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
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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 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 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

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Data Warehousing and Data Mining for Telecommunications

Rob Mattison
Artech House
Boston • London

Contents
Foreword xiii
Preface xvii
Chapter 1
Everything’s up to date in
Kansas City 1
1.1 The current industry composition 3
1.2 Why is telecommunications so
BIG? 4
1.3 Telecommunications: the major
driving economic force of the 21st
century 5
1.4 Knowledge management enablement—
the biggest factor of all 6
1.5 The ultimate environment 7
1.5.1 Failed excursions into the
new frontiers 7
1.6 Future directions 8
1.7 Telecommunications and
technological innovation 9
1.7.1 Peg counts 9
1.7.2 Business drives technological
innovation 9
1.8 The three strategic options 10
1.9 Customer intimacy—from “network
is king” to “customer is king” 10
1.9.1 Marketing as the driving force 11
1.10 Operational efficiency—being the
low-cost provider of choice 11
1.11 Technical proficiency—being the
best at what you do 12
1.12 Conclusion 12
Chapter 2
Why warehousing and
how to get started 13
2.1 Background of data warehousing 14
2.1.1 The history of the data
warehousing phenomenon 14
2.1.2 Data warehousing—in a
nutshell 16
2.1.3 What is a data warehouse? 17
2.2 Data mining 18
2.2.1 Why should one seriously consider
using these approaches? 20
2.3 Why are these approaches so
exceptionally valuable to
telecommunications firms? 20
2.3.1 Data intensity 20
2.3.2 Analysis dependency 21
2.3.3 Competitive climate 21
2.3.4 Technological change at a very
high rate 21
2.3.5 Historical precedent 22
2.4 Organizing the process 22
2.4.1 An inventory of the existing
computer systems and other
technological infrastructure 22
2.4.2 A roadmap and an approach for
how to deploy data warehouses
in general 23
2.4.3 A roadmap for understanding how
to diagnose and develop a plan for
identifying the best things to put
into the warehouse and which data
mining tools to use 23
Chapter 3
The knowledge management view of
business and warehousing 25
3.1 The knowledge management
revolution 26
3.1.1 Knowledge management
principles 27
3.1.2 The organizational footprint and
what it tells us about knowledge
transformation processes 29
3.2 Efficiency optimization—optimize
the silo or optimize the whole 32
3.2.1 Which type of warehouse is better,
or which is the right one? 34
3.2.2 The warehouse alternative 37
3.2.3 A third alternative 37
3.3 The corporate global warehouse
model 38
3.3.1 Developing a truly usable global
architecture model 40
3.3.2 An alternative foundation:
the value chain 42
3.3.3 The key to value chain
delivery 45
3.4 Overall strategy for development
(one piece at a time, fitting into the
overall architecture) 45
3.4.1 The growing warehouse
example 47
3.4.2 Ownership of knowledge
issues 49
Chapter 4
The telecommunications
value chain 51
4.1 The knowledge roadmap
solution 52
4.2 Steps in the process of deriving a
business’ value chain 52
4.3 Telecommunications functions
and systems 53
4.3.1 Creation (new product develop-
ment and exploitation) 54
4.3.2 Acquisition (acquiring the “right”
to do business) 54
4.3.3 Network infrastructure planning
and development (creating the
“phone system”) 55
4.3.4 Network infrastructure
maintenance (maintaining the
“phone system”) 56
4.3.5 Provisioning (setting up customer
services) 57
4.3.6 Activation (activating customer
services) 57
4.3.7 Service order processing 58
4.3.8 Billing (tracking service and
invoicing the customer) 58
4.3.9 Marketing (identifying prospects/
channels, advertising) 59
4.3.10 Customer service (keeping the
customer happy) 59
4.3.11 Sales (establishing and maintaining
customer relationships) 61
4.3.12 Finance and accounting 61
4.3.13 Credit management 61
4.3.14 Operations (network and
business) 62
4.3.15 A comprehensive value chain 62
4.4 Organizational structure and the
value chain 63
4.4.1 Typical organizational structure:
medium-sized cellular firm 64
4.4.2 Typical organizational structure:
