Processing probabilistic range queries over gaussian-based uncertain data

  • Authors:

  • Tingting Dong;Chuan Xiao;Xi Guo;Yoshiharu Ishikawa

  • Affiliations:

  • Nagoya University, Japan;Nagoya University, Japan;The Chinese University of Hong Kong, China;Nagoya University, Japan

  • Venue:
  • SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
  • Year:
  • 2013

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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.