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 (a) a spatiotemporal window query for location-based service or (b) 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 datasets.
| Original language | English |
|---|---|
| Pages (from-to) | 1750-1761 |
| Number of pages | 12 |
| Journal | IEEE Transactions on Communications |
| Volume | 39 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 1991 Jan 1 |
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All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
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Supporting Pattern Matching Queries over Trajectories on Road Networks. / Roh, Gook Pil; Roh, Jong Won; Hwang, Seung Won; Yi, Byoung Kee.
In: IEEE Transactions on Communications, Vol. 39, No. 12, 01.01.1991, p. 1750-1761.Research output: Contribution to journal › Article
TY - JOUR
T1 - Supporting Pattern Matching Queries over Trajectories on Road Networks
AU - Roh, Gook Pil
AU - Roh, Jong Won
AU - Hwang, Seung Won
AU - Yi, Byoung Kee
PY - 1991/1/1
Y1 - 1991/1/1
N2 - 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 (a) a spatiotemporal window query for location-based service or (b) 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 datasets.
AB - 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 (a) a spatiotemporal window query for location-based service or (b) 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 datasets.
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U2 - 10.1109/26.120162
DO - 10.1109/26.120162
M3 - Article
VL - 39
SP - 1750
EP - 1761
JO - IEEE Transactions on Communications
JF - IEEE Transactions on Communications
SN - 0096-1965
IS - 12
ER -