A Framework for Context-Aware Trajectory

  • Vania Bogorny
  • Monica Wachowicz

The recent advances in technologies for mobile devices, like GPS and mobile phones, are generating large amounts of a new kind of data: trajectories of moving objects. These data are normally available as sample points, with very little or no semantics. Trajectory data can be used in a variety of applications, but the form as the data are available makes the extraction of meaningful patterns very complex from an application point of view. Several data preprocessing steps are necessary to enrich these data with domain information for data mining. In this chapter,we present a general framework for context-aware trajectory data mining. In this framework we are able to enrich trajectories with additional geographic information that attends the application requirements. We evaluate the proposed framework with experiments on real data for two application domains: traffic management and an outdoor game.


Data Mining Application Domain Geographic Data Trajectory Data Domain Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Alvares, L.O., Bogorny, V., Kuijpers, B., de Macedo, J.A.F., Moelans, B., Vaisman, A.: A model for enriching trajectories with semantic geographical information. In: ACM-GIS, New York, NY, USA, ACM Press (2007) 162–169Google Scholar
  2. 2.
    Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: ACMSAC, New York, NY, USA, ACM Press (2008) 863–868Google Scholar
  3. 3.
    Cao, H., Mamoulis, N., Cheung, D.W.: Discovery of collocation episodes in spatiotemporal data. In: ICDM, IEEE Computer Society (2006) 823–827Google Scholar
  4. 4.
    Gudmundsson, J., van Kreveld, M.J.: Computing longest duration flocks in trajectory data.[23] 35–42Google Scholar
  5. 5.
    Laube, P., Imfeld, S., Weibel, R.: Discovering relative motion patterns in groups of moving point objects. International Journal of Geographical Information Science 19(6) (2005) 639–668Google Scholar
  6. 6.
    Lee, J., Han, J., Whang, K.Y.: Trajectory clustering: A partition-and-group framework.In: SCM SIGMOD International Conference on Management Data (SIGMOD'07), Beijing,China (June 11–14 2007)Google Scholar
  7. 7.
    Li, Y., Han, J., Yang, J.: Clustering moving objects. In: KDD '04: Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, New York,NY, USA, ACM Press (2004) 617–622Google Scholar
  8. 8.
    Nanni, M., Pedreschi, D.: Time-focused clustering of trajectories of moving objects. Journal of Intelligent Information Systems 27(3) (2006) 267–289Google Scholar
  9. 9.
    Tsoukatos, I., Gunopulos, D.: Efficient mining of spatiotemporal patterns. In Jensen, C.S.,Schneider, M., Seeger, B., Tsotras, V.J., eds.: SSTD. Volume 2121 of Lecture Notes in Computer Science., Springer (2001) 425–442Google Scholar
  10. 10.
    Verhein, F., Chawla, S.: Mining spatio-temporal association rules, sources, sinks, stationary regions and thoroughfares in object mobility databases. In Lee, M.L., Tan, K.L., Wuwongse,V., eds.: DASFAA. Volume 3882 of Lecture Notes in Computer Science., Springer (2006) 187–201Google Scholar
  11. 11.
    Bogorny, V., Kuijpers, B., Alvares, L.O.: St-dmql: a semantic trajectory data mining query language. International Journal of Geographical Information Science (2009) in PressGoogle Scholar
  12. 12.
    Giannotti, F., Nanni, M., Pinelli, F., Pedreschi, D.: Trajectory pattern mining. In Berkhin, P.,Caruana, R., Wu, X., eds.: KDD, ACM (2007) 330–339Google Scholar
  13. 13.
    Kuijpers, B., Moelans, B., de Weghe, N.V.: Qualitative polyline similarity testing with applications to query-by-sketch, indexing and classification. In de By, R.A., Nittel, S., eds.:14th ACM International Symposium on Geographic Information Systems, ACM-GIS 2006,November 10–11, 2006, Arlington, Virginia, USA, Proceedings, ACM (2006) 11–18Google Scholar
  14. 14.
    Pelekis, N., Kopanakis, I., Ntoutsi, I., Marketos, G., Theodoridis, Y.: Mining trajectory databases via a suite of distance operators. In: ICDE Workshops, IEEE Computer Society (2007) 575–584Google Scholar
  15. 15.
    OGC: Topic 5, opengis abstract specification — features (version 4) (1999). Available at: Accessed in August (2005) (1999)
  16. 16.
    Shekhar, S., Chawla, S.: Spatial Databases: A Tour. Prentice Hall (June 2002)Google Scholar
  17. 17.
    Rigaux, P., Scholl, M., Voisard, A.: Spatial Databases: with application to GIS. Morgan KaufmannGoogle Scholar
  18. 18.
    Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C.: Aconceptual view on trajectories. Data and Knowledge Engineering 65(1) (2008) 126–146Google Scholar
  19. 19.
    Han, J.: Mining knowledge at multiple concept levels. In: CIKM, ACM (1995) 19–24Google Scholar
  20. 20.
    Bogorny, V., Palma, A.T., Engel, P., Alvares, L.O.: Weka-gdpm: Integrating classical data mining toolkit to geographic information systems. In: WAAMD Workshop, SBC (2006) 9–16Google Scholar
  21. 21.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In Yu, P.S., Chen, A.L.P., eds.: ICDE,IEEE Computer Society (1995) 3–14Google Scholar
  22. 22.
    Society, W.: Frequency 1550. Available at: Accessed in September (2007) (2005)

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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Instituto de Informatica, Universidade Federal do Rio Grande do Sul (UFRGS)Porto AlegreBrasil
  2. 2.ETSI Topografia, Geodesia y Cartografa, Universidad Politecnica de MadridMadridSpain

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