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FReeP: towards parameter recommendation in scientific workflows using preference learning

Daniel Silva 1 Aline Paes 1 Esther Pacitti 2 Daniel de Oliveira 1 
2 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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 F ReeP , a parameter recommendation algorithm that suggests a value to a parameter that agrees with the user preferences. F ReeP is based on the Preference Learning technique. A preliminary experimental evaluation performed over the SciPhy workflow showed the feasibility of F ReeP to recommend parameter values for scientific workflows.
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Submitted on : Wednesday, September 5, 2018 - 3:48:30 PM
Last modification on : Friday, August 5, 2022 - 3:03:28 PM
Long-term archiving on: : Thursday, December 6, 2018 - 4:30:47 PM


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  • HAL Id : lirmm-01868574, version 1



Daniel Silva, Aline Paes, Esther Pacitti, Daniel de Oliveira. FReeP: towards parameter recommendation in scientific workflows using preference learning. 33rd Brazilian Symposium on Databases (SBBD 2018), Aug 2018, Rio de Janeiro, Brazil. pp.211-216. ⟨lirmm-01868574⟩



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