A trajectory splitting model for efficient spatio-temporal indexing

  • Authors:

  • Slobodan Rasetic;Jörg Sander;James Elding;Mario A. Nascimento

  • Affiliations:

  • University of Alberta, Edmonton, Alberta, Canada;University of Alberta, Edmonton, Alberta, Canada;University of Alberta, Edmonton, Alberta, Canada;University of Alberta, Edmonton, Alberta, Canada

  • Venue:
  • VLDB '05 Proceedings of the 31st international conference on Very large data bases
  • Year:
  • 2005

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Abstract

This paper addresses the problem of splitting trajectories optimally for the purpose of efficiently supporting spatio-temporal range queries using index structures (e.g., R-trees) that use minimum bounding hyper-rectangles as trajectory approximations. We derive a formal cost model for estimating the number of I/Os required to evaluate a spatio-temporal range query with respect to a given query size and an arbitrary split of a trajectory. Based on the proposed model, we introduce a dynamic programming algorithm for splitting a set of trajectories that minimizes the number of expected disk I/Os with respect to an average query size. In addition, we develop a linear time, near optimal solution for this problem to be used in a dynamic case where trajectory points are continuously updated. Our experimental evaluation confirms the effectiveness and efficiency of our proposed splitting policies when embedded into an R-tree structure.