Provenance Graph Integration in Scientific Machine Learning Multi-workflows - Pina, Débora; Moreira Dias, Lucas; Santos Lopes, Maria Célia; de Oliveira, Daniel; Mattoso, Marta (Anais do Simpósio Brasileiro de Banco de Dados (SBBD); 2026: Anais do XLI Simpósio Brasileiro de Bancos de Dados, 2026)
Scientific Machine Learning (SciML) multi-workflows consist of multiple independent workflows, often implemented as fragmented Jupyter Notebooks, which hinder provenance tracking and limit end-to-end querying and reproducibility. In this paper, we present a real-world SciML multi-workflow that advances methods for capturing and integrating provenance in SciML applications. The proposed approach combines scientific domain modeling of multi-workflows as prospective provenance, provenance capture mechanisms, and shared execution identifiers to enable the construction of an end-to-end provenance graph. We demonstrate the approach through queries over the integrated provenance graph of a multi-workflow seismic binary data segmentation application, highlighting its potential for long-term provenance management.