PLAGIARISM FREE BUT "A" WORK

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Course Learning Outcomes for Unit III Upon completion of this unit, students should be able to:

5. Analyze relational and spatial database theory. 5.1 Define the major attributes of database management systems. 5.2 Explain the difference between the relational model and spatial data. 5.3 Explain the various techniques used to organize data based on data type and database model. 5.4 Identify the different types of languages associated with database design.

Reading Assignment 8. Advantages of a relational database. (n.d.). Retrieved from http://www.teach-

ict.com/as_as_computing/ocr/H447/F453/3_3_9/database_design/miniweb/pg8.htm Indika. (2011). Difference between filesystem and database. Retrieved from

http://www.differencebetween.com/difference-between-filesystem-and-vs-database/ In order to access the chapters from the books mentioned below, you must first log into the myCSU Student Portal and access the ebrary within the CSU Online Library. Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: Perspectives on spatial

data analysis. Thousand Oaks, CA: Sage. Chapter 2: Spatial Data Murthy, C. S. V. (2007). Database management design. New Delhi, India: Himalaya Publishing House. Chapter 4: Relational Model

Unit Lesson We have all found ourselves writing on pieces of paper, trying to keep our calendars updated with all of our to do lists, realizing that we forgot something, or repeating out loud what it is we need to do that day. The aforementioned strategies are merely attempts to connect multiple sources of information into one place so that we can be more expedient in completing tasks. Let’s pretend that the criminal justice system is a large agency. In this agency, there are three main components (law enforcement, courts, and corrections). Each component functions independently, and each component is dependent on the other components to complete some tasks. Let’s also pretend that overnight the informal processes that were set in place to facilitate the collaboration of the components disappear. The question would then arise: How does one go about facilitating the collaboration of three distinct components into one fluid organization? One answer would be to use a database. Databases attempt to link information from independent and sometimes interconnected components in a way that the user is able to access various types of information all at once in order to make sound decisions. Pallaw (2010) defines data as “isolated facts from which the required information is produced . . . distinct pieces of information, usually formatted in a special way” (p. 1). In a database, the data is represented in three primary layers: operational, reconcile, and arrived. Data, while closely related, is not to be confused with information (“processed, organized or summarised data”), records (“collection of logically related fields or data items”), or files (“collection of related sequence of records”) (Pallaw, 2010, pp. 2-3). When related data is

UNIT III STUDY GUIDE

Databases: Spatial and Relational

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logically stored together in order to meet the “information needs of an organization” (Pallaw, 2010, p. 3), a database exists. Database management systems (DBMS) are a compilation of programs that are designed to assist in the accessing of interrelated data. DBMS assist in storing data managing transactions, maintaining the integrity of services, managing the backup and recovery of data, providing concurrency control services, managing the manipulation of data, managing data dictionary/system catalogs, managing authorization/security, providing utility services, and providing the database access application programming interfaces (Pallaw, 2010). One may ask, why should I use a database versus the “old school” file system? Although the file system is useful in providing a general understanding of the various levels of designing a database, it has several disadvantages (e.g., tremendous programming effort, data inconsistency, data redundancy, and integrity problems). Using the DBMS addresses the disadvantages associated with the file system (e.g., controlling data redundancy, enforcing integrity constraints, and improving data quality). However, the DBMS is not without its disadvantages (e.g., increased installation and management costs, increased complexity, and the need for new and/or specialized employees) (Pallaw, 2010). Nevertheless, the advantages of the DBMS outweigh the disadvantages associated with the use of the file system. The data contained in a database can be arranged in various models designed to structure the database. Such models include the entity-relationship model, object-oriented data model, hierarchical model, and the network data model. Once the database model is determined, there are different types of database systems that can be used to process the data. These typologies are based on either the number of users or the site location. Regardless of the data model for the database type, the data is useless unless it can be accessed and manipulated in a way that meets the needs of the organization. Before anyone in an organization is able to access the data to meet the organization’s needs, a database administrator (DBA) will have designed and implemented the database, tested and continued to monitor the database, granted authorization access to the data, ensured recovery mechanisms were implemented, and scheduled routine maintenance for the database. Authors Teorey, Lightstone, and Nadeau (2006) write:

