A Conceptual Data Model for Trajectory Data Mining

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6292)


Data mining has become very popular in the last years, and it is well known that data preprocessing is the most effort and time consuming step in the discovery process. In part, it is because database designers do not think about data mining during the conceptual design of a database, therefore data are not prepared for mining. This problem increases for spatio-temporal data generated by mobile devices, which involve both space and time. In this paper we propose a novel solution to reduce the gap between databases and data mining in the domain of trajectories of moving objects, aiming to reduce the effort for data preprocessing. We propose a general framework for modeling trajectory patterns during the conceptual design of a database. The proposed framework is a result of several works including different data mining case studies and experiments performed by the authors on trajectory data modeling and trajectory data mining. It has been validated with a data mining query language implemented in PostGIS, that allows the user to create, instantiate and query trajectory data and trajectory patterns.


conceptual model data mining trajectory data trajectory patterns 


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  1. 1.
    Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann, San Francisco (1999)Google Scholar
  2. 2.
    Wang, H., Zaniolo, C.: Atlas: A native extension of sql for data mining. In: Barbará, D., Kamath, C. (eds.) SDM. SIAM, Philadelphia (2003)Google Scholar
  3. 3.
    Chen, C.X., Kong, J., Zaniolo, C.: Design and implementation of a temporal extension of sql. In: Dayal, U., Ramamritham, K., Vijayaraman, T.M. (eds.) ICDE, pp. 689–691. IEEE Computer Society, Los Alamitos (2003)Google Scholar
  4. 4.
    Malerba, D., Appice, A., Ceci, M.: A data mining query language for knowledge discovery in a geographical information system. In: Meo, R., Lanzi, P.L., Klemettinen, M. (eds.) Database Support for Data Mining Applications. LNCS (LNAI), vol. 2682, pp. 95–116. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Boulicaut, J.F., Masson, C.: Data mining query languages. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 715–727. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Calders, T., Lakshmanan, L.V.S., Ng, R.T., Paredaens, J.: Expressive power of an algebra for data mining. ACM Transactions on Database Systems 31(4), 1169–1214 (2006)CrossRefGoogle Scholar
  7. 7.
    Han, J.: Mining knowledge at multiple concept levels. In: CIKM, pp. 19–24. ACM Press, New York (1995)Google Scholar
  8. 8.
    Bogorny, V., Kuijpers, B., Alvares, L.O.: St-dmql: a semantic trajectory data mining query language. International Journal of Geographical Information Science 23(10), 1245–1276 (2009)CrossRefGoogle Scholar
  9. 9.
    Bogorny, V., Kuijpers, B., Alvares, L.O.: Reducing uninteresting spatial association rules in geographic databases using background knowledge: a summary of results. International Journal of Geographical Information Science 22, 361–386 (2008)CrossRefGoogle Scholar
  10. 10.
    Alvares, L.O., Bogorny, V., de Macedo, J.F., Moelans, B., Spaccapietra, S.: Dynamic modeling of trajectory patterns using data mining and reverse engineering. In: Twenty-Sixth International Conference on Conceptual Modeling - ER2007 - Tutorials, Posters, Panels and Industrial Contributions, November 2007. CRPIT, vol. 83, pp. 149–154 (2007)Google Scholar
  11. 11.
    Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F., Vangenot, C.: A conceptual view on trajectories. Data and Knowledge Engineering 65(1), 126–146 (2008)CrossRefGoogle Scholar
  12. 12.
    GeoPKDD, P (2006),
  13. 13.
    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, pp. 162–169. ACM Press, New York (2007)Google Scholar
  14. 14.
    Baglioni, M., de Macêdo, J.A.F., Renso, C., Wachowicz, M.: An ontology-based approach for the semantic modeling and reasoning on trajectories. In: ER Workshops, pp. 344–353 (2008)Google Scholar
  15. 15.
    Bogorny, V., Wachowicz, M.: A framework for context-aware trajectory data mining. In: Cao, L., Yu, P.S., Zhang, C., Zhang, H. (eds.) Data Mining for Business Applications, pp. 225–240. Springer, Heidelberg (2008)Google Scholar
  16. 16.
    Palma, A.T., Bogorny, V., Kuijpers, B., Alvares, L.O.: A clustering-based approach for discovering interesting places in trajectories. In: ACMSAC, pp. 863–868. ACM Press, New York (2008)Google Scholar
  17. 17.
    Alvares, L.O., Oliveira, G., Heuser, C.A., Bogorny, V.: A framework for trajectory data preprocessing for data mining. In: International Conference on Software Engineering and Knowledge Engineering, pp. 698–702 (2009)Google Scholar
  18. 18.
    Wolfson, O., Xu, B., Chamberlain, S., Jiang, L.: Moving objects databases: Issues and solutions. In: Rafanelli, M., Jarke, M. (eds.) SSDBM, pp. 111–122. IEEE Computer Society, Los Alamitos (1998)Google Scholar
  19. 19.
    Güting, R.H., de Almeida, V.T., Ding, Z.: Modeling and querying moving objects in networks. VLDB Journal 15(2), 165–190 (2006)CrossRefGoogle Scholar
  20. 20.
    Brakatsoulas, S., Pfoser, D., Tryfona, N.: Modeling, storing, and mining moving object databases. In: IDEAS, pp. 68–77. IEEE Computer Society, Los Alamitos (2004)Google Scholar
  21. 21.
    Parent, C., Spaccapietra, S., Zimanyi, E.: Conceptual Modeling for Traditional and Spatio-Temporal Applications – The MADS Approach. Springer, Heidelberg (2006)zbMATHGoogle Scholar
  22. 22.
    Frank, E., Hall, M.A., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I.H., Trigg, L.: Weka - a machine learning workbench for data mining. In: Maimon, O., Rokach, L. (eds.) The Data Mining and Knowledge Discovery Handbook, pp. 1305–1314. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  23. 23.
    Abiteboul, S., Schek, H.-J., Fischer, P.C. (eds.): NF2 1987. LNCS, vol. 361. Springer, Heidelberg (1989)zbMATHGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  1. 1.Depto de Informatica e EstatisticaUniversidade Federal de Santa CatarinaFlorianopolisBrazil
  2. 2.Instituto de InformaticaUniversidade Federal do Rio Grande do SulPorto AlegreBrazil

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