{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:35:29Z","timestamp":1761165329961,"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>O aumento da complexidade em aplica\u00e7\u00f5es de aprendizado de m\u00e1quina exige sistemas que garantam rastreabilidade e reprodutibilidade. Este trabalho apresenta a abordagem Twinscie-Prov, uma adequa\u00e7\u00e3o do padr\u00e3o W3C PROV para estruturar a proveni\u00eancia de dados e processos ao longo do ciclo de vida de Machine Learning (ML) no sistema Twinscie. Os dados de proveni\u00eancia s\u00e3o armazenados no sistema NoSQL Neo4j, permitindo consultas complexas e auditoria. Estudos preliminares mostram que, para consultas envolvendo navega\u00e7\u00e3o no grafo de depend\u00eancias, t\u00edpicas em dados de proveni\u00eancia, a implementa\u00e7\u00e3o no Neo4j \u00e9 at\u00e9 cinco ordens de grandeza mais r\u00e1pida que a baseada em logs.<\/jats:p>","DOI":"10.5753\/sbbd.2025.247286","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:26:36Z","timestamp":1761074796000},"page":"576-588","source":"Crossref","is-referenced-by-count":0,"title":["Twinscie-Prov: Gerenciando a Proveni\u00eancia sobre o Ciclo-de-vida de ML no Sistema Twinscie"],"prefix":"10.5753","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5477-3605","authenticated-orcid":false,"given":"J\u00falia Neumann","family":"Bastos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabio","family":"Porto","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"F\u00e1bio Levy","family":"Siqueira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edson","family":"Gomi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ismael","family":"Santos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodrigo","family":"Barreira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Isabela","family":"Siqueira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eduardo","family":"Ogasawara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"3742","published-online":{"date-parts":[[2025,9,29]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Castro, R., Souto, Y. M., Ogasawara, E. S., Porto, F., and Bezerra, E. (2021). Stconvs2s: Spatiotemporal convolutional sequence to sequence network for weather forecasting. Neurocomputing, 426:285\u2013298.","key":"1","DOI":"10.1016\/j.neucom.2020.09.060"},{"doi-asserted-by":"crossref","unstructured":"de Almeida, V. K., de Oliveira, D. E., de Barros, C. D. T., Scatena, G. d. S., Queiroz Filho, A. N., Siqueira, F. L., Costa, Ogasawara, E., and Porto, F. e. a. (2024). A digital twin system for oil and gas industry: A use case on mooring lines integrity monitoring. In Proceedings of the ACM\/IEEE 27th International Conference on Model Driven Engineering Languages and Systems, MODELS Companion \u201924, page 322\u2013331, New York, NY, USA. Association for Computing Machinery.","key":"2","DOI":"10.1145\/3652620.3688244"},{"unstructured":"Grieves, M. (2014). Digital twin: Manufacturing excellence through virtual factory replication. Technical report, Florida Institute of Technology.","key":"3"},{"unstructured":"LNCC (2015). Sdumont. <a href=\"https:\/\/sdumont.lncc.br\"target=\"_blank\">[link]<\/a>.","key":"4"},{"unstructured":"Moreau, L. and Groth, P. (2013). Prov-overview: An overview of the prov family of documents. <a href=\"https:\/\/www.w3.org\/TR\/prov-overview\/\"target=\"_blank\">[link]<\/a>.","key":"5"},{"unstructured":"Neo4j (2003). Graph database & analytics. <a href=\"https:\/\/neo4j.com\"target=\"_blank\">[link]<\/a>.","key":"6"},{"doi-asserted-by":"crossref","unstructured":"Pina, D., Chapman, A., Kunstmann, L., de Oliveira, D., and Mattoso, M. (2024). Dlprov: A data-centric support for deep learning workflow analyses. In Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning, DEEM \u201924, page 77\u201385, New York, NY, USA. Association for Computing Machinery.","key":"7","DOI":"10.1145\/3650203.3663337"},{"doi-asserted-by":"crossref","unstructured":"Polyzotis, N., Roy, S., Whang, S. E., and Zinkevich, M. (2017). Data management challenges in production machine learning. In Proceedings of the 2017 ACM International Conference on Management of Data, SIGMOD \u201917, page 1723\u20131726, New York, NY, USA. Association for Computing Machinery.","key":"8","DOI":"10.1145\/3035918.3054782"},{"doi-asserted-by":"crossref","unstructured":"Porto, F., Ferro, M., Ogasawara, E. S., Moeda, T., de Barros, C. D. T., da Silva, A. C., Zorrilla, R., Pereira, R. S., Castro, R. N., Silva, J. V., Salles, R., Fonseca, A. J., Hermsdorff, J., Magalh\u00e3es, M., S\u00e1, V., Sim\u00f5es, A., Cardoso, C., and Bezerra, E. (2022). Machine learning approaches to extreme weather events forecast in urban areas: Challenges and initial results. Supercomput. Front. Innov., 9(1):49\u201373.","key":"9","DOI":"10.14529\/jsfi220104"},{"unstructured":"Schelter, S., B\u00f6se, J.-H., Kirschnick, J., Klein, T., and Seufert, S. (2017). Automatically tracking metadata and provenance of machine learning experiments.","key":"10"},{"unstructured":"Schlegel, A., Auer, S., and Vidal, M.-E. (2023). Mlflow2prov: Creating provenance graphs from mlflow metadata. In Proceedings of the 25th International Conference on Enterprise Information Systems (ICEIS), pages 579\u2013586.","key":"11"},{"unstructured":"Shi, X., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-k., and Woo, W.-c. (2015). Convolutional lstm network: a machine learning approach for precipitation nowcasting. In Proceedings of the 29th International Conference on Neural Information Processing Systems - Volume 1, NIPS\u201915, page 802\u2013810, Cambridge, MA, USA. MIT Press.","key":"12"}],"event":{"number":"40","acronym":"SBBD 2025","name":"Simp\u00f3sio Brasileiro de Banco de Dados","location":"Brasil"},"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\/37266\/37049","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/download\/37266\/37049","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:29:14Z","timestamp":1761074954000},"score":1,"resource":{"primary":{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/view\/37266"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":12,"URL":"https:\/\/doi.org\/10.5753\/sbbd.2025.247286","relation":{},"subject":[],"published":{"date-parts":[[2025,9,29]]}}}