Data Integration

Data Integration

AngličtinaEbook
Genesereth, Michael
Morgan & Claypool Publishers
EAN: 9781598297423
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Podrobné informace

Data integration is a critical problem in our increasingly interconnected but inevitably heterogeneous world. There are numerous data sources available in organizational databases and on public information systems like the World Wide Web. Not surprisingly, the sources often use different vocabularies and different data structures, being created, as they are, by different people, at different times, for different purposes. The goal of data integration is to provide programmatic and human users with integrated access to multiple, heterogeneous data sources, giving each user the illusion of a single, homogeneous database designed for his or her specific need. The good news is that, in many cases, the data integration process can be automated. This book is an introduction to the problem of data integration and a rigorous account of one of the leading approaches to solving this problem, viz., the relational logic approach. Relational logic provides a theoretical framework for discussing data integration. Moreover, in many important cases, it provides algorithms for solving the problem in a computationally practical way. In many respects, relational logic does for data integration what relational algebra did for database theory several decades ago. A companion web site provides interactive demonstrations of the algorithms. Table of Contents: Preface / Interactive Edition / Introduction / Basic Concepts / Query Folding / Query Planning / Master Schema Management / Appendix / References / Index / Author BiographyDon't have access? Recommend our Synthesis Digital Library to your library or purchase a personal subscription. Email [email protected] for details.
EAN 9781598297423
ISBN 1598297422
Typ produktu Ebook
Vydavatel Morgan & Claypool Publishers
Datum vydání 7. července 2010
Stránky 97
Jazyk English
Země Uruguay
Autoři Genesereth, Michael
Série Synthesis Lectures on Artificial Intelligence and Machine Learning
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