IBM Data Warehousing with IBM Business Intelligence Tools

Acknowledgments xx
Introduction xxiii
Part One Fundamentals of Business Intelligence
and the Data Warehouse 1
Chapter 1 Overview of the BI Organization 3
Overview of the BI Organization Architecture 4
Providing Information Content 10
Planning for Information Content 10
Designing for Information Content 13
Implementing Information Content 15
Justifying Your BI Effort 18
Linking Your Project to Known Business Requirements 18
Measuring ROI 18
Applying ROI 19
Questions for ROI Benefits 21
Making the Most of the First Iteration of the Warehouse 22
IBM and The BI Organization 22
Seamless Integration 23
Data Mining 24
Online Analytic Processing 24
Spatial Analysis 25
Database-Resident Tools 25
Simplified Data Delivery System 26
Zero-Latency 27
Summary 28
CChapter 2 Business Intelligence Fundamentals 29
BI Components and Technologies 31
Business Intelligence Components 31
Data Warehouse 31
Data Sources 32
Data Targets 32
Warehouse Components 36
Extraction, Transformation, and Loading 37
Extraction 38
Transformation/Cleansing 39
Data Refining 39
Data Management 40
Data Access 40
Meta Data 41
Analytical User Requirements 42
Reporting and Querying 43
Online Analytical Processing 43
Multidimensional Views 44
Calculation-Intensive Capabilities 45
Time Intelligence 45
Statistics 46
Data Mining 46
Dimensional Technology and BI 47
The OLAP Server 48
MOLAP 49
ROLAP 50
Defining the Dimensional Spectrum 50
Touch Points 52
Zero-Latency and Your Warehouse Environment 53
Closed-Loop Learning 53
Historical Integrity 54
Summary 58
Chapter 3 Planning Data Warehouse Iterations 59
Planning Any Iteration 61
Building Your BI Plan 62
Enterprise Strategy 63
Designing the Technical Architecture 64
Designing the Data Architecture 66
Implementing and Maintaining the Warehouse 69
Planning the First Iteration 70
Aligning the Warehouse with Corporate Strategy 71
Conducting a Readiness Assessment 71
Resource Planning 74
Identifying Opportunities with the DIF Matrix 1 77
Determining the Right Approach 78
Applying the DIF Matrix 78
IT JAD Sessions 80
Select Candidate Iteration Opportunities 80
Get IT Scores 81
Create DIF Matrix 81
User JAD Session and Scoring 81
Average DIF Scores 82
Select According to Score 82
Submit to Management 82
Dysfunctional 82
Impact 83
Feasibility 84
DIF Matrix Results 84
Planning Subsequent Iterations 87
Defining the Scope 87
Identifying Strategic Business Questions 87
Implementing a Project Approach 89
BI Hacking Approach 90
The Inmon Approach 90
Business Dimensional Lifecycle Approach 91
The Spiral Approach 91
Reducing Risk 92
The Spiral Approach and Your Life Cycle Model 93
Warehouse Development and the Spiral Model 94
Flattening Spiral Rounds to Time Lines 98
The IBM Approach 100
Choosing the Right Approach 103
Summary 103
Part Two Business Intelligence Architecture 105
Chapter 4 Designing the Data Architecture 107
Choosing the Right Architecture 110
Atomic Layer Alternatives 113
ROLAP Platform on a 3NF Atomic Layer 116
HOLAP Platform on a Star Schema Atomic Layer 117
Data Marts 118
Atomic Layer with Dependent Data Marts 120
Independent Data Marts 121
Data Delivery Architecture 122
EAI and Warehousing 126
Comparing ETL and EAI 126
Expected Deliverables 127
Modeling the Architecture 129
Business Logical Model 130
Atomic-Level Model 132
Modeling the Data Marts 133
Comparing Atomic and Star Data 137Operational Data Store 138
Data Architecture Strategy 140
Summary 143
Chapter 5 Technical Architecture and Data Management Foundations 145
Broad Technical Architecture Decisions 148
Centralized Data Warehousing 148
Distributed Data Warehousing 152
Parallelism and the Warehouse 154
Partitioning Data Storage 157
Technical Foundations for Data Management 158
DB2 and the Atomic Layer 158
Redistribution and Table Collocation 158
Replicated Tables 160
Indexing Options 161
Multidimensional Clusters as Indexes 161
Defined Types, User-Defined Functions, and DB2 Extenders 162
Hierarchical Storage Considerations 162
DB2 and Star Schemas 164
DB2 Technical Architecture Essentials 166
SMP, MPP, and Clusters 166
Shared-Resource vs. Shared-Nothing 168
DB2 on Hardware Architectures 169
Static and Dynamic Parallelism 170
Catalog Partition 172
High Availability 172
Online Space Management 172
Backup 172
Parallel Loading 174
OnLine Load 174
Multidimensional Clustering 174
Unplanned Outages 175
Sizing Requirements 179
Summary 181
Part Three Data Management 183
Chapter 6 DB2 BI Fundamentals 185
High Availability 186
Multidimensional Clustering 187
Online Loads 188
Load From Cursor 189
Batch Window Elimination 190
Elimination of Table Reorganization 190
Online Load and MQT Maintenance 190
MQT Staging Tables 191
Online Table Reorganization 192
x ContentsDynamic Bufferpool Management 194
Dynamic Database Configuration 195
Database Managed Storage Considerations 195
Logging Considerations 196
Administration 197
eLiza and SMART 197
Automated Health Management Framework 198
AUTOCONFIGURE 198
Administration Notification Log 199
Maintenance Mode 199
Event Monitors 200
SQL and Other Programming Features 200
INSTEAD OF Triggers 200
DML Operations through UNION ALL 201
Informational Constraints 202
User-Maintained MQTs 203
Performance 203
Connection Concentrator 203
Compression 204
Type-2 Indexes 204
MDC Performance Enhancement 206
Blocked Bufferpools 206
Extensibility 206
Spatial Extender 207
Text Extender and Text Information Extender 208
Image Extender 208
XML Extender 208
Video Extender and Audio Extender 209
Net Search Extender 209
MQSeries 209
DB2 Scoring 209
Summary 211
Chapter 7 DB2 Materialized Query Tables 213
Initializing MQTs 219
Creating 219
Populating 219
Tuning 221
MQT DROP 221
MQT Refresh Strategies 221
Deferred Refresh 221
Immediate Refresh 226
Loading Underlying Tables 227
New States 228
New LOAD Options 228
Using DB2 ALTER 231
Contents xiMaterialized View Matching 232
State Considerations 232
Matching Criteria 233
Matching Permitted 234
Matching Inhibited 240
MQT Design 243
MQT Tuning 244
Refresh Optimization 245
Materialized View Limitations 247
Summary 249
Part Four Warehouse Management 251
Chapter 8 Warehouse Management with IBM DB2 Data
Warehouse Center 253
IBM DB2 Data Warehouse Center Essentials 254
Warehouse Subject Area 254
Warehouse Source 254
Warehouse Target 255
Warehouse Server and Logger 255
Warehouse Agent and Agent Site 255
Warehouse Control Database 256
Warehouse Process and Step 257
SQL Step 258
Replication Step 258
DB2 Utilities Step 259
OLAP Server Program Step 259
File Program Step 260
Transformer Step 260
User-Defined Program Step 260
IBM DB2 Data Warehouse Center Launchpad 261
Setting Up Your Data Warehouse Environment 261
Creating a Warehouse Database 261
Browsing the Source Data 261
Establishing IBM DB2 Data Warehouse Center Security 262
Building a Data Warehouse Using the Launchpad 262
Task 1: Define a Subject Area 264
Task 2: Define a Process 264
Task 3: Define a Warehouse Source 266
Task 4: Define a Warehouse Target 267
Task 5: Define a Step 268
Task 6: Link a Source to a Step 270
Task 7 Link a Step to a Target 270
Task 8: Define the Step Parameters 272
Task 9: Schedule a Step to Run 274
Defining Keys on Target Tables 274
Maintaining the Data Warehouse 275
Authorizing Users of the Warehouse 276
Cataloging Warehouse Data for Users 276
xii ContentsProcess and Step Task Control 277
Scheduling 278
Notifying the Data Administrator 282
Scheduling a Process 283
Triggering Steps Outside IBM DB2
Data Warehouse Center 286
Starting the External Trigger Server 287
Starting the External Trigger Client 287
Monitoring Strategies with IBM DB2 Data Warehouse Center 289
IBM DB2 Data Warehouse Center Monitoring Tools 289
Monitoring Data Warehouse Population 291
Monitoring Data Warehouse Usage 298
DB2 Monitoring Tools 299
Replication Center Monitoring 300
Warehouse Tuning 303
Updating Statistics 303
Reorganizing Your Data 304
Using DB2 Snapshot and Monitor 304
Using Visual Explain 305
Tuning Database Performance 307
Maintaining IBM DB2 Data Warehouse Center 307
Log History 308
Control Database 308
DB2 Data Warehouse Center V8 Enhancements 308
Summary 312
Chapter 9 Data Transformation with IBM DB2 Data Warehouse Center 313
IBM DB2 Data Warehouse Center Process Model 316
Identify the Sources and Targets 317
Identify the Transformations 318
The Process Model 320
IBM DB2 Data Warehouse Center Transformations 322
Refresh Considerations 327
Data Volume 328
Manage Data Editions 328
User-Defined Transformation Requirements 329
