Computer Science BI, Analytics and Decision Support - Assignment

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Marko Grobelnik, Blaz Fortuna, Dunja Mladenic

Jozef Stefan Institute, Slovenia

Sydney, Oct 22nd 2013

 Big-Data in numbers

 Big-Data Definitions

 Motivation

 State of Market

 Techniques

 Tools

 Data Science

 Applications ◦ Recommendation, Social networks, Media Monitoring

 Concluding remarks

 „Big-data‟ is similar to „Small-data‟, but bigger

 …but having data bigger it requires different approaches: ◦ techniques, tools, architectures

 …with an aim to solve new problems ◦ …or old problems in a better way.

 Volume – challenging to load and process (how to index, retrieve)

 Variety – different data types and degree of structure (how to query semi- structured data)

 Velocity – real-time processing influenced by rate of data arrival

From “Understanding Big Data” by IBM

 1. Volume (lots of data = “Tonnabytes”)  2. Variety (complexity, curse of

dimensionality)  3. Velocity (rate of data and information flow)

 4. Veracity (verifying inference-based models from comprehensive data collections)

 5. Variability  6. Venue (location)  7. Vocabulary (semantics)

Comparing volume of “big data” and “data mining” queries

…adding “web 2.0” to “big data” and “data mining” queries volume

Big-Data

 Key enablers for the appearance and growth of “Big Data” are:

◦ Increase of storage capacities

◦ Increase of processing power

◦ Availability of data

Source: WikiBon report on “Big Data Vendor Revenue and Market Forecast 2012-2017”, 2013

 …when the operations on data are complex: ◦ …e.g. simple counting is not a complex problem

◦ Modeling and reasoning with data of different kinds can get extremely complex

 Good news about big-data: ◦ Often, because of vast amount of data, modeling

techniques can get simpler (e.g. smart counting can replace complex model-based analytics)…

◦ …as long as we deal with the scale

 Research areas (such as IR, KDD, ML, NLP, Se mWeb, …) are sub- cubes within the data cube

Scalability

Streaming

Context

Quality

Usage

 A risk with “Big-Data mining” is that an analyst can “discover” patterns that are meaningless

 Statisticians call it Bonferroni‟s principle: ◦ Roughly, if you look in more places for interesting

patterns than your amount of data will support, you are bound to find crap

Example:

 We want to find (unrelated) people who at least twice have stayed at the same hotel on the same day ◦ 109 people being tracked. ◦ 1000 days. ◦ Each person stays in a hotel 1% of the time (1 day out of 100) ◦ Hotels hold 100 people (so 105 hotels). ◦ If everyone behaves randomly (i.e., no terrorists) will the data

mining detect anything suspicious?

 Expected number of “suspicious” pairs of people: ◦ 250,000 ◦ … too many combinations to check – we need to have some

additional evidence to find “suspicious” pairs of people in some more efficient way

Example taken from: Rajaraman, Ullman: Mining of Massive Datasets

 Smart sampling of data ◦ …reducing the original data while not losing the

statistical properties of data

 Finding similar items ◦ …efficient multidimensional indexing

 Incremental updating of the models ◦ (vs. building models from scratch)

◦ …crucial for streaming data

 Distributed linear algebra ◦ …dealing with large sparse matrices

 On the top of the previous ops we perform usual data mining/machine learning/statistics operators: ◦ Supervised learning (classification, regression, …)

◦ Non-supervised learning (clustering, different types of decompositions, …)

◦ …

 …we are just more careful which algorithms we choose ◦ typically linear or sub-linear versions of the algorithms

 An excellent overview of the algorithms covering the above issues is the book “Rajaraman, Leskovec, Ullman: Mining of Massive Datasets”

 Downloadable from: http://infolab.stanford.edu/~ullman/mmds.html

 Where processing is hosted? ◦ Distributed Servers / Cloud (e.g. Amazon EC2)

 Where data is stored? ◦ Distributed Storage (e.g. Amazon S3)

 What is the programming model? ◦ Distributed Processing (e.g. MapReduce)

 How data is stored & indexed? ◦ High-performance schema-free databases (e.g.

