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Mining Massive-Scale Spatiotemporal Trajectories in Parallel: A Survey

  • Pengtao Huang
  • Bo Yuan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9441)

Abstract

With the popularization of positioning devices such as GPS navigators and smart phones, large volumes of spatiotemporal trajectory data have been produced at unprecedented speed. For many trajectory mining problems, a number of computationally efficient approaches have been proposed. However, to more effectively tackle the challenge of big data, it is important to exploit various advanced parallel computing paradigms. In this paper, we present a comprehensive survey of the state-of-the-art techniques for mining massive-scale spatiotemporal trajectory data based on parallel computing platforms such as Graphics Processing Unit (GPU), MapReduce and Field Programmable Gate Array (FPGA). This survey covers essential topics including trajectory indexing and query, clustering, join, classification, pattern mining and applications. We also give an in-depth analysis of the related techniques and compare them according to their principles and performance.

Keywords

Spatiotemporal Trajectory mining Parallel computing 

References

  1. 1.
    Bayardo, R., Panda, B.: Fast Algorithms for Finding Extremal Sets. In: Proceedings of the 2011 SIAM International Conference on Data Mining, pp. 25–34 (2011)Google Scholar
  2. 2.
    Löffler, M., et al. Detecting commuting patterns by clustering subtrajectories. In: Hong, S.-H., Hong, S.-H., Fukunaga, T., Fukunaga, T., Nagamochi, H., Nagamochi, H. (eds.) ISAAC 2008. LNCS, vol. 5369, pp. 644–655. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    Ding, H., Trajcevski, G., Scheuermann, P.: Efficient similarity join of large sets of moving object trajectories. In: The 15th International Symposium on Temporal Representation and Reasoning, pp. 79–87. IEEE (2008)Google Scholar
  4. 4.
    Eldawy, A., Mokbel, M.F.: A demonstration of spatialhadoop: an efficient MapReduce framework for spatial data. Proc. VLDB Endowment 6(12), 1230–1233 (2013)CrossRefGoogle Scholar
  5. 5.
    Fang, Y., Cheng, R., Tang, W., Maniu, S., Yang, X.: Evaluating Nearest-Neighbor Joins on Big Trajectory Data. Technical report (2014)Google Scholar
  6. 6.
    Fort, M., Sellarès, J.A., Valladares, N.: A parallel GPU-based approach for reporting flock patterns. Int. J. Geogr. Inf. Sci. 28(9), 1877–1903 (2014)CrossRefGoogle Scholar
  7. 7.
    Giannotti, F., Nanni, M.: Trajectory pattern mining. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 330–339. New York (2007)Google Scholar
  8. 8.
    Gowanlock, M.G., Casanova, H.: Parallel Distance Threshold Query Processing for Spatiotemporal Trajectory Databases on the GPU. Technical report (2014)Google Scholar
  9. 9.
    Gudmundsson, J., van Kreveld, M.: Computing longest duration flocks in trajectory data. In: Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Iinformation Systems, pp. 35–42. ACM Press, New York (2006)Google Scholar
  10. 10.
    Gudmundsson, J., Valladares, N.: A GPU approach to subtrajectory clustering using the Fréchet distance. IEEE Trans. Parallel Distrib. Sys. PP(99), 1–16 (2014)Google Scholar
  11. 11.
    Güting, R.H., Behr, T., Düntgen, C.: SECONDO : a platform for moving objects database research and for publishing and integrating research implementations. Bull. IEEE Comput. Soc. Tech. Committee Data Eng. 33(2), 56–63 (2010)Google Scholar
  12. 12.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. Proc. VLDB Endowment 1(1), 1068–1080 (2008)CrossRefGoogle Scholar
  13. 13.
