Abstract
Companions of moving objects are object groups that move together in a period of time. To quickly identify companion vehicles from a special kind of streaming traffic data, called Automatic Number Plate Recognition (ANPR) data, this paper proposes an approach to discover companion vehicles. Compared to related approaches, we transform the companion discovery into a frequent sequence-mining problem. We make several improvements on top of a recent frequent sequence-mining algorithm, called SeqStream, to handle customized time constraints among sequence elements when discovering traveling companions. We also use pseudo projection technique to improve the performance of our algorithm. Finally, extensive experiments are done using a real dataset to show efficiency and effectiveness of our approach.
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References
Laube, P., Imfeld, S.: Analyzing relative motion within groups of trackable moving point objects. In: Egenhofer, M., Mark, D.M. (eds.) GIScience 2002. LNCS, vol. 2478, pp. 132–144. Springer, Heidelberg (2002)
Jeung, H., Shen, H., Zhou, X.: Convoy queries in spatio-temporal databases. In: International Conference on Data Engineering, pp. 1457–1459. IEEE Computer Society, Washington, DC (2008)
Li, Z., Ding, B., Han, J., et al.: Swarm: mining relaxed temporal moving object clusters. VLDB Endowment 3(1), 723–734 (2010)
Tang, L.A., Zheng, Y., Yuan, J., et al.: A framework of traveling companion discovery on trajectory data streams. ACM Trans. Intell. Syst. Technol. 5(1), 992–999 (2013)
Zheng, K., Zheng, Y., Yuan, N.J., et al.: On discovery of gathering patterns from trajectories. In: IEEE 29th International Conference on Data Engineering, pp. 242–253. IEEE Computer Society, Washington, DC (2013)
Zhang, J., Li, J., Wang, S., et al.: On retrieving moving objects gathering patterns from trajectory data via spatio-temporal graph. In: IEEE Int. Congr. Big Data, pp. 390–397. IEEE Computer Society, Washington, DC (2014)
Li, Y., Bailey, J., Kulik, L.: Efficient mining of platoon patterns in trajectory databases. Data Knowl. Eng. 100(PA), 167–187 (2015)
Han, Y., Wang, G., Yu, J., et al.: A service-based approach to traffic sensor data integration and analysis to support community-wide green commute in China. IEEE Trans. Intell. Transp. Syst. PP(99), 1–10 (2015)
Zhu, M., Liu, C., Wang, J., et al.: Instant discovery of moment companion vehicles from big streaming traffic data. In: International Conference on Cloud Computing and Big Data, pp. 4–6. IEEE, Taipei (2015)
Zhu, M., Liu, C., Wang, J., et al.: A service-friendly approach to discover traveling companions based on ANPR data stream. In: IEEE International Conference on Services Computing, San Francisco USA (2016)
Chang, L., Wang, T., Yang, D., et al.: SeqStream: mining closed sequential patterns over stream sliding windows. In: 8th IEEE International Conference on Data Mining, pp. 83–92. IEEE Computer Society, Washington, DC (2008)
Mooney, C.H., Roddick, J.F.: Sequential pattern mining: approaches and algorithms. ACM Comput. Surv. 45(2), 94–111 (2013)
Tang, L.A., Zheng, Y., Yuan, J., et al.: On discovery of traveling companions from streaming trajectories. In: IEEE 28th International Conference on Data Engineering, pp. 186–197. IEEE Computer Society, Washington, DC (2012)
Yu, Y., Wang, Q., Wang, X., et al.: Online clustering for trajectory data stream of moving objects. Comput. Sci. Inf. Syst. 10(3), 1293–1317 (2013)
Acknowledgment
The research work is supported by the projects: Key Program of Beijing Municipal Natural Science Foundation (No. 4131001); Training Plan of Top Young Talent in North China University of Technology, “An Incremental Approach to Instant Discovery of Data Correlations among Multi-Source and Large-scale Sensor Data”.
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Liu, C., Wang, X., Zhu, M., Han, Y. (2016). Discovering Companion Vehicles from Live Streaming Traffic Data. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_10
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DOI: https://doi.org/10.1007/978-3-319-45814-4_10
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