ISM 50 - Business Information Systems
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1 ISM 50 - Business Information Systems Lecture 17 Instructor: Magdalini Eirinaki UC Santa Cruz May 29, 2007
2 Announcements News Folio #3 DUE Thursday 5/31 Database Assignment DUE Tuesday 6/5 Business Paper DUE Thursday 6/7 Pick up your midterms from Instructor s office. Pick up Assignment #3 & Pop Quiz #2 from TA s offive Next time read: Will be announced later today on website Student Presentations Thursday (5/31) Eric Gonzalez (business paper: Wal-Mart) Derek Stern (business paper: Home Depot)
3 Student Presentation Evan Price - Safeway
4 Knowledge Discovery & Data Mining Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
5 Recall - Decision Support A Data warehouse is a decision support database that is maintained separately from the operational database and stores organization s historical data (size: TB) focuses on data analysis and decision making Data mining is the process of discovering patterns in large amounts of data
6 OLTP vs. OLAP OLTP OLAP users clerk, IT professional knowledge worker function day to day operations decision support DB design application-oriented subject-oriented data current, up-to-date detailed, flat relational isolated usage repetitive ad-hoc access read/write lots of scans index/hash on prim. key unit of work short, simple transaction complex query # records accessed tens millions #users thousands hundreds DB size 100MB-GB 100GB-TB historical, summarized, multidimensional integrated, consolidated metric transaction throughput query throughput, response Slide adapted from slides for Data Mining: Concepts and Techniques By Jiawei Han and Micheline Kamber. Copyright See copyright notice
7 Largest Commercial Databases Winter Corp Survey: France Telecom: ~30TB AT&T ~ 26 TB Winter Corp Survey: Yahoo! ~ 100 TB (Largest Data Warehouse) AT&T ~ 94 TB Cingular ~ 25TB Amazon ~ 24TB (1 TB = 1000 GB) Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
8 Data Growth In 2 years, the size of the largest database TRIPLED! Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
9 Web Databases Google searches 4+ Billion pages, many hundreds TB IBM WebFountain, 160 TB (2003) Alexa internet archive: 7 years of data, 500 TB Internet Archive ( 300 TB Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
10 From terabytes to exabytes to UC Berkeley 2003 estimate: 5 exabytes (5 million terabytes) of new data was created in US produces ~40% of new stored data worldwide 2006 estimate: 161 exabytes (IDC study) projection: 988 exabytes Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
11 Data Growth Rate Twice as much information was created in 2002 as in 1999 (~30% growth rate) Other growth rate estimates even higher Very little data will ever be looked at by a human Knowledge Discovery is NEEDED to make sense and use of data. Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
12 Knowledge Discovery and Data Mining Knowledge Discovery in Data is the non-trivial process of identifying valid novel potentially useful and ultimately understandable patterns in data [Advances in Knowledge Discovery and Data Mining, Fayyad, Piatetsky-Shapiro, Smyth, and Uthurusamy, (Chapter 1), AAAI/MIT Press 1996\ Discover knowledge that is not straightforward to infer from statistically analysing the data Both terms are used interchangeably or complimentary to each other Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
13 Application Areas What do you think are some of the most important and widespread business applications of Data Mining? Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
14 Data Mining Application areas Business CRM, fraud detection, e-commerce, manufacturing, sports/entertainment, telecom, targeted marketing, health care, Web: search engines, advertising, web and text mining, recommendations, Government crime detection, profiling tax cheaters, Science astronomy, bioinformatics, drug discovery, Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
15 Data Mining for Customer Modeling Customer Tasks: attrition prediction targeted marketing Market segmentation Increase customer acquisition, customer retention, cross-selling, credit-risk fraud detection Industries banking, telecom, retail sales, Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
16 Recall - OLAP Cube Multidimensional Database One dimension is the TIME
17 Recall - OLAP Cube Enables analysis in various aggregation levels
18 Typical questions answered by data mining Which customers are most likely to drop their cell phone service? What is the probability that a customer will purchase at least $100 worth of merchandise from a particular mailorder catalog? Which customers are most likely to respond to a particular offer?
19 Combining Data Mining and Campaign Management [Data Mining and Customer Relationships,
20 Customer Attrition: Case Study Situation: Attrition rate at for mobile phone customers is around 25-30% a year! With this in mind, what is our task? Assume we have customer information for the past N months. Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
21 Customer Attrition: Case Study Task: Predict who is likely to attrite next month. Estimate customer value and what is the cost-effective offer to be made to this customer. Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
22 Customer Attrition Results Verizon Wireless built a customer data warehouse Identified potential attriters Developed multiple, regional models Targeted customers with high propensity to accept the offe Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers) (Reported in 2003) Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
23 Major Data Mining Tasks Associations: e.g. A & B & C occur frequently Classification: predicting an item class Clustering: finding clusters in data Link Analysis: finding relationships Collaborative Filtering: identify similar profiles etc Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
24 Application: Market Basket Analysis Finds associations between customers transactions TID Produce 1 MILK, BREAD, EGGS 2 BREAD, SUGAR 3 BREAD, CEREAL 4 MILK, BREAD, SUGAR 5 MILK, CEREAL 6 BREAD, CEREAL 7 MILK, CEREAL 8 MILK, BREAD, CEREAL, EGGS 9 MILK, BREAD, CEREAL e.g. Those who buy MILK and BREAD also buy CEREAL How can this knowledge be used? ==> Diapers & Beer urban legend
25 e-commerce A person buys a book (product) at Amazon.com Task: Recommend other books (products) this person is likely to buy Based on past users purchases/ratings Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
26 Recall - Recommender Systems
27 Recommender Systems Find users with similar interests/purchases/visits See what they have bought/visited/liked that you have not Recommend them! The most popular way of doing this is called collaborative filtering (Chapter 2.3)
28 Data Mining and Privacy In 2006, NSA (National Security Agency) was reported to be mining years of call info, to identify terrorism networks The web sites use cookies to track your visits Retailers track your purchases every time you use the award points card Invasion of privacy do you mind if your call/purchase/navigational information is in a database? Slide adapted from slides for Machine Learning, Knowledge Discovery, and Data Mining: An Introduction. By Gregory Piatetsky-Shapiro.
29 Networks
30 What are some examples of communications networks? Public Telephone Network Internet LANs (Local Area Networks)
31 What does a network do? Transport data from one host to another Host allocation Routing Millions of users/applications/hosts share the same network Resource sharing Congestion control
32 Routing in the Internet Host A Host C Host B Many feasible paths from source to destination.
33 Internet Routing is Hierarchical Backbone or NSP: (MCI, ATT) Autonomous System (AS) ISP or IAP (CRUZIO, AOL) ISP AS AS Customer AS
34 Network Service Provider Network Access Point Network Access Point
35 Akamai Case Matthew Payne
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