Measuring Similarity between Groups of Trajectory Data with Multiple Aspects
Resumo
This paper proposes an approach to measure similarity between groups of trajectory data using representative trajectories. By summarizing each group into a representative trajectory and comparing these, we address the challenges in group trajectory analysis. This offers a versatile solution for understanding group behaviors and interactions. Our approach is demonstrated using the Foursquare NYC dataset, which shows its potential in social behavior analysis and highlights its diverse applications, such as urban planning, transportation optimization, and animal migration analysis. The results show that our approach provides meaningful insights into group trajectory patterns, significantly advancing the field of trajectory data analysis.
Palavras-chave:
Trajetórias de Múltiplos Aspectos, Dados de Trajetórias, dados de mobilidade, Medida de Similaridade, Similaridade entre Trajetórias, Medida de similaridade entre Grupos de dados, Trajetória representativa
Referências
Alowayr, A. D., Alsalooli, L. A., Alshahrani, A. M., and Akaichi, J. (2021). A review of trajectory data mining applications. In 2021 International Conference of Women in Data Science at Taif University (WiDSTaif ), pages 1–6.
Eiter, T. and Mannila, H. (1994). Computing discrete frechet distance. Technical report cd-tr 94/64, Christian Doppler Laboratory for Expert Systems, TU Vienna - Austria.
Erwig, M. et al. (1999). Spatio-temporal data types: An approach to modeling and querying moving objects in databases. GeoInformatica, 3(3):269–296.
Furtado, A. S., Alvares, L. O. C., Pelekis, N., Theodoridis, Y., and Bogorny, V. (2018). Unveiling movement uncertainty for robust trajectory similarity analysis. Int. J. Geogr. Inf. Sci., 32(1):140–168.
Furtado, A. S., Kopanaki, D., Alvares, L. O., and Bogorny, V. (2016). Multidimensional similarity measuring for semantic trajectories. Trans. GIS, 20(2):280–298.
Gupta, A., Mishra, A., Vadlamudi, S., Chakrabarti, P., Sarkar, S., Mukherjee, T., and Gnanasambandam, N. (2013). A mobility simulation framework of humans with group behavior modeling. In 2013 IEEE 13th International Conference on Data Mining, pages 1067–1072.
Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., and Shen, H. T. (2008). Discovery of convoys in trajectory databases. Proc. VLDB Endow., page 1068–1080.
Kalnis, P., Mamoulis, N., and Bakiras, S. (2005). On discovering moving clusters in spatio-temporal data. In Advances in Spatial and Temporal Databases: 9th International Symposium, SSTD 2005, pages 364–381. Springer.
Li, Z., Wu, F., and Crofoot, M. (2013). Mining following relationships in movement data. In Proc. 2013 Int. Conf. Data Mining (ICDM’13).
Machado, V. L. (2024). On Computing Representative Data for Summarizing Multiple Aspect Trajectories. Tese (doutorado), Universidade Federal de Santa Catarina, Florianópolis.
Machado, V. L., Portela, T., Machado, A. d. L., Schreiner, G. A., and Mello, R. d. S. (2023a). A method for computing representative data for multiple aspect trajectories based on data summarization. In XXIV Brazilian Symposium on Geoinformatics.
Machado, V. L., Portela, T., Renso, C., and Mello, R. d. S. (2023b). Towards a representativeness measure for summarized trajectories with multiple aspects. In XXIV Brazilian Symposium on Geoinformatics.
Mello, R. d. S. et al. (2019). MASTER: A multiple aspect view on trajectories. Trans. GIS, 23(4):805–822.
Parent, C. et al. (2013). Semantic trajectories modeling and analysis. ACM Comput. Surv., 45(4):42:1–42:32.
Petry, L. M., Ferrero, C. A., Alvares, L. O., Renso, C., and Bogorny, V. (2019). Towards semantic-aware multiple-aspect trajectory similarity measuring. Trans. GIS, 23(5):960–975.
Wiratma, L., van Kreveld, M., and Löffler, M. (2017). On measures for groups of trajectories. In 20th AGILE conference on Geographic Information Science, pages 311–330. Springer.
Eiter, T. and Mannila, H. (1994). Computing discrete frechet distance. Technical report cd-tr 94/64, Christian Doppler Laboratory for Expert Systems, TU Vienna - Austria.
Erwig, M. et al. (1999). Spatio-temporal data types: An approach to modeling and querying moving objects in databases. GeoInformatica, 3(3):269–296.
Furtado, A. S., Alvares, L. O. C., Pelekis, N., Theodoridis, Y., and Bogorny, V. (2018). Unveiling movement uncertainty for robust trajectory similarity analysis. Int. J. Geogr. Inf. Sci., 32(1):140–168.
Furtado, A. S., Kopanaki, D., Alvares, L. O., and Bogorny, V. (2016). Multidimensional similarity measuring for semantic trajectories. Trans. GIS, 20(2):280–298.
Gupta, A., Mishra, A., Vadlamudi, S., Chakrabarti, P., Sarkar, S., Mukherjee, T., and Gnanasambandam, N. (2013). A mobility simulation framework of humans with group behavior modeling. In 2013 IEEE 13th International Conference on Data Mining, pages 1067–1072.
Jeung, H., Yiu, M. L., Zhou, X., Jensen, C. S., and Shen, H. T. (2008). Discovery of convoys in trajectory databases. Proc. VLDB Endow., page 1068–1080.
Kalnis, P., Mamoulis, N., and Bakiras, S. (2005). On discovering moving clusters in spatio-temporal data. In Advances in Spatial and Temporal Databases: 9th International Symposium, SSTD 2005, pages 364–381. Springer.
Li, Z., Wu, F., and Crofoot, M. (2013). Mining following relationships in movement data. In Proc. 2013 Int. Conf. Data Mining (ICDM’13).
Machado, V. L. (2024). On Computing Representative Data for Summarizing Multiple Aspect Trajectories. Tese (doutorado), Universidade Federal de Santa Catarina, Florianópolis.
Machado, V. L., Portela, T., Machado, A. d. L., Schreiner, G. A., and Mello, R. d. S. (2023a). A method for computing representative data for multiple aspect trajectories based on data summarization. In XXIV Brazilian Symposium on Geoinformatics.
Machado, V. L., Portela, T., Renso, C., and Mello, R. d. S. (2023b). Towards a representativeness measure for summarized trajectories with multiple aspects. In XXIV Brazilian Symposium on Geoinformatics.
Mello, R. d. S. et al. (2019). MASTER: A multiple aspect view on trajectories. Trans. GIS, 23(4):805–822.
Parent, C. et al. (2013). Semantic trajectories modeling and analysis. ACM Comput. Surv., 45(4):42:1–42:32.
Petry, L. M., Ferrero, C. A., Alvares, L. O., Renso, C., and Bogorny, V. (2019). Towards semantic-aware multiple-aspect trajectory similarity measuring. Trans. GIS, 23(5):960–975.
Wiratma, L., van Kreveld, M., and Löffler, M. (2017). On measures for groups of trajectories. In 20th AGILE conference on Geographic Information Science, pages 311–330. Springer.
Publicado
14/10/2024
Como Citar
MACHADO, Vanessa Lago; PORTELA, Tarlis Tortelli; VANINI, Lucas; MELLO, Ronaldo dos Santos.
Measuring Similarity between Groups of Trajectory Data with Multiple Aspects. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 39. , 2024, Florianópolis/SC.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 792-798.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2024.242016.
