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Last updated: July 1, 2017

Presented by: Jinfeng Ni

Abstract:

Spatial databases typically assume that the positional attributes of spatial objects are precisely known. In practice, however, they are known only approximately, with the error depending on the nature of the measurement and the source of data. In this paper, we address the problem how to perform spatial database operations in the presence of uncertainty. We first discuss a probabilistic spatial data model to represent the positional uncertainty. We then present a method for performing the probabilistic spatial join operation, which, given two uncertain data sets, finds all pairs of polygons whose probability of overlap is larger than a given threshold. This method uses an R-tree based probabilistic index structure (PR-tree) to support probabilistic filtering, and an efficient algorithm to compute the intersection probability between two uncertain polygons for the refinement step. Our experiments show that our method achieves higher accuracy than methods based on traditonal spatial joins, while reducing overall cost by a factor of more than two.