MongoDB)

 What operations are performed on data? ◦ Analytic / Semantic Processing

 Computing and storage are typically hosted transparently on cloud infrastructures ◦ …providing scale, flexibility and high fail-safety

 Distributed Servers ◦ Amazon-EC2, Google App Engine, Elastic,

Beanstalk, Heroku

 Distributed Storage ◦ Amazon-S3, Hadoop Distributed File System

 Distributed processing of Big-Data requires non-standard programming models ◦ …beyond single machines or traditional parallel

programming models (like MPI)

◦ …the aim is to simplify complex programming tasks

 The most popular programming model is MapReduce approach ◦ …suitable for commodity hardware to reduce costs

 The key idea of the MapReduce approach: ◦ A target problem needs to be parallelizable

◦ First, the problem gets split into a set of smaller problems (Map step) ◦ Next, smaller problems are solved in a parallel way ◦ Finally, a set of solutions to the smaller problems get synthesized

into a solution of the original problem (Reduce step)

Google Maps charts new territory into businesses

Google selling new tools for businesses to build their own maps

Google 4

Maps 4

Businesses 4

New 1

Charts 1

Territory 1

Tools 1

…

Google promises consumer experience for businesses with Maps Engine Pro

Google is trying to get its Maps service used by more businesses

Google Maps charts new territory into businesses

Google selling new tools for businesses to build their own maps

Businesses 2

Charts 1

Maps 2

Territory 1

…

Google promises consumer experience for businesses with Maps Engine Pro

Google is trying to get its Maps service used by more businesses

Map 2

Businesses 2

Engine 1

Maps 2

Service 1

…

Map 1

 Split according to the hash of a key

 In our case: key = word, hash = first character

Businesses 2

Charts 1

Maps 2

Territory 1

…

Businesses 2

Engine 1

Maps 2

Service 1

…

Maps 2

Territory 1

…

Maps 2

Service 1

…

Businesses 2

Charts 1

…

Businesses 2

Engine 1

…

R e d u c e 1

R e d u c e 2

T a s k 1

T a s k 2

Businesses 4

Charts 1

Engine 1

…

Maps 4

Territory 1

Service 1

…

Maps 2

Territory 1

…

Maps 2

Service 1

…

Businesses 2

Charts 1

…

Businesses 2

Engine 1

…

Reduce 2

Reduce 1

 We concatenate the outputs into final result

Businesses 4

Charts 1

Engine 1

…

Maps 4

Territory 1

Service 1

…

Businesses 4

Charts 1

Engine 1

…

Maps 4

Territory 1

Service 1

…

R e d

u c e 1

R e d u c e 2

 Apache Hadoop [http://hadoop.apache.org/] ◦ Open-source MapReduce implementation

 Tools using Hadoop: ◦ Hive: data warehouse infrastructure that provides data

summarization and ad hoc querying (HiveQL) ◦ Pig: high-level data-flow language and execution

framework for parallel computation (Pig Latin) ◦ Mahout: Scalable machine learning and data mining

library ◦ Flume: Flume is a distributed, reliable, and available

service for efficiently collecting, aggregating, and moving large amounts of log data

◦ Many more: Cascading, Cascalog, mrjob, MapR, Azkaban, Oozie, …

 “[…] need to solve a problem that relational databases are a bad fit for”, Eric Evans

 Motives: ◦ Avoidance of Unneeded Complexity – many use-case

require only subset of functionality from RDBMSs (e.g ACID properties)

◦ High Throughput - some NoSQL databases offer significantly higher throughput then RDBMSs

◦ Horizontal Scalability, Running on commodity hardware ◦ Avoidance of Expensive Object-Relational Mapping –

most NoSQL store simple data structures ◦ Compromising Reliability for Better Performance

Based on “NoSQL Databases”, Christof Strauch http://www.christof-strauch.de/nosqldbs.pdf

 BASE approach ◦ Availability, graceful degradation, performance

◦ Stands for “Basically available, soft state, eventual consistency”

