图书简介

1 The Compelling Need for Data Warehousing; Learning Objective; Case Study; 1.1 A Short Historical Note; 1.2 Need for Data Warehousing; 1.2.1 Increasing Demand for Strategic Information; 1.2.2 The Information Crisis; 1.2.3 Inability of Past Decision Support System; 1.2.4 Presence of Better Technology; 1.2.5 Expectations from the New Kind of Decision Support System; 1.2.6 Operational Vs Decisional Support System; 1.3 Data Warehouse Defined; 1.3.1 What can a Data Warehouse Do?; 1.3.2 What Data Warehouse cannot do?; 1.3.3 What is a Data Warehouse- an Environment or a Product?; 1.3.4 A Blend of Many Technologies; 1.4 Data Warehouse Users; 1.4.1 Why do they want Information?; 1.5 Benefits of Data Warehousing; 1.5.1 Tangible Benefits; 1.6 Concerns in Data Warehousing; 1.6.1 Nothing is for free; Summary; Review Questions; 2 Data Warehouse: Defining Features; Learning Objectives; Case Study; 2.1 Introduction; 2.2 Features of a Data Warehouse; 2.2.1 Subject Oriented Data; 2.2.2 Integrated Data; 2.2.2.1 Data Cleansing; 2.2.2.2 Data Transformation; 2.2.2.3 Non-Volatile Data; 2.2.2.4 Time Variant Data; 2.3 Data Granularity; 2.3.1 Benefits of Data Granularity; 2.3.2 Data granularity - Pros and Cons; 2.3.3 Dual Levels of Data Granularity; 2.4 The Information Flow Mechanism; 2.5 Metadata; 2.5.1 Role of Metadata; 2.5.2 Classification of Metadata; 2.5.3 Metadata is the Nerve Centre of the Data Warehouse; 2.5.4 Metadata Management; 2.6 Two Classes of Data; 2.7 Life Cycle of Data; 2.7.1 What is Data Velocity?; 2.7.2 Moving Data from One Medium to Another; 2.7.3 Inverted Data Warehouse; 2.8 Can Data Move from Data Warehouse to the Operational Systems?; 2.8.1 Direct Access Mode; 2.8.2 Indirect Access Mode; Summary; Review Questions; 3 Physical Architecture of a Data Warehouse and Data Mart Issues; Learning Objectives; Case Study; 3.1 Introduction; 3.2 Distinguishing Characteristics of Data Warehouse Architecture; 3.3 Data Warehouse Architectural Goals; 3.4 Data Warehouse Architecture; 3.4.1 Pros and Cons of Data Warehouse Architecture; 3.4.2 The Two Tier Architecture; 3.4.3 The Three Tier Architecture; 3.4.4 The Four Tier Architecture; 3.4.5 Three Tier Versus Two Tier Architecture; 3.4.6 Architecture Considerations and Challenges; 3.4.7 Interfacing; 3.5 Data Warehouse and Data Marts; 3.6 Issues in Building Data Marts; 3.6.1 A Change of Approaches; 3.6.2 How Are Data Warehouse Different From Data Marts; 3.6.3 Reasons for Creating Data Marts; 3.6.4 Advantages of Building a Data Mart; 3.6.5 Limitations of Building a Data Mart; 3.7 Building Data Marts; 3.8 Other Data Mart Issues; 3.8.1 Types of Data Marts Based on Underlying DBMS; 3.8.2 Loading of Data Marts; 3.8.2.1 The Types of Data Marts to Load; 3.8.2.2 Loading Temporal Data Marts; 3.8.2.3 Loading of Non- Temporal Data Marts; 3.8.3 Metadata for a Data Mart; 3.8.4 Maintenance of a Data mart; 3.8.5 Nature of data in a Data Mart; 3.8.6 Software Components of a Data Mart; 3.8.7 Performance Issues; 3.8.8 Monitoring Requirements for a Data Mart; 3.8.9 Security In A Data Mart; 3.8.10 Structure of a