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Parallel Distributed Trajectory Pattern Mining Using Hierarchical Grid with MapReduce

Parallel Distributed Trajectory Pattern Mining Using Hierarchical Grid with MapReduce

Kazuhiro Seki, Ryota Jinno, Kuniaki Uehara
Copyright: © 2013 |Volume: 5 |Issue: 4 |Pages: 18
ISSN: 1938-0259|EISSN: 1938-0267|EISBN13: 9781466635715|DOI: 10.4018/ijghpc.2013100106
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MLA

Seki, Kazuhiro, et al. "Parallel Distributed Trajectory Pattern Mining Using Hierarchical Grid with MapReduce." IJGHPC vol.5, no.4 2013: pp.79-96. http://doi.org/10.4018/ijghpc.2013100106

APA

Seki, K., Jinno, R., & Uehara, K. (2013). Parallel Distributed Trajectory Pattern Mining Using Hierarchical Grid with MapReduce. International Journal of Grid and High Performance Computing (IJGHPC), 5(4), 79-96. http://doi.org/10.4018/ijghpc.2013100106

Chicago

Seki, Kazuhiro, Ryota Jinno, and Kuniaki Uehara. "Parallel Distributed Trajectory Pattern Mining Using Hierarchical Grid with MapReduce," International Journal of Grid and High Performance Computing (IJGHPC) 5, no.4: 79-96. http://doi.org/10.4018/ijghpc.2013100106

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Abstract

This paper proposes a new approach to trajectory pattern mining, which attempts to discover frequent movement patterns from the trajectories of moving objects. For dealing with a large volume of trajectory data, traditional approaches quantize them by a grid with a fixed resolution. However, an appropriate resolution often varies across different areas of trajectories. Simply increasing the resolution cannot capture broad patterns and consumes unnecessarily large computational resources. To solve the problem, the authors propose a hierarchical grid-based approach with quadtree search. The approach initially searches for frequent patterns with a coarse grid and drills down into a finer grid level to discover more minute patterns. The algorithm is naturally parallelized and implemented in the MapReduce programming model to accelerate the computation. The authors’ evaluative experiments on real-word data show the effectiveness of the authors’ approach in mining complex patterns with lower computational cost than the previous work.

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