Probabilistic spatial queries on existentially uncertain data

Authors:
Xiangyuan Dai;Man Lung Yiu;Nikos Mamoulis;Yufei Tao;Michail Vaitis
Affiliations:
Department of Computer Science, University of Hong Kong;Department of Computer Science, University of Hong Kong;Department of Computer Science, University of Hong Kong;Department of Computer Science, City University of Hong Kong;Department of Geography, University of the Aegean
Venue:
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Year:
2005

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Abstract

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 and nearest neighbors and conduct an extensive experimental study, which evaluates the effectiveness of proposed solutions.