Multiple Table Loads 329
Ensure Warehouse Data Is Up-to-Date 329
Retry 333
SQL Transformation Steps 333
SQL Select and Insert 335
SQL Select and Update 337
DB2 Utility Steps 338
Export Utility Step 338
LOAD Utility 339
Warehouse Transformer Steps 340
Cleansing Transformer 340
Generating Key Table 343
Contents xiiiGenerating Period Table 344
Inverting Data Transformer 346
Pivoting Data 348
Date Format Changing 351
Statistical Transformers 352
Analysis of Variance (ANOVA) 352
Calculating Statistics 355
Calculating Subtotals 357
Chi-Squared Transformer 359
Correlation Analysis 362
Moving Average 364
Regression Analysis 366
Data Replication Steps 369
Setting Up Replication 371
Defining Replication Steps in IBM DB2 Data Warehouse Center 373
MQSeries Integration 379
Accessing Fixed-Length or Delimited MQSeries Messages 380
Using DB2 MQSeries Views 382
Accessing XML MQSeries Messages 384
User-Defined Program Steps 385
Vendor Integration 388
ETI•EXTRACT Integration 388
Trillium Integration 396
Ascential Integration 398
Microsoft OLE DB and Data Transformation Services 399
Accessing OLE DB 400
Accessing DTS Packages 401
Summary 401
Chapter 10 Meta Data and the IBM DB2 Warehouse Manager 403
What Is Meta Data? 404
Classification of Meta Data 406
Meta Data by Type of User 407
Meta Data by Degree of Formality at Origin 408
Meta Data by Usage Context 409
What Is the Meta Data Repository? 409
Feeding Your Meta Data Repository 410
Benefits of Meta data and the Meta Data Repository 411
Attributes of a Healthy Meta Data Repository 413
Maintaining the Repository 414
Challenges to Implementing a Meta Data Repository 415
IBM Meta Data Technology 416
Information Catalog 416
IBM DB2 Data Warehouse Center 417
Meta Data Acquisition by DWC 418
Collecting Meta Data from ETI•EXTRACT 420
Collecting Meta Data from INTEGRITY 425
Collecting Meta Data from DataStage 429
xiv ContentsCollecting Meta Data from ERwin 431
Collecting Meta Data from Axio 433
Collecting Meta Data from IBM OLAP Integration Server 434
Exchanging Meta Data between IBM DB2 Data Warehouse
Center Instances 437
Maintaining Test and Production Systems 438
Meta Data Exchange Formats 438
Tag Export and Impot 439
CWM Export and Impot 441
Transmission of DWC Meta Data to Other Tools 441
Transmission of DWC Meta Data to IBM Information Catalog 442
Transmission of DWC Meta Data to
OLAP Integration Server 445
Transmission of DWC Meta Data to IBM DB2 OLAP Server 447
Transmission of DWC Meta Data to Ascential INTEGRITY 448
Transferring Meta Data In/Out of the Information Catalog 448
Acquisition of Meta Data by the Information Catalog 450
Collecting Meta Data from IBM DB2 Data Warehouse Center 450
Collecting Meta Data from another Information Catalog 450
Accessing Brio Meta Data in the Information Catalog 450
Collecting Meta Data from BusinessObjects 451
Collecting Meta Data from Cognos 453
Collecting Meta Data from ERwin 454
Collecting Meta Data from QMF for Windows 455
Collecting Meta Data from ETI•EXTRACT 457
Collecting Meta Data from DB2 OLAP Server 459
Transmission of Information Catalog Meta Data 460
Transmitting Meta Data to Another Information Catalog 460
Enabling Brio to Access Information Catalog Meta Data 461
Transmitting Information Catalog Meta Data to BusinessObjects 462
Transmitting Information Catalog Meta Data to Cognos 463
Summary 463
Part Five OLAP and IBM 465
Chapter 11 Multidimensional Data with DB2 OLAP Server 467
Understanding the Analytic Cycle of OLAP 472
Generating Useful Metrics 474
OLAP Skills 476
Applying the Dimensional Model 477
Steering Your Organization with OLAP 478
Speed-of-Thought Analysis 478
The Outline of a Business 479
The OLAP Array 483
Relational Schema Limitations 484
Derived Measures 485
Implementing an Enterprise OLAP Architecture 486
Contents xvPrototyping the Data Warehouse 488
Database Design: Building Outlines 488
Application Manager 489
ESSCMD and MaxL 490
OLAP Integration Server 493
Support Requirements 495
DB2 OLAP Database as a Matrix 496
Block Creation Explored 498
Matrix Explosion 498
DB2 OLAP Server Sizing Requirements 499
What DB2 OLAP Server Stores 499
Using SET MSG ONLY: Pre-Version 8 Estimates 500
What is Representative Data? 501
Sizing Estimates for DB2 OLAP Server Version 8 501
Database Tuning 502
Goal Of Database Tuning 503
Outline Tuning Considerations 503
Batch Calculation and Data Storage 504
Member Tags and Dynamic Calculations 504
Disk Subsystem Utilization and Database File Configuration 506
Database Partitioning 506
Attribute Dimensions 507
Assessing Hardware Requirements 509
CPU Estimate 511
Disk Estimate 511
OLAP Auxiliary Storage Requirements 512
OLAP Backup and Disaster Recovery 512
Summary 513
Chapter 12 OLAP with IBM DB2 Data Warehouse Center 515
IBM DB2 Data Warehouse Center Step Types 516
Adding OLAP to Your Process 518
OLAP Server Main Page 519
OLAP Server Column Mapping Page 520
OLAP Server Program Processing Options 520
Other Considerations 520
OLAP Server Load Rules 521
Free Text Data Load 521
File with Load Rules 522
File without Load Rules 523
SQL Table with Load Rules 526
OLAP Server Calculation 527
Default Calculation 527
Calc with Calc Rules 528
Updating the OLAP Server Outline 530
Using a File 530
Using an SQL Table 531
Summary 533
xvi ContentsChapter 13 DB2 OLAP Functions 535
OLAP Functions 537
Specific Functions 537
RANK 537
DENSE_RANK 538
ROWNUMBER 538
PARTITION BY 539
ORDER BY 539
Window Aggregation Group Clause 540
GROUPING Capabilities: ROLLUP and CUBE 542
ROLLUP 542
CUBE 543
Ranking, Numbering, and Aggregation 544
RANK Example 545
ROW_NUMBER, RANK, and DENSE_RANK Example 546
RANK and PARTITION BY Example 546
OVER clause example 548
ROWS and ORDER BY Example 548
ROWS, RANGE, and ORDER BY Example 549
GROUPING, GROUP BY, ROLLUP, and CUBE 552
GROUPING, GROUP BY, and CUBE Example 552
ROLLUP Example 553
CUBE Example 555
OLAP Functions in Use 560
Presenting Annual Sales by Region and City 560
Data 560
BI Functions 561
Steps 561
Identifying Target Groups for a Campaign 562
Data 563
BI Functions 563
Steps 564
Summary 566
Part Six Enhanced Analytics 567
Chapter 14 Data Mining with Intelligent Miner 569
Data Mining and the BI Organization 570
Effective Data Mining 575
The Mining Process 575
Step 1: Create a Precise Definition of the Business Issue 577
Describing the Problem 578
Understanding Your Data 579
Using the Results 580
Step 2: Map Business Issue to Data Model and
Data Requirements 580
Step 3: Source and Preprocess the Data 582
Step 4: Explore and Evaluate the Data 582
Contents xviiStep 5: Select the Data Mining Technique 583
Discovery Data Mining 583
Predictive Mining 584
Step 6: Interpret the Results 585
Step 7: Deploy the Results 586
Integrating Data Mining 586
Skills for Implementing a Data Mining Project 587
Benefits of Data Mining 588
Data Quality 589
Relevant Dimensions 589
Using Mining Results in OLAP 590
Benefits of Mining DB2 OLAP Server 591
Summary 593
Chapter 15 DB2-Enhanced BI Features and Functions 595
DB2 Analytic Functions 596
AVG 597
CORRELATION 598
COUNT 598
COUNT_BIG 599
COVARIANCE 599
MAX 600
MIN 600
RAND 601
STDDEV 602
SUM 602
VARIANCE 602
Regression Functions 603
COVAR, CORR, VAR, STDDEV, and Regression Examples 606
COVARIANCE Example 606
CORRELATION Examples 607
VARIANCE Example 609
STTDEV Examples 609
Linear Regression Examples 610
BI-Centric Function Examples 612
Using Sample Data 612
Listing the Top Five Salespersons by Region This Year 615
Data Description 615
BI Functions Showcased 615
Steps 616
Determining Relationships between Product Purchases 617
Data Description 617
BI Functions Showcased 617
Steps 617
Summary 619
xviii ContentsChapter 16 Blending Spatial Data into the Warehouse 621
Spatial Analysis and the BI Organization 623
The Impact of Space 625
What Is Spatial Data? 628
The Onion Analogy 628
Spatial Data Structures 628
Vector Data 629
Raster Data 629
Triangulated Data 630
Spatial Data vs. Other Graphic Data 631
Obtaining Spatial Data 632
Creating Your Own Spatial Data 632
Acquiring Spatial Data 632
Government Data 633
Vendor Data 633
Spatial Data in DSS 634
Spatial Analysis and Data Mining 635
Serving Up Spatial Analysis 637
Typical Business Questions Directed at the Data Warehouse 639
Where are my customers coming from? 640
I don’t have customer address information-can
I still use spatial analysis tools? 641
Understanding a Spatially Enabled Data Warehouse 644
Geocoding 644
Technology Requirements for Spatial Warehouses 646
Adding Spatial Data to the Warehouse 647
Summary 649
Bibliography 651
Index 653


