Data and Information Quality

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This book offers a systematic and comparative overview of research issues related to data and information quality. It provides a comprehensive examination of the current state and future developments in this field, focusing on core techniques such as record linkage, data integration, and error localization and correction, all framed within an original methodological structure. The text analyzes quality dimension definitions and models, highlighting differences among proposed solutions. It positions data and information quality as an independent research domain while incorporating insights from related fields like probability theory, statistical data analysis, data mining, knowledge representation, and machine learning. Additionally, it presents practical solutions, including methodologies, benchmarks for effective techniques, case studies, and examples. Aimed primarily at researchers in databases and information management, as well as those in natural sciences, the book is suitable for master’s or PhD-level courses, covering essential topics without requiring additional texts. It also serves data and information system administrators and practitioners facing data-quality challenges, offering a blend of practical approaches and theoretical foundations.