New PDF release: Advances in Probabilistic Databases for Uncertain

By John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)

ISBN-10: 3642375081

ISBN-13: 9783642375088

ISBN-10: 364237509X

ISBN-13: 9783642375095

This e-book covers a fast-growing subject in nice intensity and makes a speciality of the applied sciences and functions of probabilistic info administration. It goals to supply a unmarried account of present reports in probabilistic information administration. the target of the booklet is to supply the state-of-the-art info to researchers, practitioners, and graduate scholars of data expertise of clever info processing, and even as serving the knowledge know-how specialist confronted with non-traditional functions that make the appliance of traditional ways tough or impossible.

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Additional resources for Advances in Probabilistic Databases for Uncertain Information Management

Sample text

Generalizing database relational algebra for the treatment of incomplete/uncertain information and vague queries. Information Sciences 34(2), 115–143 (1984) 24. : Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database systems. ACM Transactions on Database Systems 13(2), 129–166 (1988) 25. : Fuzzy relational algebra for possibility-distribution-fuzzyrelational model of fuzzy data. Journal of Intelligent Information Systems 3(1), 7–27 (1994) 26. : Algebraic operations in fuzzy object-oriented databases.

It is rarely the case in real life that all or most of these assumptions are met. One of some inadequacy of necessary semantics that traditional database models often suffer from can hereby be generalized as the inability to handle imprecise and uncertain information. For this reason, imprecise and uncertain data have been introduced into databases for imperfect information processing [10, 15, 16, 21]. To deal with uncertainty and imprecision, two major foundations have been developed, which are probability theory and fuzzy set theory, respectively.

Here we introduce a notation of semantic similarity between two probabilistic objects with fuzzy measures. For two probabilistic classes with fuzzy measures, say c1 and c2, let c1 contain attributes {a1, a2, …, ak, fpM1} and c2 contain attributes {b1, b2, …, bl, fpM2}, in which fpM1 and fpM2 are the fuzzy probabilistic attributes of c1 and c2, respectively. Let o1 and o2 be objects of class c1 and c2, respectively. Then for o1 with {a1, a2, …, ak} and o2 with {b1, b2, …, bl}, the probability that o1 and o2 are semantically similar each other is first defined as follows.

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Advances in Probabilistic Databases for Uncertain Information Management by John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)


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