Front cover -- Contents -- Figures -- Tables -- Examples -- Notices -- Trademarks -- Preface -- The team that wrote this redbook -- Notice -- Comments welcome -- Chapter 1. Business Intelligence overview -- 1.1 e-business drivers -- 1.1.1 Impact of e-business -- 1.1.2 Importance of BI -- 1.2 IBM's BI strategy and offerings -- 1.2.1 BI and analytic enhancements in DB2 UDB -- 1.2.2 Advantages of BI functionality in the database engine -- 1.3 Redbook focus -- 1.3.1 Materialized views -- 1.3.2 Statistics, analytic and OLAP functions -- Chapter 2. DB2 UDB's materialized views -- 2.1 Materialized view overview -- 2.1.1 Materialized view motivation -- 2.1.2 Materialized view concept overview -- 2.1.3 Materialized view usage considerations -- 2.1.4 Materialized view terminology -- 2.2 Materialized view CREATE considerations -- 2.2.1 Step 1: Create the materialized view -- 2.2.2 Step 2: Populate the materialized view -- 2.2.3 Step 3: Tune the materialized view -- 2.3 Materialized view maintenance considerations -- 2.3.1 Deferred refresh -- 2.3.2 Immediate refresh -- 2.4 Loading base tables (LOAD utility) -- 2.5 Materialized view ALTER considerations -- 2.6 Materialized view DROP considerations -- 2.7 Materialized view matching considerations -- 2.7.1 State considerations -- 2.7.2 Matching criteria considerations -- 2.7.3 Matching permitted -- 2.7.4 Matching inhibited -- 2.8 Materialized view design considerations -- 2.8.1 Step 1: Collect queries & prioritize -- 2.8.2 Step 2: Generalize local predicates to GROUP BY -- 2.8.3 Step 3: Create the materialized view -- 2.8.4 Step 4: Estimate materialized view size -- 2.8.5 Step 5: Verify query routes to "empty" the materialized view -- 2.8.6 Step 6: Consolidate materialized views -- 2.8.7 Step 7: Introduce cost issues into materialized view routing -- 2.8.8 Step 8: Estimate performance gains.

2.8.9 Step 9: Load the materialized views with production data -- 2.8.10 Generalizing local predicates application example -- 2.9 Materialized view tuning considerations -- 2.10 Refresh optimization -- 2.11 Materialized view limitations -- 2.11.1 REFRESH DEFERRED and REFRESH IMMEDIATE -- 2.11.2 REFRESH IMMEDIATE and queries with staging table -- 2.12 Replicated tables in nodegroups -- Chapter 3. DB2 UDB's statistics, analytic, and OLAP functions -- 3.1 DB2 UDB's statistics, analytic, and OLAP functions -- 3.2 Statistics and analytic functions -- 3.2.1 AVG -- 3.2.2 CORRELATION -- 3.2.3 COUNT -- 3.2.4 COUNT_BIG -- 3.2.5 COVARIANCE -- 3.2.6 MAX -- 3.2.7 MIN -- 3.2.8 RAND -- 3.2.9 STDDEV -- 3.2.10 SUM -- 3.2.11 VARIANCE -- 3.2.12 Regression functions -- 3.2.13 COVAR, CORR, VAR, STDDEV, and regression examples -- 3.3 OLAP functions -- 3.3.1 Ranking, numbering and aggregation functions -- 3.3.2 GROUPING capabilities ROLLUP & CUBE -- 3.3.3 Ranking, numbering, aggregation examples -- 3.3.4 GROUPING, GROUP BY, ROLLUP and CUBE examples -- Chapter 4. Statistics, analytic, OLAP functions in business scenarios -- 4.1 Introduction -- 4.1.1 Using sample data -- 4.1.2 Sampling and aggregation example -- 4.2 Retail -- 4.2.1 Present annual sales by region and city -- 4.2.2 Provide total quarterly and cumulative sales revenues by year -- 4.2.3 List the top 5 sales persons by region this year -- 4.2.4 Compare and rank the sales results by state and country -- 4.2.5 Determine relationships between product purchases -- 4.2.6 Determine the most profitable items and where they are sold -- 4.2.7 Identify store sales revenues noticeably different from average -- 4.3 Finance -- 4.3.1 Identify the most profitable customers -- 4.3.2 Identify the profile of transactions concluded recently -- 4.3.3 Identify target groups for a campaign.

4.3.4 Evaluate effectiveness of a marketing campaign -- 4.3.5 Identify potential fraud situations for investigation -- 4.3.6 Plot monthly stock prices movement with percentage change -- 4.3.7 Plot the average weekly stock price in September -- 4.3.8 Project growth rates of Web hits for capacity planning purposes -- 4.3.9 Relate sales revenues to advertising budget expenditures -- 4.4 Sports -- 4.4.1 For a given sporting event -- 4.4.2 Seed the players at Wimbledon -- Appendix A. Introduction to statistics and analytic concepts -- A.1 Statistics and analytic concepts -- A.1.1 Variance -- A.1.2 Standard deviation -- A.1.3 Covariance -- A.1.4 Correlation -- A.1.5 Regression -- A.1.6 Hypothesis testing -- A.1.7 HAT diagonal -- A.1.8 Wilcoxon rank sum test -- A.1.9 Chi-Squared test -- A.1.10 Interpolation -- A.1.11 Extrapolation -- A.1.12 Probability -- A.1.13 Sampling -- A.1.14 Transposition -- A.1.15 Histograms -- Appendix B. Tables used in the examples -- DDL of tables -- Appendix C. Materialized view syntax elements -- Materialized view main syntax elements -- Related publications -- IBM Redbooks -- Other resources -- Referenced Web sites -- How to get IBM Redbooks -- IBM Redbooks collections -- Index -- Back cover.