Abstract
In this paper we present a novel approach for analyzing the trajectories of moving objects and of people in particular. The mined data from these sequences can provide valuable information for understanding the surrounding locations, discovering attractive place or mining frequent sequences of visited places. Based on geotagged photos, our framework mines semantically annotated sequences. Our framework is capable of mining semantically annotated sequences of any length to discover patterns that are not necessarily immediate antecedents. The approach consists of four main steps. In the first step, every photo location is semantically annotated by assigning it to a known nearby point of interest. In the second step, a density-based clustering algorithm is applied to all unassigned photos, creating regions of unknown points of interest. In the third step, a travel sequence of every individual is built. In the final step, travel sequence patterns are mined using the semantics that were obtained from the first two steps. Case studies of Guimarães, Portugal (where the conference takes place) and Berlin, Germany demonstrate the capabilities of the proposed framework.
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References
Agrawal, R. and Srikant, R. (1995) Mining sequential patterns. Proceedings of International Conference on Data Engineering (ICDE’95), pp. 3-14, 1995.
Agrawal, R. and Srikant, R. (1994) Fast algorithms for mining association rules. Proceedings of International Conference Very Large Data Bases, pp. 487-499, 1994.
Alvares, L.O., Bogorny, V., Kuijpers, B., de Macedo, J.A.F., Moelans, B. and Vaisman, A. (2007a) A model for enriching trajectories with semantic geographical information. Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems, pp. 22, 2007.
Alvares, L.O., Bogorny, V., de Macedo, J.A.F., Moelans, B. and Spaccapietra, S. (2007b) Dynamic modeling of trajectory patterns using data mining and reverse engineering. Tutorials, posters, panels and industrial contributions at the 26th international conference on Conceptual modeling, volume 83, pp. 149- 154, 2007.
Andrienko, G., Andrienko, N., Jankowski, P., Keim D., Kraak M.-J., MacEahren A., Wrobel S. (2007) Geovisual analytics for spatial decision support: Setting the research agenda. International Journal of Geographical Information Science, 21/8, pp. 839–857.
Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings ACM KDD, pp. 226-231, 1996.
Giannotti, F., Nanni, M. and Pedreschi, D. (2006) Efficient mining of temporally annotated sequences. Proceedings of the 6th SIAM International Conference on Data Mining (SDM’06), pp. 346-357, 2006.
Giannotti, F., Nanni, M., Pinelli, F. and Pedreschi, D. (2007) Trajectory pattern mining. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 339, 2007.
Girardin, F., Vaccari, A., Gerber, A., Biderman A., and Carlo R. (2009) Quantifying urban attractiveness from the distribution and density of digital footprints. International Journal of Spatial Data Infrastructures Research, 4, pp. 175-200, 2009.
Kisilevich, S., Florian, M., Peter, Bak., Tchaikin, A., Keim, D. (2010) Where Would You Go on Your Next Vacation? A Framework for Visual Exploration of Attractive Places, accepted in Geoprocessing 2010.
Miller H. and Han J. (2009) Geographic data mining and knowledge discovery. Chapman & Hall/CRC.
Piatetsky-Shapiro G., (1991) Discovery, analysis and presentation of strong rules. In: G. Piatetsky-Shapiro and W.J. Frawley Editors, Knowledge Discovery in Databases AAAI (1991), p. 229
Rigoutsos, I. and Floratos, A. (1998) Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm. Bioinformatics, 14/1, pp. 55-67.
Rokach, L. and Maimon, O. (2005), Clustering methods, In O. Maimon & L. Rokach (Eds.), Data Mining and Knowledge Discovery Handbook, Springer, pp. 321–352.
Rokach, L., Romano R. and Maimon O. (2008A), Negation recognition in medical narrative reports, Information Retrieval, 11(6):499-538.
Rokach, L., Romano R. and Maimon O. (2008B), Mining manufacturing databases to discover the effect of operation sequence on the product quality, Journal of Intelligent Manufacturing, 19(3):313-325.
Spaccapietra, S., Parent, C., Damiani, M.L., de Macedo, J.A., Porto, F. and Vangenot, C.(2007) A conceptual view on trajectories. Technical report, Ecole Polytechnique Federal de Lausanne, 2007.
Zheng, Y., Zhang, L., Xie, X. and Ma, W.Y. (2009) Mining interesting locations and travel sequences from GPS trajectories. Proceedings of the 18th international conference on World Wide Web, pp. 791-800, 2009
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Kisilevich, S., Keim, D., Rokach, L. (2010). A Novel Approach to Mining Travel Sequences Using Collections of Geotagged Photos. In: Painho, M., Santos, M., Pundt, H. (eds) Geospatial Thinking. Lecture Notes in Geoinformation and Cartography, vol 0. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12326-9_9
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DOI: https://doi.org/10.1007/978-3-642-12326-9_9
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