{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:35:11Z","timestamp":1761165311820,"version":"build-2065373602"},"reference-count":12,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o - SBC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>Consultas por similaridade com diversidade s\u00e3o \u00fateis em aplica\u00e7\u00f5es que demandam resultados que evitam redund\u00e2ncias, como no diagn\u00f3stico m\u00e9dico, sistemas de recomenda\u00e7\u00e3o e busca por imagens. Entretanto, os Sistemas de Gerenciamento de Bases de Dados (SGBDs) ainda oferecem suporte limitado a consultas desse tipo. Este trabalho apresenta o m\u00f3dulo DiveScan, uma extens\u00e3o para o SGBD Postgres que executa tais consultas com os algoritmos FM ou BRID, e pode utilizar \u00edndices GiST para acelerar a busca. A avalia\u00e7\u00e3o experimental indica que a abordagem indexada supera a varredura sequencial em diferentes cen\u00e1rios, reduzindo significativamente o tempo de execu\u00e7\u00e3o.<\/jats:p>","DOI":"10.5753\/sbbd.2025.247779","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:26:36Z","timestamp":1761074796000},"page":"872-878","source":"Crossref","is-referenced-by-count":0,"title":["DiveScan: um m\u00f3dulo para Recupera\u00e7\u00e3o de dados por Similaridade com Diversidade em Postgres"],"prefix":"10.5753","author":[{"given":"Anna J\u00falia Costa","family":"Lauton","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4929-7258","authenticated-orcid":false,"given":"Agma Juci Machado","family":"Traina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6625-6047","authenticated-orcid":false,"given":"Caetano","family":"Traina Jr.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"Chandrasekaran, D. and Mago, V. (2021). Evolution of semantic similarity\u2014a survey. ACM Comput. Surv., 54(2):Article 41.","DOI":"10.1145\/3440755"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Eleut\u00e9rio, I. A. R., Cazzolato, M. T., Teixeira, L. R., Gutierrez, M. A., Traina, A. J. M., and Traina-Jr., C. (2024). Migue-sim: Speeding up similarity queries with native rdbms resources. In Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing, pages 321\u2013328, New York, NY, USA. Association for Computing Machinery.","DOI":"10.1145\/3605098.3636019"},{"key":"3","doi-asserted-by":"crossref","unstructured":"Gupta, S., Thakar, U., and Tokekar, S. (2025). A comprehensive survey on techniques for numerical similarity measurement. Expert Systems with Applications, 277:127235.","DOI":"10.1016\/j.eswa.2025.127235"},{"key":"4","doi-asserted-by":"crossref","unstructured":"Hambarde, K. A. and Proen\u00e7a, H. (2023). Information retrieval: Recent advances and beyond. IEEE Access, 11:76581\u201376604.","DOI":"10.1109\/ACCESS.2023.3295776"},{"key":"5","unstructured":"Hellerstein, J. M., Naughton, J. F., and Pfeffer, A. (1995). Generalized search trees for database systems. In Dayal, U., Gray, P. M. D., and Nishio, S., editors, International Conference on Very Large Databases (VLDB), pages 562\u2013573, Zurich, Switzerland. Morgan Kaufmann."},{"key":"6","doi-asserted-by":"crossref","unstructured":"Jasbick, D., Santos, L., de Oliveira, D., and Bedo, M. (2020). Some branches may bear rotten fruits: Diversity browsing vp-trees. In Similarity Search and Applications, pages 140\u2013154. Springer.","DOI":"10.1007\/978-3-030-60936-8_11"},{"key":"7","doi-asserted-by":"crossref","unstructured":"Santos, L. F. D., Oliveira, W. D. d., Ferreira, M. R. P., Traina, A. J. M., and Traina Jr, C. (2013). Parameter-free and domain-independent similarity search with diversity. In Szalay, A., Budavari, T., Balazinska, M., Meliou, A., and Sacan, A., editors, 25th International Conference on Scientific and Statistical Database Management - SSDBM\u20192013, pages 5\u201316, Baltimore, MD, USA. ACM.","DOI":"10.1145\/2484838.2484854"},{"key":"8","doi-asserted-by":"crossref","unstructured":"Shu, X. and Ye, Y. (2023). Knowledge discovery: Methods from data mining and machine learning. Social Science Research, 110:102817.","DOI":"10.1016\/j.ssresearch.2022.102817"},{"key":"9","doi-asserted-by":"crossref","unstructured":"Skopal, T., Dohnal, V., Batko, M., and Zezula, P. (2009). Distinct nearest neighbors queries for similarity search in very large multimedia databases. In Chan, C. Y. and Mitra, P., editors, 11th ACM International Workshop on Web Information and Data Management WIDM 2009, pages 11\u201314, Hong Kong, China. ACM.","DOI":"10.1145\/1651587.1651592"},{"key":"10","unstructured":"Traina-Jr., C., Moriyama, A., Rocha, G., Cordeiro, R., Ciferri, C. D. A., and Traina, A. (2019). The similarql framework: Similarity queries in plain SQL. In Proceedings of the ACM Symposium on Applied Computing, Limassol, Cyprus. ACM."},{"key":"11","doi-asserted-by":"crossref","unstructured":"Weber, M., Silva-Leite, J., Santos, L., de Oliveira, D., and Bedo, M. (2024). Adicionando suporte \u00e0 diversifica\u00e7\u00e3o de resultados em \u00edndices hnsw considerando espa\u00e7os de baixa e alta dimensionalidade. In Anais do XXXIX Simp\u00f3sio Brasileiro de Bancos de Dados, pages 14\u201326. SBC.","DOI":"10.5753\/sbbd.2024.240618"},{"key":"12","doi-asserted-by":"crossref","unstructured":"Yang, P., Wang, H., Yang, J., Qian, Z., Zhang, Y., and Lin, X. (2024). Deep learning approaches for similarity computation: A survey. IEEE Transactions on Knowledge and Data Engineering, 36(12):7893\u20137912.","DOI":"10.1109\/TKDE.2024.3422484"}],"event":{"name":"Simp\u00f3sio Brasileiro de Banco de Dados","number":"40","location":"Brasil","acronym":"SBBD 2025"},"container-title":["Anais do XL Simp\u00f3sio Brasileiro de Banco de Dados (SBBD 2025)"],"original-title":[],"link":[{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/download\/37297\/37080","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/download\/37297\/37080","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:27:24Z","timestamp":1761074844000},"score":1,"resource":{"primary":{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/view\/37297"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":12,"URL":"https:\/\/doi.org\/10.5753\/sbbd.2025.247779","relation":{},"subject":[],"published":{"date-parts":[[2025,9,29]]}}}