{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:35:06Z","timestamp":1761165306737,"version":"build-2065373602"},"reference-count":29,"publisher":"Sociedade Brasileira de Computa\u00e7\u00e3o - SBC","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:p>As Cidades Inteligentes (CIs) utilizam dados de diversas fontes para melhorar a qualidade de vida e apoiar pol\u00edticas p\u00fablicas. Garantir a rastreabilidade completa desses dados, desde a coleta at\u00e9 seu uso, \u00e9 essencial para auditoria e confiabilidade. Dados de proveni\u00eancia ajudam a representar esse caminho de deriva\u00e7\u00e3o, mas colet\u00e1-los em CIs \u00e9 complexo devido ao ecossistema variado de programas e usu\u00e1rios envolvidos. Para enfrentar esse desafio, foi desenvolvido o framework ProvInCiA, que captura dados de proveni\u00eancia em CIs. Utilizando o conceito de meta-dataflows, o framework integra as transforma\u00e7\u00f5es dos dados, formando um caminho de deriva\u00e7\u00e3o cont\u00ednuo. A efic\u00e1cia da ProvInCiA foi avaliada em um estudo de caso sobre monitoramento de alagamentos.<\/jats:p>","DOI":"10.5753\/sbbd.2025.247252","type":"proceedings-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:26:36Z","timestamp":1761074796000},"page":"371-384","source":"Crossref","is-referenced-by-count":0,"title":["Tudo em Todo Lugar ao Mesmo Tempo: Rastreabilidade de Dados em Cidades Inteligentes por meio de Proveni\u00eancia"],"prefix":"10.5753","author":[{"given":"Maria Luiza","family":"Falci","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D\u00e9bora","family":"Pina","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liliane","family":"Kunstmann","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1184-8192","authenticated-orcid":false,"given":"Vanessa","family":"Braganholo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9346-7651","authenticated-orcid":false,"given":"Daniel","family":"de Oliveira","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":"Bai, J., Lee, K. F., Hofmeister, M., Mosbach, S., Akroyd, J., and Kraft, M. (2024). A derived information framework for a dynamic knowledge graph and its application to smart cities. Future Generation Computer Systems, 152:112\u2013126.","DOI":"10.1016\/j.future.2023.10.008"},{"key":"2","doi-asserted-by":"crossref","unstructured":"Bilal, M., Usmani, R. S. A., Tayyab, M., Mahmoud, A. A., Abdalla, R. M., Marjani, M., Pillai, T. R., and Targio Hashem, I. A. (2020). Smart Cities Data: Framework, Applications, and Challenges, pages 1\u201329. Springer International Publishing, Cham.","DOI":"10.1007\/978-3-030-15145-4_6-1"},{"key":"3","doi-asserted-by":"crossref","unstructured":"Bola\u00f1os-Martinez, D., Bermudez-Edo, M., and Garrido, J. L. (2024). Clustering pipeline for vehicle behavior in smart villages. Information Fusion, 104:102164.","DOI":"10.1016\/j.inffus.2023.102164"},{"key":"4","doi-asserted-by":"crossref","unstructured":"Bonadia, S., Gama, R., Oliveira, D., Miranda, F., and Lage, M. (2023). Visual analytics using heterogeneous urban data. In Conference on Graphics, Patterns and Images, pages 25\u201330, Porto Alegre, RS, Brasil. SBC.","DOI":"10.1109\/SIBGRAPI59091.2023.10347156"},{"key":"5","doi-asserted-by":"crossref","unstructured":"Cichy, R. M. and Kaiser, D. (2019). Deep neural networks as scientific models. Trends in cognitive sciences, 23(4):305\u2013317.","DOI":"10.1016\/j.tics.2019.01.009"},{"key":"6","unstructured":"Emaldi, M., Pena, O., Lazaro, J., Lopez-de Ipina, D., Vanhecke, S., and Mannens, E. (2013). To trust, or not to trust: Highlighting the need for data provenance in mobile apps for smart cities. In International Workshop on Semantic Sensor Networks, pages 1\u20134."