By John Grant, Francesco Parisi (auth.), Zongmin Ma, Li Yan (eds.)
This e-book covers a fast-growing subject in nice intensity and specializes in the applied sciences and purposes of probabilistic information administration. It goals to supply a unmarried account of present reviews in probabilistic info administration. the target of the booklet is to supply the cutting-edge details to researchers, practitioners, and graduate scholars of knowledge expertise of clever details processing, and even as serving the data know-how specialist confronted with non-traditional functions that make the applying of traditional ways tricky or impossible.
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Additional info for Advances in Probabilistic Databases for Uncertain Information Management
790–801. VLDB Endowment (2003) 62. : Spatial reasoning in rcc-8 with boolean region terms. In: Principles of Knowledge Representation and Reasoning, ECAI 2000, pp. 244–248. IOS Press, Berlin (2000) 63. : Spatio-temporal representation and reasoning based on RCC-8. , Selman, B. ) Principles of Knowledge Representation and Reasoning, KR 2000, pp. 3–14. html 64. : Probabilistic threshold k nearest neighbor queries over moving objects in symbolic indoor space. , Ozcan, F. ) EDBT. ACM International Conference Proceeding Series, vol.
Ma all the attributes taken together. For this purpose, an additional attribute pS is introduced to the probabilistic relational scheme, where Dom (pS) = [0, 1]. , interval probability measures, in , two additional attributes LB and UB are introduced into the probabilistic relational scheme. These two attributes are used to represent the lower boundary and upper boundary of probability measures of tuples. It is clear that Dom (LB) = [0, 1] and Dom (UB) = [0, 1]. Viewed from the object-oriented paradigm, a complex event in the real world traditionally corresponds to an object which describes the status of the event.
First, some virtual objects having similar properties are the probabilistic ones with fuzzy measures, and a class defined by these virtual objects may be a probabilistic one with fuzzy measures. Second, when a class is intensionally defined, there is an attribute which domain may be fuzzy and probabilistic, and as a result a probabilistic class with fuzzy measure is formed. Third, the subclass produced by a probabilistic class with fuzzy measure by means of specialization and the superclass produced by some classes (in which there is at least one class which is a probabilistic one with fuzzy measure) by means of generalization are also fuzzy.