TRACTS: Um método para a classificação de trajetórias de objetos móveis usando séries temporais
Resumo
A classificação de trajetórias é um assunto relativamente novo de pesquisa, mas alguns métodos já foram propostos. A maioria destes métodos foi desenvolvido para uma aplicação específica. Poucos propuseram um método mais geral, aplicável a vários domínios ou conjuntos de dados. Esse trabalho apresenta um novo método de classificação que transforma as trajetórias em séries temporais de forma a obter características mais discriminativas para a classificação. Os resultados dos experimentos realizados foram comparados com os obtidos pelo método TraClass e se mostraram, em geral, superiores.Referências
Ashbrook, D., & Starner, T. (2003). “Using GPS to Learn Significant Locations and Predict Movement Across Multiple Users”. Personal and Ubiquitous Computing Journal, 275 - 286.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Databases”. AI Magazine, 37-54.
Frank, E., Hall, M., Holmes, G., Kirkby, R., & Pfahringer, B. (2005). “WEKA - A Machine Learning Workbench for Data Mining”. In: Collection of The Data Mining and Knowledge Discovery Handbook (pp. 1305-1314). Springer.
García, J., Concha, O. P., Molina, J. M., & Miguel, G. (2006). “Trajectory classification based on machine-learning techniques over tracking data”. Proceedings of 9th International Conference on Information Fusion, (pp. 1-8). Florence, Italia.
Gudmundsson, J., Kreveld, M. v., & Speckmann, B. (2007). “Efficient Detection of Patterns in 2D Trajectories”. GeoInformatica, 195-215.
Han, J., & Kamber, M. (2006). “Mining Stream, Time-Series and Sequence Data”. In: Data Mining: Concepts and Techniques. (pp. 467-489). San Francisco, CA, EUA: Morgan Kaufmann.
Hariharan, R., & Toyama, K. (2004). “Project Lachesis: Parsing and Modeling Location Histories”. Proceedings of the 3rd International Conference on Geographic Information Science (pp. 106–124). Adelphi, EUA: Springer.
Kumar, N., Lolla, V. N., Keogh, E., Lonardi, S., Ratanamahatana, C. A., & Wei, L. (2005). “Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases”. Proceedings of 5th SIAM International Conference on Data Mining - SDM'05, Proceedings (pp. 531-535). Newport Beach, CA, EUA: SIAM.
Lee, J. Y., & Hoff, W. (2007). “Activity Identification Utilizing Data Mining Techniques”. Motion and Video Computing, IEEE Workshop on (p. 12). Austin, Texas, EUA: IEEE Computer Society.
Lee, J.-G., Han, J., Gonzalez, H., & Li, X. (2008). “TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering”. VLDB Endowment (pp. 1081-1094). Auckland, Nova Zelândia: VLDB Endowment.
Liao, L., Patterson, D., Fox, D., & Kautz, H. (2006). “Building Personal Maps from GPS Data”. Annals of the New York Academy of Sciences, 249 - 265.
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (1 de Outubro de 2007). “Experiencing SAX: a novel symbolic representation of time series”. Data Mining and Knowledge Discovery, pp. 107-144.
Monterey Bay Aquarium Research Institute. (01 de Setembro de 2001). Acesso em 16 de Julho de 2010, disponível em MUSE Project: [link]
Pacific Northwest Research Station. (2005). US Forest Service. Acesso em 16 de Julho de 2010, disponível em The Starkey Project: [link]
Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008). “A Clustering-based Approach for Discovering Interesting Places in Trajectories”. Proceedings of 23rd Annual Symposium on Applied Computing, (pp. 863-868). Fortaleza, Ceara, Brasil.
Panagiotakis, C., Pelekis, N., & Kopanakis, I. (2009). “Trajectory Voting and Classification based on Spatiotemporal Similarity in Moving Object Databases”.
Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII (pp. 131-142). Lyon, France: Springer.
Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Boston: Addison-Wesley.
Tiakas, E., Papadopoulos, A. N., Nanopoulos, A., Manolopoulos, Y. P., Stojanovic, D., & Kajan, S. D. (2009). “Searching for similar trajectories in spatial networks”. Journal of Systems and Software, 772-788.
Tiesyte, D., & Jensen, C. S. (2008). “Similarity-based prediction of travel times for vehicles traveling on known routes”. Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (pp. 105114). Irvine, CA, EUA: ACM.
Unisys. (2009). Atlantic Tropical Storm Tracking by Year. Acesso em 16 de Julho de 2010, disponível em Unisys Weather: [link]
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008). “Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web”. Proceeding of the 17th international conference on World Wide Web (pp. 247-256). Pequim, China: ACM.
Zhou, C., Bhatnagar, N., Shekhar, S., & Terveen, L. (2007). “Mining Personally Important Places from GPS Tracks”. IEEE 23rd International Conference on Data Engineering Workshop (pp. 517-526). Istambul, Turquia: IEEE Computer Society.
Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). “From Data Mining to Knowledge Discovery in Databases”. AI Magazine, 37-54.
Frank, E., Hall, M., Holmes, G., Kirkby, R., & Pfahringer, B. (2005). “WEKA - A Machine Learning Workbench for Data Mining”. In: Collection of The Data Mining and Knowledge Discovery Handbook (pp. 1305-1314). Springer.
García, J., Concha, O. P., Molina, J. M., & Miguel, G. (2006). “Trajectory classification based on machine-learning techniques over tracking data”. Proceedings of 9th International Conference on Information Fusion, (pp. 1-8). Florence, Italia.
Gudmundsson, J., Kreveld, M. v., & Speckmann, B. (2007). “Efficient Detection of Patterns in 2D Trajectories”. GeoInformatica, 195-215.
Han, J., & Kamber, M. (2006). “Mining Stream, Time-Series and Sequence Data”. In: Data Mining: Concepts and Techniques. (pp. 467-489). San Francisco, CA, EUA: Morgan Kaufmann.
Hariharan, R., & Toyama, K. (2004). “Project Lachesis: Parsing and Modeling Location Histories”. Proceedings of the 3rd International Conference on Geographic Information Science (pp. 106–124). Adelphi, EUA: Springer.
Kumar, N., Lolla, V. N., Keogh, E., Lonardi, S., Ratanamahatana, C. A., & Wei, L. (2005). “Time-series Bitmaps: A Practical Visualization Tool for working with Large Time Series Databases”. Proceedings of 5th SIAM International Conference on Data Mining - SDM'05, Proceedings (pp. 531-535). Newport Beach, CA, EUA: SIAM.
Lee, J. Y., & Hoff, W. (2007). “Activity Identification Utilizing Data Mining Techniques”. Motion and Video Computing, IEEE Workshop on (p. 12). Austin, Texas, EUA: IEEE Computer Society.
Lee, J.-G., Han, J., Gonzalez, H., & Li, X. (2008). “TraClass: trajectory classification using hierarchical region-based and trajectory-based clustering”. VLDB Endowment (pp. 1081-1094). Auckland, Nova Zelândia: VLDB Endowment.
Liao, L., Patterson, D., Fox, D., & Kautz, H. (2006). “Building Personal Maps from GPS Data”. Annals of the New York Academy of Sciences, 249 - 265.
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (1 de Outubro de 2007). “Experiencing SAX: a novel symbolic representation of time series”. Data Mining and Knowledge Discovery, pp. 107-144.
Monterey Bay Aquarium Research Institute. (01 de Setembro de 2001). Acesso em 16 de Julho de 2010, disponível em MUSE Project: [link]
Pacific Northwest Research Station. (2005). US Forest Service. Acesso em 16 de Julho de 2010, disponível em The Starkey Project: [link]
Palma, A. T., Bogorny, V., Kuijpers, B., & Alvares, L. O. (2008). “A Clustering-based Approach for Discovering Interesting Places in Trajectories”. Proceedings of 23rd Annual Symposium on Applied Computing, (pp. 863-868). Fortaleza, Ceara, Brasil.
Panagiotakis, C., Pelekis, N., & Kopanakis, I. (2009). “Trajectory Voting and Classification based on Spatiotemporal Similarity in Moving Object Databases”.
Proceedings of the 8th International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis VIII (pp. 131-142). Lyon, France: Springer.
Tan, P.-N., Steinbach, M., & Kumar, V. (2006). Introduction to Data Mining. Boston: Addison-Wesley.
Tiakas, E., Papadopoulos, A. N., Nanopoulos, A., Manolopoulos, Y. P., Stojanovic, D., & Kajan, S. D. (2009). “Searching for similar trajectories in spatial networks”. Journal of Systems and Software, 772-788.
Tiesyte, D., & Jensen, C. S. (2008). “Similarity-based prediction of travel times for vehicles traveling on known routes”. Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems (pp. 105114). Irvine, CA, EUA: ACM.
Unisys. (2009). Atlantic Tropical Storm Tracking by Year. Acesso em 16 de Julho de 2010, disponível em Unisys Weather: [link]
Zheng, Y., Liu, L., Wang, L., & Xie, X. (2008). “Learning Transportation Mode from Raw GPS Data for Geographic Applications on the Web”. Proceeding of the 17th international conference on World Wide Web (pp. 247-256). Pequim, China: ACM.
Zhou, C., Bhatnagar, N., Shekhar, S., & Terveen, L. (2007). “Mining Personally Important Places from GPS Tracks”. IEEE 23rd International Conference on Data Engineering Workshop (pp. 517-526). Istambul, Turquia: IEEE Computer Society.
Publicado
19/07/2011
Como Citar
SANTOS, Irineu Jr. Pinheiro dos; ALVARES, Luis Otavio.
TRACTS: Um método para a classificação de trajetórias de objetos móveis usando séries temporais. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 8. , 2011, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2011
.
p. 797-808.
ISSN 2763-9061.
