Data Quality

  • 3h 16m
  • Mostapha Ziad, Richard Y. Wang, Yang W. Lee
  • Springer
  • 2001

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.

About the Authors

Richard Y. Wang is an associate professor at Boston University, and Co-Director for the Total Data Quality Management program at M.I.T. where he had served as a professor for a decade. He is a pioneer and leading researcher in the field of data quality. Prof. Wang received his Ph.D. degree from M.I.T.

Mostapha Ziad is an assistant professor at the Sawyer School of Management, Suffolk University, Boston. His research interests include data quality improvement tools, data production mapping, networking, and e-commerce. Prof. Ziad received his Ph.D. in Computer Science from Boston University.

Yang W. Lee is an assistant professor at Northeastern University. She has published extensively in leading journals such as Communications of the ACM, Sloan Management Review, and IEEE Computer. Prof. Lee received her Ph.D. degree from M.I.T.

In this Book

  • Introduction
  • 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
  • Bibliography

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