Batini / Scannapieco Data Quality

Concepts, Methodologies and Techniques
1. Auflage 2006
ISBN: 978-3-540-33173-5
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 262 Seiten

Reihe: Data-Centric Systems and Applications

Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the 'Data Quality Act' in the USA and the 'European 2003/98' directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone - researchers, students, or professionals - interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.

Carlo Batini is full professor of Computer Engineering at University of Milano Bicocca. He has been associate professor since 1983 and full professor since 1986. His research interests include cooperative information systems, information systems and data base modeling and design, usability of information systems, data and information quality. From 1995 to 2003 he was a member of the board of directors of the Authority for Information Technology in public administration, where he headed several large scale projects for the modernization of public administration. Monica Scannapieco is a research associate at the Computer Engineering Department of the University of Roma La Sapienza. Her research interests are data quality issues, including data quality dimensions, measurement and improvement techniques, dynamics of data quality, record matching.

Autoren/Hrsg.

Batini, Carlo

Scannapieco, Monica

Inhaltsverzeichnis

1;Preface;6
1.1;Motivation for the Book;6
1.2;Goals;7
1.3;Organization;9
1.4;Intended Audience;10
1.5;Guidelines for Teaching;12
1.6;Acknowledgements;13
2;Contents;14
3;1 Introduction to Data Quality;19
3.1;1.1 Why Data Quality is Relevant;19
3.2;1.2 Introduction to the Concept of Data Quality;22
3.3;1.3 Data Quality and Types of Data;24
3.4;1.4 Data Quality and Types of Information Systems;27
3.5;1.5 Main Research Issues and Application Domains in Data Quality;29
3.6;1.6 Summary;35
4;2 Data Quality Dimensions;37
4.1;2.1 Accuracy;38
4.2;2.2 Completeness;41
4.3;2.3 Time-Related Dimensions: Currency, Timeliness, and Volatility;46
4.4;2.4 Consistency;48
4.5;2.5 Other Data Quality Dimensions;50
4.6;2.6 Approaches to the Definition of Data Quality Dimensions;54
4.7;2.7 Schema Quality Dimensions;60
4.8;2.8 Summary;66
5;3 Models for Data Quality;69
5.1;3.1 Introduction;69
5.2;3.2 Extensions of Structured Data Models;70
5.3;3.3 Extensions of Semistructured Data Models;77
5.4;3.4 Management Information System Models;79
5.5;3.5 Summary;86
6;4 Activities and Techniques for Data Quality: Generalities;87
6.1;4.1 Data Quality Activities;88
6.2;4.2 Quality Composition;89
6.3;4.3 Error Localization and Correction;100
6.4;4.4 Cost and Benefit Classifications;106
6.5;4.5 Summary;113
7;5 Object Identification;115
7.1;5.1 Historical Perspective;116
7.2;5.2 Object Identification for Different Data Types;117
7.3;5.3 The High-Level Process for Object Identification;119
7.4;5.4 Details on the Steps for Object Identification;121
7.5;5.5 Object Identification Techniques;124
7.6;5.6 Probabilistic Techniques;124
7.7;5.7 Empirical Techniques;131
7.8;5.8 Knowledge-Based Techniques;139
7.9;5.9 Comparison of Techniques;143
7.10;5.10 Summary;149
8;6 Data Quality Issues in Data Integration Systems;151
8.1;6.1 Introduction;151
8.2;6.2 Generalities on Data Integration Systems;152
8.3;6.3 Techniques for Quality-Driven Query Processing;155
8.4;6.4 Instance-level Conflict Resolution;161
8.5;6.5 Inconsistencies in Data Integration: a Theoretical Perspective;175
8.6;6.6 Summary;178
9;7 Methodologies for Data Quality Measurement and Improvement;179
9.1;7.1 Basics on Data Quality Methodologies;179
9.2;7.2 Assessment Methodologies;185
9.3;7.3 Comparative Analysis of General-purpose Methodologies;188
9.4;7.4 The CDQM methodology;199
9.5;7.5 A Case Study in the e-Government Area;206
9.6;7.6 Summary;217
10;8 Tools for Data Quality;219
10.1;8.1 Introduction;219
10.2;8.2 Tools;220
10.3;8.3 Frameworks for Cooperative Information Systems;230
10.4;8.4 Toolboxes to Compare Tools;234
10.5;8.5 Summary;236
11;9 Open Problems;239
11.1;9.1 Dimensions and Metrics;239
11.2;9.2 Object Identification;240
11.3;9.3 Data Integration;245
11.4;9.4 Methodologies;248
11.5;9.5 Conclusions;253
12;References;255
13;Index;267