FReeP: towards parameter recommendation in scientific workflows using preference learning

• Daniel Silva Jr. Universidade Federal Fluminense
• Aline Paes Universidade Federal Fluminense
• Esther Pacitti Inria / LIRMM / University of Montpellier
• Daniel de Oliveira Universidade Federal Fluminense

DOI: https://doi.org/10.5753/sbbd.2018.22232

Resumo

Scientific workflows are a de facto standard for modeling scientific experiments. However, several workflows have too many parameters to be manually configured. Poor choices of parameter values may lead to unsuccessful executions of the workflow. In this paper, we present FReeP, a parameter recommendation algorithm that suggests a value to a parameter that agrees with the user preferences. FReeP is based on the Preference Learning technique. A preliminary experimental evaluation performed over the SciPhy workflow showed the feasibility of FReeP to recommend parameter values for scientific workflows.

Palavras-chave: Scientific Workflows, Preference Learning, Parameter Recommendation

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