1st Edition

Intelligent Data Warehousing From Data Preparation to Data Mining

By Zhengxin Chen Copyright 2001
    256 Pages 14 B/W Illustrations
    by CRC Press

    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.

    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

    Biography

    Zhengxin Chen