},{"key":"7","doi-asserted-by":"crossref","unstructured":"Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for computational tasks: A survey. Computing in science & engineering, 10(3):11\u201321.","DOI":"10.1109\/MCSE.2008.79"},{"key":"8","doi-asserted-by":"crossref","unstructured":"Hoque, M. A. and Hasan, R. (2022). A trust management framework for connected autonomous vehicles using interaction provenance. In IEEE International Conference on Communications, pages 2236\u20132241. IEEE.","DOI":"10.1109\/ICC45855.2022.9838476"},{"key":"9","doi-asserted-by":"crossref","unstructured":"Ikeda, R., Sarma, A. D., and Widom, J. (2013). Logical provenance in data-oriented workflows? In IEEE International Conference on Data Engineering, pages 877\u2013888. IEEE.","DOI":"10.1109\/ICDE.2013.6544882"},{"key":"10","doi-asserted-by":"crossref","unstructured":"Javed, B., Khan, Z., and McClatchey, R. (2017a). A network-based approach to capture provenance of a policy-making process. In International Database Engineering & Applications Symposium, pages 283\u2013286.","DOI":"10.1145\/3105831.3105850"},{"key":"11","doi-asserted-by":"crossref","unstructured":"Javed, B., Khan, Z., and McClatchey, R. (2017b). Using a model-driven approach in building a provenance framework for tracking policy-making processes in smart cities. In International Database Engineering & Applications Symposium, pages 66\u201373.","DOI":"10.1145\/3105831.3105849"},{"key":"12","doi-asserted-by":"crossref","unstructured":"Javed, B., Khan, Z., and McClatchey, R. (2018). An adaptable system to support provenance management for the public policy-making process in smart cities. Informatics, 5(1):3:1\u201326.","DOI":"10.3390\/informatics5010003"},{"key":"13","doi-asserted-by":"crossref","unstructured":"Javed, B., McClatchey, R., Khan, Z., and Shamdasani, J. (2016). A provenance framework for policy analytics in smart cities. In International Conference on Internet of Things and Big Data, pages 429\u2013434.","DOI":"10.5220\/0005931504290434"},{"key":"14","doi-asserted-by":"crossref","unstructured":"Laamech, N., Munier, M., and Pham, C. (2021). Towards a data provenance model for private data sharing management in iot. In IEEE International Enterprise Distributed Object Computing Workshop, pages 210\u2013215. IEEE.","DOI":"10.1109\/EDOCW52865.2021.00051"},{"key":"15","doi-asserted-by":"crossref","unstructured":"Lin, S., Xiao, H., Jiang, W., Li, D., Liang, J., and Li, Z. (2023). A survey of provenance in scientific workflow. J. High Speed Networks, 29(2):129\u2013145.","DOI":"10.3233\/JHS-222017"},{"key":"16","doi-asserted-by":"crossref","unstructured":"McPhillips, T. M. et al. (2015). Yesworkflow: A user-oriented, language-independent tool for recovering workflow information from scripts. CoRR, abs\/1502.02403.","DOI":"10.2218\/ijdc.v10i1.370"},{"key":"17","doi-asserted-by":"crossref","unstructured":"Moreau, L., Batlajery, B. V., Huynh, T. D., Michaelides, D., and Packer, H. (2018). A templating system to generate provenance. IEEE Transactions on Software Engineering, 44(2):103\u2013121.","DOI":"10.1109\/TSE.2017.2659745"},{"key":"18","unstructured":"Moreau, L. and Missier, P. (2013). PROV-DM: the PROV data model. W3C Recommend."