    Jinno, R., Seki, K., Uehara, K.: Parallel distributed trajectory pattern mining using MapReduce. In: 2012 IEEE 4th International Conference on Cloud Computing Technology and Science, pp. 269–274 (2012)Google Scholar
  14. 14.
    Kondekar, R., Gupta, A., Saluja, G.: A MapReduce based hybrid genetic algorithm using island approach for solving time dependent vehicle routing problem. In: International Conference on Computer&Information Science (ICCIS), pp. 263–269. No. 2003 (2012)Google Scholar
  15. 15.
    Lee, J., Han, J., Li, X., Gonzalez, H.: TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. Proc. VLDB Endowment 1(2), 1081–1094 (2008)CrossRefGoogle Scholar
  16. 16.
    Lee, J., Han, J., Whang, K.: Trajectory Clustering : A partition-and-group framework. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data, pp. 593–604. New York (2007)Google Scholar
  17. 17.
    Li, Z.: Spatiotemporal pattern mining: algorithms and applications. In: Aggarwal, C.C., Han, J. (eds.) Frequent Pattern Mining, pp. 283–306. Springer International Publishing, Heidelberg (2014)Google Scholar
  18. 18.
    Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed ttemporal moving object clusters. Proc. VLDB Endowment 3(1–2), 723–734 (2010)CrossRefGoogle Scholar
  19. 19.
    Li, Z., Ding, B., Wu, F., Lei, T.: Attraction and avoidance detection from movements. Proc. VLDB Endowment 7(3), 157–168 (2013)CrossRefGoogle Scholar
  20. 20.
    Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1099–1108. ACM Press, New York (2010)Google Scholar
  21. 21.
    Li, Z., Wu, F., Crofoot, M.C.: Mining following relationships in movement data. In: IEEE 13th International Conference on Data Mining, pp. 458–467. IEEE (2013)Google Scholar
  22. 22.
    Lu, J., Guting, R.H.: Parallel secondo: boosting database engines with hadoop. In: 2012 IEEE 18th International Conference on Parallel and Distributed Systems, pp. 738–743. IEEE, Los Alamitos (2012)Google Scholar
  23. 23.
    Lu, J., Guting, R.H.: Parallel SECONDO: a practical system for large-scale processing of moving objects. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 1190–1193. IEEE (2014)Google Scholar
  24. 24.
    Ma, Q., Yang, B., Qian, W., Zhou, A.: Query processing of massive trajectory data based on MapReduce. In: Proceeding of the First International Workshop on Cloud Data Management - CloudDB 2009, pp. 9–16. ACM Press, Hong Kong (2009)Google Scholar
  25. 25.
    Moussalli, R., Absalyamov, I., Vieira, M.R., Najjar, W., Tsotras, V.J.: High performance FPGA and GPU complex pattern matching over spatio-temporal streams. GeoInformatica 19(2), 405–434 (2014)CrossRefGoogle Scholar
  26. 26.
    Moussalli, R., Moussalli, R., Vieira, M.R., Vieira, M.R., Najjar, W., Najjar, W., Tsotras, V.J., Tsotras, V.J.: Stream-mode FPGA acceleration of complex pattern trajectory querying. In: Sellis, T., et al. (eds.) SSTD 2013. LNCS, vol. 8098, pp. 201–222. Springer, Heidelberg (2013)Google Scholar
  27. 27.
    Orellana, D., Wachowicz, M.: Exploring patterns of movement suspension in pedestrian mobility. Geogr. Anal. 43(3), 241–260 (2011)CrossRefGoogle Scholar
  28. 28.
    Qiao, S., Li, T., Peng, J., Qiu, J.: Parallel sequential pattern mining of massive trajectory data. Int. J. Comput. Intell. Sys. 3(3), 343–356 (2010)CrossRefGoogle Scholar
  29. 29.
    Qiao, S., Tang, C., Dai, S., Zhu, M., Peng, J., Li, H., Ku, Y.: PartSpan: Parallel Sequence mining of trajectory patterns. In: 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, pp. 363–367. No. 2006, IEEE (2008)Google Scholar
  30. 30.
    Sart, D., Mueen, A., Najjar, W., Keogh, E., Niennattrakul, V.: Accelerating dynamic time warping subsequence search with GPUs and FPGAs. In: 2010 IEEE International Conference on Data Mining, pp. 1001–1006. IEEE (2010)Google Scholar
