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English Information

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Authors
# Name
1 Antônio Lívio Mendonça(antonio.mendonca@ufu.br)
2 Maria Camila Barioni(camila.barioni@ufu.br)
3 Humberto Razente(humberto.razente@ufu.br)

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Reference
# Reference
1 Abelló, A. and Romero, O. (2018). Online analytical processing. In Encyclopedia of Database Systems, pages 2558–2563. Springer. doi:10.1007/978-1-4614-8265-9_252.
2 Barioni, M. C. N., Razente, H., Traina, A., and Traina-Jr, C. (2008). Accelerating kmedoid-based algorithms through metric access methods. J. Syst. Softw., 81(3):343–355. doi:10.1016/J.JSS.2007.06.019.
3 Barioni, M. C. N., Razente, H., Traina, A., and Traina-Jr., C. (2009). Seamlessly integrating similarity queries in SQL. Softw. Pract. Exp., 39(4):355–384. doi:10.1002/SPE.898.
4 Chen, L., Gao, Y., Song, X., Li, Z., Zhu, Y., Miao, X., and Jensen, C. S. (2023). Indexing metric spaces for exact similarity search. ACM Comput. Surv., 55(6):128:1–128:39. doi:10.1145/3534963.
5 Eleutério, I., Cazzolato, M., Gutierrez, M. A., Teixeira, L., Traina, A., and Traina-Jr, C. (2024). Migue-sim: Speeding up similarity queries with native rdbms resources. In Symp. Applied Computing (SAC), pages 321–328. doi:10.1145/3605098.3636019.
6 Ezugwu, A., Ikotun, A., Oyelade, O., Abualigah, L., Agushaka, J., Eke, C., and Akinyelu, A. (2022). A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects. Eng. Appl. Artif. Intell., 110:104743. doi:10.1016/J.ENGAPPAI.2022.104743.
7 Garcia-Alvarado, C. and Ordonez, C. (2015). Clustering binary cube dimensions to compute relaxed GROUP BY aggregations. Inf. Syst., 53:41–59. doi:10.1016/j.is.2014.12.008.
8 Gray, J., Chaudhuri, S., Bosworth, A., Layman, A., Reichart, D., Venkatrao, M., Pellow, F., and Pirahesh, H. (1997). Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub totals. Data Min. Knowl. Discov., 1(1):29–53. doi:10.1023/A:1009726021843.
9 Iqbal, M., Lissandrini, M., and Pedersen, T. B. (2022). A foundation for spatio-textualtemporal cube analytics. Inf. Syst., 108:102009. doi:10.1016/j.is.2022.102009.
10 ISO (1992). ISO/IEC 9075:1992: Information technology — Database languages — SQL. International Org. Standardization. https://www.iso.org/standard/16663.html.
11 ISO (2023). ISO/IEC 9075:2023: Information technology — Database languages — SQL. International Org. Standardization. https://www.iso.org/standard/76583.html.
12 Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recognit. Lett., 31(8):651–666. doi:10.1016/J.PATREC.2009.09.011.
13 Kaster, D. S., Bugatti, P. H., Traina, A. J. M., and Traina-Jr, C. (2010). FMI-SiR: A flexible and efficient module for similarity searching on Oracle database. J. Inf. Data Manag., 1(2):229–244. doi:10.5753/jidm.2010.1263.
14 Kelly, M., Longjohn, R., and Nottingham, K. (2024). The UCI Machine Learning Repository. https://archive.ics.uci.edu.
15 Kim, T., Li, W., Behm, A., Cetindil, I., Vernica, R., Borkar, V. R., Carey, M. J., and Li, C. (2020). Similarity query support in big data management systems. Inf. Syst., 88. doi:10.1016/J.IS.2019.101455.
16 Lu, W., Hou, J., Yan, Y., Zhang, M., Du, X., and Moscibroda, T. (2017). MSQL: efficient similarity search in metric spaces using SQL. VLDB J., 26(6):829–854. doi:10.1007/s00778-017-0481-6.
17 Matiazzo, M. A. L., de Castro-Silva, V., Oyamada, R. S., and Kaster, D. S. (2023). The dataset-similarity-based approach to select datasets for evaluation in similarity retrieval. In Intl Conf. Similarity Search and Applications (SISAP), volume 14289 of LNCS, pages 125–132. Springer. doi:10.1007/978-3-031-46994-7_11.
18 Oliveira, W. D., Lauton, A. J. C., Traina-Jr, C., and Santos, L. F. D. (2023). Similarity grouping by influence: Exploring result diversification in similarity group-by operators. In Simpósio Brasileiro de Bancos de Dados (SBBD), pages 402–407. SBC. doi:10.5753/sbbd.2023.233430.
19 Razente, H., Barioni, M. C. N., Traina, A., Faloutsos, C., and Traina-Jr, C. (2008). A novel optimization approach to efficiently process aggregate similarity queries in metric access methods. In Int’l Conf. Information and Knowledge Management (CIKM), pages 193–202. ACM. doi:10.1145/1458082.1458110.
20 Samet, H. (2006). Foundations of Multidimensional and Metric Data Structures. Morgan Kaufmann. San Francisco, CA.
21 Silva, Y. N., Aref,W. G., and Ali, M. H. (2009a). Similarity group-by. In Int’l Conf. Data Engineering (ICDE), pages 904–915. IEEE. doi:10.1109/ICDE.2009.113.
22 Silva, Y. N., Arshad, M. U., and Aref, W. G. (2009b). Exploiting similarity-aware grouping in decision support systems. In Int’l Conf. Extending Database Technology (EDBT), volume 360, pages 1144–1147. ACM. doi:10.1145/1516360.1516499.
23 Silva, Y. N., Sandoval, M., Prado, D., Wallace, X., and Rong, C. (2019). Similarity grouping in big data systems. In Intl Conf. Similarity Search and Applications (SISAP), volume 11807 of LNCS, pages 212–220. Springer. doi:10.1007/978-3-030-32047-8_19.
24 Stonebraker, M. and Pavlo, A. (2024). What goes around comes around... and around... SIGMOD Rec., 53(2):21–37.
25 Tang, M., Tahboub, R. Y., Aref, W. G., Atallah, M. J., Malluhi, Q. M., Ouzzani, M., and Silva, Y. N. (2016). Similarity group-by operators for multidimensional relational data. IEEE Trans. Knowl. Data Eng., 28(2):510–523. doi:10.1109/TKDE.2015.2480400.