DB2 UDB's high function business intelligence in e-business

DB2 UDB's high function business intelligence in e-business

This IBM Redbooks publication deals with exploiting DB2 UDB’s materialized views (also known as ASTs/MQTs), statistics, analytic, and OLAP functions in e-business applications to achieve superior performance and scalability. This book is aimed at a target audience of DB2 UDB application developers,...

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Detalles Bibliográficos
Otros Autores: Alur, Nagraj (-)
Formato: Libro electrónico
Idioma:Inglés
Publicado: San Jose, Calif. : IBM Corp 2002.
Edición:1st ed
Colección:IBM redbooks.
Materias:
IBM Database 2.
Electronic commerce.
Business intelligence.
Database management.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009627509206719
Tabla de Contenidos:
  • 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 &amp
  • 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 &amp
  • 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.

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