 Continuum of tradeoffs: ◦ Strict – All reads must return data from latest completed

writes

◦ Eventual – System eventually return the last written value

◦ Read Your Own Writes – see your updates immediately

◦ Session – RYOW only within same session

◦ Monotonic – only more recent data in future requests

 Consistent hashing ◦ Use same function for

hashing objects and nodes

◦ Assign objects to nearest nodes on the circle

◦ Reassign object when nodes added or removed

◦ Replicate nodes to r nearest nodes

White, Tom: Consistent Hashing. November 2007. – Blog post of 2007-11-27. http://weblogs.java.net/blog/tomwhite/archive/2007/11/consistent_hash.html

 Storage Layout ◦ Row-based

◦ Columnar

◦ Columnar with Locality Groups

 Query Models ◦ Lookup in key-value stores

 Distributed Data Processing via MapReduce

Lipcon, Todd: Design Patterns for Distributed Non-Relational Databases. June 2009. – Presentation of 2009-06-11. http://www.slideshare.net/guestdfd1ec/design-patterns-for-distributed-nonrelationaldatabases

 Map or dictionary allowing to add and retrieve values per keys

 Favor scalability over consistency ◦ Run on clusters of commodity hardware ◦ Component failure is “standard mode of operation”

 Examples: ◦ Amazon Dynamo ◦ Project Voldemort (developed by LinkedIn) ◦ Redis ◦ Memcached (not persistent)

 Combine several key-value pairs into documents

 Documents represented as JSON

 Examples: ◦ Apache CouchDB

◦ MongoDB

" Title " : " CouchDB ",

" Last editor " : "172.5.123.91" ,

" Last modified ": "9/23/2010" ,

" Categories ": [" Database ", " NoSQL ", " Document Database "],

" Body ": " CouchDB is a ..." , " Reviewed ": false

 Using columnar storage layout with locality groups (column families)

 Examples: ◦ Google Bigtable

◦ Hypertable, HBase

 open source implementation of Google Bigtable

◦ Cassandra

 combination of Google Bigtable and Amazon Dynamo

 Designed for high write throughput

Infrastructure:  Kafka [http://kafka.apache.org/]

◦ A high-throughput distributed messaging system

 Hadoop [http://hadoop.apache.org/] ◦ Open-source map-reduce implementation

 Storm [http://storm-project.net/] ◦ Real-time distributed computation system

 Cassandra [http://cassandra.apache.org/] ◦ Hybrid between Key-Value and Row-Oriented DB ◦ Distributed, decentralized, no single point of failure ◦ Optimized for fast writes

 Mahout

◦ Machine learning library working on top of Hadoop

◦ http://mahout.apache.org/

 MOA

◦ Mining data streams with concept drift

◦ Integrated with Weka

◦ http://moa.cms.waikato.ac.nz/

Mahout currently has:

• Collaborative Filtering

• User and Item based recommenders

• K-Means, Fuzzy K-Means clustering

• Mean Shift clustering

• Dirichlet process clustering

• Latent Dirichlet Allocation

• Singular value decomposition

• Parallel Frequent Pattern mining

• Complementary Naive Bayes

classifier

• Random forest decision tree based

classifier

 Interdisciplinary field using techniques and theories from many fields, including math, statistics, data engineering, pattern recognition and learning, advanced computing, visualization, uncertainty modeling, data warehousing, and high performance computing with the goal of extracting meaning from data and creating data products.

 Data science is a novel term that is often used interchangeably with competitive intelligence or business analytics, although it is becoming more common.