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Encyclopedia of Data Warehousing and Mining

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VOLUME I
Action Rules / Zbigniew W. Ras, Angelina Tzacheva, and Li-Shiang Tsay ........................................................... 1
Active Disks for Data Mining / Alexander Thomasian ........................................................................................... 6
Active Learning with Multiple Views / Ion Muslea ................................................................................................. 12
Administering and Managing a Data Warehouse / James E. Yao, Chang Liu, Qiyang Chen, and June Lu .......... 17
Agent-Based Mining of User Profiles for E-Services / Pasquale De Meo, Giovanni Quattrone,
Giorgio Terracina, and Domenico Ursino ......................................................................................................... 23
Aggregate Query Rewriting in Multidimensional Databases / Leonardo Tininini ................................................. 28
Aggregation for Predictive Modeling with Relational Data / Claudia Perlich and Foster Provost ....................... 33
API Standardization Efforts for Data Mining / Jaroslav Zendulka ......................................................................... 39
Application of Data Mining to Recommender Systems, The / J. Ben Schafer ........................................................ 44
Approximate Range Queries by Histograms in OLAP / Francesco Buccafurri and Gianluca Lax ........................ 49
Artificial Neural Networks for Prediction / Rafael Martí ......................................................................................... 54
Association Rule Mining / Yew-Kwong Woon, Wee-Keong Ng, and Ee-Peng Lim ................................................ 59
Association Rule Mining and Application to MPIS / Raymond Chi-Wing Wong and Ada Wai-Chee Fu ............. 65
Association Rule Mining of Relational Data / Anne Denton and Christopher Besemann ..................................... 70
Association Rules and Statistics / Martine Cadot, Jean-Baptiste Maj, and Tarek Ziadé ..................................... 74
Automated Anomaly Detection / Brad Morantz ..................................................................................................... 78
Automatic Musical Instrument Sound Classification / Alicja A. Wieczorkowska ................................................... 83
Bayesian Networks / Ahmad Bashir, Latifur Khan, and Mamoun Awad ............................................................... 89
Best Practices in Data Warehousing from the Federal Perspective / Les Pang ....................................................... 94
Bibliomining for Library Decision-Making / Scott Nicholson and Jeffrey Stanton ................................................. 100
Biomedical Data Mining Using RBF Neural Networks / Feng Chu and Lipo Wang ................................................ 106
Building Empirical-Based Knowledge for Design Recovery / Hee Beng Kuan Tan and Yuan Zhao ...................... 112
Business Processes / David Sundaram and Victor Portougal ............................................................................... 118
Case-Based Recommender Systems / Fabiana Lorenzi and Francesco Ricci ....................................................... 124
Categorization Process and Data Mining / Maria Suzana Marc Amoretti ............................................................. 129
Center-Based Clustering and Regression Clustering / Bin Zhang ........................................................................... 134
Classification and Regression Trees / Johannes Gehrke ........................................................................................ 141
Classification Methods / Aijun An ........................................................................................................................... 144
Closed-Itemset Incremental-Mining Problem / Luminita Dumitriu ......................................................................... 150
Cluster Analysis in Fitting Mixtures of Curves / Tom Burr ..................................................................................... 154
Clustering Analysis and Algorithms / Xiangji Huang ............................................................................................. 159
Clustering in the Identification of Space Models / Maribel Yasmina Santos, Adriano Moreira,
and Sofia Carneiro .............................................................................................................................................. 165
Clustering of Time Series Data / Anne Denton ......................................................................................................... 172
Clustering Techniques / Sheng Ma and Tao Li ....................................................................................................... 176
Clustering Techniques for Outlier Detection / Frank Klawonn and Frank Rehm ................................................. 180
Combining Induction Methods with the Multimethod Approach / Mitja Leni , Peter Kokol, Petra Povalej
and Milan Zorman ............................................................................................................................................... 184
Comprehensibility of Data Mining Algorithms / Zhi-Hua Zhou .............................................................................. 190
Computation of OLAP Cubes / Amin A. Abdulghani .............................................................................................. 196
Concept Drift / Marcus A. Maloof ............................................................................................................................ 202
Condensed Representations for Data Mining / Jean-Francois Boulicaut ............................................................. 207
Content-Based Image Retrieval / Timo R. Bretschneider and Odej Kao ................................................................. 212
Continuous Auditing and Data Mining / Edward J. Garrity, Joseph B. O’Donnell,
and G. Lawrence Sanders ................................................................................................................................... 217
Data Driven vs. Metric Driven Data Warehouse Design / John M. Artz ................................................................. 223
Data Management in Three-Dimensional Structures / Xiong Wang ........................................................................ 228
Data Mining and Decision Support for Business and Science / Auroop R. Ganguly, Amar Gupta,
and Shiraj Khan .................................................................................................................................................. 233
Data Mining and Warehousing in Pharma Industry / Andrew Kusiak and Shital C. Shah .................................... 239
Data Mining for Damage Detection in Engineering Structures / Ramdev Kanapady
and Aleksandar Lazarevic .................................................................................................................................. 245
Data Mining for Intrusion Detection / Aleksandar Lazarevic ................................................................................. 251
Data Mining in Diabetes Diagnosis and Detection / Indranil Bose ........................................................................ 257
Data Mining in Human Resources / Marvin D. Troutt and Lori K. Long ............................................................... 262
Data Mining in the Federal Government / Les Pang ................................................................................................ 268
Data Mining in the Soft Computing Paradigm / Pradip Kumar Bala, Shamik Sural,
and Rabindra Nath Banerjee .............................................................................................................................. 272
Data Mining Medical Digital Libraries / Colleen Cunningham and Xiaohua Hu ................................................... 278
Data Mining Methods for Microarray Data Analysis / Lei Yu and Huan Liu ......................................................... 283
Data Mining with Cubegrades / Amin A. Abdulghani ............................................................................................. 288
Data Mining with Incomplete Data / Hai Wang and Shouhong Wang .................................................................... 293
Data Quality in Cooperative Information Systems / Carlo Marchetti, Massimo Mecella, Monica Scannapieco,
and Antonino Virgillito ...................................................................................................................................... 297
Data Quality in Data Warehouses / William E. Winkler .......................................................................................... 302
Data Reduction and Compression in Database Systems / Alexander Thomasian .................................................. 307
Data Warehouse Back-End Tools / Alkis Simitsis and Dimitri Theodoratos ......................................................... 312
Data Warehouse Performance / Beixin (Betsy) Lin, Yu Hong, and Zu-Hsu Lee ..................................................... 318
Data Warehousing and Mining in Supply Chains / Richard Mathieu and Reuven R. Levary ............................... 323
Data Warehousing Search Engine / Hadrian Peter and Charles Greenidge ......................................................... 328
Data Warehousing Solutions for Reporting Problems / Juha Kontio ..................................................................... 334
Database Queries, Data Mining, and OLAP / Lutz Hamel ....................................................................................... 339
Database Sampling for Data Mining / Patricia E.N. Lutu ....................................................................................... 344
DEA Evaluation of Performance of E-Business Initiatives / Yao Chen, Luvai Motiwalla, and M. Riaz Khan ...... 349
Decision Tree Induction / Roberta Siciliano and Claudio Conversano ............................................................... 353
Diabetic Data Warehouses / Joseph L. Breault ....................................................................................................... 359
Discovering an Effective Measure in Data Mining / Takao Ito ............................................................................... 364
Discovering Knowledge from XML Documents / Richi Nayak .............................................................................. 372
Discovering Ranking Functions for Information Retrieval / Weiguo Fan and Praveen Pathak ............................ 377
Discovering Unknown Patterns in Free Text / Jan H. Kroeze ................................................................................. 382
Discovery Informatics / William W. Agresti ............................................................................................................. 387
Discretization for Data Mining / Ying Yang and Geoffrey I. Webb .......................................................................... 392
Discretization of Continuous Attributes / Fabrice Muhlenbach and Ricco Rakotomalala .................................. 397
Distributed Association Rule Mining / Mafruz Zaman Ashrafi, David Taniar, and Kate A. Smith ........................ 403
