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 Enzo Seraphim(seraphim@unifei.edu.br)
2 Thatyana Seraphim(thatyana@unifei.edu.br)
3 Lucio Santos(santos@ifnmg.edu.br)
4 Edmilson Moreira(edmarmo@unifei.edu.br)
5 Luiz Olmes Carvalho(olmes@unifei.edu.br)

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

Reference
# Reference
1 Afonso, F., Barbosa, F., and Rodrigues, A. (2011). Trajectory data similarity with metric data structures. In Geographical Inf. Science Research United Kingdom, 9p.
2 Ciaccia, P., Patella, M., and Zezula, P. (1997). M-tree: An Efficient Access Method for Similarity Search in Metric Spaces. In Proc. Int. Conf. VLDB, pages 426–435, Morgan Kaufmann, San Francisco, CA, USA.
3 Chen, L., Özsu, M. T., and Oria, V. (2005). Robust and fast similarity search for moving object trajectories. In Proc. ACM SIGMOD, pages 491–502, New York, NY, USA.
4 Chen, Z., Shen, H. T., Zhou, X., and Yu, J. X. (2009). Monitoring path nearest neighbor in road networks. In Proc. ACM SIGMOD, pages 591–602, New York, NY, USA.
5 CodePoint, Open CSV. (2025). https://osdatahub.os.uk/downloads/open/CodePointOpen
6 Deng, K., Xie, K., Zheng, K., and Zhou, X. (2011). Trajectory Indexing and Retrieval, pages 35–60. Springer, New York, NY.
7 Guttman. A. (1984). R-trees: a dynamic index structure for spatial searching. SIGMOD Rec., 14 (2).
8 Güting, R. H., Das, S. K., Valdés, F., and Ray, S. (2025). Exact trajectory similarity search with n-tree: An efficient metric index for knn and range queries. ACM Trans. Spatial Algorithms Syst., 11(1).
9 Kalashnikov, D., Prabhakar, S., Hambrusch, S., and Aref, W. (2002). Efficient evaluation of continuous range queries on moving objects. In Proc. DEXA, Berlin, Springer.
10 LibreOffice. (2025). https://github.com/LibreOffice/dictionaries
11 Papadias, D., Zhang, J., Mamoulis, N., and Tao, Y. (2003). Query processing in spatial network databases. In VLDB Conf., pages 802–813. Morgan Kaufmann, San Francisco.
12 Shang, S., Deng, K., and Xie, K. (2010). Best point detour query in road networks. In Proc. 18th ACM SIGSPATIAL Int. Conf. on Advances in Geographic Information Systems, pages 71–80, New York, NY, USA.
13 Tao, Y., Papadias, D., and Shen, Q. (2002). Continuous nearest neighbor search. In Proc. 28th Int. Conf. on VLDB, pages 287–298. Morgan Kaufmann, San Francisco.
14 Traina Jr., C., Traina, A. J. M., Faloutsos, C., and Seeger, B. (2002). Fast Indexing and Visualization of Metric Data Sets using Slim-Trees. IEEE TKDE. 14(2).
15 UniProtKB, TrEMBL Fasta. (2025). https://www.uniprot.org/help/downloads
16 Wang, S., Bao, Z., Culpepper, J. S., and Cong, G. (2021). A survey on trajectory data management, analytics, and learning. ACM Comput. Surv., 54(2).
17 Xu, W., and Miranker, D. P. (2004). A metric model of amino acid substitution. Bioinformatics. UK, 20(8).
18 Xuan, K., Zhao, G., Taniar, D., and Srinivasan, B. (2008). Continuous range search query processing in mobile navigation. In Int. Conf. on Parallel and Dist. Syst, pages 361–368.
19 Zezula, P., Amato, G., Dohnal, V., and Batko, M. (2010). Similarity Search: The Metric Space Approach. Springer Publishing Company, Incorporated, 1st edition.