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Data Quality - Concepts, Methodologies and Techniques
Carlo Batini, Monica Scannapieco
Verlag Springer-Verlag, 2006
ISBN 9783540331735 , 262 Seiten
Format PDF, OL
Kopierschutz Wasserzeichen
Geräte
Preface
6
Motivation for the Book
6
Goals
7
Organization
9
Intended Audience
10
Guidelines for Teaching
12
Acknowledgements
13
Contents
14
1 Introduction to Data Quality
19
1.1 Why Data Quality is Relevant
19
1.2 Introduction to the Concept of Data Quality
22
1.3 Data Quality and Types of Data
24
1.4 Data Quality and Types of Information Systems
27
1.5 Main Research Issues and Application Domains in Data Quality
29
1.6 Summary
35
2 Data Quality Dimensions
37
2.1 Accuracy
38
2.2 Completeness
41
2.3 Time-Related Dimensions: Currency, Timeliness, and Volatility
46
2.4 Consistency
48
2.5 Other Data Quality Dimensions
50
2.6 Approaches to the Definition of Data Quality Dimensions
54
2.7 Schema Quality Dimensions
60
2.8 Summary
66
3 Models for Data Quality
69
3.1 Introduction
69
3.2 Extensions of Structured Data Models
70
3.3 Extensions of Semistructured Data Models
77
3.4 Management Information System Models
79
3.5 Summary
86
4 Activities and Techniques for Data Quality: Generalities
87
4.1 Data Quality Activities
88
4.2 Quality Composition
89
4.3 Error Localization and Correction
100
4.4 Cost and Benefit Classifications
106
4.5 Summary
113
5 Object Identification
115
5.1 Historical Perspective
116
5.2 Object Identification for Different Data Types
117
5.3 The High-Level Process for Object Identification
119
5.4 Details on the Steps for Object Identification
121
5.5 Object Identification Techniques
124
5.6 Probabilistic Techniques
124
5.7 Empirical Techniques
131
5.8 Knowledge-Based Techniques
139
5.9 Comparison of Techniques
143
5.10 Summary
149
6 Data Quality Issues in Data Integration Systems
151
6.1 Introduction
151
6.2 Generalities on Data Integration Systems
152
6.3 Techniques for Quality-Driven Query Processing
155
6.4 Instance-level Conflict Resolution
161
6.5 Inconsistencies in Data Integration: a Theoretical Perspective
175
6.6 Summary
178
7 Methodologies for Data Quality Measurement and Improvement
179
7.1 Basics on Data Quality Methodologies
179
7.2 Assessment Methodologies
185
7.3 Comparative Analysis of General-purpose Methodologies
188
7.4 The CDQM methodology
199
7.5 A Case Study in the e-Government Area
206
7.6 Summary
217
8 Tools for Data Quality
219
8.1 Introduction
219
8.2 Tools
220
8.3 Frameworks for Cooperative Information Systems
230
8.4 Toolboxes to Compare Tools
234
8.5 Summary
236
9 Open Problems
239
9.1 Dimensions and Metrics
239
9.2 Object Identification
240
9.3 Data Integration
245
9.4 Methodologies
248
9.5 Conclusions
253
References
255
Index
267
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