Appendix B: Tables used in the examples
by Calisto Zuzarte, Virginia Totanes, Nicholas Summers, Paul Read, Daniela Momirosk
DB2 UDB's High-Function Business Intelligence in e-business
- Figures
- Tables
- Examples
- Notices
- Preface
- Chapter 1: Business Intelligence overview
- Chapter 2: DB2 UDB’s materialized views
- Materialized view overview
- Materialized view CREATE considerations
- Materialized view maintenance considerations
- Loading base tables (LOAD utility)
- Materialized view ALTER considerations
- Materialized view DROP considerations
- Materialized view matching considerations
- Materialized view design considerations
- Step 1: Collect queries & prioritize
- Step 2: Generalize local predicates to GROUP BY
- Step 3: Create the materialized view
- Step 4: Estimate materialized view size
- Step 5: Verify query routes to “empty” the materialized view
- Step 6: Consolidate materialized views
- Step 7: Introduce cost issues into materialized view routing
- Step 8: Estimate performance gains
- Step 9: Load the materialized views with production data
- Generalizing local predicates application example
- Materialized view tuning considerations
- Refresh optimization
- Materialized view limitations
- Replicated tables in nodegroups
- Chapter 3: DB2 UDB’s statistics, analytic, and OLAP functions
- DB2 UDB’s statistics, analytic, and OLAP functions
- Statistics and analytic functions
- OLAP functions
- Ranking, numbering and aggregation functions (1/2)
- Ranking, numbering and aggregation functions (2/2)
- GROUPING capabilities ROLLUP & CUBE
- Ranking, numbering, aggregation examples (1/3)
- Ranking, numbering, aggregation examples (2/3)
- Ranking, numbering, aggregation examples (3/3)
- GROUPING, GROUP BY, ROLLUP and CUBE examples (1/3)
- GROUPING, GROUP BY, ROLLUP and CUBE examples (2/3)
- GROUPING, GROUP BY, ROLLUP and CUBE examples (3/3)
- Chapter 4: Statistics, analytic, OLAP functions in business scenarios
- Introduction
- Retail
- Present annual sales by region and city
- Provide total quarterly and cumulative sales revenues by year
- List the top 5 sales persons by region this year
- Compare and rank the sales results by state and country
- Determine relationships between product purchases
- Determine the most profitable items and where they are sold
- Identify store sales revenues noticeably different from average
- Finance
- Identify the most profitable customers
- Identify the profile of transactions concluded recently
- Identify target groups for a campaign
- Evaluate effectiveness of a marketing campaign (1/2)
- Evaluate effectiveness of a marketing campaign (2/2)
- Identify potential fraud situations for investigation
- Plot monthly stock prices movement with percentage change
- Plot the average weekly stock price in September
- Project growth rates of Web hits for capacity planning purposes
- Relate sales revenues to advertising budget expenditures
- Sports
- Appendix A: Introduction to statistics and analytic concepts
- Appendix B: Tables used in the examples
- Appendix C: Materialized view syntax elements
- Related publications
- Index (1/2)
- Index (2/2)
- Back cover
© Copyright IBM Corp. 2002 235
Appendix B. Tables used in the examples
In this appendix we describe the DDL of the tables used in the examples
provided throughout this redbook.
B
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