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This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It�does so by delivering a sound, integrated and comprehensive �overview of the state of the art and future development of data and information quality in databases and information systems.
To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples.
The book has been written primarily for researchers in the fields of databases and information management or in �natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms.- Sales Rank: #3828943 in Books
- Published on: 2016-03-23
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x 1.13" w x 6.14" l, .0 pounds
- Binding: Hardcover
- 500 pages
Review
“This book addresses the dimensions, principles, and techniques to ensure that data and information conform to the necessary quality requirements. … Information and communication technology (ICT) professionals who touch in any way upon data and information quality … should find this book mandatory reading. … its serious depth and breadth would seem to merit building an advanced course on data and information quality around it, so computer science students would be yet another audience.” (David G. Hill, Computing Reviews, computingreviews.com, October, 2016)
From the Back Cover
This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It�does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems.
To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples.
The book has been written primarily for researchers in the fields of databases and information management�or in natural sciences who are inte
rested in investigating properties ofdata and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms. About the Author
Carlo Batini is full professor of Computer Engineering since 1986, initially at Sapienza – Universit� 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 - Universit� 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 in several European research projects on data quality and data integration.
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