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11/2/2000Database Management -- R. Larson Data Warehouses, Decision Support and Data Mining University of California, Berkeley School of Information Management.

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Presentation on theme: "11/2/2000Database Management -- R. Larson Data Warehouses, Decision Support and Data Mining University of California, Berkeley School of Information Management."— Presentation transcript:

1 11/2/2000Database Management -- R. Larson Data Warehouses, Decision Support and Data Mining University of California, Berkeley School of Information Management and Systems SIMS 257: Database Management

2 11/2/2000Database Management -- R. Larson Review Data Warehousing

3 11/2/2000Database Management -- R. Larson ORACLE Setup and Queries Things should be set up for everyone –If not, let me know. You need to include the line: –source /usr/local/skel/local.oracle –In your.cshrc file in your home directory. Refer to the diveshop tables as ray.diveords, etc.

4 11/2/2000Database Management -- R. Larson Problem: Heterogeneous Information Sources “Heterogeneities are everywhere” p Different interfaces p Different data representations p Duplicate and inconsistent information Personal Databases Digital Libraries Scientific Databases World Wide Web Slide credit: J. Hammer

5 11/2/2000Database Management -- R. Larson Problem: Data Management in Large Enterprises Vertical fragmentation of informational systems (vertical stove pipes) Result of application (user)-driven development of operational systems Sales AdministrationFinanceManufacturing... Sales Planning Stock Mngmt... Suppliers... Debt Mngmt Num. Control... Inventory Slide credit: J. Hammer

6 11/2/2000Database Management -- R. Larson Goal: Unified Access to Data Integration System Collects and combines information Provides integrated view, uniform user interface Supports sharing World Wide Web Digital LibrariesScientific Databases Personal Databases Slide credit: J. Hammer

7 11/2/2000Database Management -- R. Larson The Traditional Research Approach Source... Integration System... Metadata Clients Wrapper Query-driven (lazy, on-demand) Slide credit: J. Hammer

8 11/2/2000Database Management -- R. Larson The Warehousing Approach DataWarehouse Clients Source... Extractor/ Monitor Integration System... Metadata Extractor/ Monitor Extractor/ Monitor Information integrated in advance Stored in WH for direct querying and analysis Slide credit: J. Hammer

9 11/2/2000Database Management -- R. Larson What is a Data Warehouse? “A Data Warehouse is a –subject-oriented, –integrated, –time-variant, –non-volatile collection of data used in support of management decision making processes.” -- Inmon & Hackathorn, 1994: viz. McFadden, Chap 14

10 11/2/2000Database Management -- R. Larson A Data Warehouse is... Stored collection of diverse data –A solution to data integration problem –Single repository of information Subject-oriented –Organized by subject, not by application –Used for analysis, data mining, etc. Optimized differently from transaction- oriented db User interface aimed at executive decision makers and analysts

11 11/2/2000Database Management -- R. Larson … Cont’d Large volume of data (Gb, Tb) Non-volatile –Historical –Time attributes are important Updates infrequent May be append-only Examples –All transactions ever at WalMart –Complete client histories at insurance firm –Stockbroker financial information and portfolios Slide credit: J. Hammer

12 11/2/2000Database Management -- R. Larson Data Warehousing Architecture

13 11/2/2000Database Management -- R. Larson “Ingest” DataWarehouse Clients Source/ FileSource / ExternalSource / DB... Extractor/ Monitor Integration System... Metadata Extractor/ Monitor Extractor/ Monitor

14 11/2/2000Database Management -- R. Larson Today Applications for Data Warehouses –Decision Support Systems (DSS) –OLAP (ROLAP, MOLAP) –Data Mining Thanks again to lecture notes from Joachim Hammer of the University of Florida

15 11/2/2000Database Management -- R. Larson What is Decision Support? Technology that will help managers and planners make decisions regarding the organization and its operations based on data in the Data Warehouse. –What was the last two years of sales volume for each product by state and city? –What effects will a 5% price discount have on our future income for product X?

