Journal of Intelligent Information Systems

, Volume 42, Issue 1, pp 155–177 | Cite as

Dealing with trajectory streams by clustering and mathematical transforms

Article

Abstract

Nowadays, almost all kind of electronic devices leave traces of their movements (e.g. smartphone, GPS devices and so on). Thus, the huge number of this “tiny” data sources leads to the generation of massive data streams of geo-referenced data. As a matter of fact, the effective analysis of such amounts of data is challenging, since the possibility to extract useful information from this peculiar kind of data is crucial in many application scenarios such as vehicle traffic management, hand-off in cellular networks, supply chain management. Moreover, spatial data streams management poses new challenges both for their proper definition and acquisition, thus making the overall process harder than for classical point data. In particular, we are interested in solving the problem of effective trajectory data streams clustering, that revealed really intriguing as we deal with sequential data that have to be properly managed due to their ordering. We propose a framework that allow data pre-elaboration in order to make the mining step more effective. As for every data mining tool, the experimental evaluation is crucial, thus we performed several tests on real world datasets that confirmed the efficiency and effectiveness of the proposed approach.

Keywords

Spatial data Math transforms Clustering 

References

  1. Aggarwal, C.C., Han, J., Wang, J., Yu, P.S. (2003). A framework for clustering evolving data streams. In VLDB (pp. 81–92).Google Scholar
  2. Arthur, D., & Vassilvitskii, S. (2007). k-means++ the advantages of careful seeding. In SODA (pp. 1027–1035).Google Scholar
  3. Cadez, I.V., Gaffney, S., Smyth, P. (2000). A general probabilistic framework for clustering individuals and objects. In KDD (pp. 140–149).Google Scholar
  4. Cao, H., & Wolfson, O. (2005). Nonmaterialized motion information in transport networks. In ICDT (pp. 173–188).Google Scholar
  5. Chen, L., Özsu, M.T., Oria, V. (2005). Robust and fast similarity search for moving object trajectories. In SIGMOD (pp. 491–502). New York: ACM.Google Scholar
  6. Chihara, T.S. (1978). An introduction to orthogonal polynomials. New York: Gordon and Breach.MATHGoogle Scholar
  7. Chong, Z., Ni, W., Xu, L., Xu, Z., Shu, H., Zheng, J. (2010). Approximate k-median of location streams with redundancy and inconsistency. International Journal of Software and Informatics, 4(2), 165–182.Google Scholar
  8. Ester, M., Kriegel, H.P., Sander, J., Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD.Google Scholar
  9. Flesca, S., Manco, G., Masciari, E., Pontieri, L., Pugliese, A. (2005). Fast detection of xml structural similarity. IEEE TKDE, 17(2), 160–175.Google Scholar
  10. Gaffney, S., & Smyth, P. (1999). Trajectory clustering with mixtures of regression models. In KDD (pp. 63–72).Google Scholar
  11. Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D. (2007). Trajectory pattern mining. In KDD (pp. 330–339).Google Scholar
  12. Gonzalez, H., Han, J., Li, X., Klabjan, D. (2006). Warehousing and analyzing massive RFID data sets. In ICDE.Google Scholar
  13. Gudmundsson, J., Katajainen, J., Merrick, D., Ong, C., Wolle, T. (2007). Compressing spatio-temporal trajectories. In Int. conf. algorithms and computation (pp. 763–775).Google Scholar
  14. Han, J., & Kamber, M. (2000). Data mining: Concepts and techniques. San Mateo: Morgan Kaufmann.Google Scholar
  15. Hönle, N., Grossmann, M., Reimann, S., Mitschang, B. (2010). Usability analysis of compression algorithms for position data streams. In GIS (pp. 240–249).Google Scholar
