Data Quality

2001
ISBN: 978-1-4757-7413-9
Verlag: Springer US

Buch, Englisch, Band 23, 167 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 295 g

Reihe: Advances in Database Systems

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.

Zielgruppe

Research

Autoren/Hrsg.

Wang, Richard Y.

Lee, Yang W.

Ziad, Mostapha

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Warehouse
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Information Retrieval
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Informationstheorie, Kodierungstheorie
• Mathematik | Informatik EDV | Informatik Angewandte Informatik
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsmathematik und -statistik

Inhaltsverzeichnis

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.