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Data Warehousing - OLAP and Data Mining






CONTENTS
Preface (vii)
Acknowledgements (ix)
VOLUME I: DATA WAREHOUSING
IMPLEMENTATION AND OLAP
PART I : INTRODUCTION
Chapter 1. The Enterprise IT Architecture 5
1.1 The Past: Evolution of Enterprise Architectures 5
1.2 The Present: The IT Professional’s Responsibility 6
1.3 Business Perspective 7
1.4 Technology Perspective 8
1.5 Architecture Migration Scenarios 12
1.6 Migration Strategy: How do We Move Forward? 20
Chapter 2. Data Warehouse Concepts 24
2.1 Gradual Changes in Computing Focus 24
2.2 Data Warehouse Characteristics and Definition` 26
2.3 The Dynamic, Ad Hoc Report 28
2.4 The Purposes of a Data Warehouse 29
2.5 Data Marts 30
2.6 Operational Data Stores 33
2.7 Data Warehouse Cost-Benefit Analysis / Return on Investment 35
PART II : PEOPLE
Chapter 3. The Project Sponsor 39
3.1 How does a Data Warehouse Affect Decision-Making Processes? 39
3.2 How does a Data Warehouse Improve Financial Processes? Marketing?
Operations? 40
3.3 When is a Data Warehouse Project Justified? 41
3.4 What Expenses are Involved? 43
3.5 What are the Risks? 45
3.6 Risk-Mitigating Approaches 50
3.7 Is Organization Ready for a Data Warehouse? 51
3.8 How the Results are Measured? 51
Chapter 4. The CIO 54
4.1 How is the Data Warehouse Supported? 54
4.2 How Does Data Warehouse Evolve? 55
4.3 Who should be Involved in a Data Warehouse Project? 56
4.4 What is the Team Structure Like? 60
4.5 What New Skills will People Need? 60
4.6 How Does Data Warehousing Fit into IT Architecture? 62
4.7 How Many Vendors are Needed to Talk to? 63
4.8 What should be Looked for in a Data Warehouse Vendor? 64
4.9 How Does Data Warehousing Affect Existing Systems? 67
4.10 Data Warehousing and its Impact on Other Enterprise Initiatives 68
4.11 When is a Data Warehouse not Appropriate? 69
4.12 How to Manage or Control a Data Warehouse Initiative? 71
Chapter 5. The Project Manager 73
5.1 How to Roll Out a Data Warehouse Initiative? 73
5.2 How Imprtant is the Hardware Platform? 76
5.3 What are the Technologies Involved? 78
5.4 Are the Relational Databases Still Used for Data Warehousing? 79
5.5 How Long Does a Data Warehousing Project Last? 83
5.6 How is a Data Warehouse Different from Other IT Projects? 84
5.7 What are the Critical Success Factors of a Data Warehousing 85
Project?
PART III : PROCESS
Chapter 6. Warehousing Strategy 89
6.1 Strategy Components 89
6.2 Determine Organizational Context 90
6.3 Conduct Preliminary Survey of Requirements 90
6.4 Conduct Preliminary Source System Audit 92
6.5 Identify External Data Sources (If Applicable) 93
6.6 Define Warehouse Rollouts (Phased Implementation) 93
6.7 Define Preliminary Data Warehouse Architecture 94
6.8 Evaluate Development and Production Environment and Tools 95
Chapter 7. Warehouse Management and Support Processes 96
7.1 Define Issue Tracking and Resolution Process 96
7.2 Perform Capacity Planning 98
7.3 Define Warehouse Purging Rules 108
7.4 Define Security Management 108
7.5 Define Backup and Recovery Strategy 111
7.6 Set Up Collection of Warehouse Usage Statistics 112
Chapter 8. Data Warehouse Planning 114
8.1 Assemble and Orient Team 114
8.2 Conduct Decisional Requirements Analysis 115
8.3 Conduct Decisional Source System Audit 116
8.4 Design Logical and Physical Warehouse Schema 119
8.5 Produce Source-to-Target Field Mapping 119
8.6 Select Development and Production Environment and Tools 121
8.7 Create Prototype for this Rollout 121
8.8 Create Implementation Plan of this Rollout 122
8.9 Warehouse Planning Tips and Caveats 124
Chapter 9. Data Warehouse Implementation 128
9.1 Acquire and Set Up Development Environment 128
9.2 Obtain Copies of Operational Tables 129
9.3 Finalize Physical Warehouse Schema Design 129
9.4 Build or Configure Extraction and Transformation Subsystems 130
9.5 Build or Configure Data Quality Subsystem 131
9.6 Build Warehouse Load Subsystem 135
9.7 Set Up Warehouse Metadata 138
9.8 Set Up Data Access and Retrieval Tools 138
9.9 Perform the Production Warehouse Load 140
9.10 Conduct User Training 140
9.11 Conduct User Testing and Acceptance 141
PART IV : TECHNOLOGY
Chapter 10. Hardware and Operating Systems 145
10.1 Parallel Hardware Technology 145
10.2 The Data Partitioning Issue 148
10.3 Hardware Selection Criteria 152
Chapter 11. Warehousing Software 154
