SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Débora Pina(dbpina@cos.ufrj.br)
2 Lucas Moreira Dias(ldias@lamce.coppe.ufrj.br)
3 Maria Célia Santos Lopes(celia@lamce.coppe.ufrj.br)
4 Daniel de Oliveira(danielcmo@ic.uff.br)
5 Marta Mattoso(marta@cos.ufrj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 AlSalmi, H. and Elsheikh, A. H. (2023). Automated seismic semantic segmentation using attention u-net. Geophysics, 89(1):WA247–WA263.
2 Ferreira da Silva, R. et al. (2024). Workflows community summit 2024: Future trends and challenges in scientific workflows. (ORNL/TM-2024/3573).
3 Freire, J., Koop, D., Santos, E., and Silva, C. T. (2008). Provenance for Computational Tasks: A Survey. Computing in Science and Engineering, 10:11–21.
4 Gregori, L. et al. (2025). An llm-guided platform for multi-granular collection and management of data provenance. Journal of Big Data, 12(1):187.
5 Islam, M. S. U. and Wali, A. (2024). A comprehensive review of deep learning techniques for salt dome segmentation in seismic images. Journal of Applied Geophysics, 230:105504.
6 Liu, H., Wang, Y., Fan, W., Liu, X., Li, Y., Jain, S., Liu, Y., Jain, A., and Tang, J. (2022). Trustworthy ai: A computational perspective. ACM Trans. Intell. Syst. Technol., 14(1).
7 Missier, P. et al. (2025). From XAI to XEE: explainable end-to-end using influence and provenance. In Symposium on Advanced Database Systems, pages 560–569.
8 Moreau, L., Groth, P., Cheney, J., Lebo, T., and Miles, S. (2015). The rationale of PROV. J. Web Semant., 35:235–257.
9 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, page 77–85. ACM.
10 Pina, D., Kunstmann, L., et al. (2025). Dlprov: a suite of provenance services for deep learning workflow analyses. PeerJ Comp. Sci., 11:e2985.
11 Procko, T., Vonder Haar, L., and Ochoa, O. (2025). A survey of machine learning lifecycle provenance: Models, approaches and tools.
12 Schlegel, M. and Sattler, K.-U. (2025). Capturing end-to-end provenance for machine learning pipelines. Information Systems, 132:102495.
13 Souza, R. et al. (2022). Workflow provenance in the lifecycle of scientific machine learning. Concurrency and Computation: Practice and Experience, 34(14):e6544.
14 Souza, R. et al. (2023). Towards lightweight data integration using multi-workflow provenance and data observability. In International Conference on e-Science, pages 1–10.
15 Souza, R. et al. (2024). Workflow provenance in the computing continuum for responsible, trustworthy, and energy-efficient ai. In International Conference on e-Science, pages 1–7.
16 Souza, R. et al. (2025). Prov-agent: Unified provenance for tracking ai agent interactions in agentic workflows. In International Conference on eScience, pages 467–473.
17 Woyames, P. et al. (2025). Avaliac¸ao da capacidade de llms para especificar workflows. In Anais do XIX Brazilian e-Science Workshop, pages 81–88.