Distributed Data Management of Daily Car Pooling Problems / Roberto Wolfler Calvo, Fabio de Luigi,
Palle Haastrup, and Vittorio Maniezzo ............................................................................................................. 408
Drawing Representative Samples from Large Databases / Wen-Chi Hou, Hong Guo, Feng Yan,
and Qiang Zhu ..................................................................................................................................................... 413
Efficient Computation of Data Cubes and Aggregate Views / Leonardo Tininini .................................................. 421
Embedding Bayesian Networks in Sensor Grids / Juan E. Vargas .......................................................................... 427
Employing Neural Networks in Data Mining / Mohamed Salah Hamdi .................................................................. 433
Enhancing Web Search through Query Log Mining / Ji-Rong Wen ....................................................................... 438
Enhancing Web Search through Web Structure Mining / Ji-Rong Wen ................................................................. 443
Ensemble Data Mining Methods / Nikunj C. Oza ................................................................................................... 448
Ethics of Data Mining / Jack Cook ......................................................................................................................... 454
Ethnography to Define Requirements and Data Model / Gary J. DeLorenzo ......................................................... 459
Evaluation of Data Mining Methods / Paolo Giudici ............................................................................................. 464
Evolution of Data Cube Computational Approaches / Rebecca Boon-Noi Tan ...................................................... 469
Evolutionary Computation and Genetic Algorithms / William H. Hsu ..................................................................... 477
Evolutionary Data Mining For Genomics / Laetitia Jourdan, Clarisse Dhaenens, and El-Ghazali Talbi ............ 482
Evolutionary Mining of Rule Ensembles / Jorge Muruzábal .................................................................................. 487
Explanation-Oriented Data Mining / Yiyu Yao and Yan Zhao ................................................................................. 492
Factor Analysis in Data Mining / Zu-Hsu Lee, Richard L. Peterson, Chen-Fu Chien, and Ruben Xing ............... 498
Financial Ratio Selection for Distress Classification / Roberto Kawakami Harrop Galvão, Victor M. Becerra,
and Magda Abou-Seada ..................................................................................................................................... 503
Flexible Mining of Association Rules / Hong Shen ................................................................................................. 509
Formal Concept Analysis Based Clustering / Jamil M. Saquer .............................................................................. 514
Fuzzy Information and Data Analysis / Reinhard Viertl ......................................................................................... 519
General Model for Data Warehouses, A / Michel Schneider .................................................................................. 523
Genetic Programming / William H. Hsu .................................................................................................................... 529
Graph Transformations and Neural Networks / Ingrid Fischer ............................................................................... 534
Graph-Based Data Mining / Lawrence B. Holder and Diane J. Cook .................................................................... 540
Group Pattern Discovery Systems for Multiple Data Sources / Shichao Zhang and Chengqi Zhang ................... 546
Heterogeneous Gene Data for Classifying Tumors / Benny Yiu-ming Fung and Vincent To-yee Ng .................... 550
Hierarchical Document Clustering / Benjamin C. M. Fung, Ke Wang, and Martin Ester ....................................... 555
High Frequency Patterns in Data Mining / Tsau Young Lin .................................................................................... 560
Homeland Security Data Mining and Link Analysis / Bhavani Thuraisingham ..................................................... 566
Humanities Data Warehousing / Janet Delve .......................................................................................................... 570
Hyperbolic Space for Interactive Visualization / Jörg Andreas Walter ................................................................... 575
VOLUME II
Identifying Single Clusters in Large Data Sets / Frank Klawonn and Olga Georgieva ......................................... 582
Immersive Image Mining in Cardiology / Xiaoqiang Liu, Henk Koppelaar, Ronald Hamers,
and Nico Bruining ............................................................................................................................................... 586
Imprecise Data and the Data Mining Process / Marvin L. Brown and John F. Kros .............................................. 593
Incorporating the People Perspective into Data Mining / Nilmini Wickramasinghe .............................................. 599
Incremental Mining from News Streams / Seokkyung Chung, Jongeun Jun and Dennis McLeod ........................ 606
Inexact Field Learning Approach for Data Mining / Honghua Dai ......................................................................... 611
Information Extraction in Biomedical Literature / Min Song, Il-Yeol Song, Xiaohua Hu, and Hyoil Han .............. 615
Instance Selection / Huan Liu and Lei Yu ............................................................................................................... 621
Integration of Data Sources through Data Mining / Andreas Koeller .................................................................... 625
Intelligence Density / David Sundaram and Victor Portougal .............................................................................. 630
Intelligent Data Analysis / Xiaohui Liu ................................................................................................................... 634
Intelligent Query Answering / Zbigniew W. Ras and Agnieszka Dardzinska ........................................................ 639
Interactive Visual Data Mining / Shouhong Wang and Hai Wang .......................................................................... 644
Interscheme Properties’ Role in Data Warehouses / Pasquale De Meo, Giorgio Terracina,
and Domenico Ursino .......................................................................................................................................... 647
Inter-Transactional Association Analysis for Prediction / Ling Feng and Tharam Dillon .................................... 653
Interval Set Representations of Clusters / Pawan Lingras, Rui Yan, Mofreh Hogo, and Chad West .................... 659
Kernel Methods in Chemoinformatics / Huma Lodhi .............................................................................................. 664
Knowledge Discovery with Artificial Neural Networks / Juan R. Rabuñal Dopico, Daniel Rivero Cebrián,
Julián Dorado de la Calle, and Nieves Pedreira Souto .................................................................................... 669
Learning Bayesian Networks / Marco F. Ramoni and Paola Sebastiani ............................................................... 674
Learning Information Extraction Rules for Web Data Mining / Chia-Hui Chang and Chun-Nan Hsu .................. 678
Locally Adaptive Techniques for Pattern Classification / Carlotta Domeniconi and Dimitrios Gunopulos ........ 684
Logical Analysis of Data / Endre Boros, Peter L. Hammer, and Toshihide Ibaraki .............................................. 689
Lsquare System for Mining Logic Data, The / Giovanni Felici and Klaus Truemper ............................................ 693
Marketing Data Mining / Victor S.Y. Lo .................................................................................................................. 698
Material Acquisitions Using Discovery Informatics Approach / Chien-Hsing Wu and Tzai-Zang Lee ................. 705
Materialized Hypertext View Maintenance / Giuseppe Sindoni .............................................................................. 710
Materialized Hypertext Views / Giuseppe Sindoni ................................................................................................... 714
Materialized View Selection for Data Warehouse Design / Dimitri Theodoratos and Alkis Simitsis ..................... 717
Methods for Choosing Clusters in Phylogenetic Trees / Tom Burr ........................................................................ 722
Microarray Data Mining / Li M. Fu .......................................................................................................................... 728
Microarray Databases for Biotechnology / Richard S. Segall ................................................................................ 734
Mine Rule / Rosa Meo and Giuseppe Psaila ........................................................................................................... 740
Mining Association Rules on a NCR Teradata System / Soon M. Chung and Murali Mangamuri ....................... 746
Mining Association Rules Using Frequent Closed Itemsets / Nicolas Pasquier ................................................... 752
Mining Chat Discussions / Stanley Loh, Daniel Licthnow, Thyago Borges, Tiago Primo,
Rodrigo Branco Kickhöfel, Gabriel Simões, Gustavo Piltcher, and Ramiro Saldaña ..................................... 758
Mining Data with Group Theoretical Means / Gabriele Kern-Isberner .................................................................. 763
Mining E-Mail Data / Steffen Bickel and Tobias Scheffer ....................................................................................... 768
Mining for Image Classification Based on Feature Elements / Yu-Jin Zhang ......................................................... 773
Mining for Profitable Patterns in the Stock Market / Yihua Philip Sheng, Wen-Chi Hou, and Zhong Chen ......... 779
Mining for Web-Enabled E-Business Applications / Richi Nayak ......................................................................... 785
Mining Frequent Patterns via Pattern Decomposition / Qinghua Zou and Wesley Chu ......................................... 790
Mining Group Differences / Shane M. Butler and Geoffrey I. Webb ....................................................................... 795
Mining Historical XML / Qiankun Zhao and Sourav Saha Bhowmick .................................................................. 800
Mining Images for Structure / Terry Caelli ............................................................................................................. 805
Mining Microarray Data / Nanxiang Ge and Li Liu ................................................................................................ 810
Mining Quantitative and Fuzzy Association Rules / Hong Shen and Susumu Horiguchi ..................................... 815
Model Identification through Data Mining / Diego Liberati .................................................................................. 820
