Abstract
The increasing pervasiveness of object tracking technologies has enabled collection of huge amount of spatio-temporal trajectories. Discovering the useful movement patterns from such big data is gaining in importance and challenging. In this paper we propose an distributed mining framework on Hadoop for efficiently discovering swarm patterns from big spatio-temporal trajectories in parallel. We first define the notion of maximal objectset that captures swarms by recombining clusters in timeset domain. Second, we propose a parallel model based on timeset independent property of swarm pattern to parallel the mining process. Furthermore we propose a distributed algorithm using MapReduce chain architecture based on the proposed parallel model, which features two optimization pruning strategies designed to minimize the computation costs. Our empirical study on the real Taxi dataset demonstrates its effectiveness in finding object-closed swarms. Extensive experiments on 5 network-connected workstations also validate that our proposed algorithm nearly achieves 5-fold speedups against the serial solution.
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Acknowledgment
This work is partially supported by the National Natural Science Foundation of China (nos. 61403328 and 61572419), the open project program of Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University (no. 93K172014K13), the Key Research & Development Project of Shandong Province (no. 2015GSF115009), and the Shandong Provincial Natural Science Foundation (nos. ZR2013FQ023 and ZR2013FM011).
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Yu, Y., Qi, J., Lu, Y., Zhang, Y., Liu, Z. (2016). MR-Swarm: Mining Swarms from Big Spatio-Temporal Trajectories Using MapReduce. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_61
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DOI: https://doi.org/10.1007/978-3-319-46257-8_61
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