Probabilistic Spatial Database Operations

  • Jinfeng Ni
  • Chinya V. Ravishankar
  • Bir Bhanu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)


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 operations, which, given two uncertain data sets, find all pairs of polygons whose probability of overlap is larger than a given threshold. This method uses an R-tree based probabilistic index structure (PrR-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 traditional spatial joins, while reducing overall cost by a factor of more than two.


Spatial Data Spatial Database Spatial Object Intersection Probability Candidate Pair 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Jinfeng Ni
    • 1
  • Chinya V. Ravishankar
    • 1
  • Bir Bhanu
    • 2
  1. 1.Department of Computer Science & Engineering 
  2. 2.Department of Electrical EngineeringUniversity of California, RiversideRiversideUSA

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