Modeling Web-Based Data in a Data Warehouse / Hadrian Peter and Charles Greenidge ................................. 826
Moral Foundations of Data Mining / Kenneth W. Goodman ................................................................................... 832
Mosaic-Based Relevance Feedback for Image Retrieval / Odej Kao and Ingo la Tendresse ................................. 837
Multimodal Analysis in Multimedia Using Symbolic Kernels / Hrishikesh B. Aradhye and Chitra Dorai ............ 842
Multiple Hypothesis Testing for Data Mining / Sach Mukherjee ........................................................................... 848
Music Information Retrieval / Alicja A. Wieczorkowska ......................................................................................... 854
Negative Association Rules in Data Mining / Olena Daly and David Taniar ....................................................... 859
Neural Networks for Prediction and Classification / Kate A. Smith ......................................................................... 865
Off-Line Signature Recognition / Indrani Chakravarty, Nilesh Mishra, Mayank Vatsa, Richa Singh,
and P. Gupta ........................................................................................................................................................ 870
Online Analytical Processing Systems / Rebecca Boon-Noi Tan ........................................................................... 876
Online Signature Recognition / Indrani Chakravarty, Nilesh Mishra, Mayank Vatsa, Richa Singh,
and P. Gupta ........................................................................................................................................................ 885
Organizational Data Mining / Hamid R. Nemati and Christopher D. Barko .......................................................... 891
Path Mining in Web Processes Using Profiles / Jorge Cardoso ............................................................................. 896
Pattern Synthesis for Large-Scale Pattern Recognition / P. Viswanath, M. Narasimha Murty,
and Shalabh Bhatnagar ..................................................................................................................................... 902
Physical Data Warehousing Design / Ladjel Bellatreche and Mukesh Mohania .................................................. 906
Predicting Resource Usage for Capital Efficient Marketing / D. R. Mani, Andrew L. Betz, and James H. Drew .... 912
Privacy and Confidentiality Issues in Data Mining / Yücel Saygin ......................................................................... 921
Privacy Protection in Association Rule Mining / Neha Jha and Shamik Sural ..................................................... 925
Profit Mining / Senqiang Zhou and Ke Wang ......................................................................................................... 930
Pseudo Independent Models / Yang Xiang ............................................................................................................ 935
Reasoning about Frequent Patterns with Negation / Marzena Kryszkiewicz ......................................................... 941
Recovery of Data Dependencies / Hee Beng Kuan Tan and Yuan Zhao ................................................................ 947
Reinforcing CRM with Data Mining / Dan Zhu ........................................................................................................ 950
Resource Allocation in Wireless Networks / Dimitrios Katsaros, Gökhan Yavas, Alexandros Nanopoulos,
Murat Karakaya, Özgür Ulusoy, and Yannis Manolopoulos ............................................................................ 955
Retrieving Medical Records Using Bayesian Networks / Luis M. de Campos, Juan M. Fernández-Luna,
and Juan F. Huete ............................................................................................................................................... 960
Robust Face Recognition for Data Mining / Brian C. Lovell and Shaokang Chen ............................................... 965
Rough Sets and Data Mining / Jerzy W. Grzymala-Busse and Wojciech Ziarko .................................................... 973
Rule Generation Methods Based on Logic Synthesis / Marco Muselli .................................................................. 978
Rule Qualities and Knowledge Combination for Decision-Making / Ivan Bruha .................................................... 984
Sampling Methods in Approximate Query Answering Systems / Gautam Das ...................................................... 990
Scientific Web Intelligence / Mike Thelwall ............................................................................................................ 995
Search Situations and Transitions / Nils Pharo and Kalervo Järvelin .................................................................. 1000
Secure Multiparty Computation for Privacy Preserving Data Mining / Yehida Lindell .......................................... 1005
Semantic Data Mining / Protima Banerjee, Xiaohua Hu, and Illhoi Yoo ............................................................... 1010
Semi-Structured Document Classification / Ludovic Denoyer and Patrick Gallinari ............................................ 1015
Semi-Supervised Learning / Tobias Scheffer ............................................................................................................ 1022
Sequential Pattern Mining / Florent Masseglia, Maguelonne Teisseire, and Pascal Poncelet ............................ 1028
Software Warehouse / Honghua Dai ....................................................................................................................... 1033
Spectral Methods for Data Clustering / Wenyuan Li ............................................................................................... 1037
Statistical Data Editing / Claudio Conversano and Roberta Siciliano .................................................................. 1043
Statistical Metadata in Data Processing and Interchange / Maria Vardaki ........................................................... 1048
Storage Strategies in Data Warehouses / Xinjian Lu .............................................................................................. 1054
Subgraph Mining / Ingrid Fischer and Thorsten Meinl .......................................................................................... 1059
Support Vector Machines / Mamoun Awad and Latifur Khan ............................................................................... 1064
Support Vector Machines Illuminated / David R. Musicant .................................................................................... 1071
Survival Analysis and Data Mining / Qiyang Chen, Alan Oppenheim, and Dajin Wang ...................................... 1077
Symbiotic Data Mining / Kuriakose Athappilly and Alan Rea .............................................................................. 1083
Symbolic Data Clustering / Edwin Diday and M. Narasimha Murthy .................................................................... 1087
Synthesis with Data Warehouse Applications and Utilities / Hakikur Rahman .................................................... 1092
Temporal Association Rule Mining in Event Sequences / Sherri K. Harms ........................................................... 1098
Text Content Approaches in Web Content Mining / Víctor Fresno Fernández and Luis Magdalena Layos ....... 1103
Text Mining-Machine Learning on Documents / Dunja Mladenić ........................................................................ 1109
Text Mining Methods for Hierarchical Document Indexing / Han-Joon Kim ......................................................... 1113
Time Series Analysis and Mining Techniques / Mehmet Sayal .............................................................................. 1120
Time Series Data Forecasting / Vincent Cho ........................................................................................................... 1125
Topic Maps Generation by Text Mining / Hsin-Chang Yang and Chung-Hong Lee ............................................. 1130
Transferable Belief Model / Philippe Smets ............................................................................................................ 1135
Tree and Graph Mining / Dimitrios Katsaros and Yannis Manolopoulos ............................................................. 1140
Trends in Web Content and Structure Mining / Anita Lee-Post and Haihao Jin .................................................. 1146
Trends in Web Usage Mining / Anita Lee-Post and Haihao Jin ............................................................................ 1151
Unsupervised Mining of Genes Classifying Leukemia / Diego Liberati, Sergio Bittanti,
and Simone Garatti ............................................................................................................................................. 1155
Use of RFID in Supply Chain Data Processing / Jan Owens, Suresh Chalasani,
and Jayavel Sounderpandian ............................................................................................................................. 1160
Using Dempster-Shafer Theory in Data Mining / Malcolm J. Beynon .................................................................... 1166
Using Standard APIs for Data Mining in Prediction / Jaroslav Zendulka .............................................................. 1171
Utilizing Fuzzy Decision Trees in Decision Making / Malcolm J. Beynon .............................................................. 1175
Vertical Data Mining / William Perrizo, Qiang Ding, Qin Ding, and Taufik Abidin .............................................. 1181
Video Data Mining / JungHwan Oh, JeongKyu Lee, and Sae Hwang .................................................................... 1185
Visualization Techniques for Data Mining / Herna L. Viktor and Eric Paquet ...................................................... 1190
Wavelets for Querying Multidimensional Datasets / Cyrus Shahabi, Dimitris Sacharidis,
and Mehrdad Jahangiri ...................................................................................................................................... 1196
Web Mining in Thematic Search Engines / Massimiliano Caramia and Giovanni Felici .................................... 1201
Web Mining Overview / Bamshad Mobasher ......................................................................................................... 1206
Web Page Extension of Data Warehouses / Anthony Scime ................................................................................... 1211
Web Usage Mining / Bamshad Mobasher ............................................................................................................... 1216
Web Usage Mining and Its Applications / Yongjian Fu ......................................................................................... 1221
Web Usage Mining Data Preparation / Bamshad Mobasher ................................................................................... 1226
Web Usage Mining through Associative Models / Paolo Giudici and Paola Cerchiello .................................... 1231
World Wide Web Personalization / Olfa Nasraoui ................................................................................................. 1235
World Wide Web Usage Mining / Wen-Chen Hu, Hung-Jen Yang, Chung-wei Lee, and Jyh-haw Yeh ............... 1242



