Data Quality
Author:   Richard Y. Wang ,  Mostapha Ziad ,  Yang W. Lee
Publisher:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   23
ISBN:  

9781475774139


Pages:   167
Publication Date:   07 April 2013
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Data Quality


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Overview

Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.

Full Product Details

Author:   Richard Y. Wang ,  Mostapha Ziad ,  Yang W. Lee
Publisher:   Springer-Verlag New York Inc.
Imprint:   Springer-Verlag New York Inc.
Edition:   Softcover reprint of the original 1st ed. 2002
Volume:   23
Dimensions:   Width: 15.50cm , Height: 1.00cm , Length: 23.50cm
Weight:   0.296kg
ISBN:  

9781475774139


ISBN 10:   1475774133
Pages:   167
Publication Date:   07 April 2013
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

Extending the Relational Model to Capture Data Quality Attributes.- Extending the ER Model to Represent Data Quality Requirements.- Automating Data Quality Judgment.- Developing a Data Quality Algebra.- The MIT Context Interchange Project.- The European Union Data Warehouse Quality Project.- The Purdue University Data Quality Project.- Conclusion.

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