C
S2S1 S3
(v) (vi)C
S2S1 S3
C
S2S1 S3
G
(v) 0..n1..1
Figure A.1 Alternative notations. (a) Symbols for entity type/class, attribute, and relationship. (b) Displaying attributes. (c) Displaying cardinality ratios. (d) Various (min, max) notations. (e) Notations for displaying specialization/generalization.
Appendix A Alternative Diagrammatic Notations for ER Models 1085
Figure A.1(c) shows various notations for representing the cardinality ratio of binary relationships. We used notation (i) in Chapters 7 through 9. Notation (ii)— known as the chicken feet notation—is quite popular. Notation (iv) uses the arrow as a functional reference (from the N to the 1 side) and resembles our notation for for- eign keys in the relational model (see Figure 9.2); notation (v)—used in Bachman diagrams and the network data model—uses the arrow in the reverse direction (from the 1 to the N side). For a 1:1 relationship, (ii) uses a straight line without any chicken feet; (iii) makes both halves of the diamond white; and (iv) places arrow- heads on both sides. For an M:N relationship, (ii) uses chicken feet at both ends of the line; (iii) makes both halves of the diamond black; and (iv) does not display any arrowheads.
Figure A.1(d) shows several variations for displaying (min, max) constraints, which are used to display both cardinality ratio and total/partial participation. We mostly used notation (i). Notation (ii) is the alternative notation we used in Figure 7.15 and discussed in Section 7.7.4. Recall that our notation specifies the constraint that each entity must participate in at least min and at most max relationship instances. Hence, for a 1:1 relationship, both max values are 1; for M:N, both max values are n. A min value greater than 0 (zero) specifies total participation (existence depen- dency). In methodologies that use the straight line for displaying relationships, it is common to reverse the positioning of the (min, max) constraints, as shown in (iii); a variation common in some tools (and in UML notation) is shown in (v). Another popular technique—which follows the same positioning as (iii)—is to display the min as o (“oh” or circle, which stands for zero) or as | (vertical dash, which stands for 1), and to display the max as | (vertical dash, which stands for 1) or as chicken feet (which stands for n), as shown in (iv).
Figure A.1(e) shows some notations for displaying specialization/generalization. We used notation (i) in Chapter 8, where a d in the circle specifies that the subclasses (S1, S2, and S3) are disjoint and an o in the circle specifies overlapping subclasses. Notation (ii) uses G (for generalization) to specify disjoint, and Gs to specify over- lapping; some notations use the solid arrow, while others use the empty arrow (shown at the side). Notation (iii) uses a triangle pointing toward the superclass, and notation (v) uses a triangle pointing toward the subclasses; it is also possible to use both notations in the same methodology, with (iii) indicating generalization and (v) indicating specialization. Notation (iv) places the boxes representing sub- classes within the box representing the superclass. Of the notations based on (vi), some use a single-lined arrow, and others use a double-lined arrow (shown at the side).
The notations shown in Figure A.1 show only some of the diagrammatic symbols that have been used or suggested for displaying database conceptual schemes. Other notations, as well as various combinations of the preceding, have also been used. It would be useful to establish a standard that everyone would adhere to, in order to prevent misunderstandings and reduce confusion.
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1087
Parameters of Disks
The most important disk parameter is the timerequired to locate an arbitrary disk block, given its block address, and then to transfer the block between the disk and a main memory buffer. This is the random access time for accessing a disk block. There are three time components to consider as follows:
1. Seek time (s). This is the time needed to mechanically position the read/write head on the correct track for movable-head disks. (For fixed-head disks, it is the time needed to electronically switch to the appropriate read/write head.) For movable-head disks, this time varies, depending on the distance between the current track under the read/write head and the track specified in the block address. Usually, the disk manufacturer provides an average seek time in milliseconds. The typical range of average seek time is 4 to 10 msec. This is the main culprit for the delay involved in transferring blocks between disk and memory.
2. Rotational delay (rd). Once the read/write head is at the correct track, the user must wait for the beginning of the required block to rotate into position under the read/write head. On average, this takes about the time for half a revolution of the disk, but it actually ranges from immediate access (if the start of the required block is in position under the read/write head right after the seek) to a full disk revolution (if the start of the required block just passed the read/write head after the seek). If the speed of disk rotation is p revolu- tions per minute (rpm), then the average rotational delay rd is given by
rd = (1/2) * (1/p) min = (60 * 1000)/(2 * p) msec = 30000/p msec
A typical value for p is 10,000 rpm, which gives a rotational delay of rd = 3 msec. For fixed-head disks, where the seek time is negligible, this component causes the greatest delay in transferring a disk block.
Bappendix B
1088 Appendix B Parameters of Disks
3. Block transfer time (btt). Once the read/write head is at the beginning of the required block, some time is needed to transfer the data in the block. This block transfer time depends on the block size, track size, and rotational speed. If the transfer rate for the disk is tr bytes/msec and the block size is B bytes, then
btt = B/tr msec
If we have a track size of 50 Kbytes and p is 3600 rpm, then the transfer rate in bytes/msec is
tr = (50 * 1000)/(60 * 1000/3600) = 3000 bytes/msec
In this case, btt = B/3000 msec, where B is the block size in bytes.
The average time (s) needed to find and transfer a block, given its block address, is estimated by
(s + rd + btt) msec
This holds for either reading or writing a block. The principal method of reducing this time is to transfer several blocks that are stored on one or more tracks of the same cylinder; then the seek time is required for the first block only. To transfer con- secutively k noncontiguous blocks that are on the same cylinder, we need approxi- mately
s + (k * (rd + btt)) msec
In this case, we need two or more buffers in main storage because we are continu- ously reading or writing the k blocks, as we discussed in Chapter 17. The transfer time per block is reduced even further when consecutive blocks on the same track or cylinder are transferred. This eliminates the rotational delay for all but the first block, so the estimate for transferring k consecutive blocks is
s + rd + (k * btt) msec
A more accurate estimate for transferring consecutive blocks takes into account the interblock gap (see Section 17.2.1), which includes the information that enables the read/write head to determine which block it is about to read. Usually, the disk man- ufacturer provides a bulk transfer rate (btr) that takes the gap size into account when reading consecutively stored blocks. If the gap size is G bytes, then
btr = (B/(B + G)) * tr bytes/msec
The bulk transfer rate is the rate of transferring useful bytes in the data blocks. The disk read/write head must go over all bytes on a track as the disk rotates, including the bytes in the interblock gaps, which store control information but not real data. When the bulk transfer rate is used, the time needed to transfer the useful data in one block out of several consecutive blocks is B/btr. Hence, the estimated time to read k blocks consecutively stored on the same cylinder becomes
s + rd + (k * (B/btr)) msec
Appendix B Parameters of Disks 1089
Another parameter of disks is the rewrite time. This is useful in cases when we read a block from the disk into a main memory buffer, update the buffer, and then write the buffer back to the same disk block on which it was stored. In many cases, the time required to update the buffer in main memory is less than the time required for one disk revolution. If we know that the buffer is ready for rewriting, the system can keep the disk heads on the same track, and during the next disk revolution the updated buffer is rewritten back to the disk block. Hence, the rewrite time Trw, is usually estimated to be the time needed for one disk revolution:
Trw = 2 * rd msec = 60000/p msec
To summarize, the following is a list of the parameters we have discussed and the symbols we use for them:
Seek time: s msec
Rotational delay: rd msec
Block transfer time: btt msec
Rewrite time: Trw msec
Transfer rate: tr bytes/msec
Bulk transfer rate: btr bytes/msec
Block size: B bytes
Interblock gap size: G bytes
Disk speed: p rpm (revolutions per minute)
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1091
Overview of the QBE Language
The Query-By-Example (QBE) language is impor-tant because it is one of the first graphical query languages with minimum syntax developed for database systems. It was developed at IBM Research and is available as an IBM commercial product as part of the QMF (Query Management Facility) interface option to DB2. The language was also implemented in the Paradox DBMS, and is related to a point-and-click type inter- face in the Microsoft Access DBMS. It differs from SQL in that the user does not have to explicitly specify a query using a fixed syntax; rather, the query is formulated by filling in templates of relations that are displayed on a monitor screen. Figure C.1 shows how these templates may look for the database of Figure 3.5. The user does not have to remember the names of attributes or relations because they are dis- played as part of these templates. Additionally, the user does not have to follow rigid syntax rules for query specification; rather, constants and variables are entered in the columns of the templates to construct an example related to the retrieval or update request. QBE is related to the domain relational calculus, as we shall see, and its original specification has been shown to be relationally complete.
C.1 Basic Retrievals in QBE In QBE retrieval queries are specified by filling in one or more rows in the templates of the tables. For a single relation query, we enter either constants or example ele- ments (a QBE term) in the columns of the template of that relation. An example element stands for a domain variable and is specified as an example value preceded by the underscore character (_). Additionally, a P. prefix (called the P dot operator) is entered in certain columns to indicate that we would like to print (or display)
Cappendix C
1092 Appendix C Overview of the QBE Language
values in those columns for our result. The constants specify values that must be exactly matched in those columns.
For example, consider the query Q0: Retrieve the birth date and address of John B. Smith. In Figures C.2(a) through C.2(d) we show how this query can be specified in a progressively more terse form in QBE. In Figure C.2(a) an example of an employee is presented as the type of row that we are interested in. By leaving John B. Smith as constants in the Fname, Minit, and Lname columns, we are specifying an exact match in those columns. The rest of the columns are preceded by an underscore indicating that they are domain variables (example elements). The P. prefix is placed in the Bdate and Address columns to indicate that we would like to output value(s) in those columns.
Q0 can be abbreviated as shown in Figure C.2(b). There is no need to specify exam- ple values for columns in which we are not interested. Moreover, because example values are completely arbitrary, we can just specify variable names for them, as shown in Figure C.2(c). Finally, we can also leave out the example values entirely, as shown in Figure C.2(d), and just specify a P. under the columns to be retrieved.
To see how retrieval queries in QBE are similar to the domain relational calculus, compare Figure C.2(d) with Q0 (simplified) in domain calculus as follows:
Q0 : { uv | EMPLOYEE(qrstuvwxyz) and q=‘John’ and r=‘B’ and s=‘Smith’}
DEPARTMENT
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
EMPLOYEE
DEPT_LOCATIONS
Dnumber Dlocation
PROJECT
Pname Pnumber Plocation Dnum
WORKS_ON
Essn Pno Hours
DEPENDENT
Essn Dependent_name Sex Bdate Relationship
Dname Dnumber Mgr_ssn Mgr_start_date
Figure C.1 The relational schema of Figure 3.5 as it may be displayed by QBE.
Appendix C Overview of the QBE Language 1093
EMPLOYEE(a)
(b)
(c)
(d)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
John B Smith _123456789 P._9/1/60 P._100 Main, Houston, TX _M _25000 _123456789 _3
EMPLOYEE
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
John B Smith P._9/1/60 P._100 Main, Houston, TX
EMPLOYEE
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
John B Smith P._X P._Y
EMPLOYEE
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
John B Smith P. P.
Figure C.2 Four ways to specify the query Q0 in QBE.
We can think of each column in a QBE template as an implicit domain variable; hence, Fname corresponds to the domain variable q, Minit corresponds to r, ..., and Dno corresponds to z. In the QBE query, the columns with P. correspond to variables specified to the left of the bar in domain calculus, whereas the columns with con- stant values correspond to tuple variables with equality selection conditions on them. The condition EMPLOYEE(qrstuvwxyz) and the existential quantifiers are implicit in the QBE query because the template corresponding to the EMPLOYEE relation is used.
In QBE, the user interface first allows the user to choose the tables (relations) needed to formulate a query by displaying a list of all relation names. Then the tem- plates for the chosen relations are displayed. The user moves to the appropriate columns in the templates and specifies the query. Special function keys are provided to move among templates and perform certain functions.
We now give examples to illustrate basic facilities of QBE. Comparison operators other than = (such as > or ≥) may be entered in a column before typing a constant value. For example, the query Q0A: List the social security numbers of employees who work more than 20 hours per week on project number 1 can be specified as shown in Figure C.3(a). For more complex conditions, the user can ask for a condition box, which is created by pressing a particular function key. The user can then type the complex condition.1
1Negation with the ¬ symbol is not allowed in a condition box.
1094 Appendix C Overview of the QBE Language
For example, the query Q0B: List the social security numbers of employees who work more than 20 hours per week on either project 1 or project 2 can be specified as shown in Figure C.3(b).
Some complex conditions can be specified without a condition box. The rule is that all conditions specified on the same row of a relation template are connected by the and logical connective (all must be satisfied by a selected tuple), whereas conditions specified on distinct rows are connected by or (at least one must be satisfied). Hence, Q0B can also be specified, as shown in Figure C.3(c), by entering two dis- tinct rows in the template.
Now consider query Q0C: List the social security numbers of employees who work on both project 1 and project 2; this cannot be specified as in Figure C.4(a), which lists those who work on either project 1 or project 2. The example variable _ES will bind itself to Essn values in <–, 1, –> tuples as well as to those in <–, 2, –> tuples. Figure C.4(b) shows how to specify Q0C correctly, where the condition (_EX = _EY) in the box makes the _EX and _EY variables bind only to identical Essn values.
In general, once a query is specified, the resulting values are displayed in the template under the appropriate columns. If the result contains more rows than can be dis- played on the screen, most QBE implementations have function keys to allow scroll- ing up and down the rows. Similarly, if a template or several templates are too wide to appear on the screen, it is possible to scroll sideways to examine all the templates.
A join operation is specified in QBE by using the same variable2 in the columns to be joined. For example, the query Q1: List the name and address of all employees who
2A variable is called an example element in QBE manuals.
WORKS_ON
(a) Essn Pno Hours
P. > 20
WORKS_ON
(b) Essn Pno Hours
P. _PX _HX
_HX > 20 and (PX = 1 or PX = 2)
CONDITIONS
WORKS_ON
(c) Essn Pno Hours
P. > 201 P. > 202
Figure C.3 Specifying complex conditions in QBE. (a) The query Q0A. (b) The query Q0B with a condition box. (c) The query Q0B without a condition box.
Appendix C Overview of the QBE Language 1095
WORKS_ON (a) Essn Pno Hours
P._ES 1 P._ES 2
WORKS_ON (b) Essn Pno Hours
P._EX 1 P._EY 2
_EX = _EY
CONDITIONS
Figure C.4 Specifying EMPLOYEES who work on both projects. (a) Incorrect specification of an AND condition. (b) Correct specification.
work for the ‘Research’ department can be specified as shown in Figure C.5(a). Any number of joins can be specified in a single query. We can also specify a result table to display the result of the join query, as shown in Figure C.5(a); this is needed if the result includes attributes from two or more relations. If no result table is specified, the system provides the query result in the columns of the various relations, which may make it difficult to interpret. Figure C.5(a) also illustrates the feature of QBE for specifying that all attributes of a relation should be retrieved, by placing the P. operator under the relation name in the relation template.
To join a table with itself, we specify different variables to represent the different ref- erences to the table. For example, query Q8: For each employee retrieve the employee’s first and last name as well as the first and last name of his or her immediate supervisor can be specified as shown in Figure C.5(b), where the variables starting with E refer to an employee and those starting with S refer to a supervisor.
C.2 Grouping, Aggregation, and Database Modification in QBE
Next, consider the types of queries that require grouping or aggregate functions. A grouping operator G. can be specified in a column to indicate that tuples should be grouped by the value of that column. Common functions can be specified, such as AVG., SUM., CNT. (count), MAX., and MIN. In QBE the functions AVG., SUM., and CNT. are applied to distinct values within a group in the default case. If we want these functions to apply to all values, we must use the prefix ALL.3 This convention is different in SQL, where the default is to apply a function to all values.
3ALL in QBE is unrelated to the universal quantifier.
1096 Appendix C Overview of the QBE Language
Figure C.6(a) shows query Q23, which counts the number of distinct salary values in the EMPLOYEE relation. Query Q23A (Figure C.6(b) counts all salary values, which is the same as counting the number of employees. Figure C.6(c) shows Q24, which retrieves each department number and the number of employees and average salary within each department; hence, the Dno column is used for grouping as indicated by the G. function. Several of the operators G., P., and ALL can be specified in a single column. Figure C.6(d) shows query Q26, which displays each project name and the number of employees working on it for projects on which more than two employees work.
QBE has a negation symbol, ¬, which is used in a manner similar to the NOT EXISTS function in SQL. Figure C.7 shows query Q6, which lists the names of employees who have no dependents. The negation symbol ¬ says that we will select values of the _SX variable from the EMPLOYEE relation only if they do not occur in the DEPENDENT relation. The same effect can be produced by placing a ¬ _SX in the Essn column.
Although the QBE language as originally proposed was shown to support the equivalent of the EXISTS and NOT EXISTS functions of SQL, the QBE imple- mentation in QMF (under the DB2 system) does not provide this support. Hence, the QMF version of QBE, which we discuss here, is not relationally complete. Queries such as Q3: Find employees who work on all projects controlled by depart- ment 5 cannot be specified.
EMPLOYEE(a)
(b)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno _FN
Research
P. _FN _LN _Addr
_DX
_LN _Addr _DX
DEPARTMENT
Dname Dnumber Mgrssn Mgr_start_date
RESULT
P. _E1 _E2 _S1
RESULT _S2
EMPLOYEE
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno _E1 _E2 _Xssn
_S1 _S2 _Xssn
Figure C.5 Illustrating JOIN and result relations in QBE. (a) The query Q1. (b) The query Q8.
Appendix C Overview of the QBE Language 1097
EMPLOYEE(a)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
P.CNT.
EMPLOYEE(b)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
P.CNT.ALL
EMPLOYEE(c)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
P.AVG.ALL P.G.P.CNT.ALL
PROJECT(d)
Pname Pnumber Plocation
_PXP.
Dnum
WORKS_ON
Essn Pno Hours
P.CNT.EX G._PX
CNT._EX > 2
CONDITIONS
Figure C.6 Functions and grouping in QBE. (a) The query Q23. (b) The query Q23A. (c) The query Q24. (d) The query Q26.
There are three QBE operators for modifying the database: I. for insert, D. for delete, and U. for update. The insert and delete operators are specified in the template col- umn under the relation name, whereas the update operator is specified under the columns to be updated. Figure C.8(a) shows how to insert a new EMPLOYEE tuple. For deletion, we first enter the D. operator and then specify the tuples to be deleted by a condition (Figure C.8(b)). To update a tuple, we specify the U. operator under the attribute name, followed by the new value of the attribute. We should also select the tuple or tuples to be updated in the usual way. Figure C.8(c) shows an update
EMPLOYEE
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
P. P. _SX
DEPENDENT
Essn Dependent_name Sex Bdate Relationship _SX
Figure C.7 Illustrating negation by the query Q6.
1098 Appendix C Overview of the QBE Language
EMPLOYEE(a)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
RichardI. MariniK M 37000 987654321 4653298653 30-Dec-52 98 Oak Forest, Katy, TX
EMPLOYEE(b)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
D. 653298653
EMPLOYEE(c)
Fname Minit Lname Ssn Bdate Address Sex Salary Super_ssn Dno
John Smith U._S*1.1 U.4
Figure C.8 Modifying the database in QBE. (a) Insertion. (b) Deletion. (c) Update in QBE.
request to increase the salary of ‘John Smith’ by 10 percent and also to reassign him to department number 4.
QBE also has data definition capabilities. The tables of a database can be specified interactively, and a table definition can also be updated by adding, renaming, or removing a column. We can also specify various characteristics for each column, such as whether it is a key of the relation, what its data type is, and whether an index should be created on that field. QBE also has facilities for view definition, authoriza- tion, storing query definitions for later use, and so on.
QBE does not use the linear style of SQL; rather, it is a two-dimensional language because users specify a query moving around the full area of the screen. Tests on users have shown that QBE is easier to learn than SQL, especially for nonspecialists. In this sense, QBE was the first user-friendly visual relational database language.
More recently, numerous other user-friendly interfaces have been developed for commercial database systems. The use of menus, graphics, and forms is now becoming quite common. Filling forms partially to issue a search request is akin to using QBE. Visual query languages, which are still not so common, are likely to be offered with commercial relational databases in the future.
1099
Abbreviations Used in the Bibliography ACM: Association for Computing Machinery AFIPS: American Federation of Information Processing
Societies CACM: Communications of the ACM (journal) CIKM: Proceedings of the International Conference on
Information and Knowledge Management DASFAA: Proceedings of the International Conference on
Database Systems for Advanced Applications DKE: Data and Knowledge Engineering, Elsevier
Publishing (journal) EDS: Proceedings of the International Conference on
Expert Database Systems ER Conference: Proceedings of the International Confer-
ence on Entity-Relationship Approach (now called International Conference on Conceptual Modeling)
ICDCS: Proceedings of the IEEE International Conference on Distributed Computing Systems
ICDE: Proceedings of the IEEE International Conference on Data Engineering
IEEE: Institute of Electrical and Electronics Engineers IEEE Computer: Computer magazine (journal) of the
IEEE CS IEEE CS: IEEE Computer Society IFIP: International Federation for Information Processing JACM: Journal of the ACM KDD: Knowledge Discovery in Databases LNCS: Lecture Notes in Computer Science NCC: Proceedings of the National Computer Conference
(published by AFIPS) OOPSLA: Proceedings of the ACM Conference on
Object-Oriented Programming Systems, Languages, and Applications
PAMI: Pattern Analysis and Machine Intelligence PODS: Proceedings of the ACM Symposium on Principles
of Database Systems SIGMOD: Proceedings of the ACM SIGMOD Inter-
national Conference on Management of Data SOSP: ACM Symposium on Operating System Principles TKDE: IEEE Transactions on Knowledge and Data
Engineering (journal) TOCS: ACM Transactions on Computer Systems (journal) TODS: ACM Transactions on Database Systems (journal)
TOIS: ACM Transactions on Information Systems (jour- nal)
TOOIS: ACM Transactions on Office Information Systems (journal)
TSE: IEEE Transactions on Software Engineering (journal) VLDB: Proceedings of the International Conference on
Very Large Data Bases (issues after 1981 available from Morgan Kaufmann, Menlo Park, California)
Format for Bibliographic Citations Book titles are in boldface—for example, Database Computers. Conference proceedings names are in ital- ics—for example, ACM Pacific Conference. Journal names are in boldface—for example, TODS or Information Systems. For journal citations, we give the volume number and issue number (within the volume, if any) and date of issue. For example, “TODS, 3:4, December 1978” refers to the December 1978 issue of ACM Transactions on Database Systems, which is Volume 3, Number 4. Articles that appear in books or conference proceedings that are themselves cited in the bibliography are referenced as “in” these references—for example, “in VLDB [1978]” or “in Rustin [1974].” Page numbers (abbreviated “pp.”) are pro- vided with pp. at the end of the citation whenever avail- able. For citations with more than four authors, we will give the first author only followed by et al. In the selected bibliography at the end of each chapter, we use et al. if there are more than two authors.
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Abiteboul, S., Hull, R., and Vianu, V. [1995] Foundations of Databases, Addison-Wesley, 1995.
Abrial, J. [1974] “Data Semantics,” in Klimbie and Koffeman [1974].
