5 papers
Workflow Cards: Structured Summaries of Workflow Executions Using Provenance Data
Nicola Giuseppe Marchioro, Gabriele Padovani, Amal Gueroudji +5
Model Cards and Data Cards have demonstrated the value of structured, human-readable documentation for machine learning artifacts, capturing their context, parameters, limitations,…
yProv4DV: Reproducible Data Visualization Scripts Out of the Box
Gabriele Padovani, Sandro Fiore
While results visualization is a critical phase to the communication of new academic results, plots are frequently shared without the complete combination of code, input data, exec…
yProv4ML: Effortless Provenance Tracking for Machine Learning Systems
Gabriele Padovani, Valentine Anantharaj, Sandro Fiore
The rapid growth of interest in large language models (LLMs) reflects their potential for flexibility and generalization, and attracted the attention of a diverse range of research…
Provenance Tracking in Large-Scale Machine Learning Systems
Gabriele Padovani, Valentine Anantharaj, Sandro Fiore
As the demand for large scale AI models continues to grow, the optimization of their training to balance computational efficiency, execution time, accuracy and energy consumption r…
Trustworthy Provenance for Big Data Science: a Modular Architecture Leveraging Blockchain in Federated Settings
Nicola Giuseppe Marchioro, Yannis Velegrakis, Valentine Anantharaj +2
Ensuring the trustworthiness and long-term verifiability of scientific data is a foundational challenge in the era of data-intensive, collaborative research. Provenance metadata pl…