  31. 31.
    Scheepens, R., van de Wetering, H., van Wijk, J.J.: Contour based visualization of vessel movement predictions. Int. J. Geogr. Inf. Sci. 28(5), 891–909 (2014)CrossRefGoogle Scholar
  32. 32.
    Seki, K., Jinno, R., Uehara, K.: Parallel distributed trajectory pattern mining using hierarchical grid with MapReduce. Int. J. Grid High Perform. Comput. 5(4), 79–96 (2013)CrossRefGoogle Scholar
  33. 33.
    Sun, F., Wang, W., Zhou, B., Chen, F.: The design and application of navigation and location services data index. In: 2013 International Conference on Computational and Information Sciences, pp. 774–777. IEEE (2013)Google Scholar
  34. 34.
    Sun, Z.-Y., Sun, Z.-Y., Tsai, M.-C., Tsai, M.-C., Tsai, H.-P., Tsai, H.-P.: Mining Uncertain Sequence Data on Hadoop Platform. In: Peng, W.-C., et al. (eds.) PAKDD 2014 Workshops. LNCS, vol. 8643, pp. 204–216. Springer, Heidelberg (2014)Google Scholar
  35. 35.
    Thakur, A., Svec, P., Gupta, S.K.: GPU based generation of state transition models using simulations for unmanned surface vehicle trajectory planning. Robot. Auton. Sys. 60(12), 1457–1471 (2012)CrossRefGoogle Scholar
  36. 36.
    Tsai, H.P.: Mining Movement Pattern from Uncertain Trajectory Data with MapReduce (2011). http://nchuir.lib.nchu.edu.tw/handle/309270000/89680
  37. 37.
    Valladares, N.: GPU Parallel Algorithms For Reporting Movement Behaviour Patterns in Spatio-temporal Databases. Ph.D. thesis, University of Girona (2013)Google Scholar
  38. 38.
    Vieira, M.R., Bakalov, P., Tsotras, V.J.: On-line discovery of flock patterns in spatio-temporal data. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS 2009, pp. 286–295. ACM Press, New York (2009)Google Scholar
  39. 39.
    Wang, Z., Huang, S., Wang, L., Li, H., Wang, Y., Yang, H.: Accelerating subsequence similarity search based on ddynamic time warping Ddistance with FPGA. In: Proceedings of the ACM/SIGDA International Symposium on Field Programmable Gate Arrays - FPGA 2013, pp. 53–62. ACM Press, New York(2013)Google Scholar
  40. 40.
    You, S., Zhang, J., Gruenwald, L.: Parallel spatial query processing on GPUs using R-trees. In: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data - BigSpatial 2013, pp. 23–31 (2013)Google Scholar
  41. 41.
    Zhang, J., You, S., Gruenwald, L.: High-performance online spatial and temporal aggregations on multi-core CPUs and many-core GPUs. In: Proceedings of the Fifteenth International Workshop on Data Warehousing and OLAP (DOLAP 2012), pp. 89–96. ACM, Maui (2012)Google Scholar
  42. 42.
    Zhang, J., You, S., Gruenwald, L.: U2STRA : High-performance data management of ubiquitous urban sensing trajectories on GPGPUs. In: Proceedings of the 2012 ACM Workshop on City Data Management Workshop -CDMW 2012. pp. 5–12 (2012)Google Scholar
  43. 43.
    Zhang, J., You, S., Gruenwald, L.: parallel online spatial and temporal aggregations on multi-core CPUs and many-core GPUs. Inf. Sys. 44, 134–154 (2014)CrossRefGoogle Scholar
  44. 44.
    Zhao, Y., Sheong, F.K., Sun, J., Sander, P., Huang, X.: A fast parallel clustering algorithm for molecular simulation trajectories. J. Comput. Chem. 34(2), 95–104 (2013)CrossRefGoogle Scholar
  45. 45.
    Zheng, Y., Zhou, X.: Computing with Spatial Trajectories. Springer New York Dordrecht Heidelberg London, New York (2011) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Intelligent Computing Lab, Division of Informatics, Graduate School at ShenzhenTsinghua UniversityShenzhenPeople’s Republic of China

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