11.1 Middleware and Connectivity Tools 155
11.2 Extraction Tools 155
11.3 Transformation Tools 156
11.4 Data Quality Tools 158
11.5 Data Loaders 158
11.6 Database Management Systems 159
11.7 Metadata Repository 159
11.8 Data Access and Retrieval Tools 160
11.9 Data Modeling Tools 162
11.10 Warehouse Management Tools 163
11.11 Source Systems 163
Chapter 12. Warehouse Schema Design 165
12.1 OLTP Systems Use Normalized Data Structures 165
12.2 Dimensional Modeling for Decisional Systems 167
12.3 Star Schema 168
12.4 Dimensional Hierarchies and Hierarchical Drilling 169
12.5 The Granularity of the Fact Table 170
12.6 Aggregates or Summaries 171
12.7 Dimensional Attributes 173
12.8 Multiple Star Schemas 173
12.9 Advantages of Dimensional Modeling 174
Chapter 13. Warehouse Metadata 176
13.1 Metadata Defined 176
13.2 Metadata are a Form of Abstraction 177
13.3 Imprtance of Metadata 178
13.4 Types of Metadata 179
13.5 Metadata Management 181
13.6 Metadata as the Basis for Automating Warehousing Tasks 182
13.7 Metadata Trends 182
Chapter 14. Warehousing Applications 184
14.1 The Early Adopters 184
14.2 Types of Warehousing Applications 184
14.3 Financial Analysis and Management 185
14.4 Specialized Applications of Warehousing Technology 186
PART V: MAINTENANCE, EVOLUTION AND TRENDS
Chapter 15. Warehouse Maintenance and Evolution 191
15.1 Regular Warehouse Loads 191
15.2 Warehouse Statistics Collection 191
15.3 Warehouse User Profiles 192
15.4 Security and Access Profiles 193
15.5 Data Quality 193
15.6 Data Growth 194
15.7 Updates to Warehouse Subsystems 194
15.8 Database Optimization and Tuning 195
15.9 Data Warehouse Staffing 195
15.10 Warehouse Staff and User Training 196
15.11 Subsequent Warehouse Rollouts 196
15.12 Chargeback Schemes 197
15.13 Disaster Recovery 197
Chapter 16. Warehousing Trends 198
16.1 Continued Growth of the Data Warehouse Industry 198
16.2 Increased Adoption of Warehousing Technology by More Industries 198
16.3 Increased Maturity of Data Mining Technologies 199
16.4 Emergence and Use of Metadata Interchange Standards 199
16.5 Increased Availability of Web-Enabled Solutions 199
16.6 Popularity of Windows NT for Data Mart Projects 199
16.7 Availability of Warehousing Modules for Application Packages 200
16.8 More Mergers and Acquisitions Among Warehouse Players 200
PART VI: ON-LINE ANALYTICAL PROCESSING
Chapter 17. Introduction 203
17.1 What is OLAP ? 203
17.2 The Codd Rules and Features 205
17.3 The origins of Today’s OLAP Products 209
17.4 What’s in a Name 219
17.5 Market Analysis 221
17.6 OLAP Architectures 224
17.7 Dimensional Data Structures 229
Chapter 18. OLAP Applications 233
18.1 Marketing and Sales Analysis 233
18.2 Click stream Analysis 235
18.3 Database Marketing 236
18.4 Budgeting 237
18.5 Financial Reporting and Consolidation 239
18.6 Management Reporting 242
18.7 EIS 242
18.8 Balanced Scorecard 243
18.9 Profitability Analysis 245
18.10 Quality Analysis 246
VOLUME II: DATA MINING
Chapter 1. Introduction 249
1.1 What is Data Mining 251
1.2 Definitions 252
1.3 Data Mining Process 253
1.4 Data Mining Background 254
1.5 Data Mining Models 256
1.6 Data Mining Methods 257
1.7 Data Mining Problems/Issues 260
1.8 Potential Applications 262
1.9 Data Mining Examples 262
Chapter 2. Data Mining with Decision Trees 267
2.1 How a Decision Tree Works 269
2.2 Constructing Decision Trees 271
2.3 Issues in Data Mining with Decision Trees 275
2.4 Visualization of Decision Trees in System CABRO 279
2.5 Strengths and Weakness of Decision Tree Methods 281
Chapter 3. Data Mining with Association Rules 283
3.1 When is Association Rule Analysis Useful ? 285
3.2 How does Association Rule Analysis Work ? 286
3.3 The Basic Process of Mining Association Rules 287
3.4 The Problem of Large Datasets 292
3.5 Strengths and Weakness of Association Rules Analysis 293
Chapter 4. Automatic Clustering Detection 295
4.1 Searching for Clusters 297
4.2 The K-means Method 299
4.3 Agglomerative Methods 309
4.4 Evaluating Clusters 311
4.5 Other Approaches to Cluster Detection 312
4.6 Strengths and Weakness of Automatic Cluster Detection 313
Chapter 5. Data Mining with Neural Network 315
5.1 Neural Networks for Data Mining 317
5.2 Neural Network Topologies 318
5.3 Neural Network Models 321
5.4 Iterative Development Process 327
5.5 Strengths and Weakness of Artificial Neural Network 320

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