FReeP: towards Parameter Recommendation in Scientific Workflows using Preference Learning.
SBBD(2018)
摘要
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.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络