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1133
Index
* (asterisk), for retrieving all attrib- ute values of selected tuples, 102–103
* (wildcard symbol) types of queries in IR systems,
1008–1009 using with XPath, 433
. (concatenate operator), in PHP, 485
|| (concatenate operator), in SQL, 105
1NF (first normal form), 65, 519–523
2NF (Second normal form) general definition of, 526–527 overview of, 523
2PC (two-phase commit) protocol recovery in multidatabase sys-
tems, 825–826 transaction management in dis-
tributed databases, 908 3NF (third normal form). See Third
normal form (3NF) 3PC (three-phase commit) protocol,
908 4NF (fourth normal form). See
Fourth normal form (4NF) 5NF (fifth normal form), 534–535 Aborted transactions
recovery process and, 812 types of transactions, 750
Abstraction aggregation and, 269 association and, 270 concepts in semantic data models,
268 identification and, 269
Access control database security, 839–840 MAC (mandatory access control),
847–850 mandatory vs. discretionary,
850–851 policies for e-commerce and Web,
854–855 RBAC (role-based access control),
851–852
restricting unauthorized access, 18–19
row-level access control, 852–853 security control measures, 837 user authorization and, 841 in XML, 853–854
Access matrix model, for discre- tionary privileges, 843
Access methods, file organization and, 601
Access mode, SQL transactions, 770 Access paths
classifying DBMSs by, 50 in physical data models, 31
Access structures, 631. See also Indexes
Accounts assigning privileges at account
level, 842–843 DBAs assigning account numbers,
839 ACID (atomicity, consistency, isola-
tion, durability) properties, of transactions, 754–755
Action component, of triggers, 133 Actions, in ECA model
defined, 933–934 in STARBURST example, 940
Activate command, in active data- base systems, 938
Activation, in sequence diagrams, 332
Active database systems creating triggers in SQL, 111 design and implementation issues
with, 937–940 generalized model for, 933–937 inferencing and actions using
rules, 21 overview of, 932 potential applications of, 942–943 statement-level active rules in
STARBURST example, 940–942 technology of, 3 triggers and, 132, 942–943
Active rules overview of, 931
triggers specifying, 933 Active state, transactions, 752 Active transaction, in recovery
process, 812 Activity diagrams, UML, 334 Actuators, on hard disks, 591 Acyclic graphs (hierarchies), in
object data models, 50 Ad-hoc querying, 1075 Adleman, Len, 865 Administration, of data warehous-
ing, 1080–1081 Advanced Encryption Standard
(AES), 863 AES (Advanced Encryption
Standard), 863 AFIM (after image), in data item
updates, 810–811 After triggers, 938 Agent-based approach, to Web
content analysis, 1024–1025 Aggregate functions
collection operators in OQL, 403–405
grouping and, 166–168 implementing aggregate opera-
tions, 698–699 query processing and optimizing,
698–699 in relational algebra, 146 in SQL, 124–126 in tuple relational calculus,
182–183 Aggregation
as abstraction concept, 269–271 in QBE (Query-By-Example),
1095–1098 temporal, 957 in UML class diagrams, 227
Algebra transformation rules for relational
algebra operations, 706–708 translating SQL queries into rela-
tional algebra, 681–682 Algorithms
Apriori, 1041–1042 ARIES recovery, 821–825
deadlock prevention, 786–787 decision tree induction,
1052–1054 for ER-to-Relational mapping,
286–287 FP-growth, 1045–1047 FP-tree (frequent-pattern tree),
1043–1045 genetic algorithms (GAs), 1059 heuristic algebraic optimization,
708–709 HITS ranking, 1021–1022 k-means clustering, 1055–1056 PageRank algorithm, 1021 partition, 1047 public (asymmetric) key, 863–865 recovery, 818–819 sampling algorithm, 1042 for set operations, 697–698 symmetric key algorithms, 863
Algorithms, file related binary search on ordering key of
disk file, 603 hashing, 608 search procedure for linear hash-
ing, 616 Algorithms, indexing
B+-tree searches, 655–656 searching nondense multilevel
primary index, 646 Algorithms, normalization
closure of X under F, 548 dependency preservation and
nonadditive join decomposi- tion into 3NF, 560–563
dependency preservation into 3NF, 558–559
for finding a key of a relation, 551 for minimal sets of functional
dependencies, 550 nonadditive join decomposition
into 4NF, 570 nonadditive join decomposition
into BCNF, 559–560 problems with, 565–566 relational synthesis into 3NF with
dependency preservation and nonadditive join property, 560–562
summary of, 567 testing for nonadditive join prop-
erty, 554–555 Aliases, in SQL, 101–102 Allen’s algebra, 955 Allocation
in distributed databases, 897
example of fragmentation, alloca- tion, and replication, 898–901
Allocation schema, of databases, 896 ALTER command, 138–139 ALTER TABLE command, 90, 92,
138–139 American National Standards
Institute (ANSI), 88 Analysis phase, of ARIES recovery
algorithm, 823 Analytical components, of design
tools, 344 Anchor record (block anchor), 633 Anchor text
browsing and, 999 hyperlink component, 1020
AND, OR, NOT connectives for complex conditions in QBE,
1094 in formulas, 176 AND and OR operators in Prolog
languages, 972 in three-valued logic, 116–117
Annotations, XML schema lan- guage, 429
Anomalies, avoiding redundant information in tuples, 507–509
ANSI (American National Standards Institute), 88
Antimonotonicity property Apriori algorithm using,
1041–1042 association rules, 1041
API (application programming interface)
client-side API for calling DBMSs, 47
for data mining, 1061 for database programming, 449
Append-only databases, 953 Application-based constraints, 68 Application conversion, in database
application life cycle, 308 Application development
advantages of distributed data- bases for, 882
database approach reducing development time, 22
DBMS tools for, 44 Application layer (business logic), in
three-tier client/server architec- ture, 892–893
Application programmers, 16 Application programs, 449 Application servers
DBMS component modules, 42
federated databases and, 886 middle tier in three-tier architec-
ture, 48 Applications, database
in active database systems, 942–943
application program accessing databases, 6
as canned transactions, 449 designing, 199, 310 environment, 43–44 ER modeling for, 245 extending capabilities of, 24–26 flexibility provided by relational
databases, 23–24 life cycle of, 308–309 object-oriented, 24 software programs implementing,
477 spatial data and, 964–965 testing, 199 traditional, 3 using hierarchical and network
systems, 23 Apriori algorithm, 1041–1042 Architecture, database
centralized architecture, 44 client/server architecture, 44–46 three-tier and n-iter architectures
for Web applications, 47–49 two-tier client/server architecture,
46–47 Architecture, distributed database
federated database schema, 890–891
parallel vs. distributed architec- ture, 887–888
pure distributed databases, 889–890
three-tier client/server architec- ture, 892–894
Architecture, of Oracle Label Security, 869
Archives, magnetic tape for, 593 Arguments, in Prolog languages, 970 ARIES recovery algorithm, 821–825 Arithmetic
functions and procedures, 572–574
operators in SQL, 105–106 Armstrong’s inference rules, 548 Arrays
constructor, 359 creating UDTs, 373 dynamic, 483 PHP, 486–488
1134 Index
Arrays of texels, 967 Arrow notation, as alternative to dot
notation, 382 AS qualifier, for renaming attributes,
122 ASC keyword, for ascending order
of query results, 107 Assertions
relational schemas and, 66 specifying constraints as, 74,
131–132 Association rules
among hierarchies, 1047–1048 Apriori algorithm, 1041–1042 complications in, 1050–1051 example of, 1037 form of, 1040 FP-growth algorithms, 1045–1047 FP-tree (frequent-pattern tree)
algorithm, 1043–1045 multidimensional associations,
1048–1049 negative associations, 1049–1050 overview of, 1039–1041 Partition algorithm, 1047 in pattern discovery phase of Web
usage analysis, 1026 Sampling algorithm, 1042
Associations, 269–271 autonomy in federated databases,
887 ER relationships compared with,
227 spatial, 963
Associative arrays, PHP, 487 Asterisk (*), for retrieving all attrib-
ute values of selected tuples, 102–103
Asymmetric (public) key algo- rithms, 863–865
Atom constructors, 358 Atomic domains, in relational
model, 61 Atomic formulas, in Datalog lan-
guage, 973 Atomic literals, 378 Atomic objects
object lifetime and, 378 in ODMG object model, 386–388
Atomic values 1NF and, 519 in tuples, 65
Atomicity property, of transactions, 14, 754–755
Atoms, calculus in domain calculus formulas, 183
overview of, 176 truth values of, 184
Attribute data, GIS systems, 960 Attribute-defined specialization, of
superclass, 252 Attribute-defined subclasses, 264 Attribute preservation condition, of
a decomposition, 552 Attribute versioning, for incorporat-
ing time in OODBs, 953–954 Attributes
clear semantics for, 503–507 in data modeling, 31 dealing with ambiguous names in
SQL, 100–101 HTML tags, 419 in ODMG object model, 386 prime and nonprime, 519, 526 relationships and, 214, 218 renaming, 122 retrieving all values of selected
tuples, 102–103 semantics of, 503–507, 514 specifying attribute constraints in
SQL, 94–95 subsets of, 68–69 of superclass, 252 symbols for, 1084 time conditions and, 956 time-varying and nontime-
varying, 953 in UML class diagrams, 227 XML documents, 420
Attributes, in ER model complex, 206–207 composite vs. simple, 205–206 constraints on, 208–209 NULL values, 206 overview of, 203–205 single-valued vs. multivalued, 206 stored vs. derived, 206 value sets (domains) of, 209–210
Attributes, in relational model defined, 61 relation schema and, 62
Attributes (indexing fields) deciding whether to index, 730 ordered index on multiple, 661 records and, 631 secondary indexes, 636 single-level indexes, 632
Audio clips, in multimedia data- bases, 932, 965
Audio data sources, in multimedia databases, 966, 969
Audits, of database security, 839–840
Authentication SQL injection attack bypassing,
857 weakness of, 855
Authority, hubs and, 1020 Authorization
identifiers, 89, 842 privileges, 874 subsystem in DBMSs, 19
Automated tools, for database design, 342–346
Automatic analysis, of multimedia sources, 965
Autonomy degree of local autonomy for
DDMBS software, 884 in distributed databases, 881 in federated databases, 886–887 transparency as complement to,
882 Availability
of data, 840–841 in distributed databases, 881–882 loss of, as security threat, 836 of up-to-date information, 22
AVERAGE function grouping and, 166–168 implementing, 698
Average precision, measures of rele- vance in I, 1017
AVG function, in SQL, 124–125 Axes, of XPath expressions, 433 Axioms, in deductive databases, 975 B-link tree, 800 B-trees
file organization and, 617 overview of, 649–652 variations on, 660
B+-trees algorithms for searches with,
655–656 cost functions of selection, 714 decision making in database
design, 730 methods for simple selection, 686 overview of, 652–655 search, insertion, deletion with,
655–660 variations on, 660
Bachman diagrams, 1084–1085 Back-ends, database, 25 Backflushing data, in data ware-
houses, 1076 Backup and recovery. See also
Database recovery techniques in databases, 20
Index 1135
DBMS component modules, 42 magnetic tape for, 592 recovering from catastrophic
failure, 826–827 Backup sites, for distributed data-
base, 910–911 Backup utility, 43 Bag constructor, 359 Bag (multiset), of tuples, 103–105,
150 Base classes, 265 Base tables
specifying, 90 views and, 133
BCNF (Boyce-Codd normal form) nonadditive join decomposition
into, 559–560 overview of, 529–531
Before triggers, in active database systems, 938
Begin transaction, transaction types, 745
BEGIN_TRANSACTION operation, 751
Behavioral diagrams, UML, 329 Behaviors
of database applications, 30 in ODMG object model, 382
Bell-LaPadula model, 847 Best Match 25 (BM25), 1006 BFIM (before image), in data item
updates, 810–811 Bfr. See Blocking factor (bfr) Bidirectional associations, in UML
class diagrams, 227 Binary associations, in UML class
diagrams, 227 Binary balanced strategy, for view
integration process, 319 Binary decompositions, 553–556 Binary ladder integration strategy,
for view integration process, 319
Binary large objects (BLOBs), 595 Binary relational operations
DIVISION operation, 162–163 EQUIJOIN and NATURAL JOIN
operations, 159–161 JOIN operation, 157–158 overview of, 146
Binary relationships choosing between binary and
ternary relationships, 228–231 comparing RDB design with
ODB design, 396 constraints on, 216–218, 232
degree of relationship, 213 mapping EER schema to ODB
schema, 397 mapping ER-to-Relational data
models, 289–291 Binary representation, of hashing
function, 612 Binary searches
cost functions for, 713 files, 603 methods for simple selection, 686
Binary trees, 718 Bind variables (parameterized state-
ments), protecting against SQL injection, 858
Bindings early and late binding in ODMS,
368 impedance mismatch and, 450 to OOPLs, 376, 407
Bit-level data striping, RAID and, 620
Bit-string data types, in SQL, 92–93 Bitemporal databases, 946, 950 Bitemporal time relations, 950–952 Bitmap indexes
for B+-tree leaf nodes, 666 in multidimensional data models,
1074 overview of, 663–666
Bits, of data, 588 Blind writes, 768 BLOBs (binary large objects), 595 Block-level striping, RAID, 620 Block size, as disk parameter, 1088 Block transfer time (btt)
as disk parameter, 1088 for hard disks, 591
Blocking factor (bfr) for files, 597 multilevel indexes and, 643 in query cost estimation, 712
Blocking records, 597 Blocks of data
allocation on disk, 598 buffer management and, 593–594 pointers and, 597 in query cost estimation, 712 sequential order for accessing, 592
BM25 (Best Match 25), 1006 Body, HTML, 419 Boolean conditions, in tuple rela-
tional calculus, 176–177 Boolean data types
attributes, 295 in SQL, 93
Boolean model, for information retrieval, 1002–1003
Boolean queries, in information retrieval systems, 1007–1008
Bottom-tier database server, PHP, 482
Bottom-up approach in relational database design, 502,
544 for schema design, 316
Bottom-up conceptual synthesis, 257
Boyce-Codd normal form (BCNF) nonadditive join decomposition
into, 559–560 overview of, 529–531
Browsing. See also Web browsers interfaces, 38 modes of interaction in IR sys-
tems, 999 Buckets, of disk blocks, 609 Buffer management
DBMS component modules, 40 storage and, 593–594
Buffers address of, 590 DBMS buffer space, 683 in DBMSs, 19 performance of nested-loop joins
and, 690 Bulk transfer rate (btr)
as disk parameter, 1088 for hard disks, 592
Business metadata, in data ware- housing, 1078
Business rules applications for active databases,
943 integrity constraints in databases,
21 relational model constraints, 68
Bytes, of data, 588 C language
embedded SQL and, 451–452 as host language in SQL/CLI,
464–468 PHP interpreter written in, 482
C++ language binding, in ODMG, 407–408
Cache memory, 585 Caching
database cache, 746 in database recovery, 809–810 in DBMSs, 19
Calendars cultural differences in, 945
1136 Index
temporal databases and, 945–947 time series data and, 957
Call statement, SQL, 475 Candidate keys
database design and, 729 defined, 519 functional dependency and, 514 relational model constraints, 69
Canned transactions, 15 Cardinality
of domains, 63 selection cardinality, 712
Cardinality ratio for binary relationship, 216–217 notation for, 1084–1085
Cartesian product, 63 CARTESIAN PRODUCT operation
algorithms for, 697–698 in relational algebra, 155–157
Cartridge, Oracle, 931 Cascade option, in delete operation,
77 Cascadeless (avoid cascading roll-
back), of transactions, 759 Cascading rollback (or cascading
abort) in database recovery, 813–815 timestamp ordering and, 790 of transactions, 758
CASE (computer aided software engineering), 1083
Case, text preprocessing in informa- tion retrieval, 1011
Catalogs catalog information used in cost
functions, 712–713 DBMS, 10–11 SQL, 90 transaction management in
distributed databases, 913 Catastrophic failure, database recov-
ery techniques for, 826–827 Categories, in EER
defined, 265 modeling, 258–260
Categorization, in agent-based approach to Web content analysis, 1024
Cautious waiting algorithm, for deadlock prevention, 787
Centralized catalogs, 913 Centralized DBMSs, 44, 49 Centralized (one shot) schema
design approach, 315 Certification locking technique, 794 Certify locks, 792–793
Chaining, for collision resolution, 608–609
Character-string data types, in SQL, 92
CHECK clauses restricting attribute or domain
values with, 94–95 specifying constraints on tuples, 97
Checkpoints in ARIES recovery algorithm, 822 in database recovery, 812–813
Chicken feet notation, 1085 Child nodes, of tree structures, 646 Chronon, in temporal databases, 945 Ciphertext, 862, 864. See also
Encryption Class diagrams
ER diagrams compared with, 200 notation of, 226–228 UML and, 329–330
Class hierarchies. See Type (class) hierarchies
Class properties, in knowledge representation, 268
Class/subclass relationships, 247, 571–572
Classes built-in to ODMG object model,
383–385 compared with entities, 227 in EER model, 264 names, in UML class diagrams,
227 in object data models, 50 security classes in mandatory
access control, 847 type definitions and operation
definitions, 361–362 Classification
classification trees in data mining, 1037
concept hierarchies and, 1024 data mining and, 1051–1054 faceted classification scheme,
1028–1029 goals of data mining, 1038 knowledge discovery and, 1039 in mandatory access control, 848 overview of, 268 in pattern discovery phase of Web
usage analysis, 1027 spatial, 963
Classification attributes, in manda- tory access control, 848
Clausal form in Datalog, 973
in deductive database systems, 974–975
Clauses, in simple SQL queries, 107 Clearance, in mandatory access con-
trol, 848 Client computers
accessing specialized servers, 45 DBMS access and, 42
Client module, DBMS, 29 Client (presentation) layer, three-tier
client/server architecture, 892 Client programs, 42, 451 Client/server architecture
for DBMSs, 44–46 three-tier client/server architec-
ture, 892–894 two-tier client/server architecture,
46–47 Client tier, PHP, 482 Clients
client level in two-tier client/serv- er architecture, 47
client program calling database server, 451
in client/server architecture, 46 Close operation, files, 600 The closed world assumption, 66 Closure of X under F, 548 Cloud computing, 914–915 Clustered file, 606, 636 Clustering
concept hierarchies and, 1024 data mining and, 1054–1055 k-means clustering algorithm,
1055–1056 knowledge discovery and, 1039 in pattern discovery phase of Web
usage analysis, 1026–1027 spatial, 964
Clustering field, 635 Clustering indexes
cost functions for SELECT opera- tions, 713–714
decision making in database design, 730
implementing aggregate opera- tions, 699
methods for simple selection, 686 overview of, 635–636 tables comparing index types, 642 types of ordered indexes, 632
Clusters, on hard disks, 591 Coalescing, of time periods, 956 Codd, Ted, 59 Code generator, for query execution,
679
Index 1137
Code injection, SQL injection attacks, 856
Collaborative social searches, 1029 Collection literals, 382 Collection (multivalued) construc-
tors, 359 Collection objects, built-in to
ODMG object model, 383 Collection operators, OQL, 403–405 Collections
creating UDTs, 373 extracting single elements from,
403 persistent, 363, 367 transient, 367
Collisions, hashing and, 608–609 Colocation rules, spatial, 964 Color, automatic analysis of images,
967 Column-based storage, of relations,
669–670 Columns, in SQL, 89. See also
Attributes Commercial tools, for data mining,
1060–1062 Commercial value, of Web searches,
1019 Commit point, of transactions, 754 Committed projection, of transac-
tion schedule, 757 Committed state, of transactions,
752 Committed transactions
commit point and, 754 issues with distributed commit,
910 recovery process and, 812 types of transaction, 750
COMMIT_TRANSACTION operation, 752
Communication autonomy in distributed databases, 881 in federated databases, 886
Communication costs, in query exe- cution, 711
Communication facilities, DBMS, 43–44
Communication links, failure in distributed databases, 910
Communication software, 44 Communication variables, SQL, 454 Comparison operators
applying to domains of ordered values, 148
SQL, 98
Compatibility, object-oriented data- bases and, 19
Complete horizontal fragmentation, in distributed databases, 895
Complete schedule, transactions, 757
Complete vertical fragmentation, in distributed databases, 896
Completeness (totalness) constraint, 253
Complex attributes, in ER model, 206–207
Complex data relationships, in data- bases, 20
Complex structures attribute versioning and, 953 objects and, 355 UDT (user-defined types) and,
370–373 Complex types
for objects and literals, 358–360 specifying structures in XML
schema using, 430 Component modules, DBMS, 40–42 Component schema, in federated
database architecture, 891 Component values, of tuples, 67 Components diagrams, UML, 330 Composite attributes
in ER model, 205–207 mapping from EER schema to
ODB schema, 397 XML schema language, 430
Composite indexes, 714 Composite keys, 661 Composite (molecular) objects, 270 Computational interpretation, of
rules in deductive databases, 977
Computer aided software engineer- ing (CASE), 1083
Computer failure (system crash), recovery needed due to, 750
Concatenate operator (.), in PHP, 485
Concatenate operator (||), in SQL, 105
Concept hierarchies, in Web content analysis, 1024
Conceptual data models, 30 Conceptual database design phase,
311 Conceptual design
comparing RDB design with ODB design, 395–396
of databases, 9 high-level, 200–202 initial conceptual design for
COMPANY database example, 210–211
Rational Rose modeling using UML notation, 339–340
refining conceptual design for COMPANY database example, 220–221
synchronizing with actual data- base, 341
Conceptual (logical) level, goodness of relation schemas and, 501
Conceptual representation of data, in DBMS, 12
Conceptual schema database design and, 201 in three-schema architecture, 34
Conceptual schema design, 313–321 approaches to, 314–315 high-level data model used for,
313–314 identifying correspondences and
conflicts among schemas, 317–319
schema (view) integration, 316–317, 319–321
strategies, 315–316 Conceptualization, 272 Concurrency control
DBMS component modules, 42 distributed control based on dis-
tinguished copy of data item, 910–912
distributed control based on vot- ing, 912
protocols, 777 reasons for aborting transactions,
751 serializability used for, 765–768 software, 13 transaction issues handled by,
747–750 transaction management in
distributed databases, 909–910 Concurrency control techniques
basic timestamp ordering, 789–790
bibliographic references, 804–805
binary locks, 778–780 conversion of locks, 782 deadlock detection, 787–788 deadlock prevention, 785–787
1138 Index
granularity level in locking, 795–796
insertion, deletion, and phantom records, 800–801
interactive transactions and, 801 latches, 802 locking used in indexes, 798–800 multiple granularity level locking,
796–798 multiversion concurrency control,
791–793 overview of, 777–778 serializability guaranteed by two-
phase locking, 782–784 shared/exclusive (read/write)
locks, 780–782 starvation, 788 strict timestamp ordering,
790–791 summary and exercises, 802–804 Thomas’s write rule, 791 two-phase locking, 778 validation (optimistic) concur-
rency control, 794–795 variations on two-phase locking,
784–785 Concurrent use, of database system,
744 Condition box, in Query-By-
Example, 1093 Condition-defined subclasses, 252,
264 Condition markers, in sequence
diagrams, 332 Conditions, as component of
triggers, 133 Conditions (formulas). See
Formulas Conditions, in ECA model
defined, 933–934 in STARBURST example, 940
Confidence, of association rules, 1040 Confidentiality, loss of, 836 Conflict equivalence, of transaction
schedules, 762 Conflict serializable
testing conflict serializability of schedules, 763–765
