John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan's Advances in Probabilistic Databases for Uncertain PDF

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

ISBN-10: 3642375081

ISBN-13: 9783642375088

ISBN-10: 364237509X

ISBN-13: 9783642375095

This publication covers a fast-growing subject in nice intensity and makes a speciality of the applied sciences and functions of probabilistic facts administration. It goals to supply a unmarried account of present reviews in probabilistic facts administration. the target of the ebook is to supply the state-of-the-art info to researchers, practitioners, and graduate scholars of knowledge know-how of clever details processing, and whilst serving the data know-how expert confronted with non-traditional purposes that make the appliance of traditional ways tricky or impossible.

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Ma & L. ): Advances in Probabilistic Databases, STUDFUZZ 304, pp. 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.

Advances in Probabilistic Databases, STUDFUZZ 304, pp. 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.

The advantages of such a hybrid system are that the strengths of its partners are combined and the weaknesses of its partners are complementary one another. The fuzzy probabilistic object-oriented database model provides a flexible database model to model hybrid imprecise and uncertain information as well as complex objects. In this chapter, a probabilistic object-oriented database model is introduced, in which possibility distributions arise at the level of objects as probability measures. Such an extended object-oriented database model can be applied for modeling stochastic events which probabilities are represented by possibility distributions.

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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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