Cover -- Preface -- Acknowledgements -- Contents -- Volume I Data Warehousing Implementation and Olap -- Part I: Introduction -- Chapter 1 The Enterprise IT Architecture -- 1.1 The Past: Evolution of Enterprise Architectures -- 1.2 The Present: The IT Professional's Responsibility -- 1.3 Business Perspective -- 1.4 Technology Perspective -- 1.5 Architecture Migration Scenarios -- 1.6 Migration Strategy: How do We Move Forward? -- Chapter 2 Data Warehouse Concepts -- 2.1 Gradual Changes in Computing Focus -- 2.2 Data Warehouse Characteristics and Definition -- 2.3 The Dynamic, Ad Hoc Report -- 2.4 The Purposes of a Data Warehouse -- 2.5 Data Marts -- 2.6 Operational Data Stores -- 2.7 Data Warehouse Cost-Benefit Analysis/Return on Investment -- Part II: People -- Chapter 3 The Project Sponsor -- 3.1 How Does a Data Warehouse Affect Decision-Making Processes? -- 3.2 How Does a Data Warehouse Improve Financial Processes?Marketing? Operations? -- 3.3 When is a Data Warehouse Project Justified? -- 3.4 What Expenses Are Involved? -- 3.5 What are the Risks? -- 3.6 Risk-Mitigating Approaches -- 3.7 Is the Organization Ready For a Data Warehouse? -- 3.8 How The Results Are Measured? -- Chapte 4 The CIO -- 4.1 How is The Data Warehouse Supported? -- 4.2 How Does Data Warehouse Evolve? -- 4.3 Who Should be Involved in a Data Warehouse Project? -- 4.4 What is the Team Structure Like? -- 4.5 What New Skills Will People Need? -- 4.6 How Does Data Warehousing Fit Into it Architecture? -- 4.7 How Many Vendors Are Needed to Talk to? -- 4.8 What Should Be Looked For in a Data Warehouse Vendor? -- 4.9 How Does Data Warehousing Affect Existing Systems? -- 4.10 Data Warehousing and Its Impact on Other Enterprise Initiatives -- 4.11 When is a Data Warehouse Not Appropriate? -- 4.12 How To Manage or Control A Data Warehouse Initiative? -- Chapter 5 The Project Manager.

5.1 How to Roll Out a Data Warehouse Initiative? -- 5.2 How Important is the Hardware Platform? -- 5.3 What are the Technologies Involved? -- 5.4 Are the Relational Databases Still Used for Data Warehousing? -- 5.5 How Long Does A Data Warehousing Project Last? -- 5.6 How is a Data Warehouse Different From Other IT Projects? -- 5.7 What are the Critical Success Factors of a Data Warehousing Project? -- Part III: Process -- Chapter 6 Warehousing Strategy -- 6.1 Strategy Components -- 6.2 Determine Organizational Context -- 6.3 Conduct Preliminary Survey of Requirements -- 6.4 Conduct Preliminary Source System Audit -- 6.5 Identify External Data Sources (If Applicable) -- 6.6 Define Warehouse Rollouts (Phased Implementation) -- 6.7 Define Preliminary Data Warehouse Architecture -- 6.8 Evaluate Development and Production Environment and Tools -- Chapter 7 Warehouse Management and Support Processes -- 7.1 Define Issue Tracking and Resolution Process -- 7.2 Perform Capacity Planning -- 7.3 Define Warehouse Purging Rules -- 7.4 Define Security Management -- 7.5 Define Backup and Recovery Strategy -- 7.6 Set up Collection of Warehouse Usage Statistics -- Chapter 8 Data Warehouse Planning -- 8.1 Assemble and Orient Team -- 8.2 Conduct Decisional Requirements Analysis -- 8.3 Conduct Decisional Source System Audit -- 8.4 Design Logical and Physical Warehouse Schema -- 8.5 Produce Source-To-Target Field Mapping -- 8.6 Select Development and Production Environment and Tools -- 8.7 Create Prototype for this Rollout -- 8.8 Create Implementation Plan of this Rollout -- 8.9 Warehouse Planning Tips and Caveats -- Chapter 9 Date Warehouse Implementation -- 9.1 Acquire and Set up Development Environment -- 9.2 Obtain Copies of Operational Tables -- 9.3 Finalize Physical Warehouse Schema Design -- 9.4 Build or Configure Extraction and Transformation Subsystems.