16 11/2/2000Database Management -- R. Larson Conventional Query Tools Ad-hoc queries and reports using conventional database tools –E.g. Access queries. Typical database designs include fixed sets of reports and queries to support them –The end-user is often not given the ability to do ad-hoc queries

17 11/2/2000Database Management -- R. Larson OLAP Online Line Analytical Processing –Intended to provide multidimensional views of the data –I.e., the “Data Cube” –The PivotTables in MS Excel are examples of OLAP tools

18 11/2/2000Database Management -- R. Larson Data Cube

19 11/2/2000Database Management -- R. Larson Operations on Data Cubes Slicing the cube –Extracts a 2d table from the multidimensional data cube –Example… Drill-Down –Analyzing a given set of data at a finer level of detail

20 11/2/2000Database Management -- R. Larson Data Mining Data mining is knowledge discovery rather than question answering –May have no pre-formulated questions –Derived from Traditional Statistics Artificial intelligence Computer graphics (visualization)

21 11/2/2000Database Management -- R. Larson Goals of Data Mining Explanatory –Explain some observed event or situation Why have the sales of SUVs increased in California but not in Oregon? Confirmatory –To confirm a hypothesis Whether 2-income families are more likely to buy family medical coverage Exploratory –To analyze data for new or unexpected relationships What spending patterns seem to indicate credit card fraud?

22 11/2/2000Database Management -- R. Larson Data Mining Applications Profiling Populations Analysis of business trends Target marketing Usage Analysis Campaign effectiveness Product affinity

23 11/2/2000Database Management -- R. Larson Data Mining Algorithms Market Basket Analysis Memory-based reasoning Cluster detection Link analysis Decision trees and rule induction algorithms Neural Networks Genetic algorithms

24 11/2/2000Database Management -- R. Larson Market Basket Analysis Memory-based reasoning Cluster detection Link analysis Decision trees and rule induction algorithms Neural Networks Genetic algorithms

25 11/2/2000Database Management -- R. Larson Market Basket Analysis Memory-based reasoning Cluster detection Link analysis Decision trees and rule induction algorithms Neural Networks Genetic algorithms

26 11/2/2000Database Management -- R. Larson Market Basket Analysis A type of clustering used to predict purchase patterns. Identify the products likely to be purchased in conjunction with other products –E.g., the famous (and apocryphal) story that men who buy diapers on Friday nights also buy beer.

27 11/2/2000Database Management -- R. Larson Memory-based reasoning Use known instances of a model to make predictions about unknown instances. Could be used for sales forcasting or fraud detection by working from known cases to predict new cases

28 11/2/2000Database Management -- R. Larson Cluster detection Finds data records that are similar to each other. K-nearest neighbors (where K represents the mathematical distance to the nearest similar record) is an example of one clustering algorithm

29 11/2/2000Database Management -- R. Larson Link analysis Follows relationships between records to discover patterns Link analysis can provide the basis for various affinity marketing programs Similar to Markov transition analysis methods where probabilities are calculated for each observed transition.

30 11/2/2000Database Management -- R. Larson Decision trees and rule induction algorithms Pulls rules out of a mass of data using classification and regression trees (CART) or Chi-Square automatic interaction detectors (CHAID) These algorithms produce explicit rules, which make understanding the results simpler

31 11/2/2000Database Management -- R. Larson Neural Networks Attempt to model neurons in the brain Learn from a training set and then can be used to detect patterns inherent in that training set Neural nets are effective when the data is shapeless and lacking any apparent patterns May be hard to understand results

32 11/2/2000Database Management -- R. Larson Genetic algorithms Imitate natural selection processes to evolve models using –Selection –Crossover –Mutation Each new generation inherits traits from the previous ones until only the most predictive survive.

33 11/2/2000Database Management -- R. Larson More on ORACLE WebDB Next Time…?

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