Supporting pattern-matching queries over trajectories on road networks

Gook Pil Roh, Jong Won Roh, Seung Won Hwang, Byoung Kee Yi

Research output: Contribution to journalArticle

18 Citations (Scopus)

Abstract

With the advent of ubiquitous computing, we can easily collect large-scale trajectory data, say, from moving vehicles. This paper studies pattern-matching problems for trajectory data over road networks, which complements existing efforts focusing on 1) a spatiotemporal window query for location-based service or 2) euclidean space with no restriction. In contrast, we first identify some desirable properties for pattern-matching queries to the road network trajectories. As the existing work does not fully satisfy these properties, we develop 1) trajectory representation and 2) distance metric that satisfy all the desirable properties we identified. Based on this representation and metric, we develop efficient algorithms for three types of pattern-matching querieswhole, subpattern, and reverse subpattern matching. We analytically validate the correctness of our algorithms and also empirically validate their scalability over large-scale, real-life, and synthetic trajectory data sets.

Original languageEnglish
Article number5601717
Pages (from-to)1753-1758
Number of pages6
JournalIEEE Transactions on Knowledge and Data Engineering
Volume23
Issue number11
DOIs
Publication statusPublished - 2011

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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