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Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data
January 2009 (vol. 21 no. 1)
pp. 108-122
Man Lung Yiu, Aalborg University, Aalborg
Nikos Mamoulis, University of Hong Kong, Hong Kong
Xiangyuan Dai, University of Hong Kong, Hong Kong
Yufei Tao, University of Hong Kong, Hong Kong
Michail Vaitis, University of the Aegean, Mytilene
We study the problem of answering spatial queries in databases where objects exist with some uncertainty and they are associated with an existential probability. The goal of a thresholding probabilistic spatial query is to retrieve the objects that qualify the spatial predicates with probability that exceeds a threshold. Accordingly, a ranking probabilistic spatial query selects the objects with the highest probabilities to qualify the spatial predicates. We propose adaptations of spatial access methods and search algorithms for probabilistic versions of range queries, nearest neighbors, spatial skylines, and reverse nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.

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Index Terms:
Query processing, Spatial databases
Citation:
Man Lung Yiu, Nikos Mamoulis, Xiangyuan Dai, Yufei Tao, Michail Vaitis, "Efficient Evaluation of Probabilistic Advanced Spatial Queries on Existentially Uncertain Data," IEEE Transactions on Knowledge and Data Engineering, vol. 21, no. 1, pp. 108-122, Jan. 2009, doi:10.1109/TKDE.2008.135
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