Data Mart; 3.9 Reasons for Increased Popularity of Data Marts; 3.10 Can We Have the Data Warehouse and Data Marts on the Same Processor?; 3.11 Pushing and Pulling Data; Summary; Review Questions; 4 Gathering the Business Requirements; Learning Objective; Case Study; 4.1 Introduction; 4.2 Determining the End User Requirements; 4.2.1 Business Objectives; 4.2.2 Business Queries; 4.2.3 Determining the Functional Requirements; 4.2.4 Information Infrastructure Environment; 4.2.5 The Data Quality Levels; 4.3 Requirements Gathering Methods; 4.3.1 Interviews; 4.3.2 JAD Methodology; 4.3.3 Review of Existing Documentation; 4.3.4 Brainstorming; 4.3.5 Questionnaires; 4.3.6 Where to Stop?; 4.4 Requirements Analysis; 4.4.1 Requirements Definition Document; 4.5 Gathering Requirements for a Data Warehouse Project; 4.6 Dimensional Analysis; 4.6.1 Business Dimensions; 4.6.2 Dimension Hierarchies/Categories; 4.6.3 Facts or Metrics; 4.6.4 Example; 4.7 Information Package Diagram; 4.7.1 What Information does an IPD contain?; 4.7.2 Example; 4.7.3 Reason for Forming IPD; Summary; Review questions; 5 Planning and Project Management In A Data Warehouse; Learning Objective; Case Study; 5.1 The Project Management Principles; 5.1.1 Key Considerations; 5.1.2 The Ideal Approach; 5.2 Data Warehouse Readiness Assessment; 5.2.1 Bad Performance Indicators; 5.2.2 Indications for a Successful Data Warehouse Project; 5.3 The Data Warehouse Project Team; 5.3.1 Key Roles; 5.3.2 User Involvement; 5.4 Planning for the Data Warehouse; 5.4.1 Gathering the Business Requirements; 5.4.2 Gaining Support for the Project; 5.5 The Data Warehouse Project Plan; 5.6 Economic Feasibility Analysis; 5.6.1 Costs and Benefits of the System; 5.6.2 Economic Feasibility Measures; 5.6.3 Justifying the New System; 5.7 Planning For a Data Warehouse Server; 5.7.1 SMP; 5.7.2 Clusters; 5.7.3 MMP; 5.7.4 ccNUMA; 5.8 Capacity Planning; 5.8.1 Estimating the Load; 5.8.2 Estimating the CPU Bandwidth; 5.8.3 Estimating the Memory; 5.8.4 Estimating the Disk; 5.9 Selecting the Operating System for the Data Warehouse; 5.10 Selecting the Database Software; 5.10.1 Difference between General DBMS and Data Warehouse DBMS; 5.10.2 How to Choose?; 5.11 Selection of Tools; 5.11.1 Information Delivery Tools; 5.11.1.1 The Tool Selection Technique; 5.11.1.2 Criteria for Selecting the Information Delivery Tool; 5.11.2 Query Tools; 5.11.3 Browser Tools; 5.11.4 Metadata Tools; 5.15.5 Data Quality Tools; Summary; Review Questions; 6 Data Warehouse Schema; 6.1 Introduction; 6.2 Building the Fact Tables and Dimension Tables; 6.2.1 The Traditional Approach; 6.3 Dimensional Modeling; 6.3.1 Data Warehouse Modeling Vs Operational Database Modeling; 6.3.2 Dimensional Model Vs ER Model; 6.3.3 The Need for Dimension Model; 6.3.4 Features of a Good Dimensional Model; 6.4 The Star Schema; 6.4.1 How Does a Query Execute?; 6.4.2 Example; 6.4.3 Pros and Cons of the Star Schema; 6.5 The Snowflake Schema; 6.5.1 The Technique; 6.5.2 Example; 6.5.3 Is Snowflaking Really Helpful?; 6.5.4 Pros and Cons of the Snowflake Schema; 6.6 Aggregate