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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.
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