By Christoph Koch (auth.), Amol Deshpande, Anthony Hunter (eds.)
Managing uncertainty and inconsistency has been generally explored in - ti?cial Intelligence over a few years. Now with the arrival of huge quantities of information and data from allotted heterogeneous,and very likely con?icting, assets, there's curiosity in constructing and utilizing formalisms for uncertainty andinconsistency widelyin structures that have to higher managethis facts and information. the once a year foreign convention on Scalable Uncertainty administration (SUM) has grown out of this wide-ranging curiosity in coping with uncertainty and inconsistency in databases, the internet, the Semantic internet, and AI. It goals at bringing jointly all these drawn to the administration of huge volumes of uncertainty and inconsistency, regardless of whether or not they are in databases,the net, the Semantic net, or in AI, in addition to in different parts reminiscent of info retrieval, probability research, and desktop imaginative and prescient, the place signi?cant computational - forts are wanted. After a promising First foreign convention on Scalable Uncertainty administration was once held in Washington DC, united states in 2007, the c- ference sequence has been effectively held in Napoli, Italy, in 2008, and back in Washington DC, united states, in 2009.
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Extra info for Scalable Uncertainty Management: 4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010. Proceedings
In: Proc. of KR 1998, pp. 488–498 (1998) 8. : Logical representation of preferences for group decision making. In: Proc. of KR 2000, pp. 457–468. Morgan Kaufmann, San Francisco (2000) 9. : On the diﬀerence between merging knowledge bases and combining them. In: Proc of KR 2000, pp. 135–144. Morgan Kaufmann, San Francisco (2000) 10. : Iterated revision as prioritized merging. In: Proc. of KR 2006, pp. 210–220 (2006) 11. : Updating logical databases. In: Advances in Computing Research, pp. 1–18 (1986) 12.
It can be perceived as a surprising way of using the imprecise probability framework. It allows a new interpretation and a new way of computing the imprecision associated with an observed value. According to Use of the Domination Property 27 this interpretation, the imprecision of an observation can be due to the observation process but also to poor knowledge on the proper post-processing to be used to ﬁlter the raw measured signal. Deﬁning the pair of convex capacities also deﬁnes the convex set of impulse responses.
E. we do not perform any kind of duplicate elimination. As a result when we pose a query to any of the solutions of all LDB data exchange problems that arise from the Di possible instances of a source ULDB we expect to get the corresponding certain answer. We thus have the following deﬁnition: Deﬁnition 8 (ULDB Certain answers). Consider a data exchange setting (S, T, Σ = Σst ∪ Σt ). If I is a source ULDB instance with P I(I) = D1 , . . , Dn and q a query over target schema T then the certain answers of q with respect to I, denoted as uldb-cert(q, I) is a complete ULDB C (with no nulls) with P I(C) = D1 , .
Scalable Uncertainty Management: 4th International Conference, SUM 2010, Toulouse, France, September 27-29, 2010. Proceedings by Christoph Koch (auth.), Amol Deshpande, Anthony Hunter (eds.)