1st Edition

Intelligent Data Warehousing
From Data Preparation to Data Mining




ISBN 9780849312045
Published December 13, 2001 by CRC Press
256 Pages 14 B/W Illustrations

USD $185.00

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Book Description

Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in fact many of its advances have and will continue to come from the computer science arena.

Intelligent Data Warehousing presents the state of the art in data warehousing research and practice from a perspective that integrates business applications and computer science. It brings the intelligent techniques associated with artificial intelligence (AI) to the entire process of data warehousing, including data preparation, storage, and mining. Part I provides an overview of the main ideas and fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing. Part II presents core materials on data warehousing, and Part III explores data analysis and knowledge discovery in the data warehousing environment, including how to perform intelligent data analysis and the discovery of influential association patterns.

Bridging the gap between theoretical research and business applications, this book summarizes the main ideas behind recent research developments rather than setting forth technical details, and it presents case studies that show the how-to's of implementing these ideas. The result is a practical, first-of-its-kind book that brings together scattered research, unites MIS with computer science, and melds intelligent techniques with data warehousing.

Table of Contents

Part I:
INTRODUCTION
Why this Book is Needed
Features of the Book
Why Intelligent Data Warehousing
Organization of the Book
How to Use this Book
ENTERPRISE INTELLIGENCE AND ARTIFICIAL INTELLIGENCE
Overview
Data Warehouse and Business Intelligence
Historical Development of Data Warehousing
Basic Elements of Data Warehousing
Databases and the Web
Basics of Artificial Intelligence and Inductive Machine Learning
Data Warehousing with Intelligent Agents
Data Mining, CRM, Web Mining and Clickstream
The Future of Data Warehouses
BASICS OF DATA WAREHOUSING
Overview
An Overview of Database Management Systems
Advances in DBMS
Architecture and Design of Data Warehouses
Data Marts
Metadata
Data Warehousing and Materialized Views
Data Warehouse Performance
Data warehousing and OLAP
Part II:
DATA PREPARATION AND PREPROCESSING
Overview
Schema and Data Integration
Data Pumping
Middleware
Data Quality
Data Cleansing
Uncertainty and Inconsistency
Data Reduction
Case Study: Data Preparation for Stock Food Chain Analysis
Web log File Preparation
References
BUILDING DATA WAREHOUSES
Overview
Conceptual Data Modeling
Data Warehouse Design Using ER Approach
Aspects of Building Data Warehouses
Data Cubes
BASICS OF MATERIALIZED VIEWS
Overview
Data Cubes
Using Simple Optimization Algorithm to Select Views
Aggregates Calculation Using Pre-Constructed Data Structures in Data Cubes
View Selection for a Human Service Data Warehouse
ADVANCES IN MATERIALIZED VIEWS
Overview
Data Warehouse Design Through Materialized Views
Maintenance of Materialized Views
Consistency in View Maintenance
Integrity Constraints and Active Databases
Dynamic Warehouse Design
Implementation Issues and Online Updates
Data Cubes
Materialized Views in Advanced Database Systems
Relationship with Mobile Databases
Other Issues
Part III:
INTELLIGENT DATA ANALYSIS
Overview
Basics of Data Mining
Case Study: Stock Food Chain Analysis
Case Study: Rough Set Data Analysis
Recent Progress of Data Mining
TOWARD INTEGRATED OLAP AND DATA MINING
Overview
Integration of OLAP and Data Mining
Influential Association Rules
Significance of Influential Association Rules
Reviews of Algorithms for Discovery of Conventional Association Rules
Discovery of Influential Association Rules
Bitmap Indexing and Influential Association Rules
Mining Influential Association Rules Using Bitmap Indexing
INDEX

Each chapter also contains a Summary section and Reference

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