 Data science seeks to use all available and relevant data to effectively tell a story that can be easily understood by non-practitioners.

http://en.wikipedia.org/wiki/Data_science

Analyzing the Analyzers An Introspective Survey of Data Scientists and Their Work By Harlan Harris, Sean Murphy, Marck Vaisman Publisher: O'Reilly Media Released: June 2013

An Introduction to Data Jeffrey Stanton, Syracuse University School of Information Studies Downloadable from http://jsresearch.net/wiki/projects/teachdatascience Released: February 2013

Data Science for Business: What you need to know about data mining and data-analytic thinking by Foster Provost and Tom Fawcett Released: Aug 16, 2013

Recommendation

Social Network Analytics

Media Monitoring

 User visit logs ◦ Track each visit using embedded JavaScript

 Content ◦ The content and metadata of visited pages

 Demographics ◦ Metadata about (registered) users

User ID cookie: 1234567890

IP: 95.87.154.251 (Ljubljana, Slovenia)

Requested URL: http://www.bloomberg.com/news/2012-07- 19/americans-hold-dimmest-view-on- economic-outlook-since-january.html

Referring URL: http://www.bloomberg.com/

Date and time: 2009-08-25 08:12:34

Device: Chrome, Windows, PC

 News-source:

◦ www.bloomberg.com

 Article URL:

◦ http://www.bloomberg.com/news /2011-01-17/video-gamers- prolonged-play-raises-risk-of- depression-anxiety-phobias.html

 Author:

◦ Elizabeth Lopatto

 Produced at:

◦ New York

 Editor:

◦ Reg Gale

 Publish Date:

◦ Jan 17, 2011 6:00 AM

 Topics:

◦ U.S., Health Care, Media, Technology, Science

Topics (e.g. DMoz): ◦ Health/Mental Health/…/Depression ◦ Health/Mental Health/Disorders/Mood ◦ Games/Game Studies

Keywords (e.g. DMoz): ◦ Health, Mental Health, Disorders, Mood,

Games, Video Games, Depression, Recreation, Browser Based, Game Studies, Anxiety, Women, Society, Recreation and Sports

Locations: ◦ Singapore (sws.geonames.org/1880252/) ◦ Ames (sws.geonames.org/3037869/)

People: ◦ Duglas A. Gentile

Organizations: ◦ Iowa State University

(dbpediapa.org/resource/ Iowa_State_University)

◦ Pediatrics (journal)

 Provided only for registered users ◦ Only some % of unique users typically register

 Each registered users described with: ◦ Gender

◦ Year of birth

◦ Household income

 Noisy

 List of articles based on ◦ Current article

◦ User‟s history

◦ Other Visits

 In general, a combination of text stream (news articles) with click stream (website access logs)

 The key is a rich context model used to describe user

 “Increase in engagement” ◦ Good recommendations can make a difference when

keeping a user on a web site ◦ Measured in number of articles read in a session

 “User experience” ◦ Users return to the site ◦ Harder to measure and attribute to recommendation

module

 Predominant success metric is the attention span of a user expressed in terms of time spent on site and number of page views.

 Cold start ◦ Recent news articles have little usage history

◦ More sever for articles that did not hit homepage or section front, but are still relevant for particular user segment

 Recommendation model must be able to generalize well to new articles.

 Access logs analysis shows, that half of the articles read are less then ~8 hours old

 Weekends are exception

A rt

ic le

a g e [

m in

u te

s ]

 History

◦ Time

◦ Article

 Current request:

◦ Location

◦ Requested page

◦ Referring page

◦ Local Time

 Each article from the time window is described with the following features:

◦ Popularity (user independent)

◦ Content

◦ Meta-data

◦ Co-visits

◦ Users

 Features computed by comparing article‟s and user‟s feature vectors

 Features computed on- the-fly when preparing recommendations

recommendations

time

training

1 2-10 11-50 51- 21% 24% 32% 37%

 Measure how many times one of top 4 recommended article was actually read

 Feature space ◦ Extracted from subset of fields ◦ Using vector space model ◦ Vector elements for each field are normalized

 Training set ◦ One visit = one vector ◦ One user = a centroid of all his/her visits ◦ Users from the segment form positive class ◦ Sample of other users form negative class

 Classification algorithm ◦ Support Vector Machine ◦ Good for dealing with high dimensional data ◦ Linear kernel ◦ Stochastic gradient descent  Good for sampling

 Real-world dataset from a major news publishing website ◦ 5 million daily users, 1 million registered