transaction schedules, 763 Conflict set, rule consideration and,
942 Conflicts, in transaction schedules,
756–757 Conjunctive conditions, SELECT
operations and, 686–687
Conjunctive selection, cost functions for SELECT, 714
Connecting fields, for relationships between file records, 616
Connection records, in SQL/CLI, 464–468
Connections opening database, 451 PHP connecting to databases,
491–493 SQL command for connecting to
database, 453 Conservative two-phase locking, 784 Consistency property, transaction
properties, 754–755 Constant nodes
notation for query trees and query graphs, 703
in query graphs, 179 Constant values, in Prolog
languages, 971 Constrained write assumption, 768 Constraint specification language,
74 Constraints
on binary relationships, 216–218 on binary relationships and terna-
ry relationships, 232 database design and, 310 domain constraints, 68 on extents corresponding to type
hierarchies, 366 in federated databases, 885 inclusion dependencies and, 571 integrity, referential integrity, and
foreign keys, 73–74 key constraints and NULL value
constraints, 68–70 notation of max/min values,
1084–1085 other types of, 74–75 relational model and, 67–68 on specialization and generaliza-
tion, 251–254 state constraints vs. transition
constraints, 75 Constraints, in SQL
CHECK clauses for specifying on tuples, 97
naming, 96–97 specifying as assertions, 131–132 specifying attribute constraints
and default values, 94–95 specifying key and referential
integrity constraints, 95–96
specifying with CREATE TABLE command, 90, 92
Constructing databases defined, 5 University student database
example, 7 Constructor operations
factory objects providing, 388 objects, 362
Constructs, PHP, 485–486 Content-based retrieval, in queries,
965 Content-encryption algorithms, 863 Continuous allocation, file blocks
on disk, 597 Control measures, database security,
837–838 Conversational searches (CS),
1029–1030 Core specification, SQL, 88 Correlated nested queries, in SQL,
119–120 Cost-based query optimization
catalog information used in, 712–713
example, 719–721 JOIN and, 715–718 overview of, 711–712 SELECT and, 713–715 systematically estimating costs of
query trees, 681 Costs, in choosing a DBMS, 323 COUNT function, SQL
aggregate functions in SQL, 124–125
grouping and, 166–167 implementing, 698
Covert channels flow control and, 861–862 in mandatory access control, 850
Crawlers overview of, 999 Web crawlers, 1028
CREATE ASSERTION command, 131–132
CREATE command, 89 CREATE INDEX command, 110–111 CREATE SCHEMA command, 89 CREATE TABLE command
CHECK clauses for specifying constraints on tuples, 97
clauses for keys and referential integrity constraints in, 95
SQL (Structured Query Language), 90–92
Index 1139
CREATE TRIGGER command, 132–133, 936
CREATE VIEW command, 90, 134–135
Credentials, access control via, 854–855
CRM (Customer Relationship Management), 26
CROSS JOIN operation. See CARTESIAN PRODUCT operation
CROSS PRODUCT operation. See CARTESIAN PRODUCT operation
CS (Conversational searches), 1029–1030
Current directory, 820 Current relation state, 63 Cursors
for looping over tuples in a query result, 450
options for declaring, 457 retrieving multiple tuples using,
455–457 Customer Relationship
Management (CRM), 26 Cycles, converting graph with cycles
into hierarchical structure, 441 Cylinders, on hard disks, 589 DAC (discretionary access control),
850–851 Dangling tuples, problems in rela-
tional design, 563–565 Data
data quality as issue in database security, 867
definition of, 4 insulation between programs and,
11–13 multiple views of, 13 normalization of, 517 sensitivity of, 840–841 types of data in information
retrieval, 996 Data abstraction
data models and, 30 in EER (Enhanced Entity-
Relationship) model, 267–268 insulation between programs and
data, 12–13 relational databases and, 23–24
Data allocation. See Allocation Data blade, Informix, 931 Data blocks. See Blocks of data Data buffers, in database cache, 746 Data-centric XML documents, 422
Data content, database design and, 310
Data cubes (hypercubes), 1070–1072
Data definition in QBE, 1098 in SQL, 89
Data definition language. See DDL (data definition language)
Data dependencies, relational model constraints, 68
Data dictionaries (or data repository) DBMS tools, 43 organizations using, 306
Data-driven design, 310 Data elements, storing XML docu-
ment as, 431 Data encryption. See Encryption Data Encryption Standard (DES),
863 Data fragmentation. See
Fragmentation Data independence, in three-schema
architecture, 35–36 Data items
dealing with multiple copies of, 909
granularity of, 746, 795 updates, 810–811
Data labels, combining with user labels, 869–870
Data manipulation language. See DML (data manipulation language)
Data marts, 1070 Data mining
applications of, 1060 Apriori algorithm, 1041–1042 association rules among hierar-
chies, 1047–1048 association rules and, 1039–1041 bibliographic references,
1065–1066 classification and, 1051–1054 clustering and, 1054–1056 commercial tools for, 1060–1062 complications in mining associa-
tion rules, 1050–1051 vs. data warehousing, 1036 FP-growth algorithms, 1045–1047 FP-tree (frequent-pattern tree)
algorithm, 1043–1045 genetic algorithms (GAs), 1059 goals of, 1037–1038 multidimensional associations,
1048–1049
negative associations, 1049–1050 neural networks in, 1058 overview of, 1035–1036 as part of knowledge discovery
process, 1036–1037 Partition algorithm, 1047 pattern discovery in, 1057 regression in, 1057–1058 Sampling algorithm, 1042 spatial, 963–964 specialized database applications,
25 summary and exercises, 1063–1065 types of knowledge discovered
during, 1038–1039 Data model mapping
automated tools for database design, 344
database design and, 202 as design phase, 311 logical database design, 326
Data models categories of, 30–31 classifying DBMSs by, 49–52 converting object models to/from
logical models, 341 data abstraction in, 12, 30 for data warehouses, 1070 ER model. See ER (Entity-
Relationship) model in federated databases, 885 functional, 214 hierarchical for XML. See
Hierarchical data models, for XML
inherent rules of, 21 mapping EER to relational. See
EER-to-Relational mapping mapping ER-to-Relational. See
ER-to-Relational mapping network. See Network data mod-
els object. See Object data models Rational Rose, 338–342 semantic, 267–268
Data models, enhanced, 931–932 active databases. See Active data-
base systems bibliographic references, 989–991 deductive database systems. See
Deductive database systems multimedia databases. See
Multimedia databases spatial databases. See Spatial
databases summary and exercises, 983–989
1140 Index
temporal databases. See Temporal databases
Data normalization, 18 Data organization, transparency of,
880 Data provenance, 306 Data records, in University database
example, 6 Data replication. See Replication Data requirements, database design
and, 200 Data servers, in two-tier client/
server architecture, 47 Data sources
accessing with Java programs, 469 databases as, 415
Data striping, RAID, 618 Data structure, database design and,
310 Data sublanguage, DML as, 38 Data Surveyor, 1060 Data transfer costs, for distributed
query processing, 902–904 Data types
associated with record fields, 595 atomic (user-defined), 386–388 class hierarchies. See Type (class)
hierarchies common SQL, 92–94 complex types, 358–360, 430 constructors. See Type construc-
tors domains and, 61 entity types. See Entity types n-ary relationship types, 291–292 names and functions, 365 PHP, 485–486 reference types, 373–374 spatial types, 959–960 temporal types, 945 type-compatible relations, 697 type generator, 358–359 UNION types, 258–260 in University database example, 7 value sets specified via, 209
Data warehouses bibliographic references, 1082 building, 1075–1078 characteristics of, 1069 data mining compared with, 1036 data modeling for, 1070 difficulties with implementing,
1080–1081 functions of, 1078–1079 overview of, 1067–1068 summary and exercises, 1081–1082
types of databases, 3 views compared with, 1079–1080
Database administrators. See DBAs (database administrators)
Database applications. See Applications, database
Database architectures. See Architecture, database
Database back-ends, 25 Database-based approach, to Web
content analysis, 1025 Database cache, DBMS, 746 Database design
conceptual design choices, 222, 224
in database application life cycle, 308
denormalization as design deci- sion related to query speed, 731–732
factors influencing physical data- base design, 727–729
indexing decisions, 730–731 issues in active database systems,
937–940 practical. See Database design
methodology relational. See Relational database
design specialization and generalization
choices, 263–264 tuning, 735–736 verification of, 345
Database design methodology, 298 automated tools for, 342–346 bibliographic references, 348–350 choice of DBMS, 323–325 conceptual schema design,
313–321 data model mapping (logical
database design), 326 database application system life
cycle, 308–309 implementation process and,
309–311 information system (IS) life cycle,
307–308 organizational context for data-
base systems and, 304–307 overview of, 303–304 physical database design, 326–327 Rational Rose for. See Rational
Rose requirements collection and
analysis phase, 311–313 summary and exercises, 345–348
system implementation and tun- ing, 327–328
transaction design, 322–323 UML as design specification
standard, 328 UML diagram types, 329–334 UML for database application
design, 329 University student database
example, 334–337 Database designers
database actors on the scene, 15 design and testing of applications,
199 Database fingerprinting, 857 Database implementation. See
Implementation Database interfaces. See Interfaces Database items, in transaction pro-
cessing, 745–747 Database management systems. See
DBMSs (database management systems)
Database programming language. See Programming languages
Database recovery techniques ARIES recovery algorithm,
821–825 bibliographic references, 832 caching (buffering) disk blocks,
809–810 catastrophic failure and, 826–827 checkpoints and fuzzy check-
points, 812–813 deferred update and immediate
update techniques, 808–809 immediate update techniques,
817–820 in multidatabase systems,
825–826 NO-UNDO/REDO recovery
based on deferred update, 815–817
overview of, 807–808 recovery in distributed databases,
909–910 rollbacks, 813–815 shadow paging, 820–821 steal/no-steal and force/no-force
techniques, 811–812 summary and exercises, 827–832 system logs and, 808 write-ahead logging, 810–812
Database schemas. See also Schema instances and database state and,
32–33
Index 1141
ontologies and, 272 relational. See Relational database
design three-schema architecture. See
Three-schema architecture Database security
access control policies for e- commerce and Web, 854–855
access control, user accounts, and audits, 839–840
bibliographic references, 874–875 challenges in, 867–868 comparing mandatory access
control with discretionary access control, 850–851
control measures, 837–838 covert channels, 861–862 DBAs (database administrators)
and, 838–839 DES and AES standards, 863 digital certificates, 865–866 digital signatures, 865 discretionary privileges, 842–844 encryption, 862–863 flow control, 860–861 granting/revoking privileges,
844–846 information security vs. informa-
tion privacy, 841–842 label-based security and row-level
access control, 852–853 limits on propagation of privi-
leges, 846–847 mandatory access control,
847–850 Oracle Label Security, 868–870 overview of, 835 privacy issues, 866–867 public (asymmetric) key algo-
rithms, 863–865 role-based access control,
851–852 sensitivity of data and, 840–841 SQL injection attacks, 855–858 statistical, 859–860 summary and exercises, 870–874 symmetric key algorithms, 863 threats, 836–837 types of security, 836 XML access control, 853–854
Database security and authorization subsystem, DBMSs, 837
Database server layer, in three-tier client/server architecture, 893
Database servers, DBMS component modules, 42
Database state (snapshot), 32–33 Database storage reorganization,
DBMS utilities, 43 Database system environment
database system utilities, 42–43 DBMS component modules,
40–42 tools, application environments,
and communication facilities, 43–44
Databases, introduction to actors on the scene, 14–16 advantages of DBMS approach,
17 backup and recovery in, 20 bibliographic references, 28 characteristics of database
approach, 9–10 as collection of named data items,
746 comparing with IR systems,
997–998 complex data relationships in, 20 concurrent use of, 744 creation/conversion costs,
323–324 defined, 4 design, 9 early applications using hierarchi-
cal and network systems, 23 extending application capabilities,
24–26 inferencing and actions using
rules, 21–22 information retrieval (IR) and, 26 insulation between programs and
data, and data abstraction, 11–13
integrity constraints in, 20–21 interchanging data of Web using
XML, 24 multiple user interfaces in, 20 multiple views of data, 13 object-oriented, 24 other benefits and implications of
using database approach, 22 overview of, 3–6 persistent storage of program
objects, 19 personal, 305 query processing in, 19–20 redundancy controlled in, 17–18 relational databases providing
data abstraction and applica- tion flexibility, 23–24
self-describing nature of, 10–11
sharing data and multiuser trans- actions, 13–14
storing/extracting XML docu- ments from, 431–432, 442
summary and exercises, 27–28 unauthorized access restricted in,
18–19 University student database
example, 6–9 when not to use DBMS, 26–27 workers behind the scene, 16–17
Datalog language clausal form and Horn clauses,
974–975 evaluating nonrecursive queries,
981–983 notation, 970–973 programs and safety, 978–980 as variation of Prolog language,
970 DATE data type, in SQL, 93, 945 DB/DC system, DBMS tools, 44 DBAs (database administrators)
database actors on the scene, 15 interface for, 40 role in database security, 838–839
DBMSs (database management sys- tems)
accessible with PEAR DB, 492 advantages of, 17 backup and recovery in, 20 buffers in transaction processing,
745–747 cache, 683, 746, 809 Catalog, 10–11 centralized architecture, 44 choosing for database design,
323–325 classification of, 49–52 client/server architecture, 44–46 component modules, 40–42 conceptual representation of data
in, 12 definition of, 5 interfaces, 38–40 languages, 36–38 lock manager subsystem in, 779 modules, 29 query processing and optimiza-
tion module, 20 security and authorization sub-
system, 19, 837 single-user vs. multiuser, 744–745 SQL and, 87 storing/extracting XML docu-
ments, 431
1142 Index
system designers, 16 system utilities, 42–43 tools, application environments,
and communication facilities, 43–44
two-tier client/server architecture, 46–47
when not to use, 26–27 DCT (Discrete Cosine Transform),
966 DDBMSs (Distributed DBMSs)
architecture of, 889–891 classifying DBMSs by site distri-
bution, 49–50 defined, 878 degree of homogeneity of soft-
ware, 884 DDBs (distributed databases)
advantages of, 882 architecture of, 889–891 autonomy of, 881 bibliographic references, 924–927 catalog management, 913 cloud computing and, 914–915 concurrency control, 910–912 data fragmentation in, 894–896 data replication and allocation in,
897 data transfer costs for distributed
query processing, 902–904 defined, 878 example of fragmentation, alloca-
tion, and replication, 898–901 federated database management
systems, 885–887 functions of, 883 multiprocessor systems compared
with, 879 in Oracle, 915–919 overview of, 877–878 parallel vs. distributed architec-
ture, 887–888 peer-to-peer database systems,
915 query processing, 901–902 query processing using semijoin
operation, 904 query update and decomposition,
905–907 recovery, 912–913 reliability and availability of, 881 summary and exercises, 919–924 three-tier client/server architec-
ture, 892–894 transaction management in,
907–909
transparency in, 879–881 types of, 883–885
DDL (data definition language) DBMS languages and, 37 processing schema definitions, 40 Rational Rose for DDL genera-
tion, 338 SQL as, 88
Deactivate rule, in active database systems, 938
Deadlocks detecting, 787–788 in distributed databases, 910 preventing, 785–787
Debit-credit transactions, 769 Decision-support systems (DSS),
1068–1069 Decision tree induction algorithm,
1052–1054 Decision trees, classification with,
1051 Declarative assertions, 131 Declarative expressions, in relational
calculus, 174 Declarative languages
DBMS languages, 38 deductive database systems and,
970 SQL as, 88
Decomposition. See Properties of relational decompositions
Decrement operator, SQL, 105 Decryption algorithms, in public
key schemes, 864 Deductive axioms, in deductive
databases, 975 Deductive database systems
clausal form and Horn clauses, 974–975
Datalog notation, 973 Datalog programs and safety of,
978–980 evaluating nonrecursive Datalog
queries, 981–983 interpretation of rules, 21, 975–977 overview of, 970 Prolog notation, 970–972 relational operators, 980–981
Deductive knowledge, discovered during data mining, 1038
Deductive rules. See Rules, in deduc- tive databases
Default values, of attributes in SQL, 94–95
Deferred consideration, of rules in active databases, 938
Deferred update techniques following no-steal approach, 811 NO-UNDO/REDO recovery,
815–817 overview of, 807–809
Defining attribute, of specialization, 252
Defining databases database state and, 33 overview of, 5 University student database
example, 6–7 Defining predicate, of subclass, 252 Degree of homogeneity, of DDBMS
software, 884 Degree of local autonomy, of
DDBMS software, 884 Degree of relationship
in ER (Entity-Relationship) model, 213
greater than two, 228–232 in relational model, 62 SELECT operator and, 149
DELETE command, SQL, 109, 936 Delete operation
concurrency control techniques, 800–801
on files, 600 relational data model operations,
77–78 Deletion anomalies, avoiding redun-
dant information in tuples, 509 Deletion markers, file organization
and, 602 Denial of Service (DOS) attacks,
855, 857 Denormalization
defined, 18, 518 query speed and, 731–732
Dense indexes, 633, 636 Density-based clustering, 964 Dependency. See also Functional
dependencies modeling in pattern discovery
phase of Web usage analysis, 1027
predicate dependency graph, 982 Dependency preservation property
decomposition into third normal form (3NF), 560–563
normal forms and, 518 overview of, 552–553
Deployment diagrams, UML, 330 Deployment, operation, and main-
tenance phase, of information system (IS) life cycle, 308
Index 1143
Derived attributes in ER model, 206 in functionality of data ware-
houses, 1079 Derived tables, SQL views, 89 DES (Data Encryption Standard),
863 DES keyword, for descending order
of query results, 107 Descendant nodes, of tree struc-
tures, 646 Description records, in SQL/CLI,
464–468 Descriptor elements, SQL, 89 Design autonomy
in distributed databases, 881 in federated databases, 886–887
Design, database. See Database design
Design phase, of information system (IS) life cycle, 307
Design transparency, in distributed databases, 881
Design verification, 345 Desktop search engines, 996 Destination page, as hyperlink
component, 1020 Destructor operations, on objects,
362 Detached consideration, of rules in
active databases, 939 DFT (Discrete Fourier Transform),
966 Diagnostic area size, transaction
support in SQL, 770 Diagrams
automated tools for database design, 343–344
Class diagrams. See Class dia- grams
ER diagrams. See ER diagrams Schema diagram, 32 UML. See UML (Unified
Modeling Language) Dictionary constructor, 359 Digital certificates, 865–866 Digital libraries, searchable Web
documents in, 1018 Digital signatures, 854, 865 Digital terrain analysis, for spatial
databases, 959 Digits, in text preprocessing in
information retrieval, 1011 Dimension tables, in multidimen-
sional data model, 1073
Directories current and shadow, 820 online directories, 919–921
Directory Information Tree (DIT), 919
Directory services, in distributed databases, 919–921
Directory Services Markup Language (DSML), 855
Dirty bits, in DBMS cache, 810 Dirty Page Table, in ARIES recovery
algorithm, 822 Dirty reads, transaction support in
SQL, 770 Discrete Cosine Transform (DCT),
966 Discrete Fourier Transform (DFT),
966 Discretionary access control (DAC),
850–851 Discretionary privileges, 842–844 Discriminator key, in UML class
diagrams, 228 Disjointness (disjointedness) con-
straint, 253, 264 Disjoints, 895 Disjunctive selection conditions, 688 Disk blocks (pages)
on hard disks, 589 parameters, 1087–1089
Disk controllers, 591 Disk devices
hard drives, 591 hardware structures of, 588–592 placing file records on disk, 594 as random access storage devices,
592 recovery needed due to failure of,
751 Disk packs, 589 Disk speed, as disk parameter, 1088 Disk storage, cost components of
query execution, 711 Distance function, in image data-
base queries, 966 DISTINCT keyword, with SELECT
command, 103 Distributed architecture, vs. parallel,
887–888 Distributed computing systems, 877 Distributed databases. See DDBs
(distributed databases) Distributed DBMSs. See DDBMSs
(Distributed DBMSs) Distributed warehouses, 1078
Distribution transparency, 880, 894 DIT (Directory Information Tree),
919 DIVISION operation, in relational
algebra, 162–163 DKNF (domain-key normal form),
574–575 DML (data manipulation language)
DBMS languages, 37 precompilers and, 42 SQL as, 88
Document-centric XML documents, 422
Document header, HTML, 419 Document Object Model (DOM),
423 Document Type Definitions
(DTDs), 423, 425 Documentation, in XML schema
language, 429 Documents
hypertext documents, 415 information retrieval from, 1014 in multimedia databases, 932,
965–966 Semantic Web and, 272 SMART retrieval system, 998
Documents, XML extracting from databases,
431–432, 442 publishing, 431 types of, 422 well-formed and valid, 422–425
DOM (Document Object Model), 423
Domain-key normal form (DKNF), 574–575
Domain of knowledge, in ontolo- gies, 268
Domain (relational) calculus, 183 Domain variables, 183 Domains
of attributes in UML class diagrams, 227
cardinality of, 63 constraints and, 68 relation schema and, 62 in relational model, 61
DOS (Denial of Service) attacks, 855, 857
Dot notation in ODMG object model, 382 operations, 362 for path expressions in SQL, 376
Double buffering, 593
1144 Index
Double-quoted strings, in PHP text processing, 485–486
Double-sided disks, 589 Downward closure property
Apriori algorithm using, 1041–1042
association rules and, 1041 Drill-down display
in data warehouses, 1078 working with data cubes,
1070–1072 Driver manager class, JDBC, 469 Drop command, in active database
systems, 938 DROP SCHEMA command, 138 DROP TABLE command, 138 DROP VIEW command, 135 DSML (Directory Services Markup
Language), 855 DSS (decision-support systems),
1068–1069 DTDs (Document Type
Definitions), 423, 425 Duplicate elimination, PROJECT
operation and, 150 Durability property, of transactions,
755 Duration events (facts), in temporal
databases, 946 Dynamic arrays, PHP, 483 Dynamic database application, 30 Dynamic database programming
approaches Dynamic SQL, 448, 458–459 overview of, 464 SQL/CLI (Call Level Interface),
464–468 Dynamic files, 601 Dynamic hashing, 612, 614, 731 Dynamic (late) binding, in ODMS,
368 Dynamic multilevel indexes,
646–647 Dynamic operators, spatial, 961 Dynamic SQL
defined, 448 specifying queries at runtime,
458–459 Dynamic Web pages
defined, 481 vs. static, 420 XML for, 416
E-commerce access control policies for,
854–855
interchanging data on Web using XML, 27–28
E-mail servers, 45 Early (static) binding, in ODMS,
368 ECA (Event-Condition-Action)
model, 933 Economies of scale, as benefit of
database approach, 22 ECR (Entity-Category-Relationship)
model, 258 Edges
detecting in automatic analysis of images, 967
notation for query trees and query graphs, 703
