Processing and Managing Complex Data for Decision Support, 1st Edition

  • Published By:
  • ISBN-10: 1591406579
  • ISBN-13: 9781591406570
  • Grade Level Range: College Freshman - College Senior
  • 429 Pages | eBook
  • Original Copyright 2006 | Published/Released November 2006
  • This publication's content originally published in print form: 2006

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About

Overview

In many decision support fields the data that is exploited tends to be more and more complex. To take this phenomenon into account, classical architectures of data warehouses or data mining algorithms must be completely re-evaluated.

Processing and Managing Complex Data for Decision Support provides readers with an overview of the emerging field of complex data processing by bringing together various research studies and surveys in different subfields, and by highlighting the similarities between the different data, issues, and approaches. This book deals with important topics such as: complex data warehousing, including spatial, XML, and text warehousing; and complex data mining, including distance metrics and similarity measures, pattern management, multimedia, and gene sequence mining.

Table of Contents

Front Cover.
Title Page.
Copyright Page.
Processing and Managing Complex Data for Decision Support Table of Contents.
Foreword.
Preface.
Acknowledgments.
1: Complex Data Warehousing.
2: Spatial Data Warehouse Modelling.
3: Goal-Oriented Requirement Engineering for XML Document Warehouses.
4: Building an Active Content Warehouse.
5: Text Warehousing: Present and Future.
6: Morphology, Processing, and Integrating of Information from Large Source Code Warehouses for Decision Support.
7: Managing Metadata in Decision Environments.
8: DWFIST: The Data Warehouse of Frequent Itemsets Tactics Approach.
9: Complex Data Mining.
10: On the Usage of Structural Distance Metrics for Mining Hierarchical Structures.
11: Structural Similarity Measures in Sources of XML Documents.
12: Pattern Management: Practice and Challenges.
13: VRMiner: A Tool for Multimedia Database Mining with Virtual Reality.
14: Mining in Music Databases.
15: Data Mining in Gene Expression Data Analysis: A Survey.
About the Authors.
Index.