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Superseding Nearest Neighbor Search on Uncertain Spatial Databases
July 2010 (vol. 22 no. 7)
pp. 1041-1055
Sze Man Yuen, Chinese University of Hong Kong, Hong Kong
Yufei Tao, Chinese University of Hong Kong, Hong Kong
Xiaokui Xiao, Nanyang Technological University
Jian Pei, Simon Fraser University, Burnaby
Donghui Zhang, Northeastern University, Boston
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 supersedes o_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.

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Index Terms:
Nearest neighbor, uncertain, spatial database.
Citation:
Sze Man Yuen, Yufei Tao, Xiaokui Xiao, Jian Pei, Donghui Zhang, "Superseding Nearest Neighbor Search on Uncertain Spatial Databases," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp. 1041-1055, July 2010, doi:10.1109/TKDE.2009.137
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