of query graphs, 179 EER (Enhanced Entity-
Relationship) model aggregation and association,
269–271 bibliographic references, 284 classification and instantiation,
268 constraints on specialization and
generalization, 251–254 data abstraction, knowledge rep-
resentation, and ontological concepts, 267–268
design choices for specialization/generalization, 263–264
formal definitions of concepts, 264–265
generalization, 250–251, 269 hierarchies and lattices for spe-
cialization and generalization, 254–257
identification, 269 mapping to ODB schema,
397–398 modeling union types, 258–260 ontologies and the Semantic Web
and, 272–273 overview of, 245–246 refining conceptual schemas,
257–258 specialization, 248–250, 269 subclasses, superclasses, and
inheritance, 246–248 summary and exercises, 273–284 UML as alternative notation to,
265–266 UNIVERSITY example of EER
schema, 260–263
EER-to-Relational mapping mapping shared subclasses (mul-
tiple inheritance), 297 mapping specialization or gener-
alization, 294–297 mapping union types (cate-
gories), 297–299 EIS (executive information systems),
1068 Element operator, in OQL, 403 Elements, in SQL schema, 89 Elements, XML documents
complex vs. simple, 421–422 notation for specifying, 424 overview of, 420 storing XML document as data
element, 431 XML schema language, 429
Embedded SQL advantages/disadvantages,
476–477 approaches to database program-
ming, 449 communicating between program
and DBMS, 454 connecting to databases, 453 defined, 448 overview of, 451–452 programming example, 454–455 retrieving multiple tuples using
cursors, 455–457 retrieving single tuples with,
452–453 SQLJ, 448, 459–464 as static database programming
approach, 464 Empty elements, XML schema lan-
guage, 429 Encapsulation
defined, 369 ODB features included in SQL,
370 in OO systems, 356 overview of, 361–363 UDTs and, 374–375
Encryption DES and AES standards, 863 overview of, 862–863 public (asymmetric) key
algorithms, 863–865 RSA encryption algorithm, 865 symmetric key algorithms, 863
End transaction, transaction types, 745
End users, 15–16
Index 1145
END_TRANSACTION operation, 751–752
Enhanced Entity-Relationship model. See EER (Enhanced Entity-Relationship) model
Enterprise Resource Planning. See ERP (Enterprise Resource Planning)
Enterprise search systems, 996 Enterprise-wide data warehouses,
1070 Entity-Category-Relationship (ECR)
model, 258 Entity integrity
constraint in relational data model, 73
rules for multilevel relations, 850 Entity-Relationship model. See ER
(Entity-Relationship) model Entity sets
overlapping, 253 overview of, 207–208
Entity types attributes of, 203–207 classes compared with, 227 in data modeling, 31 generalized, 264 key attributes of, 208–209 mapping ER-to-Relational data
models, 287–289 notation for, 1083–1084 overview of, 207–208 relations, 288 subtypes or subclasses of,
246–247 supertypes or superclasses of, 247 symbols for, 1084 type inheritance in EER model,
248 weak entity types, 219–220
Entry points to objects, 363 in ODMG object model, 377 in OQL, 399–400
Environment records, in SQL/CLI, 464–468
Environments database system environment,
40–44 SQL, 90
EQUIJOIN operation, in relational algebra, 159–161
Equivalence of schedules, 768–770 Equivalence of sets, of functional
dependencies, 549
ER diagrams class diagrams compared with,
200 defined, 199 notation of, 221–226, 1083–1085
ER (Entity-Relationship) model attributes of relationship types,
218 bibliographic references, 243 conceptual design choices, 222,
224 constraints on binary relationship
types, 216–218 correspondence to relational
model, 293 as data model type, 31 degree of relationship, 213 degree of relationship greater
than two, 228–232 entities and attributes, 203–207 entity types and entity sets,
207–208 ER diagram notation, 221–226,
1083–1085 as high-level conceptual model,
200–202 initial conceptual design for
COMPANY database example, 210–211
key attributes of entity types, 208–209
laboratory exercises, 241–243 naming conventions for schema
constructs, 222 overview of, 199–200 refining conceptual design for
COMPANY database example, 220–221
relationship types, sets, and instances, 212–213
relationships as attributes, 214 role names and recursive relation-
ships, 215 sample database application,
202–203 summary and exercises, 232–241 UML class diagram notation,
226–228 value sets (domains) of attributes,
209–210 weak entity types, 219–220
ER-to-Relational mapping algorithm for, 286–287 mapping binary 1:1 relationship
types, 289–290
mapping binary 1:N relationship types, 290
mapping binary M:N relationship types, 290–291
mapping multivalued attributes, 291
mapping N-ary relationship types, 291–292
mapping regular entity types, 287–288
mapping weak entity types, 288–289
summary of mapping constructs, 292–294
ERP (Enterprise Resource Planning) database back-ends and, 25 federated databases and, 886 SANs (Storage Area Networks)
and, 621 Errors/exceptions
in Java, 459–461 in ODMG object model, 383 recovery needed due to, 750 reliability and availability and,
881 ETL (extraction, transformation,
and loading) tools, 1069 Event-based processing, in XML,
423 Event-Condition-Action (ECA)
model, 933 Events
as component of triggers, 133 specifying, 943
Events, in ECA model defined, 933–934 in STARBURST example, 940
Example elements, QBE, 1091 EXCEPT operation
in relational algebra, 152–155 as SQL set operations, 104
Exception objects, in knowledge representation, 268
Exceptions. See Errors/exceptions Execution autonomy
in distributed databases, 881 in federated databases, 886–887
Execution transparency, in distrib- uted databases, 881
Executive information systems (EIS), 1068
Existence dependency, in binary relationships, 217
Existential quantifiers transforming, 180
1146 Index
in tuple relational calculus, 177–178
EXISTS function, SQL, 120–122 Expanding or growing (first) phase,
in two-phase locking, 782 Expert database systems, 932 Export schema, in federated data-
base architecture, 891 Expressions
in-line expressions in relational algebra, 151
safe/unsafe expressions in tuple relational algebra, 182–183
in tuple relational calculus, 176–177 Extends keyword, extending inheri-
tance, 383 Extensible hashing, 612–614 Extensible Stylesheet Language
Transformations (XSLT), 415, 436
Extensible Stylesheet Language (XSL), 415, 436
Extension. See Database state (snapshot)
Extensions, SQL, 88 Extents
constraints corresponding to type hierarchies, 366
defined, 369 object persistence and, 363 in ODMG object model, 388–390
External hashing, 609–611 External schema
in federated database architecture, 891
in three-schema architecture, 34 Extraction, transformation, and
loading (ETL) tools, 1069 F-score, measures of relevance in IR,
1017 Faceted searches, 1028–1029 Facets, 1028–1029 Fact constellation, in multidimen-
sional data models, 1074 Fact-defined predicates (relations),
978 Fact tables, in multidimensional
data model, 1073 Factory objects, in ODMG object
model, 388–390 Facts, in deductive databases, 970 Failed state, transactions, 752 Fan-out, of multilevel index, 643 Faults, reliability and availability
and, 881
FCIP (Fiber Channel over IP), 624 FCoE (Fiber Channel over
Ethernet), 624 FDBMS (federated database man-
agement systems), 50 FDBSs (federated database systems)
architecture of, 890–891 defined, 885 overview of, 885–887
Feasibility analysis, 307 Features, in vector space model for
IR, 1003 Federated database management
systems (FDBMS), 50 Federated database systems. See
FDBSs (federated database systems)
Federated schema, in federated data- base architecture, 891
Federated warehouses, 1078 Feedback loops, between phases of
database design, 310 Fiber Channel over Ethernet
(FCoE), 624 Fiber Channel over IP (FCIP), 624 Field models, spatial data, 960 Fields
connecting fields for relationships between file records, 616
hash field, 606, 608 indexing fields (or attributes). See
Indexing fields (or attributes) logical field references for rela-
tionships between file records, 617
ordering and key fields, 603 of records, 594–595 search field, 648
Fifth normal form (5NF), 534–535 File of commands, in SQL program-
ming, 449 File servers, in client/server archi-
tecture, 45 Files (of records)
allocation of file blocks on disk, 598
B-trees and data structures in organization of, 617
data organization and, 587 database approach compared with
file processing, 9–10 dynamic file expansion, 611–612 dynamic hashing, 614 extensible hashing, 612–614 external hashing, 609–611
fixed-length records and variable- length records, 595–597
hashing techniques, 606 headers (descriptors), 598 internal hashing, 606–609 linear hashing, 614–616 of mixed records, 616–617 operations on, 599–601 of ordered records (sorted files),
603–606 organization of, 601 placing file records on disk, 594 simple searches, 685 of unordered records (heap files),
601–602 Filtering
in agent-based approach to Web content analysis, 1024
classification process in manda- tory access control, 848
protecting against SQL injection, 858
Finances, applying data mining to, 1060
Find operation, on files, 599–600 FindAll operation, on files, 600 FindNext operation, on files, 600 FindOrdered operation, on files, 600 Fingerprinting, database, 857 First-come-first-served queues, deal-
ing with starvation, 788 First normal form (1NF), 65,
519–523 Fixed-head disks, 591 Fixed-length records, 595–597 Flash memory, 585 Flat data models, 436–440 Flat files, in relational model, 60 Flat relational model, 65 Flexibility, as benefit of database
approach, 22 Flow analysis, for spatial databases,
959 Flow control
covert channels, 861–862 overview of, 860–861 security control measures, 838
Flow policies, 860 FLWR expressions, XQua, 434 Folding technique, in hashing, 608 Force/no-force techniques
in database recovery, 811–812 UNDO/NO-REDO recovery algo-
rithm using, 818–819 Force-writing, log buffers, 754
Index 1147
FOREIGN KEY clause, of CREATE TABLE command, 96
Foreign keys ER-to-Relational mapping of
binary 1:1 relationship types, 289
inclusion dependencies and, 571 in RDBs, 396 relational data model and, 73–74
Formatting disks, 589 Forms
collecting data from/inserting record into, 493–494
PHP, 490–491 Forms-based interfaces, DBMS, 38 Forms specification languages,
38–39 Formulas
in domain calculus, 183 in tuple relational calculus,
176–177 Forward engineering, Rational Rose
and, 338 Fourth normal form (4NF)
formal definition of, 533–534 multivalued dependency and,
531–533 restating definition of, 568–570
FP-growth algorithms, in data min- ing, 1045–1047
FP-tree (frequent-pattern tree) algo- rithm, in data mining, 1043–1045
Fragmentation in distributed databases, 894–896 example of, 898–901 transparency of, 880
Fragmentation schema, of data- bases, 896
Free-form search requests, 995 FROM clause
of SELECT command, 107 in SQL retrieval queries, 129–130
Full functional dependency, in 2NF, 523
Fully inverted files, 669 Fully replicated catalogs, 913 Function-based indexes, 666–667 Function call injection attack,
856–857 Function calls, database program-
ming with advantages/disadvantages, 477 approaches to database program-
ming, 449
JDBC for Java programming, 469–473
overview of, 461–464 PHP and, 491 SQL/CLI with C as host language,
464–468 Functional data models, 214 Functional dependencies
based on arithmetic functions and procedures, 572–574
equivalence of sets of, 549 existence dependency and, 217 full functional dependency in
2NF, 523 inclusion dependencies, 571 inference rules for, 545–549, 568 JD (join dependencies), 534–535 minimal sets of, 549–551 MVD (multivalued dependency),
68, 531–533 overview of, 513–516 relational model constraints, 68 template dependencies, 572 transitive dependencies in 3NF,
523–524 types of constraints, 74
Functional requirements, in data- base design, 200–201
Functions. See also Operations as attributes and operations, 365 identifying functional behavior
for transactions, 322 PHP, 488–490 protecting against SQL injection,
858 QBE (Query-By-Example), 1095 SQL/PSM (SQL/Persistent Stored
Modules), 473–475 Fuzzy checkpoints
in ARIES recovery algorithm, 822
in database recovery, 812–813 Garbage collection, when transac-
tion commits, 821 GAs (genetic algorithms), 1059 GCS (global conceptual schema),
315, 889 Generalization. See
Specialization/generalization Generalized model, for active data-
base systems, 933–937 GENERALIZED PROJECTION, in
relational algebra, 165–166 Generic IR pipeline, 1000–1001 Genetic algorithms (GAs), 1059
Geographic Information Systems. See GIS (Geographic Information Systems)
GIF image format, 966 GIS (Geographic Information
Systems) spatial databases and, 958 types of databases, 3
Global conceptual schema (GCS), 315, 889
Global query optimization, 901 Global recovery manager, 825, 908 Global transaction manager, 907 Granting privileges
example, 845–846 GRANT OPTION, 844 overview of, 843
Granularity level, in locking multiple granularity level locking,
796–798 overview of, 795–796
Granularity, of points in temporal databases, 945
Graphical user interfaces (GUIs), 20, 39
Graphs. See also Diagrams converting graph data into XML
trees, 441 creating hierarchical XML views
over flat or graph-based data, 436–440
hierarchies (acyclic graphs), 50 predicate dependency graph, 982 query graphs, 179–180, 679,
701–703 wait-for graph, 787
Grid files for organizing file data, 662–663 for spatial indexing, 962
Ground axioms, in deductive data- bases, 975
Group by clause, in OQL, 405–406 GROUP BY clause, in SQL
aggregation and, 698 overview of, 126–129 in retrieval queries, 129–130 of SELECT command, 107
Grouping aggregation and, 124–125,
166–168 GROUP BY and HAVING clauses
for, 126–129 Grouping attributes, 126 Grouping operator, in QBE (Query-
By-Example), 1095–1098
1148 Index
Growing (first) phase, in two-phase locking, 782
Guided participation, in social searches, 1029
GUIs (graphical user interfaces), 20, 39
Handle, C pointer variable and, 465–466
Hard drives, 591. See also Disk devices
Hardware addresses, 590 costs in choosing a DBMS, 323 structures of disk devices,
588–592 Hash field, 606 Hash field space, 608 Hash file, 606 Hash function (randomizing func-
tion), 606 Hash keys, 606, 686 Hash tables, 606 Hash values, of a record, 612 Hashing
cost functions for SELECT opera- tions, 713
implementing set operations, 697–698
partitioned, 661–662 Hashing indexes
for decision making in database design, 730–731
overview of, 663 Hashing techniques, files
dynamic file expansion, 611–612 dynamic hashing, 614 extensible hashing, 612–614 external hashing, 609–611 internal hashing, 606–609 linear hashing, 614–616 overview of, 606
Having clause, in OQL, 406 HAVING clause, in SQL
aggregate functions used in, 125 overview of, 127–129 in retrieval queries, 129–130 of SELECT command, 107
Health care, applying data mining to, 1060
Heterogeneous databases DDBMSs, 50 in Oracle, 918
Heuristic components, of design tools, 344
Heuristic rules, 681, 701
Heuristics, in query optimization converting query trees into query
execution plans, 709–710 heuristic algebraic optimization
algorithm, 708–709 notation for query trees and
query graphs, 701–703 optimization of query trees,
703–706 overview of, 700–701 transformation rules for relational
algebra operations, 706–708 Hierarchical data models
classifying DBMSs and, 49 overview of, 52 types of data models, 31
Hierarchical data models, for XML converting graph data into XML
tree, 441 creating hierarchical XML views
over flat or graph-based data, 436–440
overview of, 420–422 Hierarchies
association rules among, 1047–1048
in object data models, 50 specialization hierarchies,
254–255 type hierarchies, 364–367
High-level data models, 30 High-level (nonprocedural) DMLs, 37 High-level query languages, 361 Histograms, catalog information
used in cost functions, 713 HITS ranking algorithm, 1021–1022 Homogeneous DDBMSs, 49 Horizontal fragmentation, 880, 895 Horizontal partitioning, in database
tuning, 736 Horizontal propagation, of privi-
leges, 846 Horn, Alfred, 973 Horn clauses, in deductive database
systems, 974–975 Host languages
C as host language in SQL/CLI, 464–468
in database programming, 452 DML and, 38
HSV (hue, saturation, and value), 967
HTML (HyperText Markup Language)
hypertext documents and, 415
long text strings in HTML files, 484
unstructured data and, 418–420 Web publishing language, 24
Hubs, of Web pages or Web sites, 1020
Hue, saturation, and value (HSV), 967
Hybrid hash-join, 696 Hybrid XML documents, 422 Hyperlinks
browsing and, 999 destination page and anchor text
components, 1020 for linking Web documents, 24
Hypertext documents, on Web, 415 HyperText Markup Language. See
HTML (HyperText Markup Language)
Hypertext Preprocessor, 482 Hyphens, text preprocessing in
information retrieval, 1011 Hypothesis tuples, in template
dependencies, 572 I/O performance, in database
tuning, 733 Idempotent operations, UNDO and
REDO, 809 Identification
as abstraction process, 269 goals of data mining, 1038 of multimedia sources, 965
Identifiers, 269 Identifying (or owner) entity type,
219 Identifying relationship, of weak
entities, 219 Identity Management, in security,
852 IE (information extraction), 1012 Image data, in GIS systems, 960 Images, in multimedia databases
analysis of, 967–968 formats, 965–966 object recognition, 967 overview of, 932, 965 semantic tagging, 969 storage/retrieval, 25
Immediate consideration, rule con- sideration in active databases, 938
Immediate update techniques, 808–809
overview of, 807, 809 recovery based on, 817–820
Index 1149
UNDO/NO-REDO recovery algorithm, 818–819
UNDO/REDO recovery algorithm, 819
Immutable property, of OIDs, 357 Impedance mismatch problem, in
traditional databases, 19 Implementation
in database application life cycle, 308
database design and, 309–311 of database operations, 12 of database system, 327–328 encapsulation and, 361 goodness of relation schemas and,
502 issues in active database systems,
937–940 of object operations, 356 tuning and, 311
Implementation data models, 31 Implementation phase, of informa-
tion system (IS) life cycle, 307 In-line expressions, in relational
algebra, 151 Inclusion dependencies, 571–572 Incorrect summary problem, in
concurrency control, 748–749 Increment operator, SQL, 105 Incremental updates, 136 Indexed allocation, of file blocks on
disk, 597 Indexed (ordered) collection expres-
sions, in OQL, 405 Indexed Sequential Access Method
(ISAM), 631, 644–645 Indexed sequential file, 605, 644 Indexes
as access paths in physical data models, 31
B-trees, 649–652 B+-trees, 652–655 bibliographic references, 674–675 bitmap, 663–666 clustering, 635–636 column-based storage of relations
and, 669–670 cost functions for SELECT opera-
tions, 713 database storage and, 583 database tuning and, 734–735 DBMS queries and, 19 dynamic multilevel, 646–647 function-based, 666–667 grid files, 662–663 hash, 663
inverted indexing for information retrieval, 1012–1014
issues with, 668–669 locks for concurrency control in,
798–801 methods for complex selection,
686–687 methods for simple selection, 686 multilevel, 643–646 on multiple keys, 660–661 ordered on multiple attributes,
661 partitioned hashing, 661–662 physical database design and,
730–731 physical vs. logical, 668 primary, 605, 633–635 rebuilding to improve perfor-
mance, 735 search, insertion, deletion with
B+-trees, 655–660 search trees, 647–649 secondary, 636–642 simple searches, 685 single-level, 632–633 spatial, 961–963 summary and exercises, 670–674 tables comparing index types, 642 text-based indexing of audio data,
969 variations on B-trees and
B+-trees, 660 Indexing fields (or attributes)
records and, 631 secondary indexes, 636 single-level indexes, 632
Inductive knowledge, 1038 Inference
control measures, 837–838 in databases, 21–22 in knowledge representation, 268 statistical databases and, 859–860
Inference (deduction mechanism) engine
deductive database systems and, 970
nonrecursive Datalog queries and, 981
in Prolog/Datalog, 975 Inference rules
Armstrong’s, 548 for functional and multivalued
dependencies, 568 for functional dependencies,
545–549 Information extraction (IE), 1012
Information privacy, vs. information security, 841–842
Information repository DBMS tools, 43 organizations using, 306
Information Retrieval. See IR (Information Retrieval)
Information system (IS) vs. information privacy, 841–842 life cycle of, 307–308
Informational searches, 996 INFORMATION_SCHEMA, SQL, 90 Inherent rules, of data models, 21 Inheritance
comparing RDB design with ODB design, 396
extending, 383 multiple, 256–257, 368 ODB features included in SQL,
370 ODL and, 395 in ODMG object model, 383 in OO systems, 356 selective, 368 shared subclasses, 297 single, 256–257 specifying in SQL, 375–376 table inheritance in SQL, 376 type hierarchies and, 364–367 type inheritance in EER model,
248 Initial state, populating (loading)
databases and, 33 Inline views, SQL, 137 Inmon, W. H., 1068 Inner joins
vs. outer joins, 160–161 in SQL, 123–124
Input/output, identifying for trans- actions, 322
Input validation filtering, 858 INSERT command, SQL
active rules and, 936 overview of, 107–109
Insert operation concurrency control techniques,
800–801 on files, 600 relational data model operations,
76–77 Insertion anomalies, reducing
redundant information in tuples, 507–508
Inside-out strategy, for schema design, 316
Instance variables, specifying, 356
1150 Index
Instances current set in database state, 32 database schemas, 32–33 relational database state, 70 of specialization, 249
Instantiation, as inverse of classifica- tion, 268
Instead triggers, in active database systems, 938
Integrity constraints active databases enforcing, 943 in databases, 20–21 entity integrity constraint, 73 in mandatory access control, 850 in relational data model, 73–74 relational database schema and, 70
Integrity loss, as security threat, 836 Intellectual property rights, 867 Intension entity type, describing for
entity set, 208 Interaction diagrams, UML, 331 Interactive query interface
SQL programming, 448 user interaction with databases
via, 40 Interactive transactions, concur-
rency control and, 801 Interblock gaps
disk parameter for, 1088 structures of disk devices, 589
Interfaces for database operations, 12 DBA, 40 DBMS, 38–40 design tools and, 344 GUIs (graphical user interfaces),
20, 39 for high-level query languages, 38 for interactive queries, 40 multiple user, 20 in ODMG object model, 382–385 of operations, 356, 361
Interinstance integrity, 850 Interleaved concurrency
on disks, 593–594 multiprogramming and, 745
Internal hashing, 606–609 Internal schema, in three-schema
architecture, 34 International Standards
Organization (ISO), 88 Internet SCSI (ISCSI), 623–624 Interpolating variable, within
strings, 485 INTERSECT operation, SQL set
operations, 104
INTERSECTION operation algorithms for, 697–698 in relational algebra, 152–155
INTERVAL data type, SQL overview of, 93 as temporal data type, 945
Intranets, searches over, 996 Invalid state, of relational databases,
71 Inverted indexing, for information
retrieval, 1012–1014 IR (Information Retrieval)
bibliographic references, 1033–1034
Boolean model for, 1002–1003 comparing databases to, 26,
997–998 comparing Web search and analy-
sis to, 1018–1019 generic IR pipeline, 1000–1001 history of, 998–999 inverted indexing, 1012–1014 measures of search relevance,
1014–1015 mode of interaction in,
999–1000 overview of, 26, 993–997 probabilistic model for, 1005–1006 query types in, 1007–1009 recall and precision metrics in,
1015–1017 semantic model for, 1006–1007 summary and exercises,
1030–1032 text preprocessing in, 1009–1012 trends in, 1028–1030 vector space model for,
1003–1005 IS-A-COMPONENT-OF, 270 IS-A-MEMBER-OF, 268 IS-A (or IS-AN) relationships
class/subclass relationships in EER model, 247
in EER model, 264 IS-A-PART-OF, 270 IS-A-SUBCLASS-OF, 269 IS-AN-INSTANCE-OF, 268 IS-ASSOCIATED-WITH, 270 IS (information system)
vs. information privacy, 841–842 life cycle of, 307–308
