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Computing with Spatial Trajectories pp 143–177Cite as

Trajectory Pattern Mining

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Abstract

In step with the rapidly growing volumes of available moving-object trajectory data, there is also an increasing need for techniques that enable the analysis of trajectories. Such functionality may benefit a range of application area and services, including transportation, the sciences, sports, and prediction-based and social services, to name but a few. The chapter first provides an overview trajectory patterns and a categorization of trajectory patterns from the literature. Next, it examines relative motion patterns, which serve as fundamental background for the chapter's subsequent discussions. Relative patterns enable the specification of patterns to be identified in the data that refer to the relationships of motion attributes among moving objects. The chapter then studies disc-based and density-based patterns, which address some of the limitations of relative motion patterns. The chapter also reviews indexing structures and algorithms for trajectory pattern mining.

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References

  1. Digital battle field. http://www.defenselink.mil/news/newsarticle.aspx?id=45084

    Google Scholar 

  2. Porcupine caribou herd satellite collar project. http://www.taiga.net/satellite/.

    Google Scholar 

  3. Al-Naymat, G., Chawla, S., Gudmundsson, J.: Dimensionality reduction for long duration and complex spatio-temporal queries. In: Proceedings of the ACM symposium on Applied computing, pp. 393–397 (2007)

    Google Scholar 

  4. Andersson, M., Gudmundsson, J., Laube, P., Wolle, T.: Reporting leadership patterns among trajectories. In: SAC, pp. 3–7 (2007)

    Google Scholar 

  5. Andersson, M., Gudmundsson, J., Laube, P.,Wolle, T.: Reporting leaders and followers among trajectories of moving point objects. GeoInformatica 12(4), 497–528 (2008)

    Article  Google Scholar 

  6. Arumugam, S., Jermaine, C.: Closest-point-of-approach join for moving object histories. In: Proceedings of the IEEE International Conference on Data Engineering, p. 86 (2006)

    Google Scholar 

  7. Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.Y.: An optimal algorithm for approximate nearest neighbor searching. In: SODA, pp. 573–582 (1994)

    Google Scholar 

  8. Aung, H.H., Tan, K.L.: Discovery of evolving convoys. In: Proceedings of the 22nd international conference on Scientific and statistical database management, pp. 196–213 (2010)

    Google Scholar 

  9. Bakalov, P., Hadjieleftheriou, M., Keogh, E., Tsotras, V.J.: Efficient trajectory joins using symbolic representations. In: Proceedings of the international conference on Mobile data management, pp. 86–93 (2005)

    Google Scholar 

  10. Bakalov, P., Hadjieleftheriou, M., Tsotras, V.J.: Time relaxed spatiotemporal trajectory joins. In: Proceedings of the ACM international symposium on Advances in geographic information systems, pp. 182–191 (2005)

    Google Scholar 

  11. Benkert, M., Gudmundsson, J., Hbner, F., Wolle, T.: Reporting ock patterns. Computational Geometry 41(1), 111125 (2008)

    Google Scholar 

  12. Brilingaite, A.: Location-related context in mobile services. Ph.D. dissertation, Aalborg University (2006)

    Google Scholar 

  13. Brinkhoff, T., Kriegel, H.P., Seeger, B.: Efficient processing of spatial joins using r-trees. In: SIGMOD Conference, pp. 237–246 (1993)

    Google Scholar 

  14. Cao, H., Mamoulis, N., Cheung, D.W.: Discovery of periodic patterns in spatiotemporal sequences. TJDE 19, 453–467 (2007)

    Google Scholar 

  15. Chen, L., Ng, R.: On the marriage of lp-norms and edit distance. In: Proceedings of the international conference on Very large data bases, pp. 792–803 (2004)

    Google Scholar 

  16. Chen, L., O¨ zsu,M.T., Oria, V.: Robust and fast similarity search for moving object trajectories. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 491–502 (2005)

    Google Scholar 

  17. Chen, Y., Patel, J.M.: Design and evaluation of trajectory join algorithms. In: GIS, pp. 266–275 (2009)

    Google Scholar 

  18. Chen, Z., Shen, H.T., Zhou, X.: Discovering popular routes from trajectories. In: ICDE, pp. 900–911 (2011)

    Google Scholar 

  19. Dodge, S., Weibel, R., Lautensch¨utz, A.K.: Towards a taxonomy of movement patterns. Information Visualization 7, 240–252 (2008)

    Google Scholar 

  20. Douglas, D., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a line or its character. The American Cartographer 10(42), 112–123 (1973)