 Tested prediction of three demographic dimensions: ◦ Gender, Age, Income

 Three user groups based on the number of visits: ◦ ≥2, ≥10, ≥50

 Evaluation: ◦ Break Even Point (BEP) ◦ 10-fold cross validation

Category Size Category Size Category Size

Male 250,000 21-30 100,000 0-24k 50,000

Female 250,000 31-40 100,000 25k-49k 50,000

41-50 100,000 50k-74k 50,000

51-60 100,000 75k-99k 50,000

61-80 100,000 100k- 149k

50,000

150k- 254k

50,000

50.00%

55.00%

60.00%

65.00%

70.00%

75.00%

80.00%

Male Female

≥2

≥10

20.00%

25.00%

30.00%

35.00%

40.00%

45.00%

21-30 31-40 41-50 51-60 61-80

≥2

≥10

≥50

14.00%

15.00%

16.00%

17.00%

18.00%

19.00%

20.00%

21.00%

22.00%

0-24 50-74 150-254

Text Features

Named Entities

All Meta Data

 Observe social and communication phenomena at a planetary scale

 Largest social network analyzed till 2010

Research questions:

 How does communication change with user demographics (age, sex, language, country)?

 How does geography affect communication?

 What is the structure of the communication network?

90 “Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

 We collected the data for June 2006

 Log size:

150Gb/day (compressed)

 Total: 1 month of communication data:

4.5Tb of compressed data

 Activity over June 2006 (30 days) ◦ 245 million users logged in

◦ 180 million users engaged in conversations

◦ 17,5 million new accounts activated

◦ More than 30 billion conversations

◦ More than 255 billion exchanged messages

91 “Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

92“Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

93“Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

 Count the number of users logging in from particular location on the earth

94 “Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

 Logins from Europe

95“Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

 6 degrees of separation [Milgram ‟60s]

 Average distance between two random users is 6.6

 90% of nodes can be reached in < 8 hops

Hops Nodes

1 10

2 78

3 396

4 8648

5 3299252

6 28395849

7 79059497

8 52995778

9 10321008

10 1955007

11 518410

12 149945

13 44616

14 13740

15 4476

16 1542

17 536

18 167

19 71

20 29

21 16

22 10

23 3

24 2

25 3“Planetary-Scale Views on a Large Instant-Messaging Network” Leskovec & Horvitz WWW2008

Social-networkSocial-network

 The aim of the project is to collect and analyze global main-stream and social media ◦ …documents are crawled from 100 thousands of

sources

◦ …each crawled document gets cleaned, linguistically and semantically enriched

◦ …we connect documents across languages (cross-lingual technology)

◦ …we identify and connect events

http://render-project.eu/ http://www.xlike.org/

 The NewsFeed.ijs.si system collects ◦ 40.000 main-stream news

sources ◦ 250.000 blog sources ◦ Twitter stream

 …resulting in ~500.000 documents + #N of twits per day

 Each document gets cleaned, linguistically and semantically annotated

Plain text

Text Enrichment

Extracted graph of triples from text

“Enrycher” is available as as a web-service generating Semantic Graph, LOD links, Entities, Keywords, Categories, Text Summarization

 Reporting has bias – same information is being reported in different ways

 DiversiNews system allows exploring news diversity along:

◦ Topicality

◦ Geography

◦ Sentiment

 Having stream of news & social media, the task is to structure documents into events

 “Event Registry” system allows for: ◦ Identification of events from documents ◦ Connecting documents across many languages ◦ Tracking events and constructing story-lines ◦ Describing events in a (semi)structured way ◦ UI for exploration through Search & Visualization ◦ Export into RDF (Storyline ontology)

 Prototype operating at ◦ http://mustang.ijs.si:8060/searchEvents

 Big-Data is everywhere, we are just not used to deal with it

 The “Big-Data” hype is very recent ◦ …growth seems to be going up ◦ …evident lack of experts to build Big-Data apps

 Can we do “Big-Data” without big investment? ◦ …yes – many open source tools, computing machinery is

cheap (to buy or to rent) ◦ …the key is knowledge on how to deal with data ◦ …data is either free (e.g. Wikipedia) or to buy (e.g.

twitter)