9.5 Build or Configure Data Quality Subsystem -- 9.6 Build Warehouse Load Subsystem -- 9.7 Set up Warehouse Metadata -- 9.8 Set Up Data Access and Retrieval Tools -- 9.9 Perform the Production Warehouse Load -- 9.10 Conduct User Training -- 9.11 Conduct User Testing and Acceptance -- Part IV: Technology -- Chapter 10 Hardware and Operating Systems -- 10.1 Parallel Hardware Technology -- 10.2 The Data Partitioning Issue -- 10.3 Hardware Selection Criteria -- Chapter 11 Warehousing Software -- 11.1 Middleware and Connectivity Tools -- 11.2 Extraction Tools -- 11.3 Transformation Tools -- 11.4 Data Quality Tools -- 11.5 Data Loaders -- 11.6 Database Management Systems -- 11.7 Metadata Repository -- 11.8 Data Access and Retrieval Tools -- 11.9 Data Modeling Tools -- 11.10 Warehouse Management Tools -- 11.11 Source Systems -- Chapter 12 Warehouse Schema Design -- 12.1 OLTP Systems Use Normalized Data Structures -- 12.2 Dimensional Modeling for Decisional Systems -- 12.3 Star Schema -- 12.4 Dimensional Hierarchies and Hierarchical Drilling -- 12.5 The Granularity of the Fact Table -- 12.6 Aggregates or Summaries -- 12.7 Dimensional Attributes -- 12.8 Multiple Star Schemas -- 12.9 Advantages of Dimensional Modeling -- Chapter 13 Warehouse Metadata -- 13.1 Metadata Defined -- 13.2 Metadata are a Form of Abstraction -- 13.3 Importance of Metadata -- 13.4 Types of Metadata -- 13.5 Metadata Management -- 13.6 Metadata As the Basis for Automating Warehousing Tasks -- 13.7 Metadata Trends -- Chapter 14 Warehousing Applications -- 14.1 The Early Adopters -- 14.2 Types of Warehousing Applications -- 14.3 Financial Analysis and Management -- 14.4 Specialized Applications of Warehousing Technology -- Part V: Maintenance Evolution and Trends -- Chapter 15 Warehouse Maintenance and Evolution -- 15.1 Regular Warehouse Loads -- 15.2 Warehouse Statistics Collection.

15.3 Warehouse User Profiles -- 15.4 Security and Access Profiles -- 15.5 Data Quality -- 15.6 Data Growth -- 15.7 Updates to Warehouse Subsystems -- 15.8 Database Optimization and Tuning -- 15.9 Data Warehouse Staffing -- 15.10 Warehouse Staff and User Training -- 15.11 Subsequent Warehouse Rollouts -- 15.12 Chargeback Schemes -- 15.13 Disaster Recovery -- Chapter 16 Warehousing Trends -- 16.1 Continued Growth of the Data Warehouse Industry -- 16.2 Increased Adoption of Warehousing Technology by More Industries -- 16.3 Increased Maturity of Data Mining Technologies -- 16.4 Emergence and Use of Metadata Interchange Standards -- 16.5 Increased Availability of Web-Enabled Solutions -- 16.6 Popularity of Windows Nt for Data Mart Projects -- 16.7 Availability of Warehousing Modules for Application Packages -- 16.8 More Mergers and Acquisitions Among Warehouse Players -- Part VI: On-Line Analytical Processing -- Chapter 17 Introduction -- 17.1 What is OLAP? -- 17.2 The Codd Rules and Features -- 17.3 The Origins of Today's OLAP Products -- 17.4 What's in a Name? -- 17.5 Market Analysis -- 17.6 OLAP Architectures -- 17.7 Dimensional Data Structures -- Chapter 18 Olap Applications -- 18.1 Marketing and Sales Analysis -- 18.2 Clickstream Analysis -- 18.3 Database Marketing -- 18.4 Budgeting -- 18.5 Financial Reporting and Consolidation -- 18.6 Management Reporting -- 18.7 EIS -- 18.8 Balanced Scorecard -- 18.9 Profitability Analysis -- 18.10 Quality Analysis -- Volume II Data Mining -- Chapter 1: Introduction -- 1.1 What is Data Mining? -- 1.2 Definitions -- 1.3 Data Mining Process -- 1.4 Data Mining Background -- 1.5 Data Mining Models -- 1.6 Data Mining Methods -- 1.7 DATA MINING PROBLEMS/ISSUES -- 1.8 Potential Applications -- 1.9 Data Mining Examples -- Chapter 2: Data Mining with Decision Trees -- 2.1 How A Decision Tree Works.

2.2 Constructing Decision Trees -- 2.3 Issues in Data Mining With Decision Trees -- 2.4 Visualization of Decision Trees in System CABRO -- 2.5 Strengths and Weakness of Decision Tree Methods -- Chapter 3: Data Mining With Association Rules -- 3.1 When is Association Rule Analysis Useful? -- 3.2 How Does Association Rule Analysis Work? -- 3.3 The Basic Process of Mining Association Rules -- 3.4 The Problem of Large Datasets -- 3.5 Strengths and Weaknesses of Association Rules Analysis -- Chapter 4: Automatic Clustering Detection -- 4.1 Searching for Clusters -- 4.2 The K-Means Method -- 4.3 Agglomerative Methods -- 4.4 Evaluating Clusters -- 4.5 Other Approaches to Cluster Detection -- 4.6 Strengths and Weaknesses of Automatic Cluster Detection -- Chapter 5: Data Mining With Neural Networks -- 5.1 Neural Networks for Data Mining -- 5.2 Neural Network Topologies -- 5.3 Neural Network Models -- 5.4 Iterative Development Process -- 5.5 Strengths and Weaknesses of Artificial Neural Networks.