ISAM (Indexed Sequential Access Method), 631, 644–645
ISCSI (Internet SCSI), 623–624 ISO (International Standards
Organization), 88
Isolation level, transaction support in SQL, 770
Isolation property, transactions, 14, 754–755
Items, stored in records, 594 Itemsets
Apriori algorithm finding fre- quent (large), 1041–1042
association rules and, 1040–1041 FP-growth algorithms for finding
frequent, 1045–1047 FP-tree algorithms for finding fre-
quent, 1044–1045 partition algorithm for local fre-
quent, 1047 sampling algorithm and, 1043
Iteration markers, in sequence dia- grams, 332
Iterator objects, in ODMG object model, 383
Iterator variables, in OQL, 399–400 Iterators
looping in SQL queries, 98 looping over tuples in a query
result, 450 retrieving multiple tuples in
SQLJ, 461–464 Java
embedding SQL commands in, 459–461
JDBC programming, 469–473 SQLJ and, 452
Java Database Connectivity. See JDBC (Java Database Connectivity)
JD (join dependencies), 534–535 JDBC class libraries, 469 JDBC driver, 469 JDBC (Java Database Connectivity)
client/server architecture for DBMSs, 47
for Java programming, 469–473 library of functions, 448
Job mix, in database design, 728 Join attributes, 159, 689 Join conditions
in domain calculus, 184 SELECT command and, 100 temporal intersection join, 952
Join dependencies (JD), 534–535 Join indexing, in multidimensional
data models, 1075 JOIN operations
cost functions for, 715–718 distributed query processing using
SEMIJOIN operation, 904
Index 1151
EQUIJOIN and NATURAL JOIN variations, 159–161
hybrid hash-join, 696 implementing, 689–690 join selection factors, 693–694 multiple relation queries and
JOIN ordering, 718–719 nested-loop joins, 690–693 overview of, 157–158 partition-hash joins, 694–696
Join operations, in QBE, 1094–1095 Join selection factors, 693–694 Join selectivity ratio, 160, 715 Joined tables (relations), 123–124 JPEG image format, 966 K-means clustering algorithm,
1055–1056 KDD (Knowledge Discovery in
Databases), 1036 Key attribute, 209 Key constraints
on entity attributes, 208–209 integrity constraints in databases,
21 overview of, 68–70 specifying in SQL, 95–96
Key field, sorted files and, 603 Keys
candidate and primary in rela- tional databases, 518–519
indexes on multiple, 660–661 methods for simple selection, 686 in ODMG object model, 388–390 specifying in XML schema, 430
Keyword queries overview of, 39 searching with, 995 types of queries in IR systems,
1007 Kleinberg, Jon, 1021 Knowledge-based systems, 932, 1007 Knowledge Discovery in Databases
(KDD), 1036 Knowledge discovery process
data mining in, 1036–1037 goals of, 1037–1038 types of knowledge discovered,
1038–1039 KR (knowledge representation)
aggregation and association, 269–271
classification and instantiation, 268
compared with semantic data models, 267–268
identification, 269
overview of, 245 specialization and generalization,
269 Label-based security
administrator defining policy for, 853
Oracle Label Security, 868–870 Label Security administrator, 853 Labels, semistructured data and, 417 LANs (local area networks), 44, 879 Large databases, 304 Latches, for short term locks, 802 Late (dynamic) binding, in ODMS,
368 Latency. See Rotational delay (rd) Lattices, for specialization, 255–256 LCS (local conceptual schema), 889 LDAP (Lightweight Directory Access
Protocol), 919–921 Leaf classes, 265 Leaf-deep trees, 718 Leaf nodes, of tree structures, 646 Learning approaches
classification and, 1051 clustering and, 1054 neural networks and, 1058
Legacy data models, 51 Legacy database systems, 49, 60 Legal relation states (legal exten-
sions), 514 Levels of isolation, of transaction,
755 Libraries of functions. See Function
calls, database programming with
Lifespan temporal attributes, 953–954
Lightweight Directory Access Protocol (LDAP), 919–921
LIKE comparison operator, in string pattern matching, 105
Linear hashing, 614–616 Linear regression, 1058 Linear searches
with brute force algorithm, 685–686
cost functions for SELECT opera- tions, 713
of file blocks on disk, 597 of files, 602
Lines, on maps, 960 Link structure, of Web pages,
1020–1021 Linked allocation, of file blocks on
disk, 597 Links, in UML class diagrams, 227
LIS (local internal schema), 889 List constructor, 359 Literals (values), 378–382
atomic formulas as, 973 atomic literals, 378 collection literals, 382 complex types for, 358–360 in OO systems, 358 structured literals, 378
Loading/converting data, in data- base application life cycle, 308
Loading databases, initial state and, 33
Loading utility, for loading data files to database, 42–43
Local area networks (LANs), 44, 879 Local conceptual schema (LCS), 889 Local internal schema (LIS), 889 Local query optimization, 902 Local schema, in federated database
architecture, 890 Localization, in distributed query
processing, 901 Location analysis, for spatial data-
bases, 959 Location transparency, 880 Lock compatibility table, 792 Lock manager subsystem, in DBMS,
779 Lock table, 779 Locking. See also Two-phase locking
for concurrency control, 777 granularity level in, 795–796 index locking and predicate lock-
ing, 801 multiple granularity level,
796–798 used in indexes, 798–800
Locks binary, 778–780 certify locks, 792–793 conversion of, 782 shared/exclusive (read/write),
780–782 in two-phase locking, 778
Log buffers, 753–754 Log records, 753 Log sequence number (LSN), 822 Logic databases, 932. See also
Deductive database systems Logic programming, 970 Logical (conceptual) level, goodness
of relation schemas and, 501 Logical data independence, in three-
schema architecture, 35–36 Logical data models, 341
1152 Index
Logical definitions, of domains, 61 Logical design, 9, 202 Logical field references, for relation-
ships between file records, 617
Logical indexes, vs. physical indexes, 668
Logical operators. See AND, OR, NOT connectives
Logical ordering, secondary indexes and, 642
Logical theory, in ontologies, 272 Login sessions, 839 Logs/logging
auditing and, 839–840 database recovery and, 808 tracking transaction operations,
753–754 during undo process, 821
Long-haul networks, 879 Long text strings, in HTML files,
484 Loss of integrity, as security threat,
836 Lossless (nonadditive) join property
decomposition into 3NF rela- tions, 560–563
decomposition into 4NF rela- tions, 570
normal forms and, 518 overview of, 553–556 successive, 557 testing binary decompositions for,
557 Lossy design, 554 Lost updates, in concurrency con-
trol, 748–749 Low-level data models, 30 Low-level (procedural) DMLs,
37–38 LSN (log sequence number), 822 MAC (mandatory access control)
classification attributes, tuple classification, and multilevel relations, 848–850
comparing with discretionary access control, 850–851
overview of, 847 security classes in, 847–848
Macro life cycle, 307 Magnetic tape
for archiving and backup, 586 as storage devices, 592–593
Main memory, 585 Main memory database, 585 Main or master files, 605
Maintenance database, 6 of derived data, 943 maintenance costs in choosing
DBMS, 323 Maintenance personnel, 17 Mandatory access control. See MAC
(mandatory access control) Manipulating databases
overview of, 5 University student database
example, 9 Manufacturing, applying data min-
ing to, 1060 Map data, as spatial data, 959 Mapping
data model mapping. See Data model mapping
from EER model to relational model. See EER-to-Relational mapping
from EER schema to ODB schema, 397–398
from ER model to relational model. See ER-to-Relational mapping
between levels of three-schema architecture, 35
Query mapping, 901 SELECT command, 97 tuples, 64
Market-basket data, association rules and, 1040
Marketing, applying data mining to, 1060
Mass storage devices, 585 Massively parallel processing
(MMP), 1079 Master files, 605 Materialized evaluation, converting
query trees into query execu- tion plans, 710
Materialized views active rules for maintaining con-
sistency of, 943 data warehouses compared with,
1079 Mathematical relations, 59, 63 Mathematical set theory, 104 MAX function
aggregate functions in SQL, 124–125
grouping and, 166 implementing, 698
MBR (minimum bounding rectan- gle), in R-Trees, 962
Measurement operations, for spatial databases, 958
Mechanical arm, on hard disks, 591 Memory hierarchies, storage devices
and, 584–586 Memory usage, cost components of
query execution, 711 Menu-based interfaces, 38 Merge phase, of sort-merge strategy,
683 Messages, passing to objects, 356 Meta-classes, in knowledge repre-
sentation, 268 Meta-data
in DBMS catalog, 33 DBMSs managing, 306 defined, 5 describing structure of primary
database, 10–11 Metadata repository, in data ware-
housing, 1078 Metasearch engines, 1018 Methods. See also Operations
of object classes, 50 of object operations, 356
Metric operators, as spatial operator, 961
Metrics, for evaluating relevance in IR, 1015–1017
MGL (multiple granularity locking), 797–798
Micro life cycle, 307 Middle tier, in three-tier architec-
ture, 48 Middle-tier Web servers, PHP, 482 Middleware software
federated databases and, 886 heterogeneous DDBMSs and, 50
MIN function aggregate functions in SQL,
124–125 grouping and, 166 implementing aggregate opera-
tions, 698 Minimal model, for interpretation
of rules in deductive databases, 976
Minimal sets, of functional depen- dencies, 549–551
Minimum bounding rectangle (MBR), in R-Trees, 962
Minimum cardinality constraint, 217
Miniworld, 4 MINUS operation, in relational
algebra, 152–155
Index 1153
Mirroring (shadowing), RAID, 619 Mixed fragmentation, in distributed
databases, 896 Mixed strategy
for schema design, 316 for view integration process, 319
Mixed transactions, 322 MMP (massively parallel process-
ing), 1079 Model mapping. See Data model
mapping Model-theoretic interpretation, of
rules in deductive databases, 976
Models, data. See Data models Models, for interpretation of rules
in deductive databases, 976 Models, spatial, 959–960 Modification anomalies, avoiding
redundant information in tuples, 509
Modifier operations, objects, 362 Modify operations, on files, 600.
See also Update operations Modules
client and server, 29 DBMS, 16 DBMS component, 40–42
MOLAP (multidimensional OLAP), 1079
Monitoring and maintenance phase, in database application life cycle, 308
Morphological analysis, in semantic model for IR, 1006
Movable-head disks, 591 MPEG image format, 966 Multidatabase systems
recovery techniques for, 825–826 types of distributed databases,
885 Multidimensional associations,
1048–1049 Multidimensional data models
decision-support technologies, 1069
dimension tables and fact tables, 1073
indexing, 1074–1075 roll-up and drill-down displays,
1072 schemas, 1073–1074
Multidimensional OLAP (MOLAP), 1079
Multilevel indexes B-trees, 649–652
B+-trees, 652–655 dynamic, 646–647 overview of, 643–646 search, insertion, deletion,
655–660 search trees compared with,
647–649 variations on B-trees and
B+-trees, 660 Multilevel relations, in mandatory
access control, 848 Multimedia databases
analysis of audio data sources, 969
analysis of images, 967–968 object recognition, 968–969 overview of, 965–967 semantic tagging of images, 969 types of databases, 3
Multiple granularity locking (MGL), 797–798
Multiple hashing, in collision reso- lution, 609
Multiple inheritance in ODBs (object databases), 368 ODL (object definition language)
and, 395 subclasses and, 256
Multiple relations options for mapping specializa-
tion or generalization, 295 queries and JOIN ordering,
718–719 Multiplicities, in UML class dia-
grams, 227 Multiprocessor systems, 879 Multiprogramming operating
systems, 744–745 Multiset (bag), of tuples, 103–105,
150 Multiuser DBMS systems, 49 Multiuser transactions
in databases, 13–14 processing system, 744–745
Multivalued attributes declaring, 397 in ER model, 206 mapping ER-to-Relational data
models, 291 Multivalued dependency. See MVD
(multivalued dependency) Multiversion concurrency control,
777, 791–793 Multiway joins, 124, 689 Mutually exclusive role, in role-
based access control, 851
MVD (multivalued dependency) 4NF and, 531–533 formal definition of, 533 inference rules for, 568 relational model constraints, 68
N-ary relationship types, 291–292 strategies for view integration
process, 319 N-tier architectures, for Web appli-
cations, 47–49 Named iterator, SQLJ, 461 Named queries, in OQL, 402–403 Namespaces, XML, 428–429 Naming conventions
for constraints, in SQL, 96–97 for relations, 62 for schema constructs, 222
Naming mechanism, object persis- tence and, 363
Naming transparency, 880 NAS (network-attached storage),
622–623 Native XML format
DBMSs, 49 overview of, 425 storing, 431
NATURAL JOIN operations, 123–124, 159–161
Natural language interfaces, 39 Natural language queries, 1009 Navigational searches, 996 Nearest neighbor, in spatial queries,
961 Negation symbol, in QBE, 1096 Negative associations, 1049–1050 Negative literals, in Datalog lan-
guage, 973 Nested-loop joins
cost functions for, 716 factors impacting performance of,
690–693 implementing, 689
Nested queries correlated, 119–120 innermost, 119 overview of, 117–119
Nested relations INF and, 521 SQL and, 111
Network-attached storage (NAS), 622–623
Network data models classifying DBMSs and, 49 overview of, 51 types of data models, 31
1154 Index
Network transparency, 880 Neural networks, 1058 NO-UNDO/REDO recovery tech-
nique based on deferred update, 815–817 overview of, 807, 809
No waiting algorithm, for deadlock prevention, 787
Nodes, of tree structures, 646 Non-identifying relationships, in
Rational Rose, 340 Nonadditive (lossless) join property
decomposition into 3NF rela- tions, 560–563
decomposition into 4NF rela- tions, 570
normal forms and, 518 overview of, 553–556 successive, 557 testing binary decompositions for,
557 Nonprime attributes, 519 Nonprocedural languages, relational
calculus as, 174 Nonrecoverable schedules, transac-
tions, 758 Nonrecursive queries, evaluating in
Datalog, 981–983 Nonrelevant sets, in probabilistic
model for IR, 1005 Nonrepeatable reads, transaction
support in SQL, 771 Nonserial schedules, 761 Nontime-varying attributes, 953 Nonvolatile storage, in databases,
586 Normal forms
based on primary keys, 516–517 Boyce-Codd normal form
(BCNF), 529–531, 559–560 domain-key normal form
(DKNF), 531–533, 574–575 fifth normal form (5NF), 534–535 first normal form (1NF), 65,
519–523 fourth normal form (4NF),
531–534, 568–570 insufficiency of, 552 normalization of relations, 517 practical use of, 518 second normal form (2NF), 523,
526–527 temporal normal form, 952 tests, 517 third normal form (3NF),
523–525, 528, 558–563
Normalization algorithms. See algorithms, nor-
malization automated tools for database
design, 344 functional dependencies and, 545 relational database design based
on, 60 of relational schema design, 516 of relations, 517–518
NOT EXISTS functions, SQL, 120–122
NOT logical connective. See AND, OR, NOT connectives
NOT NULL, specifying attribute defaults in SQL, 94
Notation diagrammatic, 516 in ER diagrams, 221–223,
1083–1085 for Prolog/Datalog languages,
970–973 for query graphs, 179–180,
701–703 for query trees, 163–165, 701–703 in relational data model, 66–67 in UML class diagrams, 226–228
Notification application, for active databases, 942
NULL values access control integrity, 850 comparisons involving, 116–117 constraints, 68–70 in ER model, 206 examples illustrating, 116 grouping tuples with, 128 problems in relational design,
563–565 reducing in tuples, 509–510 specifying attribute defaults in
SQL, 94 in tuples, 65–66
Numeric arrays, PHP, 487 Numeric data types, in SQL, 92 Object Data Management Group.
See ODMG (Object Data Management Group)
Object data management systems. See ODMS (object data man- agement systems)
Object data models classifying DBMSs and, 49 converting to/from logical
models, 341 object modeling, 245 ODMG object model, 376
overview of, 50 spatial data, 960 types of data models, 31
Object databases. See ODBs (object databases)
Object definition language. See ODL (object definition language)
Object diagrams, UML, 330 Object Identifiers. See OIDs (object
identifiers) Object identifiers, in SQL, 111 Object lifeline, in sequence dia-
grams, 332 Object-orientation (OO), 355–357 Object-oriented analysis (OOA),
1083 Object-oriented database manage-
ment systems (OODBMSs), 49 Object-oriented databases. See
OODBs (object-oriented data- bases)
Object-oriented programming languages. See OOPLs (object- oriented programming languages)
Object recognition, in images, 968–969
Object-relational database manage- ment systems (ORDBMS), 354
Object-relational (extended rela- tional) systems, 51, 111
Object-relational model creating tables based on UDTs, 374 encapsulation of operations,
374–375 ODB extensions to SQL, 369–370 OIDs using reference types,
373–374 specifying inheritance and over-
loading of functions, 375–376 specifying relationships via refer-
ence, 376 UDT (user-defined types) and
complex structures, 370–373 Objects
atomic (user-defined) objects, 386–388
complex types for, 358 in ODMG object model, 377–378 operations, 362–363 persistence of, 19, 363–364 state and behavior components
of, 355 visible/hidden attributes of, 361
Occurrences, current set in database state, 32
Index 1155
ODBC (Open Database Connectivity)
client-side API for calling DBMSs, 47
data mining tools using, 1060 library of functions, 448
ODBs (object databases) bibliographic references, 412–413 complex types for objects and lit-
erals, 358–360 conceptual design, 395–396 encapsulation of operations,
361–363 extensions to SQL, 369–370 list of concepts in, 369 mapping EER schema to ODB
schema, 397–398 multiple and selective inheritance,
368 OID (object identifiers), 357–358 OO (object-orientation) concepts
and features, 355–357 overview of, 353–355 persistence of objects, 363–364 polymorphism (operator over-
loading), 367–368 selective inheritance, 368 summary and exercises, 408–411 type hierarchies and inheritance,
364–367 ODL (object definition language)
binding ODL constructs to C++, 407–408
inheritance and, 395 in ODMG standard, 376 supporting semantic constructs of
ODMG object model, 390 type constructors in, 359–360 University student database
example, 391–395 ODMG (Object Data Management
Group) C++ language binding in, 407 OQL (object query language) in
ODMG standard, 398 standards, 354, 376
ODMG object model atomic (user-defined) types,
386–388 built-in interfaces and classes,
383–385 extents, keys, and factory objects,
388–390 inheritance in, 383 literals in, 378–382 objects in, 377–378
ODL (object definition language) and, 390–395
overview of, 376–377 ODMS (object data management
systems). See also ODBs (object databases)
complex types for objects and lit- erals, 358
early (static) binding and late (dynamic) binding, 368
high-level query languages used by, 361
list of concepts in, 369 OIDs, 357, 377–378 overview of, 353 standard for, 376
Offline, storing data, 587 OIDs (object identifiers)
comparing RDB design with ODB design, 395–396
ODB features included in SQL, 370
in ODMG object model, 377–378 reference types, 373–374 unique identity and, 357–358, 369
Okapi relevance system, in proba- bilistic model for IR, 1006
OLAP (online analytical processing) data mining tools, 1061 data warehousing and, 1067–1068 overview of, 3 relational and multidimensional,
1079 OLTP (online transaction process-
ing) classifying DBMSs by purpose, 50 database support for, 1068 sharing data and multiuser trans-
actions, 13 transaction processing and, 79
Online analytical processing. See OLAP (online analytical pro- cessing)
Online data storage, 587 Online directories, 919–921 Online transaction processing. See
OLTP (online transaction pro- cessing)
Ontologies concepts, 267–268 defined, 272 OWL (Web Ontology Language),
969 Semantic Web and, 272–273 Web content analysis and,
1023–1024
OO (object-orientation), 355–357 OOA (object-oriented analysis),
1083 OODBMSs (object-oriented data-
base management systems), 49 OODBs (object-oriented databases)
attribute versioning for incorpo- rating time in, 953–954
complexity of data and, 24 persistent storage and, 19
OOPLs (object-oriented program- ming languages)
bindings to, 376 class declarations of, 364 instance variables in, 356 ODBs closely coupled with, 363 OO concepts, 355–356
Open addresses, collision resolution, 608
Open Database Connectivity. See ODBC (Open Database Connectivity)
Open operation, on files, 599 Operating costs, in choosing a
DBMS, 323–324 Operating systems. See OSs (operat-
ing systems) Operations. See also Functions;
Methods comparing RDB design with
ODB design, 396 in data models, 30 database, 12 in database application life cycle,
308 database design and, 201 encapsulation of, 361–363, 369 on files, 599–601 objects, 362–363 in ODMG object model, 387 pipelining, 700 query processing and optimizing,
700 transaction, 751–752 transformation rules for relation-
al algebra operations, 706–708 in UML class diagrams, 227
Operations, in relational data model Delete operation, 77–78 Insert operation, 76–77 overview of, 75–76 Update (Modify) operation, 78–79
Operators arithmetic operators in SQL,
105–106 Boolean, 1007
1156 Index
collection operators in OQL, 403–405
comparison operators in SQL, 98 concatenate operator in PHP, 485 grouping operator in QBE,
1095–1098 logical connectives. See AND, OR,
NOT connectives overloading. See Polymorphism
(operator overloading) relational, 980–981, 983 SELECT operator (σ), 147–149 spatial, 960–961
Operators, database workers behind the scene, 17
Optical jukebox memories, 586 Optimization, in data mining, 1038 Optimizing queries. See Query pro-
cessing and optimizing Optional fields, in file records, 595 OQL (object query language)
collection operators and, 403–405 extracting single elements from
singleton collections, 403 group by clause in, 405–406 in ODMG standard, 376, 398 ordered (indexed) collection
expressions, 405 overview of, 398–399 query results and path expres-
sions, 400–402 simple OQL queries, database
entry points, and iterator vari- ables, 399–400
specifying views as named queries, 402–403
OR logical connective. See AND, OR, NOT connectives
Oracle Cartridge, 931 distributed databases, 915–919 query optimization in, 721–722
Oracle Internet Directory, 919–921 Oracle Label Security
architecture of, 869 combining data labels and user
labels, 869–870 overview of, 868 virtual private database technolo-
gy, 868–869 ORDBMS (object-relational data-
base management systems), 354 ORDER BY clause, SQL
ordering query results, 106–107 in retrieval queries, 129–130 sorting query results, 682–683
Order preserving functions, hashing and, 609
Ordered (indexed) collection expressions in OQL, 405 cost functions for SELECT opera-
tions, 713 query results in SQL, 106–107
Ordered (sorted files), in records, 603–606
Ordering field, file organization and, 603
Ordering key, sorted files and, 603 Organization context, for database
systems, 304–307 OSs (operating systems)
DBMS access and disk read/write, 40
multiprogramming, 744–745 support for transaction process-
ing in distributed databases, 909
OUTER JOIN operations implementing, 699–700 vs. inner joins, 160–161 overview of, 169–170 in SQL, 123–124
Outer queries, 117 OUTER UNION operation, in rela-
tional algebra, 170–171 Outliers, spatial, 965 Overflow (transaction) files, 605 Overlapping
entity sets, 253 specialization and, 264
OWL (Web Ontology Language), 969
Owner accounts, granting/revoking privileges, 843–844
Package diagrams, UML, 330 PageRank algorithm, 1021 Parallel architecture, for servers,
1079 Parallel database management
systems, vs. distributed archi- tecture, 887–888
Parallel processing on disks, 593–594 handling multiple processes, 745
Parameterized statements (bind variables), protecting against SQL injection, 858
Parameters disk blocks (pages), 1087–1089 SQL/PSM (SQL/Persistent Stored
Modules), 474 Parametric users, interfaces for, 39
Parser, checking query syntax with, 679
Partial categories, 260 Partial dependencies, 523 Partial keys, 219 Partial order, of transaction sched-