  16. Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T. (2008). Discovery of convoys in trajectory databases. In Proceedings of the VLDB Endowement, vol. 1(1) (pp. 1068–1080).Google Scholar
  17. Keogh, E. (2002). Exact indexing of dynamic time warping. In VLDB (pp. 406–417). VLDB Endowment.Google Scholar
  18. Lee, J.G., Han, J., Li, X. (2008a). Trajectory outlier detection: A partition-and-detect framework. In ICDE (pp. 140–149).Google Scholar
  19. Lee, J.G., Han, J., Li, X., Gonzalez, H. (2008b). TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. PVLDB, 1(1), 1081–1094.Google Scholar
  20. Lee, J.G., Han, J., Whang, K.Y. (2007). Trajectory clustering: A partition-and-group framework. In SIGMOD.Google Scholar
  21. Li, Y., Han, J., Yang, J. (2004). Clustering moving objects. In KDD (pp. 617–622).Google Scholar
  22. Li, Z., Lee, J.G., Li, X., Han, J. (2010). Incremental clustering for trajectories. In DASFAA (2) (pp. 32–46).Google Scholar
  23. Liu, Y., Chen, L., Pei, J., Chen, Q., Zhao, Y. (2007). Mining frequent trajectory patterns for activity monitoring using radio frequency tag arrays. In PerCom (pp. 37–46).Google Scholar
  24. Lloyd, S. (1982). Least squares quantization in pcm. IEEE TOIT, 28.Google Scholar
  25. Masciari, E. (2009a). A complete framework for clustering trajectories. In ICTAI (pp. 9–16).Google Scholar
  26. Masciari, E. (2009b). Trajectory clustering via effective partitioning. In FQAS (pp. 358–370).Google Scholar
  27. Nehme, R.V., & Rundensteiner, E.A. (2006). Scuba: scalable cluster-based algorithm for evaluating continuous spatio-temporal queries on moving objects. In EDBT (pp. 1001–1019).Google Scholar
  28. Oppenheim, A.V., & Shafer, R.W. (1999). Discrete-time signal processing. Englewood Cliffs: Prentice Hall.Google Scholar
  29. Press, W.H., et al. (2001). Numerical recipes in C++. Cambridge: Cambridge University Press.Google Scholar
  30. Puschel, M., & Rotteler, M. (2005). Fourier transform for the directed quincunx lattice. In ICASSP.Google Scholar
  31. Secker, A., & Taubman, D. (2003). Lifting-based invertible motion adaptive transform (limat) framework for highly scalable video compression. IEEE Transactions on Image Processing, 12(12), 1530–1542.CrossRefGoogle Scholar
  32. Veenman, C.J., & Reinders, M.J.T. (2005). The nearest subclass classifier: a compromise between the nearest mean and nearest neighbor classifier. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(9), 1417–1429.CrossRefGoogle Scholar
  33. Vlachos, M., Gunopoulos, D., Kollios, G. (2002). Discovering similar multidimensional trajectories. In ICDE (p. 673).Google Scholar
  34. Wang, W., Yang, J., Muntz, R.R. (1997). Sting: a statistical information grid approach to spatial data mining. In VLDB (pp. 186–195).Google Scholar
  35. Yang, J., & Trajpattern, M.Hu. (2006). Mining sequential patterns from imprecise trajectories of mobile objects. In EDBT (pp. 664–681).Google Scholar
  36. Yi, B., Jagadish, H.V., Faloutsos, C. (1998). Efficient retrieval of similar time sequences under time warping. In ICDE (pp. 201–208).Google Scholar
  37. Zhang, T., Ramakrishnan, R., Livny, M. (1996). Birch: an efficient data clustering method for very large databases. In SIGMOD (pp. 103–114).Google Scholar
  38. Zhang, X., Wu, X., Wu, F. (2007). Image coding on quincunx lattice with adaptive lifting and interpolation. In Data compression conf. (pp. 193–202).Google Scholar
  39. Zheng, Y., Zhang, L., Xie, X., Ma, W.Y. (2009). Mining interesting locations and travel sequences from gps trajectories. In WWW (pp. 791–800).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.ICAR-CNRRendeItaly

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