large telecommunications
firms 66
4.5 Allocating the business units to the
value chain and the knowledge
management process 66
4.5.1 Aligning the value chain and
the organization—large
megacorporation 68
4.5.2 Aligning the value chain with the
information systems 71
4.5.3 Kingpin systems: the beginning of
computer systems alignment 72
4.5.4 Alignment problems and their
symptoms 74
4.5.5 Data warehousing as an
alternative 76
4.5.6 Data warehousing as a migration
path 77
4.5.7 The fully aligned model—
a summary 77
Chapter 5
Building the warehouse—
one step at a time 79
5.1 Challenges to infrastructure
design 80
5.2 The functional components of a
warehouse environment 84
5.2.1 Acquisition 85
5.2.2 Storage 87
5.2.3 Access 88
5.2.4 The operational
infrastructure 89
5.2.5 The physical infrastructure 89
5.3 The step-by-step, cost-justified
approach 89
5.3.1 What is a value proposition? 90
5.3.2 Gathering value propositions 90
5.4 How do you build a warehouse? 92
Chapter 6
Value propositions in
telecommunications 95
6.1 Mining tools and value delivery 96
6.1.1 Operational monitoring and
control 96
6.1.2 Discovery and exploration 97
6.2 Value propositions by functional
area 98
6.2.1 Marketing value propositions
(historical/cross-silo/
discovery) 99
6.2.2 Credit value propositions 99
6.2.3 Customer service value
propositions—(real-time and
historical/cross-silo/
operational monitoring) 100
6.2.4 Sales value propositions 101
6.2.5 Network planning value
propositions 101
6.2.6 Network maintenance value
propositions 103
6.2.7 Creation 103
6.2.8 Activation and provisioning and
service order processing 104
6.2.9 Billing (historical/single-silo/
discovery and monitoring) 104
6.2.10 Operations 104
6.3 Conclusions 105
6.3.1 Knowledge management
approach 105
Chapter 7
Simple sales analysis:
an introduction to operational moni-
toring using Microsoft Query 107
7.1 Operational efficiency—an
overview 109
7.2 Sales monitoring and control 110
7.3 A universal problem 111
7.4 Using Microsoft Query and Excel to
do sales tracking 111
7.4.1 The sales database 112
7.5 Managing more complicated
needs 115
7.6 Alternative methods of
accessing data 115
Chapter 8
Sales and product management:
advanced operational monitoring
using COGNOS PowerPlay 117
8.1 Monitoring complex business
organizations 118
8.1.1 Determining the different levels at
which to report 119
8.1.2 Preparing the data for use 121
8.2 Exploring sales and product
performance 121
8.3 Additional PowerPlay features 124
8.3.1 Alerts 125
8.3.2 Schedulers 126
8.4 Summary 127
Chapter 9
Customer intimacy:
an introduction using SPSS 129
9.1 An introduction to analytical
mining 130
9.2 Statistical analysis—options and
objectives 131
9.3 Descriptive approaches 133
9.4 Inferential approaches—regression
analysis 137
9.5 Conclusions on statistical
analysis 140
Chapter 10
Predicting customer behavior:
an introduction to neural
networks 143
10.1 Unraveling complex
situations 144
10.2 How can a neural network help
with marketing? 145
10.3 Step-by-step use of a neural
network 145
10.3.1 What does the training report tell
us? 146
10.3.2 Creating and interpreting the
gains table 147
10.3.3 Analyzing the gains chart 149
10.3.4 Making marketing programs as
profitable as possible 151
10.4 Applying the model to
prospects 152
10.5 Conclusion on neural
networks 152
Chapter 11
Engineering and competitive analysis
support: an introduction to geographi-
cal systems and MapInfo 155
11.1 An introduction to MapInfo
Professional 156
11.1.1 MapInfo telecommunications
offerings 156
11.2 Using geographical information to
solve telecommunications
problems 158
11.3 Cellsite analysis with MapInfo
Professional 159
11.4 Market analysis capabilities 162
11.5 Viewing a local market in greater
detail 164
11.6 Accessibility to fiber analysis 165
11.7 Working with the underlying
database 167
11.8 Conclusion 168
Appendix A
Real world warehousing: France
Telecom and STATlab tools 169
Appendix B
The business case
for business intelligence 211
Appendix C
SPSS 239
Appendix D
The DecisionWORKS suite
from Advanced Software
Applications 245
Glossary 251
Selected bibliography 257
About the author 261
Index 263

