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Found 3 papers, 1 papers with code
Workflow Provenance in the Lifecycle of Scientific Machine Learning
We contribute with (i) characterization of the lifecycle and taxonomy for data analyses; (ii) design principles to build this view, with a W3C PROV compliant data representation and a reference system architecture; and (iii) lessons learned after an evaluation in an Oil & Gas case using an HPC cluster with 393 nodes and 946 GPUs.
Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering
To handle this problem, in this paper we contribute with a detailed characterization of provenance data in the ML lifecycle in CSE; a new provenance data representation, called PROV-ML, built on top of W3C PROV and ML Schema; and extensions to a system that tracks provenance from multiple workflows to address the characteristics of ML and CSE, and to allow for provenance queries with a standard vocabulary.
BioWorkbench: A High-Performance Framework for Managing and Analyzing Bioinformatics Experiments
This framework automatically collects provenance data, including both performance data from workflow execution and data from the scientific domain of the workflow application.
Distributed, Parallel, and Cluster Computing Databases
