Superseding Nearest Neighbor Search on Uncertain Spatial Databases

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 non-zero probability to be the NN of q. Given two NN candidates o1 and o2, o1 supersedes o2 if o1 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 SNNcore — 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 experi...

• Type: Journal
• Year: 2010
• Where: TKDE
• Authors: Sze Man Yuen, Yufei Tao, Xiaokui Xiao, Jian Pei, Donghui Zhang
• Keywords: Nearest Neighbor, Nearest Neighbor Search, Nn Candidate, TKDE 2010