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

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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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Data Strategy

By Sid Adelman, Larissa T. Moss, Majid Abai
...............................................
Publisher: Prentice Hall PTR
Pub Date: June 15, 2005
ISBN: 0-321-24099-5
Pages: 384



Table of Contents | Index


The definitive best-practices guide to enterprise data-management strategy.You can no longer manage enterprise data "piecemeal." To maximize the business value of your data assets, you must define a coherent, enterprise-wide data strategy that reflects all the ways you capture, store, manage, and use information.In this book, three renowned data management experts walk you through creating the optimal data strategy for your organization. Using their proven techniques, you can reduce hardware and maintenance costs, and rein in out-of-control data spending. You can build new systems with less risk, higher quality, and improve data access. Best of all, you can learn how to integrate new applications that support your key business objectives.Drawing on real enterprise case studies and proven best practices, the author team covers everything from goal-setting through managing security and performance. You'll learn how to: Identify the real risks and bottlenecks you face in delivering data—and the right solutions Integrate enterprise data and improve its quality, so it can be used more widely and effectively Systematically secure enterprise data and protect customer privacy Model data more effectively and take full advantage of metadata Choose the DBMS and data storage products that fit best into your overall plan Smoothly accommodate new Business Intelligence (BI) and unstructured data applications Improve the performance of your enterprise database applications Revamp your organization to streamline day-to-day data management and reduce cost Data Strategy is indispensable for everyone who needs to manage enterprise data more efficiently—from database architects to DBAs, technical staff to senior IT decision-makers. © Copyright Pearson Education. All rights reserved.

Table of Contents | Index

--------------------------------------------------------------------------------

Copyright
Acknowledgments
About the Authors
Foreword
Chapter 1. Introduction
Current Status in Contemporary Organizations
Why a Data strategy Is Needed
Vision and Goals of the Enterprise
Components of a Data Strategy
How Will You Develop and Implement a Data Strategy?
References
Chapter 2. Data Integration
Ineffective "Silver-Bullet" Technology Solutions
Gaining Management Support
Integrating Business Data
Deciding What Data Should Be Integrated
Consolidation and Federation
Getting Started
Conclusion
References
Chapter 3. Data Quality
Current State of Data Quality
Recognizing Dirty Data
Data Quality Rules
Data Quality Improvement Practices
Enterprise-Wide Data Quality Disciplines
Enterprise Architecture
Business Sponsorship
Conclusion
References
Chapter 4. Metadata
Why Metadata Is Critical to the Business
Metadata Categories
Metadata Sources
Metadata Repository
Developing a Metadata Repository
Managed Metadata Environment
Conclusion
References
Chapter 5. Data Modeling
Origins of Data Modeling
Significance of Data Modeling
Logical Data Modeling Concepts
Enterprise Logical Data Model
Physical Data Modeling Concepts
Physical Data Modeling Techniques
Dimensionality
Factors that Influence the Physical Data Model
Conclusion
References
Chapter 6. Organizational Roles and Responsibilities
Building the Teams Who Create and Maintain the Strategy
Resistance to Change
Optimal Organizational Structures
Training
Roles and Responsibilities
Data Ownership
Information Stewardship
Worst Practices
Agenda for Weekly Data Strategy Team Meeting
Conclusion
Chapter 7. Performance
Performance Requirements
Service Level Agreements
Capacity Planning: Performance Modeling
Capacity Planning: Benchmarks
Application Packages: Enterprise Resource Planning (ERPs)
Designing, Coding, and Implementing
Setting User Expectations
Monitoring (Measurement)
Tuning
Case Studies
Performance Tasks
Conclusion
References
Chapter 8. Security and Privacy of Data
Data Identification for Security and Privacy
Roles and Responsibilities
Regulatory Compliance
Auditing Procedures
Design Solutions
Impact of the Data Warehouse
Vendor Issues
Communicating and Selling Security
Best Practices and Worst Practices
Identify Your Own Sensitive Data Exercise
Conclusion
Chapter 9. DBMS Selection
Existing Environment
DBMS Choices
Why Standardize the DBMS?
Total Cost of Ownership
Application Packages and ERPs
Criteria for Selection
Selection Process
Reference Checking
RFPs for DBMSs
Response Format
Evaluating Vendors
Dealing with the Vendor
Exercise—How Well Are You Using Your DBMS?
Conclusion
References
Chapter 10. Business Intelligence
What Is Business Intelligence?
BI Components
Imprtant BI Tools and Processes
Emerging Trends and Technologies
BI Myths and Pitfalls
Conclusion
References
Chapter 11. Strategies for Managing Unstructured Data
What Is Unstructured Data?
A Unified Content Strategy for the Organization
Emerging Technologies
Conclusion
References
Chapter 12. Business Value of Data and ROI
The Business Value of Data
Align Data with Strategic Goals
The Cost of Developing a Data Strategy
Benefits of a Data Strategy
Conclusion
Reference
Appendix A. ROI Calculation Process, Cost Template, and Intangible Benefits Template
Cost of Capital
Risk
ROI Example
Cost Calculation Template
Intangible Benefits Calculation Template
Reference
Appendix B. Resources
Publications
Websites
Index

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