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Scaling Cautious Selection in Spatial Probabilistic Temporal Databases

  • Francesco Parisi
  • Austin Parker
  • John Grant
  • V. S. Subrahmanian
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 256)

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 SPOT PDF 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.

Keywords

Convex Polytope Convex Region Convex Envelope Pruning Technique Deductive Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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