Poor data quality can seriously hinder 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. This book presents a comprehensive and systematic introduction to the wide array of issues related to data quality. Beginning with a detailed description of the parameters of data quality, the text gives an excellent overview of the current state of the art, describing 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. The presentation concludes with 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 sound theoretical foundation and practical approach.

Details and analyzes different quality dimension definitions and parameters Combines approaches from data modeling, data mining, knowledge representation, probability theory, statistical data analysis, and machine learning Combines solid formal foundations with concrete practical solutions and approaches Ideally suited for self-study or specialized courses Includes supplementary material: sn.pub/extras

Autorentext

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.

Inhalt
to Data Quality.- Data Quality Dimensions.- Models for Data Quality.- Activities and Techniques for Data Quality: Generalities.- Object Identification.- Data Quality Issues in Data Integration Systems.- Methodologies for Data Quality Measurement and Improvement.- Tools for Data Quality.- Open Problems.