In emerging applications such as location-based service (LBS), the values of spatial database items are naturally uncertain. This paper focuses on the problem of finding probabilistic closest pairs between two uncertain spatial datasets in a given range, namely, probabilistic spatialrange closest pair (PSRCP) query. In particular, given two uncertain spatial datasets which contain uncertain objects modeled by a set of sampled instances, a PSRCP query retrieves the pairs that satisfy the query with probabilities higher than a given threshold. Several pruning strategies are proposed to filter the objects that cannot constitute an answer, which can significantly improve the query performance. Extensive experiments are performed to examine the effectiveness of our methods.
['Mo Chen', 'Zixi Jia', 'Yu Gu', 'Ge Yu', 'Chuanwen Li']
Evaluating probabilistic spatial-range closest pairs queries over uncertain objects