Tables; 6.6.1 Need for Building Aggregate Fact Tables; 6.6.2 Limitations of Aggregate Tables; 6.7 Fact Constellation Schema or Families of Star; 6.7.1 Pre-requisite for a Fact Constellation Schema; 6.7.2 Pros and Cons of Fact Constellation Schema; 6.8 Strengths of Dimensional Modeling; 6.9 Data Warehouse and the Data Model; Summary; Review Questions; 7 Fact Tables and Dimension Tables: Miscellaneous Issues; Learning Objective; Case Study; 7.1 Characteristics of a Dimension Table; 7.2 Characteristics of a Fact Table; 7.3 The Factless Fact Table; 7.4 Updates To Dimension Tables; 7.4.1 Slowly Changing Dimensions; 7.4.1.1 Type 1 Changes; 7.4.1.2 Type 2 Changes; 7.4.1.3 Type 3 Changes; 7.4.1.4 Example; 7.5 Cyclicity of Data - Wrinkle of Time; 7.6 Other Types of Dimension Tables; 7.6.1 Large Dimension Tables; 7.6.2 Rapidly Changing or Large Slowly Changing Dimensions; 7.6.3 Junk Dimensions; 7.7 Keys in the Data Warehouse Schema; 7.7.1 Primary Keys; 7.7.2 Surrogate Keys; 7.7.3 Foreign Keys; 7.8 Enhancing the Data Warehouse Performance; 7.8.1 Table Compression; 7.8.2 Parallel Execution; 7.8.3 Table Partitioning; 7.8.3.1 The Partitioning Technique; 7.8.3.2 Advantages of Partitioning; 7.8.4 Data Clustering; 7.8.5 Data Summarization; 7.8.6 Bypassing the Referential Integrity Checks; 7.8.7 Indexing the Data Warehouse; 7.9 Data Warehousing and the Technology; Summary; Review Questions; 8 THE ETL PROCESS; Learning Objective; Case Study; 8.1 Introduction; 8.1.1 Challenges in ETL Functions; 8.2 Data Extraction; 8.2.1 Identification of Data Sources; 8.2.2 Extracting Data for Data Warehouse Refreshing; 8.2.2.1 Immediate Data Extraction Technique; 8.2.2.2 Deferred Data Extraction Technique; 8.2.2.3 Evaluation of Extraction Techniques; 8.2.3 Managing Reference Tables in a Data Warehouse; 8.3 Data Transformation; 8.3.1 Tasks Involved in Data Transformation; 8.3.2 Role of Data Transformation Process; 8.4 Data Loading; 8.4.1 Techniques of Data Loading; 8.4.2 When should we go for Data Update rather than Data Refresh?; 8.4.3 Loading the Fact Tables and Dimension Tables; 8.5 Data Quality; 8.5.1 The Need for Data Quality; 8.5.2 Categories of Errors Which Effect data Quality; 8.5.2.1 Incomplete Errors; 8.5.2.2 Incorrect Errors; 8.5.2.3 Incomprehensibility Errors; 8.5.2.4 Inconsistency Errors; 8.5.3 Issues in Data Cleansing; 8.5.4 Conclusion about Data Quality; Summary; Review Questions; 9 Testing, Growth and Maintenance Of Data Warehouse; Learning Objective; Case Study; 9.1 Data Warehouse Design Review; 9.1.1 Contents of a Typical Design Review; 9.2 Developing the Data Warehouse Iteratively; 9.3 Testing; 9.3.1 Testing the Data Warehouse; 9.3.2 Developing the Test Plan; 9.3.3 Testing the Backup and Recovery Processes; 9.3.4 Testing the Data Warehouse Environment; 9.3.5 Testing the Database; 9.3.6 Logging of Test Results; 9.4 Monitoring the Data Warehouse; 9.4.1 Why Are Statistics Monitored?; 9.5 Tuning the Data Warehouse; 9.5.1 Tuning the Data Load; 9.5.2 Tuning Queries; 9.6 The Feedback Loop; Summary; Review Questions; 10 OLAP in the Data Warehouse; Learning