Efficient similarity join of large sets of moving object trajectories

Hui Ding*, Goce P Trajcevski, Peter I Scheuermann

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

32 Scopus citations

Abstract

We address the problem of performing efficient similarity join for large sets of moving objects trajectories. Unlike previous approaches which use a dedicated index in a transformed space, our premise is that in many applications of location-based services, the trajectories are already indexed in their native space, in order to facilitate the processing of common spatio-temporal querìes, e.g., range, nearest neighbor etc. We, introduce a novel distance measure adapted from the classìc Frèchet distance, which can be naturally extended to support lower/upper bounding using the underlying indices of moving object databases in the native space. This, in turn, enables efficient implementation of various trajectory similarity joins. We report on extensive experiments demonstrating that our methodology provides performance speed-up of trajectory similarity join by more than 50% on average, while maintaining effectiveness comparable to the well-known approaches for identifying trajectory similarity based on time-series analysis.

Original languageEnglish (US)
Pages79-87
Number of pages9
DOIs
StatePublished - Sep 17 2008
Event15th International Symposium on Temporal Representation and Reasoning, TIME 2008 - Montreal, QC, Canada
Duration: Jun 16 2008Jun 18 2008

Other

Other15th International Symposium on Temporal Representation and Reasoning, TIME 2008
CountryCanada
CityMontreal, QC
Period6/16/086/18/08

ASJC Scopus subject areas

  • Mathematics(all)

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