The database life cycle incorporates the basic steps involved in designing a global schema of the logical database, allocating data across a computer network, and defining local DBMS-specific schemas. Once the design is completed, the life cycle continues with database implementation and maintenance. (p. 3)

However, it is during the design phase that one identifies the type of data that will be used to construct a database and determines the type of database that will be utilized. Thus, for the remainder of this unit, we will focus our attention of two types of databases: relational and spatial. Relational Databases In a relational database, data is considered to be a collection of relations and is usually represented in tables (Murthy, 2008). The tables used in a relational database have unique characteristics. For example, “a row is called a tuple, a column header is called an attribute, and a table is called a relation. The datatype describing the types of values that can appear in each column is called a domain” (Murthy, 2008, p. 95). Tables are distinguished by using a primary key, which ensures that “no two roles of the table contain the same value in that column or column combination” (Murthy, 2008, p. 96). Relationships are depicted in one of three ways: one-to-one, one-to-many, and many-to-many. Data records contained in a relational database do not have a predefined set of relationships, allowing the user to determine his or her relationships when the data is accessed. Because the user has so much control over the data, relational databases can perform a variety of tasks, such as defining the database; querying the database; adding, editing, and deleting data from the database; modifying the structure of the database; securing data from public access; communicating within the network; and exporting and importing data (Murthy, 2008). There are advantages and disadvantages associated with using a relational database. Advantages include ease of use due to query languages, not needing to learn the inner structure of the database, and easy structure modification due to data independence (Murthy, 2008). Disadvantages include the consumption of processing time and memory by the query languages, the need for data to be related in order to be accessed within the database, the unsuitability

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of set processing to conventional programming languages, and the effect that the commands used to operate the database have on system performance (Murthy, 2008). Spatial Databases Spatial databases differ from relational databases in that spatial databases attempt to store, organize, and analyze data that is spatial (in space—not to be confused with “outer space”). According to Fotheringham, Brunsdon, and Charlton (2000):

Spatial data comprise observations of some phenomenon that possess a spatial reference. The spatial reference may be explicit, as an address or grid reference, or it may [be] implicit, as in a pixel in the middle of a satellite image. (p. 15)

Spatial data is captured through a variety of processes. One such spatial data-capturing process is digitization, which “involves the transfer of locational information about features on paper maps into some computer-processable form” (Fotheringham et al., 2000, p. 16). Spatial reference points are depicted in spatial databases by spatial objects (points, lines, or areas). Location in the spatial database is often represented using conventional means: latitude and longitude points. In a computer, spatial data is either represented using a vector model (ordered coordinate pair, vector of attributes) or a raster model (lattice or grid). The ultimate goal of entering the data into a spatial database is to be able to actually analyze the spatial data of interest. Two major components associated with the analysis of spatial data are the spatial component, which “describes the location of the object of interest,” and the attribute component, which “describes the characteristics of the object” (Fotheringham et al., 2000, p. 21). Although analyzing spatial data is appealing due to its ability to be utilized in the creation of visual representations of the data being analyzed, there is a fair mix of problems and opportunities associated with spatial analyses. Thus, one must remember to be careful and thoughtful when conducting spatial analyses.

References Fotheringham, A. S., Brunsdon, C., & Charlton, M. (2000). Quantitative geography: Perspectives on spatial

data analysis. Thousand Oaks, CA: Sage. Murthy, C. S. V. (2008). Database management design. New Delhi, India: Himalaya Publishing House. Pallaw, V. K. (2010). Database management systems (2nd ed.). New Delhi, India: Asian Books Private

Limited. Teorey, T., Lightstone, S., & Nadeau, T. (2006). Database modeling and design: Logical design (4th ed.). San

Francisco, CA: Morgan Kaufmann.