Objective; Case Study; 10.1 Need for Online Analytical Processing; 10.1.1 Multi Dimensional Analysis; 10.1.2 Fast Access and Powerful Calculations; 10.2 OLAP; 10.2.1 OLAP Defined; 10.2.2 OLAP is a Data Warehouse Tool; 10.3 OLAP and Multidimensional Analysis; 10.3.1 The Multi-Dimensional Logical Data Model; 10.3.2 Multi Dimensional Model’s Users; 10.3.3 The Multi Dimensional Structure; 10.3.4 Multi- Dimensional Operations; 10.3.5 The Business Need; 10.4 OLAP Functions; 10.4.1 Dimensional Analysis; 10.4.2 Hypercubes; 10.4.3 OLAP Operations in Multidimensional Data Model; 10.5 OLAP Applications; 10.5.1 Integrating OLAP with GIS; 10.6 OLAP Models; 10.6.1 MOLAP; 10.6.2 ROLAP; 10.6.3 HOLAP; 10.6.4 DOLAP; 10.6.5 OLAP Survey; 10.6.6 OLAP Trends; 10.7 OLAP Design Considerations; 10.8 OLAP Tools and Products; 10.8.1 Report Scheduling and Sharing; 10.8.2 Ad hoc Reporting; 10.8.3 OLAP Customization; 10.8.4 The Human Angle; 10.9 Existing OLAP Tools; 10.9.1 Spreadsheet OLAP Clients; 10.9.2 Other OLAP Clients; 10.9.3 Embedded OLAP; 10.10 Data Design; 10.10 Administration and Performance; 10.11 OLAP Platforms; Summary; Review Questions; 11 Overview of Building and Maintaining A Data Warehouse; Learning Objective; Case Study; 11.1 Problem Definition; 11.2 Critical Success Factors; 11.3 Requirement Analysis; 11.4 Planning for the Data Warehouse; 11.4.1 Project Staff; 11.4.2 Project Plan; 11.4.3 Outsourcing Vs Custom Planning; 11.4.4 Detailed Project Plan; 11.5 Data Warehouse Design Stage; 11.5.1 Design the Dimensional Model; 11.5.2 Develop the Architecture; 11.5.3 Design for Update and Expansion; 11.5.4 Design the Relational Database and OLAP Cubes; 11.5.5 Decisions in Design; 11.5.6 Detail Design; 11.5.7 Other Design Considerations; 11.6 Building and Implementing Data Marts; 11.7 Building Data Warehouse; 11.7.1 Test and Deploy the System; 11.7.2 Transition to Production; 11.7.3 User Training and Support; 11.7.3.1 The Success Factors of a Training Program; 11.7.3.2 Issues in User Support; 11.8 Backup and Recovery; 11.9 Establish the Data Quality Framework; 11.9.1 Data Purification Process; 11.10 Security Issues in a Data Warehouse; 11.11 Operating the Data Warehouse; 11.11.1 Day-to-Day Operations of the Data Warehouse; 11.11.2 Administering the Data Warehouse; 11.11.3 Overnight Processing; 11.12 Recipe for a Successful Data Warehouse; 11.13 Data Warehouse Pitfalls; Summary; Review Questions; 12 Data Mining Basics; Learning Objective; Case Study; 12.1 Introduction; 12.1.1 What Is Data Mining; 12.1.2 Foundation of Data Mining; 12.1.3 An Analogy; 12.1.4 What Can Be Discovered; 12.1.5 What Type of Data Can Be Mined; 12.2 Architecture of Data Mining System; 12.3 The KDD Process; 12.4 Integrating Data Mining and the Data Warehouse; 12.4.1 KDD versus Data Mining; 12.4.2 DBMS versus Data Mining; 12.4.3 OLAP versus Data Mining; 12.5 Related Areas of Data Mining; 12.6 Data Mining Techniques; 12.6.1 Association Rule Mining; 12.6.2 Decision Tress; 12.6.3 Clustering Analysis; 12.6.4 Memory Based Reasoning; 12.6.5 Genetic Algorithm; 12.6.6 Neural networks; 