    Google Scholar 

  21. Eppstein, D., Goodrich, M.T., Sun, J.Z.: The skip quadtree: a simple dynamic data structure for multidimensional data. In: SCG, pp. 296–305 (2005)

    Google Scholar 

  22. Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 226–231 (1996)

    Google Scholar 

  23. Frank, A., Raper, J., Cheylan, J.P.: Life and motion of spatial socio-economic units. Taylor & Francis, London (2001)

    Google Scholar 

  24. Gaffney, S., Smyth, P.: Trajectory clustering with mixtures of regression models. In: KDD, pp. 63–72 (1999)

    Google Scholar 

  25. Gid´ofalvi, G., Pedersen, T.B.: Cab-sharing: An effective, door-to-door, on-demand transportation service. In: Proceedings of the 6th European Congress on Intelligent Transport Systems and Services (2007)

    Google Scholar 

  26. Gudmundsson, J., van Kreveld, M.: Computing longest duration ocks in trajectory data. In: Proceedings of the ACM international symposium on Advances in geographic information systems, pp. 35–42 (2006)

    Google Scholar 

  27. Gudmundsson, J., van Kreveld, M., Speckmann, B.: Efficient detection of motion patterns in spatio-temporal data sets. In: Proceedings of the ACM international symposium on Advances in geographic information systems, pp. 250–257 (2004)

    Google Scholar 

  28. Hadjieleftheriou, M., Kollios, G., Bakalov, P., Tsotras, V.J.: Complex spatio-temporal pattern queries. In: VLDB, pp. 877–888 (2005)

    Google Scholar 

  29. Han, J., Li, Z., Tang, L.A.: Mining moving object, trajectory and traffic data. In: DASFAA, pp. 485–486 (2010)

    Google Scholar 

  30. Iwase, S., Saito, H.: Tracking soccer player using multiple views. In: Proceedings of the IAPR Workshop on Machine Vision Applications (2002)

    Google Scholar 

  31. Jeong, S.H., Paton, N.W., Fernandes, A.A., Griffiths, T.: An experimental performance evaluation of spatiotemporal join strategies. Transactions in GIS 9(2), 129–156 (2005)

    Article  Google Scholar 

  32. Jeung, H., Liu, Q., Shen, H.T., Zhou, X.: A hybrid prediction model for moving objects. In: ICDE, pp. 70–79 (2008)

    Google Scholar 

  33. Jeung, H., Shen, H.T., Zhou, X.: Convoy queries in spatio-temporal databases. In: Proceedings of the IEEE International Conference on Data Engineering, pp. 1457–1459 (2008)

    Google Scholar 

  34. Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S.: Path prediction and predictive range querying in road network databases. The VLDB Journal 19(4), 585–602 (2010)

    Article  Google Scholar 

  35. Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment 1(1), 1068–1080 (2008)

    Google Scholar 

  36. Kalnis, P., Mamoulis, N., Bakiras, S.: On discovering moving clusters in spatio-temporal data. In: Proceedings of the International Symposium on Spatial and Temporal Databases, pp. 364–381 (2005)

    Google Scholar 

  37. Karimi, H.A., Liu, X.: A predictive location model for location-based services. In: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, pp. 126–133 (2003)

    Google Scholar 

  38. Laube, P., Imfeld, S.: Analyzing relative motion within groups of trackable moving point objects. In: GIScience, pp. 132–144 (2002)

    Google Scholar 

  39. Laube, P., Imfeld, S., Weibel, R.: Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science 19(6), 639–668 (2005)

    Article  Google Scholar 

  40. Laube, P., van Kreveld, M., Imfeld, S.: Finding remo - detecting relative motion patterns in geospatial lifelines. In: Proceedings of the International Symposium on Spatial Data Handling, pp. 201–214 (2004)

    Google Scholar 

  41. Laube, P., Purves, R.S.: An approach to evaluating motion pattern detection techniques in spatio-temporal data. Computers, Environment and Urban Systems 30(3), 347–374 (2006)

    Article  Google Scholar 

  42. Lee, J., Han, J., Whang, K.: Trajectory clustering: a partition-and-group framework. In: Proceedings of the ACMSIGMOD international conference on Management of data, pp. 593–604 (2007)

    Google Scholar 

  43. Lee, J.G., Han, J., Li, X., Gonzalez, H.: TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering. PVLDB 1(1), 1081–1094 (2008)

