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Poor data quality can hinder or seriously damage the efficiency and effectiveness of organizations and businesses. The growing awareness of these repercussions has led to important public initiatives such as the promulgation of the "Data Quality Act" in the United States and the Directive 2003/98 of the European Parliament. The authors present a complete and systematic introduction to the wide range of problems related to data quality. The book starts with a detailed description of different dimensions of data quality, such as accuracy, completeness and consistency, and discusses its importance in relation to both different types of data, such as federated data, data present on the web and data with temporal dependencies, which to the different categories in which the data can be classified. The comprehensive description of techniques and methodologies from not only research in the area of data quality but also related areas, such as data mining, probability theory, statistical data analysis and machine learning, provides an excellent introduction to the state of the art. current art. The presentation is complemented by a short description and a critical comparison of practical tools and methodologies, which will help the reader to solve their quality problems.