Fuzzy Logic in Data Modeling: Semantics, Constraints and Database Design addresses fundamental and important issues of fuzzy data modeling, such as fuzzy data representation, fuzzy integrity constraints, fuzzy conceptual modeling, and fuzzy database design. The purpose of introducing fuzzy logic in data modeling is to enhance the classical models such that uncertain and imprecise information can be represented and manipulated. Fuzzy data representation reflects how, where and to what extent fuzziness is incorporated into classical models. Fuzzy integrity constraints are a sort of fuzziness-involved business rules and semantic restrictions that need to be specified and enforced. Fuzzy conceptual modeling describes and treats high-level data concepts and related semantics in a fuzzy context, allowing the model to tolerate imprecision at different degrees. Fuzzy database design provides guidelines for how relation schemes of fuzzy databases should be formed and develops remedies to...

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