FReeP: towards parameter recommendation in scientific workflows using preference learning - Silva Jr., Daniel; Paes, Aline; Pacitti, Esther; de Oliveira, Daniel (Anais do Simpósio Brasileiro de Banco de Dados (SBBD), 2018)
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.