Scaling Cautious Selection in Spatial Probabilistic Temporal Databases

  • Francesco Parisi
  • Austin Parker
  • John Grant
  • V. S. Subrahmanian

Abstract

SPOT databases have been proposed as a paradigm for efficiently reasoning about probabilistic spatio-temporal data. A selection query asks for all pairs of objects and times such that the object is within a query region with a probability within a stated probability interval. Two alternative semantics have been introduced for selection queries: optimistic and cautious selection.

It has been shown in past work that selection is characterized by a linear program whose solutions correspond to certain kinds of probability density functions (pdfs). In this chapter, we define a space called the SPOTPDF Space (SPS for short) and show that the space of solutions to a cautious selection query is a convex polytope in this space. This convex polytope can be approximated both by an interior region and a containing region. We show that both notions can be jointly used to prune the search space when answering a query. We report on experiments showing that cautious selection can be executed in about 4 seconds on databases containing 3 million SPOT atoms.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Francesco Parisi
    • 1
  • Austin Parker
    • 2
  • John Grant
    • 2
    • 3
  • V. S. Subrahmanian
    • 2
  1. 1.Università della CalabriaRendeItaly
  2. 2.University of MarylandCollege ParkUSA
  3. 3.Towson UniversityTowsonUSA

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