ule, 757 Partial specialization, 253–254, 264 Partially committed state, transac-
tions, 752 Partially replicated catalogs, 913 Participation constraints, on binary
relationships, 217 Partition algorithm, for local fre-
quent itemsets, 1047 Partition-hash joins
methods for implementing joins, 690
overview of, 694–696 Partitioned hashing, 661–662 Passwords, DBAs assigning, 839 Path expressions
dot notation for build path expressions in SQL, 376
in OQL, 400 specifying with XPath, 432–434
Patterns analysis phase of Web usage
analysis, 1027 data mining for discovering, 1057 substring pattern matching in
SQL, 105–106 within time series, 1039
PEAR (PHP Extension and Application Repository), 491
Peer-to-peer database systems, 915 Performance
advantages of distributed data- bases for, 882
DBMS utilities for monitoring, 43 Persistence
collections, 367 data, 586 objects, 363–364, 378
Persistent storage, of program objects in databases, 19
Persistent stored modules (PSM), 474–476
Personal databases, 305 Personalization, of information in
Web searches, 1019 Personnel costs, in choosing a
DBMS, 323–324 PGP (Pretty Good Privacy), 854 Phantom records, concurrency con-
trol techniques, 800–801
Index 1157
Phantoms, transaction support in SQL, 771
PHP arrays, 486–488 bibliographic references, 497 collecting data from forms and
inserting records, 493–494 connecting to databases, 491–493 features, 484–485 functions, 488–490 overview of, 481–482 retrieval queries, 494–495 server variables and forms,
490–491 simple example of, 482–484 summary and exercises, 496–497 variables, data types, and con-
structs, 485–486 PHP Extension and Application
Repository (PEAR), 491 Phrase queries, types of queries in
IR systems, 1008 Physical clustering, of records on
disks, 617 Physical data independence, in
three-schema architecture, 36 Physical data models, 30 Physical database design. See also
Database design bibliographic references, 740 data organization in, 587 denormalization as design deci-
sion related to query speed, 731–732
in ER (Entity-Relationship) model, 202
factors influencing, 727–729 indexing decisions, 730–731 overview of, 9, 326–327 summary and exercises, 739–740 tuning and, 735–736
Physical database file structures, 583 Physical database phase, in database
design, 311 Physical indexes
vs. logical, 668 ordering primary and clustering
indexes, 642 Physical problems/catastrophes,
recovery needed due to, 751 Physical relationships, between file
records, 617 Pile file (heap), 602 Pipelined evaluation, converting
query trees into query execu- tion plans, 710
Pipelining, combining operations using, 700
Pivoting (rotation) functionality of data warehouses,
1078 working with data cubes,
1070–1072 PL/SQL
designing database programming language from scratch, 449
impedance mismatch and, 450 writing database applications
with, 447 Plaintext, 864 Point events (facts), in temporal
databases, 946 Pointers, blocks of data and, 597 Points
on maps, 959–960 in temporal databases, 945
Policies access control for e-commerce
and Web, 854–855 flow policies, 860 for label-based security, 853 security policies, 836
Polygons, on maps, 960 Polyinstantiation, in mandatory
access control, 849–850 Polymorphism (operator overload-
ing) defined, 369 in OO systems, 357 overview of, 367–368 specifying in SQL, 375–376
populating (loading) databases, 33 Populations, in statistical database
security, 859 Positional iterator, SQLJ, 461–462 Positive literals, in Datalog language,
973 Precedence graph (serialization
graph), 763–765 Precision metrics
finding relevant information and, 1019
measures of relevance in IR, 1015–1017
Precision, vs. security, 841 Precompilers
DML commands and, 42 embedded SQL and, 452 in SQL programming, 449
Predicate-defined (condition- defined) subclasses, 252, 264
Predicate dependency graph, 982
Predicate locking, 801 Predicates
as arity or degree of p, 973 built-in, 972–973 fact-defined and rule-defined, 978 interpretation of, 976 in Prolog languages, 970–972 relational schemas and, 66
Prediction, as goal of data mining, 1037
Preprocessors embedded SQL and, 452 in SQL programming, 449 in Web usage analysis, 1025–1027
Presentation layer (client), in three- tier client/server architecture, 892
Pretty Good Privacy (PGP), 854 Primary file organization
B-trees as, 651 data organization and, 587
Primary indexes cost functions for SELECT opera-
tions, 713 methods for simple selection, 686 for ordered records (sorted files),
605 overview of, 633–635 searching nondense multilevel
primary index, 646 tables comparing index types, 642 types of ordered indexes, 632
PRIMARY KEY clause, CREATE TABLE command, 95
Primary keys defined, 519 normal forms based on, 516–517 primary indexes and, 633 relational model constraints, 69
Primary site, concurrency control techniques for distributed data- bases, 910–911
Primary storage, 584 Prime attributes, 519, 526 Printer servers, in client/server
architecture, 45 Privacy
information privacy vs. informa- tion security, 841–842
issues in database security, 866–867
protecting in statistical databases, 859
Private keys, in public (asymmetric) key algorithms, 864
Privileged software, 19
1158 Index
Privileges discretionary, 842–844 granting/revoking, 111, 844–846 limits on propagation of, 846–847 unauthorized escalation and
abuse, 855, 858 views for specifying, 844
Proactive updates, valid time rela- tions and, 949
Probabilistic model, for information retrieval, 1005–1006
Procedural DMLs, 37–38 Process-driven design, 310 PROCESS RULES, in active data-
base systems, 938 Processes
in database design, 322 multiprogramming and, 744
Processors, parallel, 1079 Program-data independence, 11–12,
23–24 Program-operation independence, 12 Program variables, 599 Programming languages
advantages/disadvantages of, 477 approaches to database program-
ming, 449 DBMS, 36–38 impedance mismatch and, 450 object-orientation creating com-
patibility between, 369 Web databases. See PHP XML, 432–436
Programs, insulation between pro- grams and data, 11–13
PROJECT operations algorithms for, 696–697 Query processing and optimizing,
696–697 in relational algebra, 149–150
Projection attributes, SELECT com- mand and, 98
Projective operators, types of spatial operators, 961
Prolog language. See also Datalog language
logic programming and, 970 notation, 970–973
Proof-theoretic interpretation, of rules in deductive databases, 975
Properties, of association rules, 1041 Properties of relational decomposi-
tions dependency preservation,
552–553
dependency-preserving and non- additive join decomposition into 3NF schemas, 560–563
dependency-preserving decompo- sition into 3NF schemas, 558–559
insufficiency of normal forms and, 552
nonadditive join decomposition into BCNF schemas, 559–560
nonadditive (lossless) join, 553–556
overview of, 544, 551 successive nonadditive join
decompositions, 557 testing binary decompositions for
nonadditive join property, 553–556
Protocols concurrency control, 777 deadlock prevention, 785–787 for ensuring serializability of
transaction schedules, 767–768 Proximity queries, 1008 PSM (Persistent stored modules),
474–476 Public (asymmetric) key algorithms,
863–865 Public keys, in public (asymmetric)
key algorithm, 864 Publishing XML documents, 431 Punctuation marks, text preprocess-
ing in information retrieval, 1011
Pure time conditions, 955 QBE (Query-By-Example)
basic retrieval in, 1091–1095 domain calculus and, 183, 185 grouping, aggregation, and data-
base modification in, 1095–1098
overview of, 1091 QMF (Query Management Facility),
185 Quadtrees, 963 Qualified aggregations, in UML class
diagrams, 228 Qualified associations, in UML class
diagrams, 228 Qualifier conditions, XPath, 432 Quality control, data warehousing
and, 1080 Quantifiers
collection operators in OQL, 403–405
existential and universal, 177–178
transforming, 180 using in queries, 180–182
Queries. See also OQL (object query language); SQL (Structured Query Language)
content-based retrieval, 965 database tuning and, 736–738 defined, 6 design decisions related to query
speed, 731–732 evaluating nonrecursive Datalog
queries, 981–983 information retrieval, 1007–1009 interactive interface for, 40 IR systems, 1007–1009 keyword-based, 39 modes of interaction in IR sys-
tems, 999 physical database design and,
728–729 processing in databases, 19–20 in Prolog languages, 973 retrieval queries from database
tables, 494–495 spatial, 958, 961 statistical, 859 TSQL2, 954–956
Query blocks, 681 Query-By-Example. See QBE
(Query-By-Example) Query compilers, 41 Query decomposition, 905–907 Query execution plans
converting query trees into, 709–710
creating, 679 Query graphs
creating, 679 notation for, 179–180, 701–703
Query languages DML as, 38 for federated databases, 886 SQL. See also SQL (Structured
Query Language) TSQL2. See also SQL (Structured
Query Language) Query Management Facility (QMF),
185 Query mapping, 901 Query modification, 135 Query optimizer, 41, 679 Query processing and optimizing
aggregate functions, 698–699 bibliographic references, 725 catalog information used in cost
functions, 712–713
Index 1159
converting query trees into query execution plans, 709–710
cost components of query execu- tion, 711–712
cost functions for JOIN, 715–718 cost functions for SELECT,
713–715 DBMS module for, 20 disjunctive selection conditions,
688 external sorting, 682–685 heuristic algebraic optimization
algorithm, 708–709 heuristic optimization of query
trees, 703–706 heuristics used in query optimiza-
tion, 700–701 hybrid hash-join, 696 implementing JOIN operations,
689–690 implementing SELECT opera-
tions, 685 join selection factors, 693–694 multiple relation queries and
JOIN ordering, 718–719 nested-loop joins, 690–693 notation for query trees and
query graphs, 701–703 operations, 700 OUTER JOIN operations, 699–700 overview of, 679–681 partition-hash joins, 694–696 PROJECT operations, 696–697 query optimization in Oracle,
721–722 search methods for complex
selection, 686–687 search methods for simple selec-
tion, 685–686 selectivity and cost estimates in
query optimization, 710–711 selectivity of conditions and,
687–688 semantic query optimization,
722–723 set operations, 697–698 summary and exercises, 723–725 transformation rules for relation-
al algebra operations, 706–708 translating SQL queries into rela-
tional algebra, 681–682 Query processing and optimizing, in
distributed databases data transfer costs for distributed
query processing, 902–904
distributed query processing using semijoin operation, 904
overview of, 901–902 query update and decomposition,
905–907 Query results
cursors for looping over tuples in, 450
ordering, 106–107 path expressions and, 400–402 retrieval queries from database
tables, 494–495 Query (transaction) server, in two-
tier client/server architecture, 47
Query trees converting into query execution
plans, 709–710 creating, 679 notation for, 163–165, 701–703 optimization of, 703–706
R-Trees, for spatial indexing, 962 RAID (Redundant Array of
Inexpensive Disks) levels, 620–621 overview of, 617–619 performance improvements,
619–620 reliability improvements, 619
RAM (Random Access Memory), 585
Random access storage devices, 592 Randomizing function (hash func-
tion), 606 Range queries, 686, 961 Range relations, of tuple variables,
175–176 Rational Rose
data modeler, 338 database design with, 337 tools and options for data model-
ing, 338–342 RBAC (role-based access control),
851–852 RBG (red, blue, green) colors, 967 RDBMS (relational database man-
agement systems) creating indexes, 731 ORDBMS (object-relational data-
base management systems), 354
providing application flexibility, 23–24
two-tier client/server architec- tures and, 46
RDBs (relational databases) designing. See relational database
design overview of, 395–396 schemas. See relational database
schemas RDF (Resource Description
Framework), 436 Reachability, of objects, 363 Read command, hard disks, 591 Read-only transaction, 745 READ operation, transactions, 751 Read (or Get) operation, on files, 600 Read phase, of optimistic concur-
rency control, 794 Read-set, of transaction, 747 Read timestamp, 789 Read-write conflicts, in transaction
schedules, 757 Read/write heads, on hard disks, 591 Read/write, OSs controlling disk
read/write, 40 Read-write transactions, 745–747 read_item(X), 746 Real-time database technology, 3 Reasoning mechanisms, in knowl-
edge representation, 268 Recall metrics, in IR, 1015–1017,
1019 Recall/precision curve, in IR, 1017 Record-at-a-time DMLs, 38 Record-based data models, 31 Record pointers, 609 Records. See also Files (of records)
anchor record (block anchor), 633 blocking, 597 catalog information used in query
cost estimation, 712 fixed-length and variable-length,
595–597 inserting, 493–494 mixed, 616–617 ordered (sorted files), 603–606 phantom records, concurrency
control techniques, 800–801 placing file records on disk, 594 spanned/unspanned, 597–598 in SQL/CLI, 464–468 types of, 594–595 unordered (heap files), 601–602
Recoverability, transaction sched- ules based o, 757–759
Recovery. See also Backup and recovery; Database recovery techniques
1160 Index
transaction management in dis- tributed databases, 912–913
types of failures and, 750–751 Recursive closure operations, in
relational algebra, 168–169 Recursive relationships, 168, 215 Recursive rules, in Prolog languages,
972 Red, blue, green (RBG) colors, 967 REDO phase, of ARIES recovery
algorithm, 823 Redo transaction, 753 REDO, write-ahead logging and,
810–811 Redundancy, controlling in data-
bases, 17–18 Redundant Array of Inexpensive
Disks (RAID). See RAID (Redundant Array of Inexpensive Disks)
REF keyword, specifying relation- ships via reference, 376
Reference types, OIDs using, 373–374
References foreign key, 73 representing object relationships,
360 specifying relationships via
reference, 376 Referencing relations, 73 Referential integrity constraints
inclusion dependencies and, 571 integrity constraints in databases,
21 relational data model and, 73–74 specifying in SQL, 95–96
Reflexive associations, in UML class diagrams, 227
Regression function, 1058 Regression, in data mining,
1057–1058 Regression rule, 1057 Regular entity types, 219, 287–288 Relation extension, 62 Relation intension, 62 Relation nodes
notation for, 703 in query graphs, 179
Relation schemas domains and, 61 goodness of, 501–502 in relational databases, 501
Relation (table) level, assigning priv- ileges at, 842–843
Relational algebra aggregate functions and grouping,
166–168 bibliographic references, 194–195 CARTESIAN PRODUCT opera-
tion, 155–157 complete set of relational algebra
operations, 161, 164 DIVISION operation, 162–163 EQUIJOIN and NATURAL JOIN
operations, 159–161 examples of queries in, 171–174 generalized projection, 165–166 JOIN operation, 157–158 notation for query trees, 163–165 OUTER JOIN operations,
169–170 OUTER UNION operation,
170–171 overview of, 145–146 PROJECT operation, 149–150 recursive closure operations,
168–169 RENAME operation, 151–152 SELECT operation, 147–149 sequences of operations, 151 summary and exercises, 185–194 transformation rules for opera-
tions, 706–708 translating SQL queries into,
681–682 UNION, INTERSECTION, and
MINUS operations, 152–155 Relational calculus
domain (relational) calculus, 183–185
overview of, 146–147 tuple relational calculus. See Tuple
relational calculus Relational completeness, of rela-
tional query languages, 174 Relational data model
bibliographic references, 85 characteristics of relations, 63–66 classifying DBMSs and, 49 concepts, 60–61 constraints, 67–70 correspondence to ER model, 293 Delete operation, 77–78 domains, attributes, tuples, and
relations, 61–63 formal languages for. See
Relational algebra; Relational calculus
Insert operation, 76–77
integrity, referential integrity, and foreign keys, 73–74
in list of data model types, 31 mapping from EER model to. See
EER-to-Relational mapping mapping from ER model to. See
ER-to-Relational mapping notation, 66–67 other types of constraints, 74–75 overview of, 50, 59–60 practical language for. See SQL
(Structured Query Language) schemas, 70–73 SQL compared with, 97 summary and exercises, 79–85 transactions and, 79 update operations, 75–76, 78–79
Relational database design algorithms for, 557, 566–567 attribute semantics in, 503–507 bibliographic references, 302, 579 bottom-up approach to, 544 Boyce-Codd normal form
(BCNF), 529–531 dependency preservation proper-
ties of decompositions, 552–553
dependency-preserving and non- additive join decomposition into 3NF schemas, 560–563
dependency-preserving decompo- sition into 3NF schemas, 558–559
disallowing possibility for spuri- ous tuples, 510–513
domain-key normal form (DKNF), 574–575
equivalence of sets of functional dependencies, 549
first normal form (1NF), 519–523 formal analysis of relational
schemas, 513 formal definition of fourth nor-
mal form, 533–534, 568–570 functional dependencies based on
arithmetic functions and pro- cedures, 572–574
functional dependency and, 513–516
general definition of second nor- mal form, 526–527
general definition of third normal form, 528
goodness of relational schemas, 501–502
Index 1161
inclusion dependencies, 571–572 inference rules for functional and
multivalued dependencies, 568 inference rules for functional
dependencies, 545–549 informal guidelines for relational
schemas, 503, 513 join dependencies and fifth nor-
mal form, 534–535 key definitions, 518–519 mapping from EER model to
relational model. See EER-to- Relational mapping
mapping from ER model to rela- tional model. See ER-to- Relational mapping
minimal sets of functional dependencies, 549–551
multivalued dependency and fourth normal form, 531–533
nonadditive join decomposition into 4NF relations, 570
nonadditive join decomposition into BCNF schemas, 559–560
nonadditive (lossless) join prop- erties of decompositions, 553–556
normal forms based on primary keys, 516–517
normalization of relations, 517–518
NULL values and dangling tuples and, 563–565
overview of, 285 practical use of normal forms,
518 reducing NULL values in tuples,
509–510 reducing redundant information
in tuples, 507–509 relational decomposition and
insufficiency of normal forms, 552
second normal form (2NF), 523 successive nonadditive join
decompositions, 557 summary and exercises, 299–301,
575–578 template dependencies, 572 testing binary decompositions for
nonadditive join property, 557 third normal form (3NF),
523–525 top-down and bottom-up
approaches, 502 tuning and, 733
Relational database management systems. See RDBMS (relational database management systems)
Relational database schemas algorithms for schema design,
557 bibliographic references, 542 clear semantics for attributes in,
503–507 components of, 70–73 disallowing possibility for spuri-
ous tuples, 510–513 formal analysis of, 513 functional dependency and,
513–516 informal guidelines, 503, 513 overview of, 501–502 reducing NULL values in tuples,
509–510 reducing redundant information
in tuples, 507–509 relation schemas in, 501 summary and exercises, 535–542
Relational database state, 70 Relational design by analysis, 543 Relational design by synthesis, 544 Relational expressions, 983 Relational OLAP (ROLAP), 1079 Relational operators
in deductive database systems, 980–981
relational expressions and, 983 Relations (relation states). See also
Tables alternative definition of, 64–65 column-based storage of, 669–670 defined, 61 interpretation (meaning) of, 66 legality of, 514 normalization of, 517–518 ordering tuples in, 63 ordering values within tuples, 64 overview of, 62–63 values and NULLS in tuples,
65–66 Relations, temporal
bitemporal time, 950–952 transaction time, 949–950 valid time, 947–949
Relationship relation (lookup table) mapping of binary 1:1 relation-
ship types, 289 mapping of binary 1:N relation-
ship types, 290 mapping of binary M:N relation-
ship types, 290–291
Relationships in data modeling, 31 in ODMG object model, 386 references to, 360 representing in OO systems, 356 specifying by reference, 376 symbols for, 1084 University student database
example, 7 Relationships, in EER model
class/subclass relationships, 247 specific relationship types and,
249–250 Relationships, in ER model
attributes of relationship types, 218
constraints on binary relationship types, 216–218
degree of relationship greater than two, 228–232
degree of relationship type, 213–214
overview of, 212 relationship types, sets, and
instances, 212–213 relationships as attributes, 214 role names and recursive relation-
ships, 215 Relevant sets, in probabilistic model
for IR, 1005 Reliability, in distributed databases,
881, 882 Remote commands, for SQL injec-
tion attacks, 857 RENAME operation, in relational
algebra, 151–152 Reorganize operation, on files, 600 Repeating field or groups, in file
records, 595 Repeating history, in ARIES recov-
ery algorithm, 821 Replication
active rules for maintaining con- sistency of replicated tables, 943
in distributed databases, 897 example of fragmentation, alloca-
tion, and replication, 898–901 transparency of, 880
Representational (or implementa- tion) data models, 31
Requirements collection and analy- sis phase
in database design, 200, 311–313 database design starting with, 9 of information system (IS) life
cycle, 307
1162 Index
Reset operations, on files, 599 Resource Description Framework
(RDF), 436 Response time, physical database
design and, 326 Restrict option, of delete operation,
77 Result equivalence, of transaction
schedules, 762 Result relations, 75 Result tables, in QBE, 1095 Retrieval operations
database design and, 728 from database tables, 494–495 on files, 599 modes of interaction in IR
systems, 999 objects, 362 QBE (Query-By-Example),
1091–1095 types of relational data model
operations, 75 Retrieval transactions, 322 Retroactive update, valid time rela-
tions and, 949 Return values, of PHP functions,
490 Reverse engineering, Rational Rose
and, 338 Revoking privileges, 844, 845–846 Rewrite blocks, file organization
and, 602 Rewrite time, as disk parameter,
1089 RIFT (rotation invariant feature
transform), 968 Rigorous two-phase locking, 785 Rivest, Ron, 865 ROLAP (relational OLAP), 1079 Role-based access control (RBAC),
851–852 Role hierarchy, in role-based access
control, 851 Role names, and recursive relation-
ships, 215 Roll-up display
functionality of data warehouses, 1078
working with data cubes, 1070–1072
ROLLBACK (or ABORT) operation, 752
Rollbacks, in database recovery, 813–815, 950
Root element, XML schema lan- guage, 429
Root tag, XML documents, 423 Roots, of tree structures, 646 Rotation. See Pivoting (rotation) Rotation invariant feature transform
(RIFT), 968 Rotational delay (rd)
as disk parameter, 1087 on hard disks, 591
Row-level access control, 852–853 Row-level triggers, 937 Rows. See Tuples (rows) Rows, in SQL, 89 RSA encryption algorithm, 865 Rule consideration, in active
databases deferred consideration, 942 overview of, 938–939
Rule-defined predicates (views), 978
Rule sets, in active database systems, 938
Rules, in deductive databases interpretation of, 975–977 overview of, 21, 932 in Prolog/Datalog notation,
970–972 safe, 979–980
Runtime database processor DBMS component modules, 42 query execution and, 679
Runtime, specifying SQL queries at, 458–459
Safe expressions, in tuple relational calculus, 182–183
Safe rules, in deductive databases, 979–980
Sampling algorithm, in data mining, 1042
SANs (Storage Area Networks), 621–622
Saturation, hue, saturation, and value (HSV), 967
SAX (Simple API for XML), 423 Scale-invariant feature transform
(SIFT), 968 Scan operations, files, 600 Scanner, for SQL, 679 Schedules (histories), of transactions
characterizing based on recover- ability, 757–759
characterizing based on serializ- ability, 759–760
equivalence of, 768–770 overview of, 755–757 serial, nonserial, and conflict-
serializable schedules, 761–763
testing conflict serializability of, 763–765
