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.
|Number of pages||6|
|Journal||IEEE Transactions on Knowledge and Data Engineering|
|Publication status||Published - 2011|
Bibliographical noteFunding Information:
This research was supported by Microsoft Research Asia and the Ministry of Knowledge Economy (MKE), Korea, under the Information Technology Research Center (ITRC ) support program supervised by the Institute for Information Technology Advancement (IITA) (NIPA-2010-C1090-1031-0009) and the Engineering Research Center of Excellence Program of Korea Ministry of Education, Science and Technology (MEST) / National Research Foundation of Korea (NRF) (Grant 2010-0001728).
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Computational Theory and Mathematics