},{"key":"19","doi-asserted-by":"crossref","unstructured":"Nepal, A., Amanullah, M. A., Doss, R., and Jiang, F. (2024a). Secure data provenance in internet of vehicles with data plausibility for security and trust. In IEEE World AI IoT Congress, pages 612\u2013618. IEEE.","DOI":"10.1109\/AIIoT61789.2024.10578965"},{"key":"20","doi-asserted-by":"crossref","unstructured":"Nepal, A., Doss, R., and Jiang, F. (2023). Secure data provenance for internet of vehicles with verifiable credentials. In IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference, pages 0210\u20130218. IEEE.","DOI":"10.1109\/UEMCON59035.2023.10315994"},{"key":"21","doi-asserted-by":"crossref","unstructured":"Nepal, A., Doss, R., and Jiang, F. (2024b). Secure data provenance in internet of vehicles with verifiable credentials for security and privacy. In Annual IEEE\/IFIP International Conference on Dependable Systems and Networks-Supplemental Volume, pages 59\u201361. IEEE.","DOI":"10.1109\/DSN-S60304.2024.00025"},{"key":"22","doi-asserted-by":"crossref","unstructured":"Pasquier, T., Han, X., Goldstein, M., Moyer, T., Eyers, D., Seltzer, M., and Bacon, J. (2017). Practical whole-system provenance capture. In Symposium on Cloud Computing, page 405\u2013418, New York, NY, USA. Association for Computing Machinery.","DOI":"10.1145\/3127479.3129249"},{"key":"23","unstructured":"Rodrigues, A. J., Vieira, J., Fontana, R. L., de C\u00e1ssia Barroso, R., Silva, J. A., et al. (2015). a urbaniza\u00e7\u00e3o no mundo e no brasil sob um enfoque geogr\u00e1fico. Caderno de Gradua\u00e7\u00e3o-Ci\u00eancias Humanas e Sociais-UNIT-SERGIPE, pages 95\u2013106."},{"key":"24","doi-asserted-by":"crossref","unstructured":"Roriz Junior, M., de Oliveira, R. P., Carvalho, F., Lifschitz, S., and Endler, M. (2019). M ensageria: A smart city framework for real-time analysis of traffic data streams. In Big Social Data and Urban Computing Workshop, pages 59\u201373. Springer.","DOI":"10.1007\/978-3-030-11238-7_4"},{"key":"25","doi-asserted-by":"crossref","unstructured":"Sadineni, L., Pilli, E. S., and Battula, R. B. (2023). Provlink-iot: A novel provenance model for link-layer forensics in iot networks. Forensic Science International: Digital Investigation, 46:301600.","DOI":"10.1016\/j.fsidi.2023.301600"},{"key":"26","doi-asserted-by":"crossref","unstructured":"Silva, V., de Oliveira, D., Valduriez, P., and Mattoso, M. (2018). Dfanalyzer: Runtime dataflow analysis of scientific applications using provenance. Proceedings of the VLDB Endowment.","DOI":"10.14778\/3229863.3236265"},{"key":"27","doi-asserted-by":"crossref","unstructured":"Silva, V., Leite, J., Camata, J. J., De Oliveira, D., Coutinho, A. L. G. A., Valduriez, P., and Mattoso, M. (2017). Raw data queries during data-intensive parallel workflow execution. Future Generation Computer Systems, 75:402\u2013422.","DOI":"10.1016\/j.future.2017.01.016"},{"key":"28","doi-asserted-by":"crossref","unstructured":"Victorino, F., Amorim, A., et al. (2023). Pluv-web: um gateway cient\u00edfico orientado a dados para an\u00e1lise e monitoramento de chuvas na cidade de niter\u00f3i. In Anais Estendidos do Simp\u00f3sio Brasileiro de Bancos de Dados, pages 108\u2013113, Belo Horizonte, Brasil. SBC.","DOI":"10.5753\/sbbd_estendido.2023.233224"},{"key":"29","doi-asserted-by":"crossref","unstructured":"Wilms, D., Stoecker, C., and Caballero, J. (2021). Data provenance in vehicle data chains. In IEEE Vehicular Technology Conference, pages 1\u20135. IEEE.","DOI":"10.1109\/VTC2021-Spring51267.2021.9448697"}],"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\/37251\/37034","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/download\/37251\/37034","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T19:30:24Z","timestamp":1761075024000},"score":1,"resource":{"primary":{"URL":"https:\/\/sol.sbc.org.br\/index.php\/sbbd\/article\/view\/37251"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,29]]},"references-count":29,"URL":"https:\/\/doi.org\/10.5753\/sbbd.2025.247252","relation":{},"subject":[],"published":{"date-parts":[[2025,9,29]]}}}