SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Wilken Melo(wilken.dantas@ufc.br)
2 Lívia Cruz( livia@insightlab.ufc.br)
3 Francesco Lettich(francesco.lettich@gmail.com)
4 Ticiana Silva(ticianalc@insightlab.ufc.br)
5 Regis Magalhães( regis@insightlab.ufc.br )

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Cao, H., Xu, F., Sankaranarayanan, J., Li, Y., and Samet, H. (2020). Habit2vec: Trajectory semantic embedding for living pattern recognition in population. IEEE Transactions on Mobile Computing, 19(5):1096–1108.
2 Crivellari, A., Resch, B., and Shi, Y. (2022). TraceBERT – a feasibility study on recon- structing spatial-temporal gaps from incomplete motion trajectories via BERT training process on discrete location sequences. Sensors, 22(4):1682.
3 Cruz, L., Coelho da Silva, T., Magalh ̃aes, R., Melo, W., Cordeiro, M., de Macedo, J., and Zeitouni, K. (2022). Modeling trajectories obtained from external sensors for location prediction via NLP approaches. Sensors, 22(19).
4 Cruz, L., Zeitouni, K., and Macedo, J. (2019). Trajectory prediction from a mass of sparse and missing external sensor data. In IEEE MDM.
5 Damiani, M. L., Acquaviva, A., Hachem, F., and Rossini, M. (2020). Learning behavioral representations of human mobility. In ACM SIGSPATIAL, page 367–376, New York, NY, USA. Association for Computing Machinery.
6 Devlin, J., Chang, M., Lee, K., and Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Conference of the North American Chapter of the ACL: Human Language Technologies, Vol.1, pages 4171– 4186, Minneapolis, Minnesota. ACL.
7 Fang, Z., Du, Y., Zhu, X., Hu, D., Chen, L., Gao, Y., and Jensen, C. (2022). Spatio- temporal trajectory similarity learning in road networks. In 28th ACM SIGKDD, KDD ’22, page 347–356, New York, NY, USA. Association for Computing Machinery.
8 Fu, T.-Y. and Lee, W.-C. (2020). Trembr: Exploring road networks for trajectory repre- sentation learning. ACM TIST, 11(1):1–25.
9 Gruver, N., Finzi, M. A., Qiu, S., and Wilson, A. G. (2023). Large language models are zero-shot time series forecasters. In NeurIPS.
10 Hung, C.-C., Peng, W.-C., and Lee, W.-C. (2015). Clustering and aggregating clues of trajectories for mining trajectory patterns and routes. The VLDB Journal, 24(2):169– 192.
11 Jing, Y., Yu, Z., Chengyang, Z., Wenlei, X., Xing, X., Guangzhong, S., and Yan, H. (2018). Tdrive: driving directions based on taxi trajectories. In 18th ACM SIGSPA- TIAL, GIS ’10, pages 99–108, New York, NY, USA. Association for Computing Ma- chinery.
12 Kruskal, J. (1983). An overview of sequence comparison: time warps, string edits, and macromolecules. SIAM, 2(25):201–237.
13 Le, Q. and Mikolov, T. (2014). Distributed representations of sentences and documents. In Proceedings of the 31st ICML, ICML’14, page II–1188–II–1196. JMLR.org.
14 Levenshtein, V. I. (1966). Binary codes capable of correcting deletions, insertions, and reversals. Soviet physics doklady, 10(8):707–710.
15 Li, X., Zhao, K., Cong, G., Jensen, C. S., and Wei, W. (2018). Deep representation learning for trajectory similarity computation. In 34th IEEE ICDE, pages 617–628.
16 Mikolov, T., Chen, K., Corrado, G., and Dean, J. (2013). Efficient estimation of word representations in vector space. In 1st ICLR Workshop Track Proceedings.
17 Shuncheng, L., Su, H., Zheng, B., Zhou, X., and Zheng., K. (2019). A survey of trajectory distance measures and performance evaluation. VLDB, 408:3–32.
18 Taghizadeh, S., Elekes, A., Schaler, M., and Bohn, K. (2021). How meaningful are similarities in deep trajectory representations? In Information Systems, volume 98, page 101452. Elsevier.
19 Wang, S., Cao, J., and Philip, S. Y. (2020). Deep learning for spatio-temporal data mining: A survey. IEEE TKDE, 34(8):3681–3700.
20 Yang, P., Wang, H., Zhang, Y., Qin, L., Zhang, W., and Lin, X. (2021). T3S: Effective representation learning for trajectory similarity computation. In 37th IEEE ICDE, pages 2183–2188.
21 Yao, D., Cong, G., Zhang, C., and Bi, J. (2019). Computing trajectory similarity in linear time: A generic seed-guided neural metric learning approach. In 35th IEEE ICDE 2019, pages 1358–1369.
22 Zhang, H., Zhang, X., Jiang, Q., Zheng, B., Sun, Z., Sun, W., and Wang, C. (2021). Trajectory similarity learning with auxiliary supervision and optimal matching. In 29th IJCAI, IJCAI’20.
23 Zhang, Y., Liu, A., Liu, G., Li, Z., and Li, Q. (2019). Deep representation learning of activity trajectory similarity computation. In 2019 IEEE ICWS, pages 312–319. IEEE.