    Google Scholar 

  44. Li, Z., Ding, B., Han, J., Kays, R.: Swarm: mining relaxed temporal moving object clusters. PVLDB 3, 723–734 (2010)

    Google Scholar 

  45. Li, Z., Ding, B., Han, J., Kays, R., Nye, P.: Mining periodic behaviors for moving objects. In: SIGKDD, pp. 1099–1108 (2010)

    Google Scholar 

  46. Li, Z., Ji, M., Lee, J.G., Tang, L.A., Yu, Y., Han, J., Kays, R.: MoveMine: Mining moving object databases. In: Proceedings of the ACM SIGMOD international conference on Management of data, pp. 1203–1206 (2010)

    Google Scholar 

  47. Mamoulis, N., Cao, H., Kollios, G., Hadjieleftheriou, M., Tao, Y., Cheung, D.W.: Mining, indexing, and querying historical spatiotemporal data. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 236–245 (2004)

    Google Scholar 

  48. Sakr, M.A., Shams, A.: Spatiotemporal pattern queries. Geoinformatica 14 (2010)

    Google Scholar 

  49. Samet, H.: Foundations of Multidimensional and Metric Data Structures (The Morgan Kaufmann Series in Computer Graphics and Geometric Modeling). Morgan Kaufmann Publishers Inc. (2005)

    Google Scholar 

  50. Sumpter, N., Bulpitt, A.: Learning spatio-temporal patterns for predicting object behaviour. Image Vision Computing 18, 697V704 (2000)

    Google Scholar 

  51. Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 611–622 (2004)

    Google Scholar 

  52. Tao, Y., Papadias, D., Sun, J.: The tpr*-tree: An optimized spatio-temporal access method for predictive queries. In: Proceedings of the International Conference on Very Large Data Bases, pp. 790–801 (2003)

    Google Scholar 

  53. Tao, Y., Sun, J., Papadias, D.: Analysis of predictive spatio-temporal queries. ACM Transaction on Database Systems 28(4), 295–336 (2003)

    Article  Google Scholar 

  54. Vieira, M.R., Bakalov, P., Tsotras, V.J.: On-line discovery of ock patterns in spatio-temporal data. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 286–295 (2009)

    Google Scholar 

  55. Vlachos, M., Gunopoulos, D., Kollios, G.: Discovering similar multidimensional trajectories. In: Proceedings of the IEEE International Conference on Data Engineering, pp. 673–684 (2002)

    Google Scholar 

  56. Saltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, pp. 331–342 (2000)

    Google Scholar 

  57. Wang, Y., Lim, E.P., Hwang, S.Y.: Efficient mining of group patterns from user movement data. DKE 57, 240–282 (2006)

    Article  Google Scholar 

  58. Yi, B.K., Jagadish, H.V., Faloutsos, C.: Efficient retrieval of similar time sequences under time warping. In: Proceedings of the IEEE International Conference on Data Engineering, pp. 201–208 (1998)

    Google Scholar 

  59. Yoon, H., Shahabi, C.: Accurate discovery of valid convoys from moving object trajectories. In: IEEE International Conference on Data Mining Workshops, pp. 636–643 (2009)

    Google Scholar 

  60. Zheng, K., Trajcevski, G., Zhou, X., Scheuermann, P.: Probabilistic range queries for uncertain trajectories on road networks. In: EDBT, pp. 283–294 (2011)

    Google Scholar 

  61. Zhou, P., Zhang, D., Salzberg, B., Cooperman, G., Kollios, G.: Close pair queries in moving object databases. In: Proceedings of the ACM international symposium on Advances in geographic information systems, pp. 2–11 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Hoyoung Jeung

  2. Department of Computing, Hong Kong Polytechnic University, Hong Kong, China

    Man Lung Yiu

  3. Department of Computer Science, Aarhus University, Aarhus, Denmark

    Christian S. Jensen

Authors
  1. Hoyoung Jeung
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  2. Man Lung Yiu
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  3. Christian S. Jensen
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Corresponding author

Correspondence to Hoyoung Jeung .

Editor information

Editors and Affiliations

  1. Microsoft Research Asia, Beijing, 100080, China, People's Republic

    Yu Zheng

  2. , School of Information Technology, University of Queensland, Brisbane, 4072, Queensland, Australia

    Xiaofang Zhou

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Jeung, H., Yiu, M.L., Jensen, C.S. (2011). Trajectory Pattern Mining. In: Zheng, Y., Zhou, X. (eds) Computing with Spatial Trajectories. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-1629-6_5

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  • DOI: https://doi.org/10.1007/978-1-4614-1629-6_5

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