By John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)
This booklet covers a fast-growing subject in nice intensity and specializes in the applied sciences and functions of probabilistic facts administration. It goals to supply a unmarried account of present stories in probabilistic facts administration. the target of the booklet is to supply the cutting-edge details to researchers, practitioners, and graduate scholars of data expertise of clever info processing, and whilst serving the data know-how expert confronted with non-traditional functions that make the applying of traditional ways tough or impossible.
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Additional info for Advances in Probabilistic Databases for Uncertain Information Management
39–66. 1007/978-3-642-37509-5_3 40 B. Kimelfeld and P. Senellart inconsistent, or simply presented in incompatible forms, the result of integrating these sources necessarily involves uncertainty as to which fact is correct or which is the best mapping to a global schema. When data result from automatic and imprecise tasks, such as information extraction, data mining, or computer vision, it is commonly annotated by a score representing the confidence of the system in the correctness of the data.
Ma & L. ): Advances in Probabilistic Databases, STUDFUZZ 304, pp. 23–38. 1007/978-3-642-37509-5_2 © Springer-Verlag Berlin Heidelberg 2013 24 L. Yan and Z. Ma While the object-oriented database model provides powerful object-oriented modeling capabilities, it suffers from some inadequacy of necessary semantics. In real-world applications, information is often imperfect, and human knowledge and natural language have a big deal of imprecision and vagueness. Traditional database models assume that the models are a correct reflection of the world and further assume that the stored data is known, accurate and complete.
Updating extended possibility-based fuzzy relational databases. International Journal of Intelligent Systems 22(3), 237–258 (2007) 14. : Fuzzy XML data modeling with the UML and relational data models. Data & Knowledge Engineering 63(3), 970–994 (2007) 15. : A literature overview of fuzzy database models. Journal of Information Science and Engineering 24(1), 189–202 (2008) 16. : A literature overview of fuzzy conceptual data modeling. Journal of Information Science and Engineering 26(2), 427–441 (2010) 17.
Advances in Probabilistic Databases for Uncertain Information Management by John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)