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Data Warehousing - OLAP and Data Mining
Data Warehousing and Data Mining Techniques for Cyber Security
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The Architecture for the Next Generation of Data Warehousing

Data warehousing has been around for about 2 decades now and has become an essential part of the information technology infrastructure. Data warehousing originally grew
in response to the corporate need for information—not data. A data warehouse is a construct that supplies integrated, granular, and historical data to the corporation.

But there is a problem with data warehousing. The problem is that there are many different renditions of what a data warehouse is today. There is the federated data warehouse.

There is the active data warehouse. There is the star schema data warehouse. There is the data mart data warehouse. In fact there are about as many renditions of the data warehouse as there are software and hardware vendors.

The problem is that there are many different renditions of what the proper structure of a data warehouse should look like. And each of these renditions is architecturally very different from the others. If you were to enter a room in which a proponent of the federated data warehouse was talking to a proponent of the active data warehouse, you would be hearing the same words, but these words would be meaning very different things.

Even though the words were the same, you would not be hearing meaningful communication. When two people from very different contexts are talking, even though they are using the same words, there is no assurance that they are understanding each other.

And thus it is with fi rst-generation data warehousing today.

Into this morass of confusion as to what a data warehouse is or is not comes DW 2.0.
DW 2.0 is a definition of the next generation of data warehousing. Unlike the term “ data warehouse, ” DW 2.0 has a crisp, well defined meaning. That meaning is identified and defined in this book.

There are many impotant architectural features of DW 2.0. These architectural features represent an advance in technology and architecture beyond first-generation data ware-houses.


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Data Warehousing for Dummies

About the Author
Tom Hammergren is known worldwide as an innovator, writer, educator,
speaker, and consultant in the field of information management. Tom’s
information management and software career spans more than 20 years and
includes key roles in successful business intelligence and information man-
agement solution companies such as Cognos, Cincom, and Sybase. Tom is the
founder of Balanced Insight, Inc., a leading vendor of business intelligence
lifecycle management software and services that also works on innovation in
semantically driven business intelligence.
While working for Sybase, Hammergren helped design and develop
WarehouseStudio, a comprehensive set of tools for delivering enterprise
data warehousing solutions. At Cincom, Tom helped deliver the SupraServer
product line to market, one of the first fully distributed data management
solutions for highly survivable network implementations. During an earlier
position at Cognos, he was one of the founding members of the PowerPlay
and Impromptu product teams.
Tom has published numerous articles in industry journals and is the
author of two widely read books, Data Warehousing: Building the Corporate
Knowledge Base and Offi cial Sybase Data Warehousing on the Internet:
Accessing the Corporate Knowledge Base (both from International Thomson
Computer Press).

Contents at a Glance
Introduction ................................................................ 1
Part I: The Data Warehouse: Home for Your Data Assets ...7
Chapter 1: What’s in a Data Warehouse? ........................................................................ 9
Chapter 2: What Should You Expect from Your Data Warehouse? ........................... 25
Chapter 3: Have It Your Way: The Structure of a Data Warehouse ........................... 37
Chapter 4: Data Marts: Your Retail Data Outlet ........................................................... 59
Part II: Data Warehousing Technology ........................ 71
Chapter 5: Relational Databases and Data Warehousing ........................................... 73
Chapter 6: Specialty Databases and Data Warehousing ............................................. 85
Chapter 7: Stuck in the Middle with You: Data Warehousing Middleware .............. 95
Part III: Business Intelligence and Data Warehousing ...113
Chapter 8: An Intelligent Look at Business Intelligence............................................ 115
Chapter 9: Simple Database Querying and Reporting .............................................. 125
Chapter 10: Business Analysis (OLAP) ....................................................................... 135
Chapter 11: Data Mining: Hi-Ho, Hi-Ho, It’s Off to Mine We Go ................................ 149
Chapter 12: Dashboards and Scorecards ................................................................... 155
Part IV: Data Warehousing Projects:
How to Do Them Right ............................................. 163
Chapter 13: Data Warehousing and Other IT Projects: The Same but Different ... 165
Chapter 14: Building a Winning Data Warehousing Project Team .......................... 179
Chapter 15: You Need What? When? — Capturing Requirements .......................... 193
Chapter 16: Analyzing Data Sources............................................................................ 203
Chapter 17: Delivering the Goods ................................................................................ 213
Chapter 18: User Testing, Feedback, and Acceptance .............................................. 225
Part V: Data Warehousing: The Big Picture ................ 231
Chapter 19: The Information Value Chain:
Connecting Internal and External Data ..................................................................... 233
Chapter 20: Data Warehousing Driving Quality and Integration ............................. 247
Chapter 21: The View from the Executive Boardroom ............................................. 263
Chapter 22: Existing Sort-of Data Warehouses: Upgrade or Replace? .................... 271
Chapter 23: Surviving in the Computer Industry (and Handling Vendors) ............ 281
Chapter 24: Working with Data Warehousing Consultants ...................................... 291
Part VI: Data Warehousing in the
Not-Too-Distant Future ............................................. 297
Chapter 25: Expanding Your Data Warehouse with Unstructured Data ................. 299
Chapter 26: Agreeing to Disagree about Semantics .................................................. 305
Chapter 27: Collaborative Business Intelligence ....................................................... 311
Part VII: The Part of Tens ......................................... 317
Chapter 28: Ten Questions to Consider When You’re Selecting User Tools ......... 319
Chapter 29: Ten Secrets to Managing Your Project Successfully ............................ 325
Chapter 30: Ten Sources of Up-to-Date Information about Data Warehousing ..... 331
Chapter 31: Ten Mandatory Skills for a Data Warehousing Consultant ................. 335
Chapter 32: Ten Signs of a Data Warehousing Project in Trouble .......................... 339
Chapter 33: Ten Signs of a Successful Data Warehousing Project .......................... 343
Chapter 34: Ten Subject Areas to Cover with Product Vendors ............................. 347
Index ...................................................................... 351

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Data Warehousing Design and Advanced Engineering Applications Other Data Warehous books
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Data Warehousing Architecture and Implementation

