Chen Intelligent Data Warehousing

From Data Preparation to Data Mining
Erscheinungsjahr 2001
ISBN: 978-1-4200-4061-6
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 256 Seiten

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.

Zielgruppe

Database administrators and designers, Web designers, and systems programmers

Autoren/Hrsg.

Chen, Zhengxin

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken

Inhaltsverzeichnis

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