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Processing Probabilistic Range Queries over Gaussian-Based Uncertain Data

  • Tingting Dong
  • Chuan Xiao
  • Xi Guo
  • Yoshiharu Ishikawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8098)

Abstract

Probabilistic range query is an important type of query in the area of uncertain data management. A probabilistic range query returns all the objects within a specific range from the query object with a probability no less than a given threshold. In this paper we assume that each uncertain object stored in the databases is associated with a multi-dimensional Gaussian distribution, which describes the probability distribution that the object appears in the multi-dimensional space. A query object is either a certain object or an uncertain object modeled by a Gaussian distribution. We propose several filtering techniques and an R-tree-based index to efficiently support probabilistic range queries over Gaussian objects. Extensive experiments on real data demonstrate the efficiency of our proposed approach.

Keywords

Data Object Query Processing Index Structure Probability Threshold Uncertain Data 
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 2013

Authors and Affiliations

  • Tingting Dong
    • 1
  • Chuan Xiao
    • 1
  • Xi Guo
    • 2
  • Yoshiharu Ishikawa
    • 1
  1. 1.Nagoya UniversityJapan
  2. 2.The Chinese University of Hong KongChina

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