Humphries
Hawkins
Dy
Publisher: Prentice Hall PTR

Data Warehousing Architecture and Implementation
Preface
I: Introduction
I: Introduction
1. The Enterprise IT Architecture
The Past: Evolution of Enterprise Architectures
The Present: The IT Professional's Responsibility
Business Perspective
Technology Perspective
Architecture Migration Scenarios
Migration Strategy: How Do We Move Forward?
In Summary
2. Data Warehouse Concepts
Gradual Changes in Computing Focus
The Data Warehouse Defined
The Dynamic, Ad Hoc Report
The Purposes of a Data Warehouse
A Word About Data Marts
A Word About Operational Data Stores
Data Warehouse Cost-Benefit Analysis / Return on Investment
In Summary
II: People
II: People
3. The Project Sponsor
How Will a Data Warehouse Affect our Decision-Making Processes?
How Does a Data Warehouse Improve My Financial Processes? Marketing? Operations?
When Is a Data Warehouse Project Justified?
What Expenses Are Involved?
What Are the Risks?
Risk-Mitigating Approaches
Is My Organization Ready for a Data Warehouse?
How Do I Measure the Results?
In Summary
4. The CIO
How Do I Support the Data Warehouse?
How Will My Data Warehouse Evolve?
Who Should Be Involved in a Data Warehouse Project?
What Is the Team Structure Like?
What New Skills Will My People Need?
How Does Data Warehousing Fit into My IT Architecture?
How Many Vendors Do I Need to Talk to?
What Should I Look for in a Data Warehouse Vendor?
How Does Data Warehousing Affect My Existing Systems?
Data Warehousing and Its Impact on Other Enterprise Initiatives
When Is a Data Warehouse Not Appropriate?
How Do I Manage or Control a Data Warehouse Initiative?
In Summary
5. The Project Manager
How Do I Roll Out a Data Warehouse Initiative?
How Imprtant Is the Hardware Platform?
What Technologies Are Involved?
Do I Still Use Relational Databases for Data Warehousing?
How Long Does a Data Warehousing Project Last?
How Is a Data Warehouse Different from Other IT Projects?
What Are the Critical Success Factors of a Data Warehousing Project?
In Summary
III: Process
III: Process
6. Warehousing Strategy
Strategy Components
Determine Organizational Context
Conduct Preliminary Survey of Requirements
Conduct Preliminary Source System Audit
Identify External Data Sources (If Applicable)
Define Warehouse Roolouts (Phased Implementation)
Define Preliminary Data Warehouse Architecture
Evaluate Development and Production Environment and Tools
In Summary
7. Warehouse Management and Support Processes
Define Issue Tracking and Resolution Process
Perform Capacity Planning
Define Warehouse Purging Rules
Define Security Measures
Define Backup and Recovery Strategy
Set Up Collection of Warehouse Usage Statistics
In Summary
8. Data Warehouse Planning
Assemble and Orient Team
Conduct Decisional Requirements Analysis
Conduct Decisional Source System Audit
Design Logical and Physical Warehouse Schema
Produce Source-to-Target Field Mapping
Select Development and Production Environment and Tools
Create Prototype for This Rollout
Create Implementation Plan of This Rollout
Warehouse Planning Tips and Caveats
In Summary
9. Data Warehouse Implementation
Acquire and Set Up Development Environment
Obtain Copies of Operational Tables
Finalize Physical Warehouse Schema Design
Build or Configure Extraction and Transformation Subsystems
Build or Configure Data Quality Subsystem
Build Warehouse Load Subsystem
Set Up Warehouse Metadata
Set Up Data Access and Retrieval Tools
Perform the Production Warehouse Load
Conduct User Training
Conduct User Testing and Acceptance
In Summary
IV: Technology
IV: Technology
10. Hardware and Operating Systems
Parallel Hardware Technology
Hardware Selection Criteria
In summary
11. Warehousing Software
Middleware and Connectivity Tools
Extraction Tools
Transformation Tools
Data Quality Tools
Data Loaders
Database Management Systems
Metadata Repository
Data Access and Retrieval Tools
Data Modeling Tools
Warehouse Management Tools
Source Systems
In Summary
12. Warehouse Schema Design
OLTP Systems Use Normalized Data Structures
Dimensional Modeling for Decisional Systems
Two Types of Tables: Facts and Dimensions
A Schema Is a Fact Table Plus Its Related Dimension Tables
Facts Are Fully Normalized, Dimensions Are Denormalized
Dimensional Hierarchies and Hierarchical Drilling
The Time Dimension
The Granularity of the Fact Table
The Fact Table Key Concatenates Dimension Keys
Aggregates or Summaries
Dimensional Attributes
Multiple Star Schemas
Core and Custom Tables
In Summary
13. Warehouse Metadata
Metadata Are a Form of Abstration
Why Are Metadata Imprtant?
Metadata Types
Versioning
Metadata as the Basis for Automating Warehousing Tasks
In Summary
14. Warehousing Applications
The Early Adopters
Types of Warehousing Applications
Financial Analysis and Management
Specialized Applications of Warehousing Technology
In Summary
V: Where to Now?
V: Where to Now?
15. Warehouse Maintenance and Evolution
Regular Warehous Loads
Warehouse Statistics Collection
Warehouse User Profiles
Security and Access Profiles
Data Quality
Data Growth
Updates to Warehouse Subsystems
Database Optimization and Tuning
Data Warehouse Staffing
Warehouse Staff and User Training
Subsequent Warehouse Rollouts
Chargeback Schemes
Disaster Recovery
In Summary
16. Warehousing Trends
Continued Growth of the Data Warehouse Industry
Increased Adoption of Warehousing Technology by More Industries
Increased Maturity of Data Mining Technologies
Emergence and Use of Metadata Interchange Standards
Increased Availability of Web-Enabled Solutions
Popularity of Windows NT for Data Mart Projects
Availability of Warehousing Modules for Application Packages
More Mergers and Acquisitions Among Warehouse Players
In Summary
VI: Appendices
VI: Appendices
A. R/ OLAP XL® User's Manual
Welcome to R/ OLAP XL!
Installation
Tutorial
User's Guide
Working with R/ OLAP XL Columns
Setting R/ OLAP XL Options
The R/ OLAP XL Toolbars
Macro Programming
R/ OLAP XL Messages
B. Warehouse Designer® User's Manual
Welcome to Warehouse Designer!
Basic Consepts
The Warehouse Designer Toolbars
Applications
Dimensions
Schemas
Custom Dimensions
Custom Schemas
Aggregate Dimensions
Aggregate Schemas
C. Online Data Warehousing Resources
C. Online Data Warehousing Resources
D. Tool and Vendor Inventory
D. Tool and Vendor Inventory
E. Software License Agreement


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Data Warehousing Design and Advanced Engineering Applications
Biological Data Mining
Complex Data Warehousing and Knowledge Discovery for Development - Advanced Retrieval Innovative Methods and Applications
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Data Warehousing and Knowledge Discovery