12.6.7 Outlier Analysis; Summary; Review Questions; 13 Moving into Data Mining; Learning Objective; Case Study; 13.1 Introduction; 13.2 How Do We Categorize Data Mining System; 13.3 Is all that is Discovered Interesting and Useful; 13.4 Applications of Data Mining; 13.4.1 Benefits of Data Mining; 13.4.2 Data Mining For Retail Industry; 13.4.3 Data Mining For Telecommunication Industry; 13.4.4 Data Mining For Banking and Finance; 13.4.5 Data Mining For Biomedical and DNA Data Analysis; 13.4.6 Data Mining For Customer Retention; 13.4.7 Data Mining For Targeted Marketing; 13.4.8 Data Mining For Customer Relationship Management; 13.5 Other Data Mining Application Areas; 13.6 Advantages and Disadvantages of Data Mining; 13.7 Web Mining; 13.7.1 Web Content Mining; 13.7.2 Web Structure Mining; 13.7.3 Web Usage Mining; 13.8 Text Mining; 13.9 Temporal Data Mining; 13.10 Sequence Mining; 13.11 Time Series Analysis; 13.12 Spatial Data Mining; 13.13 Issues and Challenges in Data Mining; 13.14 Current Trends Affecting Data Mining; Summary; Review Questions; 14 Trends In Data Warehousing; Learning Objective; Case Study; 14.1 Introduction; 14.2 Data Warehouse Solutions; 14.2.1 Data Warehouse Implementation Alternatives; 14.2.2 Host-Based Data Warehouses; 14.2.2.1 Single host Based Data Warehouses; 14.2.2.2 Host Based Single Stage (LAN)-Based Data Warehouses; 14.2.3 LAN- Based Workgroup Data Warehouses; 14.2.4 Multistage Data Warehouses; 14.2.5 Stationary Data Warehouses; 14.3 Web Enabled Data Warehouse; 14.3.1 Using the Web for Information Delivery; 14.3.2 Expectations from the Web as an Information Delivery Medium; 14.3.3 Super Growth Problem; 14.3.4 Data Webhouse Prominent Features; 14.3.5 The Need for Data Webhouse; 14.3.6 The Data Webhouse Architecture; 14.3.7 Similarities with Traditional Data Warehouses; 14.3.8 Building Clickstream Data Webhouse; 14.3.9 The Granularity Manager; 14.3.10 Challenges in the Clickstream Data Webhouse Lifecycle; 14.4 Distributed Data Warehouses; 14.4.1 Advantages of Distributed Data Warehousing; 14.4.2 Distributed versus Centralized Warehouse; 14.5 The Virtual Data Warehouse; 14.5.1 Why to Go For a Virtual Data Warehouse; 14.5.2 Problems with a Virtual Data Warehouse; 14.5.3 Advantages of Using a Virtual Data Warehouse; 14.6 Data Warehouse and the ODS; 14.7 Integration of Data Warehousing with other Technologies; 14.7.1 Data Warehousing and ERP; 14.7.1.1 Integrating ERP and Data Warehouse; 14.7.1.2 Issues in integrating ERP with Data Warehousing; 14.7.1.3 Common Misconceptions about DW and ERP; 14.7.1.4 Conclusion; 14.7.2 Data Warehousing and Knowledge Management; 14.7.3 Data Warehousing and EIS; 14.7.3.1 Executive information System; 14.7.3.2 Data Warehouse as a Basis for EIS; 14.7.4 Data Warehousing and CRM; 14.7.4.1 Active Data Warehousing; 14.8 Trends in Data Warehousing; 14.8.1 Multiple Data Types; 14.8.2 Data Visualization; 14.8.3 Parallel Processing; 14.8.4 Agent Technology; 14.9 Data Warehouse Futures; Summary; Review Questions; Appendix; Glossary

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