On some geometric problems of color-spanning sets
FAW-AAIM'11 Proceedings of the 5th joint international frontiers in algorithmics, and 7th international conference on Algorithmic aspects in information and management
Top-k similarity search on uncertain trajectories
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Evaluating probabilistic spatial-range closest pairs queries over uncertain objects
WAIM'11 Proceedings of the 12th international conference on Web-age information management
MUD: Mapping-based query processing for high-dimensional uncertain data
Information Sciences: an International Journal
Nearest-neighbor searching under uncertainty
PODS '12 Proceedings of the 31st symposium on Principles of Database Systems
Nearest neighbor searching under uncertainty II
Proceedings of the 32nd symposium on Principles of database systems
On some geometric problems of color-spanning sets
Journal of Combinatorial Optimization
Entity resolution for distributed probabilistic data
Distributed and Parallel Databases
Aggregate nearest neighbor queries in uncertain graphs
World Wide Web
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This paper proposes a new problem, called superseding nearest neighbor search, on uncertain spatial databases, where each object is described by a multidimensional probability density function. Given a query point q, an object is a nearest neighbor (NN) candidate if it has a nonzero probability to be the NN of q. Given two NN-candidates o_1 and o_2, o_1 supersedeso_2 if o_1 is more likely to be closer to q. An object is a superseding nearest neighbor (SNN) of q, if it supersedes all the other NN-candidates. Sometimes no object is able to supersede every other NN-candidate. In this case, we return the SNN-core—the minimum set of NN-candidates each of which supersedes all the NN-candidates outside the SNN-core. Intuitively, the SNN-core contains the best objects, because any object outside the SNN-core is worse than all the objects in the SNN-core. We show that the SNN-core can be efficiently computed by utilizing a conventional multidimensional index, as confirmed by extensive experiments.