Data Quality: Concepts, Methodologies and Techniques

✍ Scribed by Carlo Batini, Monica Scannapieca (auth.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 2006
• Tongue: English
• Leaves: 275
• Series: Data-Centric Systems and Applications
• Edition: 1
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Introduction to Data Quality....Pages 1-18
Data Quality Dimensions....Pages 19-49
Models for Data Quality....Pages 51-68
Activities and Techniques for Data Quality: Generalities....Pages 69-95
Object Identification....Pages 97-132
Data Quality Issues in Data Integration Systems....Pages 133-160
Methodologies for Data Quality Measurement and Improvement....Pages 161-200
Tools for Data Quality....Pages 201-219
Open Problems....Pages 221-235

✦ Subjects

Database Management; Information Storage and Retrieval; Business Information Systems; Information Systems Applications (incl.Internet)