Schema conceptual design, 313–321 entity type describing for entity
sets, 208 instances and database state and,
32–33 ontologies and, 272 relational. See Relational database
schemas relational data model and, 70–73 three-schema architecture. See
Three-schema architecture Schema construct, 32, 222 Schema diagram, 32 Schema evolution, 33 Schema matching, types of Web
information integration, 1023 Schema, SQL
change statements, 137–139 names, 89 overview of, 89–90
Schema (view) integration, 316–317, 319–321
Schemaless XML documents, 422 Scientific applications, 25 Scope, variable, 490 Scripting languages, PHP as, 482 SCSI (Small Computer System
Interface), 591 SDL (storage definition language),
37, 110 Search engines
overview of, 998–999 vertical and metasearch, 1018
Search fields, 648 Search trees, 647–649 Searches
conversational, 1029–1030 faceted, 1028–1029 information retrieval. See IR
(Information Retrieval) measures of relevance, 1014–1015 methods for complex selection,
686–687 methods for simple selection,
685–686 navigational, informational, and
transactional, 996 social searches, 1029 Web. See Web search and analysis
Second normal form (2NF) general definition of, 526–527 overview of, 523
Secondary access path, 631
Index 1163
Secondary file organization, 587 Secondary indexes
advantages of, 668 cost functions for SELECT, 714 methods for simple selection, 686 overview of, 636–642 tables comparing index types, 642 types of ordered indexes, 632–633
Secondary keys, 636 Secondary storage, 584, 711 Secret key algorithms, 863 Sectors, of hard disk, 589 Security
vs. precision, 841 Web security, 1028
Security and authorization subsys- tem, DBMS, 19
Security, database. See Database security
Seek time (s) as disk parameter, 1087 on hard disks, 591
Segmentation, automatic analysis of images, 967
SELECT command, SQL aggregate functions used in, 125 basic form of, 97–98 FROM clause, 107 DISTINCT keyword with, 103 information retrieval with, 97 projection attributes and selec-
tion conditions, 98, 100 in SQL retrieval queries, 129–130
SELECT-FROM-WHERE structure, of SQL queries, 98–100
SELECT operations cost functions for, 713–715 disjunctive selection conditions,
688 on files, 599 implementing, 685 in relational algebra, 147–149 search methods for complex
selection, 686–687 search methods for simple selec-
tion, 685–686 selectivity of conditions, 687–688
SELECT operator (σ), 147 Select-project-join queries, 179 Selection cardinality, 712 Selection conditions
in domain calculus, 184 SELECT command and, 98, 100 SELECT operation and, 147
Selection, functionality of data warehouses, 1079
Selective inheritance, in ODBs (object databases), 368
Selectivity and cost estimates, in query optimization
catalog information used in cost functions, 712–713
cost components of query execu- tion, 711–712
cost functions for JOIN, 715–718 cost functions for SELECT,
713–715 multiple relation queries and
JOIN ordering, 718–719 overview of, 710–711
Selectivity, of conditions, 687–688 Self-describing data, 10–11, 416 Semantic constraints
relational model constraints, 68 template dependencies and, 572 types of constraints, 74
Semantic data models abstraction concepts in, 268 aggregation and association,
269–271 classification and instantiation, 268 compared with knowledge repre-
sentation, 267–268 ER (Entity-Relationship) model,
245 identification, 269 for information retrieval,
1006–1007 specialization and generalization,
269 Semantic query optimization,
722–723 Semantic relationships, in semantic
model for IR, 1006 Semantic Web, 272–273 Semantics
approach to IR, 1000 of attributes, 503–507, 514 equivalence of transaction sched-
ules and, 769–770 heterogeneity of in federated
databases, 886–887 integrity constraints and, 21 tagging images, 969
Semijoin operation, 904 Semistructured data, 416–417 Separators, XPath, 432 Sequence diagrams, UML, 329, 331 Sequential order, in accessing data
blocks, 592 Sequential patterns
in data mining, 1037
describing knowledge discovered by data mining, 1039
discovery of, 1057 in pattern discovery phase of Web
usage analysis, 1027 Serial schedules, 761 Serializability, of transaction
schedules characterizing schedules based
on, 759–760 serial, nonserial, and conflict-
serializable schedules, 761–763 testing conflict serializability of
schedules, 763–765 used for concurrency control,
765–768 view serializability, 768–769
Serialization (precedence) graph, 763–765
Servers client program calling database
server, 451 database servers, 42 DBMS module for, 29 parallel architecture for, 1079 PHP variables, 490–491 server level in two-tier client/
server architecture, 47 specialized servers in client/server
architecture, 45–46 Set-at-a-time DMLs, 38 Set constructor, 359 SET DIFFERENCE operation
algorithms for, 697–698 in relational algebra, 152–155
Set null (set default) option, in delete operations, 77–78
Set operations algorithms for, 697–698 query processing and optimizing,
697–698 SQL, 104
Set types, in network data model, 51 Sets
equivalence of, 549 explicit sets of values in SQL, 122 SQL table as multiset of tuples, 97 tables as, 103–105
Shadow directory, 820 Shadow paging, 820–821 Shamir, Adi, 865 Shape, automatic analysis of images,
967 Shape descriptors, 965 Shared nothing architecture,
887–888
1164 Index
Shared subclasses (multiple inheri- tance), 256, 297
Shared variables, embedded SQL and, 452
Sharing data and multiuser transac- tions, 13–14
Sharing databases, 6 Shrinking (second) phase, in two-
phase locking, 782 SIFT (scale-invariant feature
transform), 968 Simple API for XML (SAX), 423 Simple (atomic) attributes, in ER
model, 205–207 Simple Object Access Protocol
(SOAP), 436 Simultaneous update, 949 Single inheritance, subclasses and,
256–257 Single-level indexes
clustering indexes, 635–636 overview of, 632–633 primary indexes, 633–635 secondary indexes, 636–642 tables comparing index types, 642
Single-loop joins cost functions for, 716 methods for implementing joins,
689 Single-quoted strings, PHP text
processing, 485–486 Single-relation options, for mapping
specialization or generalization, 295
Single-sided disks, 589 Single time points, in temporal
databases, 946 Single-user systems, 49 Single-user transaction processing
system, 744–745 Single-valued attributes, in ER
model, 206 Singular value decompositions
(SVD), 967 Slice and dice, functionality of data
warehouses, 1078 Small Computer System Interface
(SCSI), 591 SMART document retrieval system,
998 SMP (symmetric multiprocessor),
1079 Snowflake schema, for multidimen-
sional data models, 1073–1074 SOAP (Simple Object Access
Protocol), 436
Social searches, 1029 Software costs, choosing a DBMS,
323 Software developers, 16 Software engineers
database actors on the scene, 16 design and testing of applications,
199 Sort-merge joins
cost functions for, 717 methods for implementing joins,
689–690 Sort-merge strategy, 683 Sorting
external, 682–685 functionality of data warehouses,
1078 implementing aggregate opera-
tions, 699 ordered records (sorted files),
603–606 Space utilization, physical database
design and, 326 Spamming, Web spamming, 1028 Spanned/unspanned organization,
of records, 597 Sparse indexes, 633 Spatial analysis, 959 Spatial applications, 25 Spatial databases
applications of spatial data, 964–965
data indexing, 961–963 data mining, 963–964 data types and models, 959–960 dynamic operators, 961 operators, 960–961 overview of, 957–959
Spatial joins/overlays, 961 Spatial outliers, 965 Special purpose DBMSs, 50 Specialization/generalization
constraints on, 251–254 definitions, 264 design choices for, 263–264 EER-to-Relational mapping,
294–297 generalization, 250–251 hierarchies and lattices, 254–257 in knowledge representation, 269 notation for, 1084–1085 refining conceptual schemas,
257–258 specialization, 248–250 UML (Unified Modeling
Language), 265–266
Specialized servers, in client/server architecture, 45
Specific attributes (local attributes), of subclass, 249
Specific relationship types, sub- classes and, 249–250
Specification, conceptualization and, 272
Speech input and output, queries and, 39
SQL-99, 942–943 SQL/CLI (Call Level Interface)
database programming with, 464–468
library of functions, 448 SQL injection attacks
code injection, 856 function call injection, 856–857 protecting against, 858 risks associated with, 857–858 SQL manipulation, 856 types of, 855
SQL programming techniques approaches to database program-
ming, 449–450 bibliographic references, 479 database programming tech-
niques and issues, 448–449 dynamic SQL, 448, 458–459 embedded SQL. See Embedded
SQL function calls. See Function calls,
database programming with impedance mismatch, 450 overview of, 447–448 sequence of interactions in, 451 SQL/PSM (SQL/Persistent Stored
Modules). See SQL/PSM (SQL/ Persistent Stored Modules)
summary and exercises, 477–478 SQL/PSM (SQL/Persistent Stored
Modules) overview of, 473 specifying persistent stored
modules, 475–476 stored procedures and functions,
473–475 SQL (Structured Query Language).
See also Embedded SQL * (asterisk) for retrieving all
attribute values of selected tuples, 102–103
aliases, 101–102 bibliographic references, 114 CHECK clauses for specifying
constraints on tuples, 97
Index 1165
clauses in simple SQL queries, 107
common data types, 92–94 CREATE TABLE command, 90–92 data definition in, 89 dealing with ambiguous attribute
names, 100–101 DELETE command, 109 embedding SQL commands in
Java, 459–461 external sorting, 682–685 INSERT command, 107–109 list of features in, 110–111 manipulation by SQL injection
attacks, 856 missing or unspecified WHERE
clauses, 102 naming constraints, 96–97 object-relational features in, 354 ordering query results, 106–107 overview of, 87–89 QBE compared with, 1098 schema and catalog concepts in,
89–90 SELECT-FROM-WHERE structure
of queries, 98–100 servers, 47 specifying attribute constraints
and default values, 94–95 specifying key and referential
integrity constraints, 95–96 substring pattern matching and
arithmetic operators, 105–106 summary and exercises, 111–114 tables as sets in, 103–105 temporal data types, 945 transaction support, 770–772 translating SQL queries into rela-
tional algebra, 681–682 UDT (user-defined types) in, 111 UPDATE command, 109–110
SQL (Structured Query Language), advanced features
aggregate functions, 124–126 ALTER command, 138–139 bibliographic references, 143 clauses in retrieval queries,
129–130 comparisons involving NULL and
three-valued logic, 116–117 correlated nested queries,
119–120 CREATE ASSERTION command,
131–132 CREATE TRIGGER command,
132–133
CREATE VIEW command, 134–135
DROP command, 138 EXISTS and NOT EXISTS func-
tions, 120–122 explicit sets and renaming of
attributes, 122 GROUP BY clause, 126–129 HAVING clause, 127–129 inline views, 137 nested queries, 117–119 outer and inner joins, 123–124 overview of, 115 schema change statements, 137 summary and exercises, 139–143 UNIQUE function, 122 view implementation and update,
135–137 views (virtual tables) in, 133–134
SQL (Structured Query Language), ODB extensions to
dot notation for build path expressions, 376
encapsulation of operations, 374–375
inheritance and polymorphism, 375–376
OIDs (object identifiers) using reference types, 373–374
overview of, 369–370 specifying relationships via refer-
ence, 376 tables based on UDTs, 374 UDTs and complex structures for
objects, 370–373 SQLJ
embedding SQL command in Java, 459–461
retrieving multiple tuples using iterators, 461–464
SQLODE communication variable, 454
SQLSTATE communication variable, 454
Standards database approach and, 22 database design specification, 328 SQL, 88
Star schema, 1073 Starvation, concurrency control
and, 788 State
in ODMG object model, 382 relational database state, 70–72 transaction, 751–752
State constraints, 75
Statechart diagrams, UML, 329, 333 Statement-level active rules, in
STARBURST example, 940–942 Statement-level triggers
overview of, 937 in STARBURST example, 940
Statement records, in SQL/CLI, 464–468
Static (early) binding, in ODMS, 368
Static files, 601 Static hashing, 610 Static Web pages, 420 Statistical analysis, in pattern dis-
covery phase of Web usage analysis, 1026
Statistical approach, to IR, 1000–1002
Statistical database security, 859–860 Statistical databases, 837–838, 874 Statistical queries, 859 Steal/no-steal techniques
in database recovery, 811–812 UNDO/REDO recovery algorithm,
819 Stem, of words, 1010 Stemming, text preprocessing in
information retrieval, 1010 Stopwords
in keyword queries, 1007 removal, 1009–1010 text/document sources, 966
Storage allocation of file blocks on disk,
598 bibliographic references, 630 buffer management and, 593–594 column-based storage of rela-
tions, 669–670 cost components of query execu-
tion, 711 covert channels, 861 database storage, 586–587 database storage reorganization,
43 database tuning and, 733 file headers (descriptors) and, 598 file systems and. See Files (of
records) files, fixed-length records, and
variable-length records, 595–597
hardware structures of disk devices, 588–592
iSCSI (Internet SCSI), 623–624 magnetic tape devices, 592–593
1166 Index
measuring capacity, 585 memory hierarchies and, 584–586 NAS (network-attached storage),
622–623 overview of, 583–584 parallelization of access. See RAID
(Redundant Array of Inexpensive Disks)
placing file records on disk, 594 record blocking and, 597 records and record types, 594–595 SANs (Storage Area Networks),
621–622 secondary storage devices, 587 spanned/unspanned records,
597–598 summary and exercises, 624–630
Storage Area Networks (SANs), 621–622
Storage definition language (SDL), 37, 110
Storage medium, physical, 584 Stored attributes, in ER model, 206 Stored data manager module,
DBMS, 40, 42 Stored procedures, 21, 473–475 Stream-based processing, 700 Streaming XML documents, 423 Strict hierarchies, 255 Strict schedules, 759 Strict timestamp ordering, 790–791 Strict two-phase locking, 784–785 Strings
pattern matching, 105 PHP text processing, 485
Strong entity types, 219, 287 Struct (tuple) constructors, 358–359 Structural constraints, of relation-
ships, 218 Structural diagrams, UML, 329 Structured data
extracting, 1022 overview of, 416 vs. unstructured, 993–994
Structured domains, in UML class diagrams, 227
Structured literals, 378 Subclasses
in EER model, 246–248, 264 generalizing into superclasses, 250 as leaf classes in UML, 265 options for mapping specializa-
tion or generalization, 294 predicate-defined and user-
defined, 252 shared, 256
specific attributes (local attrib- utes) of, 249
specific relationship types and, 249–250
union types or categories, 258–260
Subset of Cartesian product, 63 Subsets, of attributes, 68–69 Substring pattern matching, in SQL,
105–106 Subtrees, 646 Subtypes, 247, 365–366 SUM function
aggregate functions in SQL, 124–125
grouping and, 166, 168 implementing aggregate opera-
tions, 698 Superclass/subclass relationships
in EER model, 264 overview of, 247 union types or categories,
258–260 Superclasses
base class and, 265 in EER model, 246–248, 264 generalization and, 250 options for mapping specializa-
tion or generalization, 294 specialization and, 248
Superkeys defined, 518 relational model constraints, 69
Supertypes, 247, 365 Superuser accounts, 838 Supervised learning
classification and, 1051 neural networks and, 1058
Support, for association rules, 1040 Surrogate keys, 298 Survivability, challenges in database
security, 867 SVD (singular value decomposi-
tions), 967 Symmetric key algorithms, 863 Symmetric multiprocessor (SMP),
1079 Synonyms, thesaurus as collection
of, 1010 Syntactic analysis, in semantic
model for IR, 1006 System
accounts, 838 catalog, 42 definition in database application
life cycle, 308
recovery needed due to system error, 750
security issues at system level, 836 System designers, 16 System environment
DBMS module, 40–42 tools, application environments,
and communication facilities, 43–44
utilities for, 42–43 System independent mapping, in
choosing a DBMS, 326 System logs. See also Logs/logging
auditing and, 839–840 database recovery and, 808 tracking transaction operations,
753–754 Systems analyst, 16 Table inheritance, in SQL, 376 Tables
ALTER TABLE command, 138–139 assigning privileges at table level,
842–843 base tables (relations) vs. virtual
relations, 90 basing on UDTs, 374 DROP TABLE command, 138 in relational model, 60, 61 retrieval queries from database
tables, 494–495 in SQL, 89 SQL table as multiset of tuples,
97, 103–105 virtual. See Views
Tags HTML, 418–419 semistructured data and, 417
Tape jukeboxes, 586 Tape, magnetic, 592–593 Tape reel, 592 Taxonomies, 272 Technical metadata, in data ware-
housing, 1078 Templates
dependencies, 572 in Query-By-Example, 1091
Temporal aggregation, 957 Temporal databases
attribute versioning for incorpo- rating time in OODBs, 953–954
bitemporal time relations, 950–952
options for storing tuples in tem- poral relations, 952–953
overview of, 943–945
Index 1167
querying constructs using TSQL2 language, 954–956
time representation, calendars and time dimensions, 945–947
time series data, 957 transaction time relations,
949–950 valid time relations, 947–949
Temporal intersection join, 952 Temporal normal form, 952 Temporal variables, 948 Temporary updates (dirty reads),
concurrency control and, 748–749
Term frequency-inverse document frequency. See TF-IDF (term frequency-inverse document frequency)
Terminated state, transactions, 752 Terms (keywords)
modes of interaction in IR systems, 999
sets of terms in Boolean model for IR, 1002
Ternary relationships choosing between binary and ter-
nary relationships, 228–231 constraints on, 232 in ER (Entity-Relationship)
model, 213–214 Tertiary storage, 584, 586 Testing
conflict serializability of sched- ules, 763–765
in database application life cycle, 308
Texels (texture elements), 967 Text
preprocessing in information retrieval, 1009–1012
sources in multimedia databases, 966
storing XML document as, 431 Texture, automatic analysis of
images, 967 TF-IDF (term frequency-inverse
document frequency) applying to inverted indexing, 1013 in vector space model for IR,
1003–1004 Thematic analysis, for spatial data-
bases, 959 Theorem proving, in deductive
databases, 976 Thesaurus
ontologies, 272
text preprocessing in information retrieval, 1010–1011
Third normal form (3NF) dependency-preserving and non-
additive join decomposition into, 558–563
dependency-preserving decompo- sition into, 558–559
general definition of, 528 overview of, 523–525
Thomas’s write rule, 791 Threats, to database security,
836–837 Three-phase commit (3PC) proto-
col, 908 three-schema architecture
data independence and, 35–36 levels of, 34–35 overview of, 33
Three-tier architectures client/server architecture,
892–894 PHP, 482 for Web applications, 47–49
Three-valued logic, 116–117 Time constraints, on queries and
transactions, 729 TIME data type, 945 Time dimensions, in temporal data-
bases, 945–947 Time periods, in temporal data-
bases, 946 Time representation, in temporal
databases, 945–947 Time series
management systems, 957 patterns in, 1039, 1057 as specialized database applica-
tions, 25 in temporal databases, 946, 957
Time-varying attributes, 953 Timeouts, for dealing with dead-
locks, 788 TIMESTAMP data type, SQL, 93, 945 Timestamp ordering (TO)
basic, 789–790 for concurrency control, 777 multiversion technique based on,
792 strict timestamp ordering,
790–791 Thomas’s write rule, 791
Timestamps overview of, 789 read and write, 789 transaction time relations and, 949
Timing channels, covert, 861 TO. See Timestamp ordering (TO) Tool developers, 17 Tools, DBMS, 43–44 Top-down methodology
for conceptual refinement, 257 for database design, 502 for schema design, 315–316
Topical relevance, in IR, 1015 Topological operators, 960 Topological relationships, among
spatial objects, 959 Topologies, network, 879 Total categories, 260 Total participation, binary relation-
ships and, 217 Total specialization constraint, 253 Tracks, on hard disks, 589 Trade-off analysis, 345 Training costs, in choosing a DBMS,
323–324 Transaction-id, 753 Transaction processing systems
ACID properties, 754–755 bibliographic references, 775 characterizing schedules based on
recoverability, 757–759 characterizing schedules based on
serializability, 759–760 commit point of transactions,
754 concurrency control, 747–750 database design and, 306 equivalence of schedules, 769–770 overview of, 743–744 recovery, 750–751 schedules (histories) of transac-
tions, 756–757 serial, nonserial, and conflict-
serializable schedules, 761–763 serializability used for concurren-
cy control, 765–768 single-user vs. multiuser, 744–745 SQL support for transactions,
770–772 summary and exercises, 772–774 system log, 753–754 testing conflict serializability of
schedules, 763–765 transaction states and operations,
751–752 transactions, database items,
read/write operations, and DBMS buffers, 745–747
view equivalence and view serial- izability, 768–769
1168 Index
Transaction processing systems, in distributed databases
catalog management, 913 concurrency control, 909–912 operating system support, 909 overview of, 907–908 recovery, 912–913 two-phase and three-phase com-
mit protocols, 908–909 Transaction Table, in ARIES recov-
ery algorithm, 822 Transaction time, in temporal data-
bases, 946 Transaction time relations, in tem-
poral databases, 949–950 Transaction timestamp, 786 Transactional databases, distinguish-
ing data warehouses from, 1069
Transactional searches, 996 Transactions
ACID properties, 754–755 canned, 15 commit point of, 754 committed and aborted, 750 defined, 6 designing, 322–323 interactive, 801 multiuser, 13–14 recovery needed due to transac-
tion error, 750 relational data model and, 79 schedules (histories) of, 756–757 SQL transaction control com-
mands, 111 states and operations, 751–752 throughput in physical database
design, 327 types of, 745
Transfer rate (tr), disk blocks, 1088 Transformation approach, to image
database queries, 966 Transience
collections, 367 data, 586 object lifetime and, 378 objects, 355, 363
Transition constraints, 75 Transition tables, in STARBURST
example, 940 Transitive closure, of relations, 168 Transitive dependencies, in 3NF,
523–524 Transparency
autonomy as complement to, 882 in distributed databases, 879–881
Tree data models. See Hierarchical data models
Tree structures. See also B+-trees; B-trees
decision making in database design, 730
FP-tree (frequent-pattern tree) algorithm, 1043–1045
leaf-deep trees, 718 overview of, 646–647 R-trees, 962 search trees, 647–649 specialization hierarchy, 255 TV-trees (telescoping vector
trees), 967 Triggers
active rules specified by, 933 associating with database tables,
21 before, after, and instead triggers,
938 CREATE TABLE command,
132–133 CREATE TRIGGER command,
936 creating in SQL, 111 overview of, 932 row-level and statement-level,
937 specifying constraints, 74 in SQL-99, 942–943
Truth values, of atoms, 184 TSQL2 language, 954–956 Tuning databases
design, 735–736 guidelines for, 738–739 implementation and, 311 indexes, 734–735 overview of, 733–734 queries, 736–738 system implementation and
tuning, 327–328 Tuple-based constraints, 97 Tuple relational calculus
examples of queries in, 178–179 existential and universal quanti-
fiers, 177–178 expressions and formulas,
176–177 notation for query graphs,
179–180 overview of, 174–175 safe expressions, 182–183 SQL based on, 88 transforming universal and exis-
tential quantifiers, 180
tuple variables and range rela- tions, 175–176
universal quantifier used in queries, 180–182
Tuple versioning approach, to implementing temporal data- bases, 947–953
bitemporal time relations, 950–952
implementation considerations, 952–953
transaction time relations and, 949–950
valid time relations and, 947–949
Tuples (rows) classification in mandatory access
control, 848 combining using JOIN operation,
157–158 comparison of values in, 118 component values of, 67 dangling tuples in relational
