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.
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.
Über den Autor
Carlo Batini is full professor of Computer Engineering since 1986, initially at Sapienza - Università di Roma, then since 2002 at University of Milano Bicocca. His research interests include eGoverment, information systems and data base modeling and design, data and information quality, and service science. 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 researcher at Istat, the Italian National Institute of Statistics since 2006. She earned a University Degree in Computer Engineering with honors and a Ph.D. in Computer Engineering at Sapienza - Università di Roma. She is the author of more than 100 papers mainly on data quality, privacy preservation and data integration, published in leading conferences and journals in databases and information systems. She has been involved inseveral European research projects on data quality and data integration.
Zusammenfassung
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.
Inhaltsverzeichnis
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.
