This volume presents the results of approximately 15 years of work from researchers around the world on the use of fuzzy set theory to represent imprecision in databases. The maturity of the research in the discipline and the recent developments in commercial/industrial fuzzy databases provided an opportunity to produce this survey. Fuzzy Databases: Principles and Applications is self-contained, providing background material on fuzzy sets and database theory. It is comprehensive, covering all of the major approaches and models of fuzzy databases that have been developed, including coverage of commercial/industrial systems and applications. Background and introductory material are provided in the first two chapters. The major approaches in fuzzy databases comprise the second part of the volume. This includes the use of similarity and proximity measures as the fuzzy techniques used to extend the relational data modeling and the use of possibility theory approaches in the relational model. Coverage includes extensions to the data model, querying approaches, functional dependencies and other topics including implementation issues, information measures, database security, alternative fuzzy data models, the IFO model, and the network data models. A number of object-oriented extensions are also discussed. The use of fuzzy data modeling in geographical information systems (GIS) and use of rough sets in rough and fuzzy rough relational data models are presented. Major emphasis has been given to applications and commercialization of fuzzy databases. Several specific industrial/commercial products and applications are described. These include approaches to developing fuzzy front-end systems and special-purpose systems incorporating fuzziness.

Provides comprehensive coverage of the use of fuzzy set theory to represent imprecision in databases, focusing on recent development in commercial and industrial products and applications. In addition to introductory materials, Petry (Tulane U.) details the use of similarity and proximity measures as the fuzzy techniques to extend the relational data modeling, querying approaches, implementation, security, and a number of object-oriented extensions. A discussion of fuzzy data modeling in geographical information systems (GIS) is also featured.
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