Issue No. 11 - November (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.189
Gook-Pil Roh , POSTECH, Pohang
Jong-Won Roh , POSTECH, Pohang
Seung-Won Hwang , POSTECH, Pohang
Byoung-Kee Yi , Kyungpook National University, Daegu
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 queries—whole, 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.
Trajectory, road network, pattern-matching query.
G. Roh, B. Yi, J. Roh and S. Hwang, "Supporting Pattern-Matching Queries over Trajectories on Road Networks," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 1753-1758, 2010.