Discovering Local and Global Co-Location Patterns in Trajectories with Different Properties

• Fernando José Braz IFC
• Vania Bogorny UFSC

DOI: https://doi.org/10.5753/sbsi.2013.5732

Resumo

Most trajectory mining approaches consider a very small set of properties to extract patterns from trajectories. Besides, a lot of them consider those properties separately. In this paper we present a method to find co-location patterns based on different properties of trajectories along time. The proposal allows to identify a sequence of co-locations composed by different properties (distance, acceleration, speed, time etc) that represents the behavior of a set of trajectories. The sequences of co-locations patterns present the evolution of an event, for example traffic jam. By using this knowledge is possible to anticipate the phenomenon occurrence, and to take actions to solve problems regarding to the event.

Referências

Agrawal, R. and Srikant, R. (1995). Mining sequential patterns. In Yu, P. S. and Chen, A. S. P., editors, Eleventh International Conference on Data Engineering, pages 3–14, Taipei, Taiwan. IEEE Computer Society Press.

Bogorny, V., Kuijpers, B., and Alvares, L. O. (2009). St-dmql: A semantic trajectory data mining query language. International Journal of Geographical Information Science, 23(10):1245–1276.

Braz, F. (2008). Knowledge discovery on trajectory data warehouses: Possible usage of the data mining techniques. In SBSI 2008: Proceedings of the 4th Brazilian Symposium on Informatics, Rio de Janeiro,Rio de Janeiro,Brazil. SBC Brazilian Computer Society.

Brinkhoff, T. (2000). Generating network-based moving objects. In SSDBM ’00: Proceedings of the 12th international Conference on Scientic and Statistical Database Management, page 253, Washington,DC,USA. IEEE Computer Society.

Cao, H., Mamoulis, N., and Cheung, D. W. (2006). Discovery of collocation episodes in spatiotemporal data. In ICDM ’06: Proceedings of the Sixth International Conference on Data Mining, pages 823–827, Washington, DC, USA. IEEE Computer Society.

Celik, M., Kang, J. M., and Shekhar, S. (2007). Zonal co-location pattern discovery with In ICDM ’07: Proceedings of the 2007 Seventh IEEE Interdynamic parameters. national Conference on Data Mining, pages 433–438, Washington, DC, USA. IEEE Computer Society.

Celik, M., Shekhar, S., Rogers, J. P., and Shine, J. A. (2008). Mixed-drove spatio-temporal co-occurence pattern mining. In IEEE Transactions on Knowledge and Data Engineering, Washington, DC, USA. IEEE Computer Society.

Han, J. (1995). Mining knowledge at multiple concept levels. In CIKM, pages 19–24. ACM.

Huang, Y., Shekhar, S., and Xiong, H. (2004). Discovering colocation patterns from IEEE Transactions on Knowledge and Data spatial data sets: A general approach. Engineering, 16:2004.

Li, Z., Ding, B., Han, J., Kays, R., and Nye, P. (2010). Mining periodic behaviors for moving objects. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’10, pages 1099–1108, New York, NY, USA. ACM.

Palma, A. T., Bogorny, V., Kuijpers, B., and Alvares, L. O. (2008). A clustering-based approach for discovering interesting places in trajectories. Proceedings of the 2008 ACM symposium on Applied computing SAC 08, (December):863.

Shekhar, S. and Huang, Y. (2001). Discovering spatial co-location patterns: A summary of results. In Lecture Notes in Computer Science, pages 236–256.

Yoo, J. S. and Shekhar, S. (2006). A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng., 18(10):1323–1337.

Zaki, M. J. (2001). SPADE: An efcient algorithm for mining frequent sequences. Machine Learning, 42(1/2):31–60.

Zheng, Y. and Xie, X. (2010). Learning location correlation from gps trajectories.