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English Information

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Authors
# Name
1 João Leite(joaovitorleite@id.uff.br)
2 Wagner Telles(wtelles@id.uff.br)
3 Rodolfo Oliveira(rodolfooliveira@id.uff.br)
4 Daniel de Oliveira(danielcmo@ic.uff.br)
5 Marcos Bedo(marcosbedo@id.uff.br)

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Reference
# Reference
1 Chávez, E., Navarro, G., Baeza-Yates, R., and Marroquín, J. L. (2001). Searching in metric spaces. ACM Computing Surveys, 33(3):273–321.
2 Chen, L., Gao, Y., Zheng, B., Jensen, C., Yang, H., and Yang, K. (2017). Pivot-based metric indexing. Proceedings of the VLDB Endowment, 10(10):1058–1069.
3 Hetland, M. (2009). The basic principles of metric indexing. In Swarm Intelligence for Multi-objective Problems in Mining, pages 199–232. Springer.
4 Hjaltason, G. and Samet, H. (2003). Index-driven similarity search in metric spaces. ACM Transactions on Database Systems, 28(4):517–580.
5 Mao, R., Zhang, P., Li, X., Liu, X., and Lu, M. (2016). Pivot selection for metric-space indexing. International Journal of Machine Learning and Cybernetics, 7(2):311–323.
6 Traina Jr, C., Filho, R., Traina, A., Vieira, M., and Faloutsos, C. (2007). The omni-family of all-purpose access methods: a simple and effective way to make similarity search more efficient. The VLDB Journal, 16(4):483–505.
7 Yianilos, P. (1993). Data structures and algorithms for nearest neighbor. In Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, volume 66, page 311. SIAM.
8 Zhu, Y., Chen, L., Gao, Y., and Jensen, C. (2022). Pivot selection algorithms in metric spaces: A survey and experimental study. The VLDB Journal, 31(1):23–47.