Selectivity estimation for predictive spatio-temporal queries

Yufei Tao, Jimeng Sun, Dimitris Papadias

Research output: Contribution to conferencePaperpeer-review


This paper proposes a cost model for selectivity estimation of predictive spatio-temporal window queries. Initially, we focus on uniform data proposing formulae that capture both points and rectangles, and any type of object/query mobility combination (i.e., dynamic objects, dynamic queries or both). Then, we apply the model to non-uniform datasets by introducing spatio-temporal histograms, which in addition to the spatial, also consider the velocity distributions during partitioning. The advantages of our techniques are (i) high accuracy (1-2 orders of magnitude lower error than previous techniques), (ii) ability to handle all query types, and (iii) efficient handling of updates.

Original languageEnglish (US)
Number of pages12
StatePublished - 2003
Externally publishedYes
EventNineteenth International Conference on Data Ingineering - Bangalore, India
Duration: Mar 5 2003Mar 8 2003


OtherNineteenth International Conference on Data Ingineering

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems


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