Supporting Pattern-Matching Queries over Trajectories on Road Networks

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-ba...

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Bibliographic Details
Published inIEEE transactions on knowledge and data engineering Vol. 23; no. 11; pp. 1753 - 1758
Main Authors ROH, Gook-Pil, ROH, Jong-Won, HWANG, Seung-Won, YI, Byoung-Kee
Format Journal Article
LanguageEnglish
Published New York, NY IEEE 01.11.2011
IEEE Computer Society
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Algorithms
Applied sciences
Computer science; control theory; systems
Computer systems and distributed systems. User interface
Economic models
Exact sciences and technology
Focusing
Global Positioning System
Ground, air and sea transportation, marine construction
Mathematical analysis
Measurement
Networks
Noise
Pattern matching
pattern-matching query
Queries
Representations
road network
Road transportation and traffic
Roads
Software
Studies
Trajectories
Trajectory
Pervasive computing
Scalability
Road network
Temporal databases
pattern-matching query
Context aware
Euclidean space
Metric
Trajectory
Localization
Pattern matching
Moving body
Mobile computing
Spatial database
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Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2010.189