Data-Centric Systems and Applications
2006
Data QualityConcepts, Methodologies and Techniques Authors:

• Carlo Batini,
• Monica Scannapieca
• …show all 2hide
  

ISBN: 978-3-540-33172-8 (Print) 978-3-540-33173-5  (Online)

About this book
Poor data quality can seriously hinder the effectiveness of organizations and businesses. Growing awareness of this has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament.
Here is a systematic introduction to the array of issues related to data quality. The book opens by describing the parameters of data quality: 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. The text gives an excellent overview of the current state of the art, describing techniques and methodologies from core data quality research and from related fields like data mining, statistical data analysis, and machine learning. The presentation concludes with a critical comparison of tools and practical methodologies, to help readers resolve their own quality problems. This book is a useful combination of the theoretical and the practical.

• Authors & Editors
  

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.