Issue No.07 - July (2010 vol.22)
Yufei Tao , Chinese University of Hong Kong, Hong Kong
Xiaokui Xiao , Nanyang Technological University
Sze Man Yuen , Chinese University of Hong Kong, Hong Kong
Donghui Zhang , Northeastern University, Boston
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2009.137
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.
Nearest neighbor, uncertain, spatial database.
Yufei Tao, Xiaokui Xiao, Sze Man Yuen, Donghui Zhang, "Superseding Nearest Neighbor Search on Uncertain Spatial Databases", IEEE Transactions on Knowledge & Data Engineering, vol.22, no. 7, pp. 1041-1055, July 2010, doi:10.1109/TKDE.2009.137