Table of Contents
Conceptual Design and Modeling
UML-Based Modeling for What-If Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Matteo Golfarelli and Stefano Rizzi
Model-Driven Metadata for OLAP Cubes from the Conceptual
Modelling of Data Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Jes´us Pardillo, Jose-Norberto Maz´on, and Juan Trujillo
An MDA Approach for the Development of Spatial Data Warehouses . . . 23
Octavio Glorio and Juan Trujillo
OLAP and Cube Processing
Built-In Indicators to Discover Interesting Drill Paths in a Cube . . . . . . . 33
V´eronique Cariou, J´erˆome Cubill´e, Christian Derquenne,
Sabine Goutier, Fran¸coise Guisnel, and Henri Klajnmic
Upper Borders for Emerging Cubes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
S´ebastien Nedjar, Alain Casali, Rosine Cicchetti, and Lotfi Lakhal
Top Keyword: An Aggregation Function for Textual Document
OLAP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Franck Ravat, Olivier Teste, Ronan Tournier, and Gilles Zurfluh
Distributed Data Warehouse
Summarizing Distributed Data Streams for Storage in Data
Warehouses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Raja Chiky and Georges H´ebrail
Efficient Data Distribution for DWS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
Raquel Almeida, Jorge Vieira, Marco Vieira,
Henrique Madeira, and Jorge Bernardino
Data Partitioning in Data Warehouses: Hardness Study, Heuristics and
ORACLE Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Ladjel Bellatreche, Kamel Boukhalfa, and Pascal Richard
Data Privacy in Data Warehouse
A Robust Sampling-Based Framework for Privacy Preserving OLAP . . . . 97
Alfredo Cuzzocrea, Vincenzo Russo, and Domenico Sacc`a
Generalization-Based Privacy-Preserving Data Collection . . . . . . . . . . . . . 115
Lijie Zhang and Weining Zhang
Processing Aggregate Queries on Spatial OLAP Data . . . . . . . . . . . . . . . . . 125
Kenneth Choi and Wo-Shun Luk
Data Warehouse and Data Mining
Efficient Incremental Maintenance of Derived Relations and BLAST
Computations in Bioinformatics Data Warehouses . . . . . . . . . . . . . . . . . . . . 135
Gabriela Turcu, Svetlozar Nestorov, and Ian Foster
Mining Conditional Cardinality Patterns for Data Warehouse Query
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
Miko laj Morzy and Marcin Krystek
Up and Down: Mining Multidimensional Sequential Patterns Using
Hierarchies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
Marc Plantevit, Anne Laurent, and Maguelonne Teisseire
Clustering I
Efficient K-Means Clustering Using Accelerated Graphics Processors . . . 166
S.A. Arul Shalom, Manoranjan Dash, and Minh Tue
Extracting Knowledge from Life Courses: Clustering and
Visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
Nicolas S. M¨uller, Alexis Gabadinho, Gilbert Ritschard, and
Matthias Studer
A Hybrid Clustering Algorithm Based on Multi-swarm Constriction
PSO and GRASP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186
Yannis Marinakis, Magdalene Marinaki, and Nikolaos Matsatsinis
Clustering II
Personalizing Navigation in Folksonomies Using Hierarchical Tag
Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196
Jonathan Gemmell, Andriy Shepitsen, Bamshad Mobasher, and
Robin Burke
Clustered Dynamic Conditional Correlation Multivariate GARCH
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
Tu Zhou and Laiwan Chan
Document Clustering by Semantic Smoothing and Dynamic Growing
Cell Structure (DynGCS) for Biomedical Literature . . . . . . . . . . . . . . . . . . 217
Min Song, Xiaohua Hu, Illhoi Yoo, and Eric Koppel
Mining Data Streams
Mining Serial Episode Rules with Time Lags over Multiple Data
Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227
Tung-Ying Lee, En Tzu Wang, and Arbee L.P. Chen
Efficient Approximate Mining of Frequent Patterns over Transactional
Data Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Willie Ng and Manoranjan Dash
Continuous Trend-Based Clustering in Data Streams . . . . . . . . . . . . . . . . . 251
Maria Kontaki, Apostolos N. Papadopoulos, and
Yannis Manolopoulos
Mining Multidimensional Sequential Patterns over Data Streams . . . . . . . 263
Chedy Ra¨ıssi and Marc Plantevit
Classification
Towards a Model Independent Method for Explaining Classification for
Individual Instances . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
Erik
ˇ
Strumbelj and Igor Kononenko
Selective Pre-processing of Imbalanced Data for Improving
Classification Performance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
Jerzy Stefanowski and Szymon Wilk
A Parameter-Free Associative Classification Method . . . . . . . . . . . . . . . . . . 293
Lo¨ıc Cerf, Dominique Gay, Nazha Selmaoui, and
Jean-Fran¸cois Boulicaut
Text Mining and Taxonomy I
The Evaluation of Sentence Similarity Measures . . . . . . . . . . . . . . . . . . . . . 305
Palakorn Achananuparp, Xiaohua Hu, and Xiajiong Shen
Labeling Nodes of Automatically Generated Taxonomy for Multi-type
Relational Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 317
Tao Li and Sarabjot S. Anand
Towards the Automatic Construction of Conceptual Taxonomies . . . . . . . 327
Dino Ienco and Rosa Meo
Text Mining and Taxonomy II
Adapting LDA Model to Discover Author-Topic Relations for Email
Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
Liqiang Geng, Hao Wang, Xin Wang, and Larry Korba
A New Semantic Representation for Short Texts . . . . . . . . . . . . . . . . . . . . . 347
M.J. Mart´ın-Bautista, S. Mart´ınez-Folgoso, and M.A. Vila
Document-Base Extraction for Single-Label Text Classification . . . . . . . . 357
Yanbo J. Wang, Robert Sanderson, Frans Coenen, and Paul Leng
Machine Learning Techniques
How an Ensemble Method Can Compute a Comprehensible Model . . . . . 368
Jos´e L. Trivi˜no-Rodriguez, Amparo Ruiz-Sep´ulveda, and
Rafael Morales-Bueno
Empirical Analysis of Reliability Estimates for Individual Regression
Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Zoran Bosni´c and Igor Kononenko
User Defined Partitioning - Group Data Based on Computation
Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 389
Qiming Chen and Meichun Hsu
Data Mining Applications
Workload-Aware Histograms for Remote Applications . . . . . . . . . . . . . . . . 402
Tanu Malik and Randal Burns
Is a Voting Approach Accurate for Opinion Mining? . . . . . . . . . . . . . . . . . . 413
Michel Planti´e, Mathieu Roche, G´erard Dray, and Pascal Poncelet
Mining Sequential Patterns with Negative Conclusions . . . . . . . . . . . . . . . . 423
Przemys law Kazienko
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433



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Data Warehousing Design and Advanced Engineering Applications
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Data Warehousing and Data Mining Techniques for Cyber Security

Anoop Singhal
NIST, Computer Security Division
USA
Springer

T A B L E O F C O N T E N T S
Chapter 1: An Overview of Data Warehouse, OLAP and
Data Mining Technology 1
l.Motivationfor a Data Warehouse 1
2.A Multidimensional Data Model 3
3.Data Warehouse Architecture 6
4. Data Warehouse Implementation 6
4.1 Indexing of OLAP Data 7
4.2 Metadata Repository 8
4.3 Data Warehouse Back-end Tools 8
4.4 Views and Data Warehouse 10
5.Commercial Data Warehouse Tools 11
6.FromData Warehousing to Data Mining 11
6.1 Data Mining Techniques 12
6.2 Research Issues in Data Mining 14
6.3 Applications of Data Mining 14
6.4 Commercial Tools for Data Mining 15
7.Data Analysis Applications for NetworkyWeb Services 16
7.1 Open Research Problems in Data Warehouse 19
7.2 Current Research in Data Warehouse 21
8.Conclusions 22
Chapter 2: Network and System Security 25
1. Viruses and Related Threats 26
1.1 Types of Viruses 27
1.2 Macro Viruses 27
1.3 E-mail Viruses 27
1.4 Worms 28
1.5 The Morris Worm 28
1.6 Recent Worm Attacks 28
1.7 Virus Counter Measures 29
2. Principles of Network Security 30
2.1 Types of Networks and Topologies 30
2.2 Network Topologies 31
3.Threats in Networks 31
4.Denial of Service Attacks 33
4.1 Distributed Denial of Service Attacks 34
4.2 Denial of Service Defense Mechanisms 34
5.Network Security Controls 36
6. Firewalls 38
6.1 What they are 38
6.2 How do they work 39
6.3 Limitations of Firewalls 40
7.Basics of Intrusion Detection Systems 40
8. Conclusions 41
Chapter 3: Intrusion Detection Systems 43
l.Classification of Intrusion Detection Systems 44
2.Intrusion Detection Architecture 48
3.IDS Products 49
3.1 Research Products 49
3.2 Commercial Products 50
3.3 Public Domain Tools 51
3.4 Government Off-the Shelf (GOTS) Products 53
4. Types of Computer Attacks Commonly Detected by IDS 53
4.1 Scanning Attacks 53
4.2 Denial of Service Attacks 54
4.3 Penetration Attacks 55
5.Significant Gaps and Future Directions for IDS 55
6. Conclusions 57
Chapter 4: Data Mining for Intrusion Detection 59
1. Introduction 59
2.Data Mining for Intrusion Detection 60
2.1 Adam 60
2.2 Madam ID 63
2.3 Minds 64
2.4 Clustering of Unlabeled ID 65
2.5 Alert Correlation 65
3.Conclusions and Future Research Directions 66
Chapter 5: Data Modeling and Data Warehousing Techniques
to Improve Intrusion Detection 69
1. Introduction 69
2. Background 70
3.Research Gaps 72
4.A Data Architecture for IDS 73
5. Conclusions 80
Chapter 6: MINDS - Architecture & Design 83
1. MINDS- Minnesota Intrusion Detection System 84
2. Anomaly Detection 86
3. Summarization 90
4. Profiling Network Traffic Using Clustering 93
5. Scan Detection 97
6. Conclusions 105
7. Acknowledgements 105
Chapter 7: Discovering Novel Attack Strategies from
INFOSEC Alerts 109
1. Introduction 110
2. Alert Aggregation and Prioritization 112
3. Probabilistic Based Alert Correlation 116
4. Statistical Based Correlation 122
5. Causal Discovery Based Alert Correlation 129
6. Integration of three Correlation Engines 136
7. Experiments and Performance Evaluation 140
8. Related Work 150
9. Conclusion and Future Work 153
Index 159

Keywords : Data warehouse - Wikipedia, the free encyclopedia. Data Warehousing Concepts, data warehouse concepts, enterprise data warehouse, data warehouse architecture, data warehouse tools, data warehouse institute, what is a data warehouse, data warehouses, data warehouse certification, data warehouse consulting, kimball data warehouse, data warehouse products, data warehouse design, data warehouse architect, data warehouse solution, data warehouse vendors, management data warehouse, ods data warehouse, open source data warehouse, data warehouse tutorial, federated data warehouse, data warehouse companies, data warehouse consultant, software data warehouse, data warehouse applications, data warehouse systems, data warehouse reporting, data warehouse tool, cognos data warehouse, data warehouse interview questions, etl data warehouse, sql server data warehouse, shared data warehouse, data warehouse basics, data warehouse training, what is data warehouse, data warehouse manager, data warehouse application, data warehouse example, data warehouse software, healthcare data warehouse, data warehouse diagram, sql data warehouse, data warehouse etl, the data warehouse toolkit, data warehouse and data mining, data warehouse vendor, data warehouse testing, data warehouse specialist, bi data warehouse, data warehouseing

Other Data Warehouse Books
Other Data Mining Books
Data Warehousing Design and Advanced Engineering Applications
Biological Data Mining
Download