Abstract
Trajectory similarity is one of the most fundamental operations in spatial-temporal data analysis. Although many recent works focus on improving the efficiency on single machine, their solutions are not directly applicable to DSPEs (Distributed Stream Processing Engine) in an online manner. On one hand, the similarity processing on DSPEs is always susceptible to data skew and completeness issue. On the other hand, their methods only support a single trajectory similarity measure which could not serve for adaptive adjustment strategies in different scenario. In this paper, we propose a new general framework for online Trajectory Similarity Processing, named TraSP. Specifically, our proposal includes a matrix-based data dispatcher to provide balance and completeness guarantee for stream join, an atomic table generator to accommodate different similarity criteria and a lightweight filter to shed irrelevant workloads. Empirical studies on real world trajectory data sets validate the usefulness of our proposals and the comparison experiment shows the high performance of our framework.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Apache Flink Project. http://flink.apache.org/
Cao, P., Wang, Z.: Efficient top-k query calculation in distributed networks. In: PODC, pp. 206–215 (2004)
Chen, L., Özsu, M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: SIGMOD, pp. 491–502 (2005)
Ding, J., Fang, J., Zhang, Z., Zhao, P., Xu, J., Zhao, L.: HPCC/SmartCity/DSS, pp. 1398–1405. IEEE (2019)
Dittrich, J., Seeger, B., Taylor, D.S., Widmayer, P.: Progressive merge join: a generic and non-blocking sort-based join algorithm. In: VLDB, pp. 299–310 (2002)
Fang, J., Zhao, P., Liu, A., Li, Z., Zhao, L.: Scalable and adaptive joins for trajectory data in distributed stream system. J. Comput. Sci. Technol. 34(4), 747–761 (2019). https://doi.org/10.1007/s11390-019-1940-x
Frentzos, E., Gratsias, K., Theodoridis, Y.: Index-based most similar trajectory search. In: ICDE, pp. 816–825 (2007)
Ives, Z.G., Florescu, D., Friedman, M., Levy, A.Y., Weld, D.S.: An adaptive query execution system for data integration. In: SIGMOD, pp. 299–310 (1999)
Mokbel, M.F., Lu, M., Aref, W.G.: Hash-merge join: a non-blocking join algorithm for producing fast and early join results. In: ICDE, pp. 251–262 (2004)
Ranu, S., Deepak, P., Telang, A.D., Deshpande, P., Raghavan, S.: Indexing and matching trajectories under inconsistent sampling rates. In: ICDE, pp. 999–1010 (2015)
Shang, S., Chen, L., Wei, Z., Jensen, C.S., Zheng, K., Kalnis, P.: Parallel trajectory similarity joins in spatial networks. VLDB J. 27(3), 395–420 (2018). https://doi.org/10.1007/s00778-018-0502-0
Shang, Z., Li, G., Bao, Z.: DITA: distributed in-memory trajectory analytics. In: SIGMOD, pp. 725–740 (2018)
Su, H., Liu, S., Zheng, B., Zhou, X., Zheng, K.: A survey of trajectory distance measures and performance evaluation. VLDB J. 29(1), 3–32 (2019). https://doi.org/10.1007/s00778-019-00574-9
Urhan, T., Franklin, M.J.: Dynamic pipeline scheduling for improving interactive query performance. In: VLDB, pp. 501–510 (2001)
Wilschut, A.N., Apers, P.M.G.: Dataflow query execution in a parallel main-memory environment. Distrib. Parallel Databases 1(1), 103–128 (1993). https://doi.org/10.1007/BF01277522
Xie, D., Li, F., Phillips, J.M.: Distributed trajectory similarity search. Proc. VLDB Endow. 10(11), 1478–1489 (2017)
Yuan, H., Li, G.: Distributed in-memory trajectory similarity search and join on road network. In: ICDE, pp. 1262–1273 (2019)
Acknowledgements
This work is partially supported by NSFC (No.61802273), the Postdoctoral Science Foundation of China under Grant (No. 2017M621813), the Postdoctoral Science Foundation of Jiangsu Province of China under Grant (No. 2018K029C), and the Natural Science Foundation for Colleges and Universities in Jiangsu Province of China under Grant (No. 18KJB520044).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Pan, Z., Chao, P., Fang, J., Chen, W., Li, Z., Liu, A. (2020). TraSP: A General Framework for Online Trajectory Similarity Processing. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-62005-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62004-2
Online ISBN: 978-3-030-62005-9
eBook Packages: Computer ScienceComputer Science (R0)