design, 563–565 defined, 61 disallowing spurious, 510–513 eliminating duplicates, 150 hypothesis tuples, 572 n-tuple for relations, 62 ordering in relations, 64 ordering values within, 64–65 reducing NULL values in,
509–510 reducing redundant information
in, 507–509 retrieving all attribute values of
selected, 102–103 retrieving multiple tuples in
SQLJ, 461–464 retrieving multiple tuples using
cursors, 455–457 SQL table as multiset of, 97 storing in temporal relations,
952–953 unspecified WHERE clause and,
102 valid time relations and, 948 values and NULLS in, 65–66 versioning for incorporating time
in relational databases, 953 Tuples variables
aliases and, 101 looping with iterators, 98 range relations and, 175–176
TV-trees (telescoping vector trees), 967
Index 1169
Two-phase commit (2PC) protocol recovery in multidatabase sys-
tems, 825–826 transaction management in dis-
tributed databases, 908 Two-phase locking
basic locks, 784 binary locks, 778–780 conversion of locks, 782 overview of, 777–778 serializability guaranteed by,
782–784 shared/exclusive (read/write)
locks, 780–782 variations on two-phase locking,
784–785 Two-tier client/server architecture,
46–47 Two-way joins, 689 Type (class) hierarchies
constraints on extents corres- ponding to, 366–367
inheritance and, 369 in OO systems, 356 simple model for inheritance,
364–366 Type-compatible relations, 697 Type constructors
atom constructor, 358 collection constructor, 359 defined, 369 ODB features included in SQL,
370 ODL and, 359–360 struct (tuple) constructor,
358–359 Type generator, 358–359 UDT (user-defined types)
creating, 370–373 in SQL, 111 tables based on, 374
UML (Unified Modeling Language) class diagrams, 226–228 for database application design,
329 as design specification standard,
328 diagram types, 329–334 notation for ER diagrams, 224 object modeling with, 200 representing specialization/gener-
alization in, 265–266 University student database
example, 334–337 UMLS metathesaurus, 1010–1011
Unary relational operations CARTESIAN PRODUCT opera-
tion, 155–157 overview of, 146 PROJECT operation, 149–150 SELECT operation, 147–149 UNION, INTERSECTION, and
MINUS operations, 152–155 Unbalanced trees, 646 Unconstrained write assumption,
769 UNDO/NO-REDO recovery
immediate update techniques, 818–819
overview of, 807, 809 Undo operations, transactions, 753 UNDO phase, of ARIES recovery
algorithm, 823 UNDO/REDO recovery
immediate update techniques, 819
overview of, 807, 809 UNDO, write-ahead logging and,
810–811 Unidirectional associations, in UML
class diagrams, 227 Unified Modeling Language. See
UML (Unified Modeling Language)
UNION operation algorithms for, 697–698 in relational algebra, 152–155 SQL set operations, 104
Union types (categories) EER-to-Relational mapping,
297–299 modeling, 258–260
UNIQUE function, SQL, 122 Unique identity, in ODMS, 357 UNIQUE KEY clause, CREATE
TABLE command, 96 Unique keys, in relational models, 70 Uniqueness constraints
on entity attributes, 208–209 factors influencing physical data-
base design, 729 integrity constraints in databases,
21 overview of, 68–70 specifying in SQL, 95–96
Universal quantifiers transforming, 180 in tuple relational calculus,
177–178 used in queries, 180–182
Universal relation assumption, 552 Universal relation schema, 552 Universal relations, 544 Universe of discourse (UoD), 4 University student database example
data records in, 6–9 EER schema applied to, 260–263
Unordered (heap files) records, 601–602
Unrepeatable read problem, 750 Unstructured data
HTML and, 418–420 information retrieval dealing
with, 993–994 Unsupervised learning
clustering and, 1054 neural networks and, 1058
UoD (universe of discourse), 4 Update anomalies, avoiding redun-
dant information in tuples, 507
UPDATE command, SQL active rules and, 936 overview of, 109–110
Update operations bitemporal databases and, 950 database design and, 728 factors influencing physical data-
base design, 729 operations on files, 599 query processing in distributed
databases, 905–907 in relational data model, 78–79 types of relational data model
operations, 75 Update transactions, 322 Usage projections, data warehousing
and, 1080 Use case diagrams, UML, 329–331 User accounts, database security
and, 839–840 User-defined subclasses, 252, 264 User-defined time, 947 User-defined types. See UDT (user-
defined types) User-friendly interfaces, 38 User interfaces
GUIs (graphical user interfaces), 20, 39, 1061
multiple users, 20 User labels, combining with data
labels, 869–870 Users
classifying DBMSs by number of, 49
1170 Index
database actors on the scene, 15–16
measures of relevance in IR, 1015 multiuser transactions, 13–14 types of users in information
retrieval, 995–996 Utilities, DBMS system, 42–43 Valid event data, 957 Valid state
database states, 33 relational databases, 71
Valid time databases, 946 Valid time, in temporal databases,
946 Valid time relations, in temporal
databases, 947–949 valid XML documents, 422–425 Validation
in database application life cycle, 307–308
of queries, 679 Validation (optimistic) concurrency
control, 777, 794–795 Validation phase, of optimistic con-
currency control, 794 Value, hue, saturation, and, 967 Value references, in RDBs, 396 Value sets (domains), of attributes,
209–210 Values
stored in records, 594 in tuples, 65–66
Values (literals) atomic formulas as, 973 atomic literals, 378 collection literals, 382 complex types for, 358–360 in OO systems, 358 structured literals, 378
Variable-length records, 595–597 Variables
bind variables (parameterized statements), 858
communication variables in SQL, 454
domain, 183 instance, 356 iterator variables, in OQL,
399–400 limited, 980 PHP, 485–486 PHP server, 490–491 PHP variable names, 484–485 program, 599 in Prolog languages, 971
scope, 490 shared, 452 temporal, 948 tuple, 98, 101, 175–176
VDL (view definition language), 37 Vector space model, for information
retrieval, 1003–1005 Vertical fragmentation, in distrib-
uted databases, 881, 895 Vertical partitioning, database tun-
ing and, 735 Vertical propagation, of privileges,
847 Vertical search engines, 1018 Very large databases, 586 Victim selection algorithm, for
deadlock prevention, 788 Video applications, 25 Video clips, in multimedia data-
bases, 932, 965 Video segments, in multimedia
databases, 966 Video sources, in multimedia data-
bases, 966 View definition language (VDL), 37 View equivalence, of transaction
schedules, 768–769 View integration approach, in con-
ceptual schema design, 315 View materialization, 135 View serializability, of transaction
schedules, 768–769 Views
data warehouses compared with, 1079–1080
database designers creating, 15 granting/revoking privileges, 844 multiple views of data supported
in databases, 12 specifying as named queries in
OQL, 402–403 Views (virtual tables), SQL
vs. base tables, 134 CREATE VIEW command,
134–135 implementation and update,
135–137 inline views, 137 overview of, 89, 133–134
Virtual data, in views, 12 Virtual data warehouses, 1070 Virtual private databases (VPDs),
868–869 Virtual relations, specifying with
CREATE VIEW command, 90
Virtual tables. See Views (virtual tables), SQL
Visible/hidden attributes, of objects, 361
Vocabularies in inverted indexing, 1012 searching, 1013–1014
Volatile storage, 586 Voting method, distributed concur-
rency control based on, 912 VPDs (virtual private databases),
868–869 Wait-die transaction timestamp, 786 Wait-for graph, 787 WAL (write-ahead logging),
810–812 WANs (wide area networks), 879 Weak entity types, 219–220,
288–289 Web
access control policies for, 854–855
hypertext documents and, 415 interchanging data on, 24
Web analysis, 1019, 1027 Web applications, architectures for,
47–49 Web-based user interfaces, 38 Web browsers, 38 Web clients, 38 Web content analysis
agent-based approach to, 1024–1025
concept hierarchies in, 1024 database-based approach to, 1025 ontologies and, 1023–1024 overview of, 1022 segmenting Web pages and
detecting noise, 1024 structured data extraction, 1022 types of Web analysis, 1019 Web information integration,
1022–1023 Web crawlers, 1028 Web databases, programming. See
PHP Web forms, collecting data
from/inserting record into, 493–494
Web interface, for database applica- tions, 449
Web Ontology Language (OWL), 969 Web pages
analyzing link structure of, 1020–1021
Index 1171
content analysis, 1024 ranking, 1000
Web query interface integration, 1023
Web search and analysis analyzing link structure of Web
pages, 1020–1021 comparing with information
retrieval, 1018–1019 HITS ranking algorithm,
1021–1022 overview of, 1018 PageRank algorithm, 1021 practical uses of Web analysis,
1027–1028 searching the Web, 1020 Web content analysis, 1022–1025 Web searches combining brows-
ing and retrieval, 1000 Web usage analysis, 1025–1027
Web security, 1028 Web servers
middle tier in three-tier architec- ture, 48
specialized servers in client/server architecture, 45
Web Services Description Language (WSDL), 436
Web spamming, 1028 Web structure analysis
analyzing link structure of Web pages, 1020–1022
types of Web analysis, 1019 Web usage analysis
pattern analysis phase of, 1027 pattern discovery phase of,
1026–1027
preprocessing phase of, 1025–1026
types of Web analysis, 1019 Well-formed XML, 422–425 WHERE clause
DELETE command, 109 explicit sets of values in, 122 missing or unspecified, 102 in SQL retrieval queries, 129–130 UPDATE command, 109–110
Wide area networks (WANs), 879 Wildcard (*)
types of queries in IR systems, 1008–1009
using with XPath, 433 WITH CHECK OPTION, view
updates and, 137 WordNet thesaurus, 1011 Wound-wait transaction timestamp,
786 Wrappers, structured data extrac-
tion and, 1022 Write-ahead logging (WAL),
810–812 Write command, hard disks and,
591 Write phase, of optimistic concur-
rency control, 794 Write-set, of transactions, 747 Write timestamp, 789 Write-write conflicts, in transaction
schedules, 757 write_item(X), 746 WSDL (Web Services Description
Language), 436 XML access control, 853–854 XML declaration, 423
XML (eXtended Markup Language) data model, 51 interchanging data on Web using,
24 XML (Extensible Markup Language)
bibliographic references, 443 converting graphs into trees, 441 hierarchical (tree) data model,
420–422 hierarchical XML views over flat
or graph-based data, 436–440 languages, 432 languages related to, 436 overview of, 415–416 storing/extracting XML docu-
ments from databases, 431–432, 442
structured, semistructured, and unstructured data, 416–420
summary and exercises, 442–443 well-formed and valid docu-
ments, 422–425 XML schema language, 425–430 XPath, 432–434 XQuery, 434–435
XML schema language, 425–430 example schema file, 426–428 list of concepts in, 428–429 overview of, 425
XPath, 432–434 XQuery, 434–435 XSL (Extensible Stylesheet
Language), 415, 436 XSLT (Extensible Stylesheet
Language Transformations), 415, 436
1172 Index
- Cover������������
- Title Page�����������������
- Copyright����������������
- Contents
- Part 1 Introduction to Databases
- Chapter 1 Databases and Database Users
- 1.1 Introduction
- 1.2 An Example
- 1.3 Characteristics of the Database Approach
- 1.4 Actors on the Scene
- 1.5 Workers behind the Scene
- 1.6 Advantages of Using the DBMS Approach
- 1.7 A Brief History of Database Applications
- 1.8 When Not to Use a DBMS
- 1.9 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 2 Database System Concepts and Architecture
- 2.1 Data Models, Schemas, and Instances
- 2.2 Three-Schema Architecture and Data Independence
- 2.3 Database Languages and Interfaces
- 2.4 The Database System Environment
- 2.5 Centralized and Client/Server Architectures for DBMSs
- 2.6 Classification of Database Management Systems
- 2.7 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Part 2 The Relational Data Model and SQL
- Chapter 3 The Relational Data Model and Relational Database Constraints
- 3.1 Relational Model Concepts
- 3.2 Relational Model Constraints and Relational Database Schemas
- 3.3 Update Operations, Transactions, and Dealing with Constraint Violations
- 3.4 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 4 Basic SQL
- 4.1 SQL Data Definition and Data Types
- 4.2 Specifying Constraints in SQL
- 4.3 Basic Retrieval Queries in SQL
- 4.4 INSERT, DELETE, and UPDATE Statements in SQL
- 4.5 Additional Features of SQL
- 4.6 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 5 More SQL: Complex Queries, Triggers, Views, and Schema Modification
- 5.1 More Complex SQL Retrieval Queries
- 5.2 Specifying Constraints as Assertions and Actions as Triggers
- 5.3 Views (Virtual Tables) in SQL
- 5.4 Schema Change Statements in SQL
- 5.5 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 6 The Relational Algebra and Relational Calculus
- 6.1 Unary Relational Operations: SELECT and PROJECT
- 6.2 Relational Algebra Operations from Set Theory
- 6.3 Binary Relational Operations: JOIN and DIVISION
- 6.4 Additional Relational Operations
- 6.5 Examples of Queries in Relational Algebra
- 6.6 The Tuple Relational Calculus
- 6.7 The Domain Relational Calculus
- 6.8 Summary
- Review Questions
- Exercises
- Laboratory Exercises
- Selected Bibliography
- Part 3 Conceptual Modeling and Database Design
- Chapter 7 Data Modeling Using the Entity-Relationship (ER) Model
- 7.1 Using High-Level Conceptual Data Models for Database Design
- 7.2 A Sample Database Application
- 7.3 Entity Types, Entity Sets, Attributes, and Keys
- 7.4 Relationship Types, Relationship Sets, Roles, and Structural Constraints
- 7.5 Weak Entity Types
- 7.6 Refining the ER Design for the COMPANY Database
- 7.7 ER Diagrams, Naming Conventions, and Design Issues
- 7.8 Example of Other Notation: UML Class Diagrams
- 7.9 Relationship Types of Degree Higher than Two
- 7.10 Summary
- Review Questions
- Exercises
- Laboratory Exercises
- Selected Bibliography
- Chapter 8 The Enhanced Entity-Relationship (EER) Model
- 8.1 Subclasses, Superclasses, and Inheritance
- 8.2 Specialization and Generalization
- 8.3 Constraints and Characteristics of Specialization and Generalization Hierarchies
- 8.4 Modeling of UNION Types Using Categories
- 8.5 A Sample UNIVERSITY EER Schema, Design Choices, and Formal Definitions
- 8.6 Example of Other Notation: Representing Specialization and Generalization in UML Class Diagrams
- 8.7 Data Abstraction, Knowledge Representation, and Ontology Concepts
- 8.8 Summary
- Review Questions
- Exercises
- Laboratory Exercises
- Selected Bibliography
- Chapter 9 Relational Database Design by ER- and EER-to-Relational Mapping
- 9.1 Relational Database Design Using ER-to-Relational Mapping
- 9.2 Mapping EER Model Constructs to Relations
- 9.3 Summary
- Review Questions
- Exercises
- Laboratory Exercises
- Selected Bibliography
- Chapter 10 Practical Database Design Methodology and Use of UML Diagrams
- 10.1 The Role of Information Systems in Organizations
- 10.2 The Database Design and Implementation Process
- 10.3 Use of UML Diagrams as an Aid to Database Design Specification
- 10.4 Rational Rose: A UML-Based Design Tool
- 10.5 Automated Database Design Tools
- 10.6 Summary
- Review Questions
- Selected Bibliography
- Part 4 Object, Object-Relational, and XML: Concepts, Models, Languages, and Standards
- Chapter 11 Object and Object-Relational Databases
- 11.1 Overview of Object Database Concepts
- 11.2 Object-Relational Features: Object Database Extensions to SQL
- 11.3 The ODMG Object Model and the Object Definition Language ODL
- 11.4 Object Database Conceptual Design
- 11.5 The Object Query Language OQL
- 11.6 Overview of the C++ Language Binding in the ODMG Standard
- 11.7 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 12 XML: Extensible Markup Language
- 12.1 Structured, Semistructured, and Unstructured Data
- 12.2 XML Hierarchical (Tree) Data Model
- 12.3 XML Documents, DTD, and XML Schema
- 12.4 Storing and Extracting XML Documents from Databases
- 12.5 XML Languages
- 12.6 Extracting XML Documents from Relational Databases
- 12.7 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Part 5 Database Programming Techniques
- Chapter 13 Introduction to SQL Programming Techniques
- 13.1 Database Programming: Techniques and Issues
- 13.2 Embedded SQL, Dynamic SQL, and SQLJ
- 13.3 Database Programming with Function Calls: SQL/CLI and JDBC
- 13.4 Database Stored Procedures and SQL/PSM
- 13.5 Comparing the Three Approaches
- 13.6 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 14 Web Database Programming Using PHP
- 14.1 A Simple PHP Example
- 14.2 Overview of Basic Features of PHP
- 14.3 Overview of PHP Database Programming
- 14.4 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Part 6 Database Design Theory and Normalization
- Chapter 15 Basics of Functional Dependencies and Normalization for Relational Databases
- 15.1 Informal Design Guidelines for Relation Schemas
- 15.2 Functional Dependencies
- 15.3 Normal Forms Based on Primary Keys
- 15.4 General Definitions of Second and Third Normal Forms
- 15.5 Boyce-Codd Normal Form
- 15.6 Multivalued Dependency and Fourth Normal Form
- 15.7 Join Dependencies and Fifth Normal Form
- 15.8 Summary
- Review Questions
- Exercises
- Laboratory Exercises
- Selected Bibliography
- Chapter 16 Relational Database Design Algorithms and Further Dependencies
- 16.1 Further Topics in Functional Dependencies: Inference Rules, Equivalence, and Minimal Cover
- 16.2 Properties of Relational Decompositions
- 16.3 Algorithms for Relational Database Schema Design
- 16.4 About Nulls, Dangling Tuples, and Alternative Relational Designs
- 16.5 Further Discussion of Multivalued Dependencies and 4NF
- 16.6 Other Dependencies and Normal Forms
- 16.7 Summary
- Review Questions
- Exercises
- Laboratory Exercises
- Selected Bibliography
- Part 7 File Structures, Indexing, and Hashing
- Chapter 17 Disk Storage, Basic File Structures, and Hashing
- 17.1 Introduction
- 17.2 Secondary Storage Devices
- 17.3 Buffering of Blocks
- 17.4 Placing File Records on Disk
- 17.5 Operations on Files
- 17.6 Files of Unordered Records (Heap Files)
- 17.7 Files of Ordered Records (Sorted Files)
- 17.8 Hashing Techniques
- 17.9 Other Primary File Organizations
- 17.10 Parallelizing Disk Access Using RAID Technology
- 17.11 New Storage Systems
- 17.12 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 18 Indexing Structures for Files
- 18.1 Types of Single-Level Ordered Indexes
- 18.2 Multilevel Indexes
- 18.3 Dynamic Multilevel Indexes Using B-Trees and B+-Trees
- 18.4 Indexes on Multiple Keys
- 18.5 Other Types of Indexes
- 18.6 Some General Issues Concerning Indexing
- 18.7 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Part 8 Query Processing and Optimization, and Database Tuning
- Chapter 19 Algorithms for Query Processing and Optimization
- 19.1 Translating SQL Queries into Relational Algebra
- 19.2 Algorithms for External Sorting
- 19.3 Algorithms for SELECT and JOIN Operations
- 19.4 Algorithms for PROJECT and Set Operations
- 19.5 Implementing Aggregate Operations and OUTER JOINs
- 19.6 Combining Operations Using Pipelining
- 19.7 Using Heuristics in Query Optimization
- 19.8 Using Selectivity and Cost Estimates in Query Optimization
- 19.9 Overview of Query Optimization in Oracle
- 19.10 Semantic Query Optimization
- 19.11 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 20 Physical Database Design and Tuning
- 20.1 Physical Database Design in Relational Databases
- 20.2 An Overview of Database Tuning in Relational Systems
- 20.3 Summary
- Review Questions
- Selected Bibliography
- Part 9 Transaction Processing, Concurrency Control, and Recovery
- Chapter 21 Introduction to Transaction Processing Concepts and Theory
- 21.1 Introduction to Transaction Processing
- 21.2 Transaction and System Concepts
- 21.3 Desirable Properties of Transactions
- 21.4 Characterizing Schedules Based on Recoverability
- 21.5 Characterizing Schedules Based on Serializability
- 21.6 Transaction Support in SQL
- 21.7 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 22 Concurrency Control Techniques
- 22.1 Two-Phase Locking Techniques for Concurrency Control
- 22.2 Concurrency Control Based on Timestamp Ordering
- 22.3 Multiversion Concurrency Control Techniques
- 22.4 Validation (Optimistic) Concurrency Control Techniques
- 22.5 Granularity of Data Items and Multiple Granularity Locking
- 22.6 Using Locks for Concurrency Control in Indexes
- 22.7 Other Concurrency Control Issues
- 22.8 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 23 Database Recovery Techniques
- 23.1 Recovery Concepts
- 23.2 NO-UNDO/REDO Recovery Based on Deferred Update
- 23.3 Recovery Techniques Based on Immediate Update
- 23.4 Shadow Paging
- 23.5 The ARIES Recovery Algorithm
- 23.6 Recovery in Multidatabase Systems
- 23.7 Database Backup and Recovery from Catastrophic Failures
- 23.8 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Part 10 Additional Database Topics: Security and Distribution
- Chapter 24 Database Security
- 24.1 Introduction to Database Security Issues
- 24.2 Discretionary Access Control Based on Granting and Revoking Privileges
- 24.3 Mandatory Access Control and Role-Based Access Control for Multilevel Security
- 24.4 SQL Injection
- 24.5 Introduction to Statistical Database Security
- 24.6 Introduction to Flow Control
- 24.7 Encryption and Public Key Infrastructures
- 24.8 Privacy Issues and Preservation
- 24.9 Challenges of Database Security
- 24.10 Oracle Label-Based Security
- 24.11 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 25 Distributed Databases
- 25.1 Distributed Database Concepts
- 25.2 Types of Distributed Database Systems
- 25.3 Distributed Database Architectures
- 25.4 Data Fragmentation, Replication, and Allocation Techniques for Distributed Database Design
- 25.5 Query Processing and Optimization in Distributed Databases
- 25.6 Overview of Transaction Management in Distributed Databases
- 25.7 Overview of Concurrency Control and Recovery in Distributed Databases
- 25.8 Distributed Catalog Management
- 25.9 Current Trends in Distributed Databases
- 25.10 Distributed Databases in Oracle
- 25.11 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Part 11 Advanced Database Models, Systems, and Applications
- Chapter 26 Enhanced Data Models for Advanced Applications
- 26.1 Active Database Concepts and Triggers
- 26.2 Temporal Database Concepts
- 26.3 Spatial Database Concepts
- 26.4 Multimedia Database Concepts
- 26.5 Introduction to Deductive Databases
- 26.6 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 27 Introduction to Information Retrieval and Web Search
- 27.1 Information Retrieval (IR) Concepts
- 27.2 Retrieval Models
- 27.3 Types of Queries in IR Systems
- 27.4 Text Preprocessing
- 27.5 Inverted Indexing
- 27.6 Evaluation Measures of Search Relevance
- 27.7 Web Search and Analysis
- 27.8 Trends in Information Retrieval
- 27.9 Summary
- Review Questions
- Selected Bibliography
- Chapter 28 Data Mining Concepts
- 28.1 Overview of Data Mining Technology
- 28.2 Association Rules
- 28.3 Classification
- 28.4 Clustering
- 28.5 Approaches to Other Data Mining Problems
- 28.6 Applications of Data Mining
- 28.7 Commercial Data Mining Tools
- 28.8 Summary
- Review Questions
- Exercises
- Selected Bibliography
- Chapter 29 Overview of Data Warehousing and OLAP
- 29.1 Introduction, Definitions, and Terminology
- 29.2 Characteristics of Data Warehouses
- 29.3 Data Modeling for Data Warehouses
- 29.4 Building a Data Warehouse
- 29.5 Typical Functionality of a Data Warehouse
- 29.6 Data Warehouse versus Views
- 29.7 Difficulties of Implementing Data Warehouses
- 29.8 Summary
- Review Questions
- Selected Bibliography
- Appendix A: Alternative Diagrammatic Notations for ER Models
- Appendix B: Parameters of Disks
- Appendix C: Overview of the QBE Language
- C.1 Basic Retrievals in QBE
- C.2 Grouping, Aggregation, and Database Modification in QBE
- Selected Bibliography
- Index
- A
- B
- C
- D
- E
- F
- G
- H
- I
- J
- K
- L
- M
- N
- O
- P
- Q
- R
- S
- T
- U
- V
- W
- X
