Abstract

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.

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