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Related Experiment Video

Updated: Oct 26, 2025

A Workflow for Lipid Nanoparticle LNP Formulation Optimization using Designed Mixture-Process Experiments and Self-Validated Ensemble Models SVEM

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Published on: August 18, 2023

Provenance-and machine learning-based recommendation of parameter values in scientific workflows.

Daniel Silva Junior¹, Esther Pacitti², Aline Paes¹

¹Institute of Computing, Universidade Federal Fluminense, Niteroi, RJ, Brazil.

Peerj. Computer Science

|July 26, 2021

Summary

Scientific Workflows (SWfs) require careful parameter configuration. FReeP (Feature Recommender from Preferences) uses machine learning to recommend parameter values based on user history, improving workflow execution success.

Keywords:

Machine Learning Preference Learning Recommender systems Scientific workflows

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Area of Science:

Computational science
Bioinformatics
Data science

Background:

Scientific Workflows (SWfs) are essential tools for modern scientific experimentation.
Managing SWfs involves complex tools for composition, execution, and data handling.
Incorrect parameter configuration in SWfs can lead to execution failures and wasted resources.

Purpose of the Study:

To introduce FReeP (Feature Recommender from Preferences), a novel method for recommending parameter values in SWfs.
To leverage Machine Learning, specifically Preference Learning, for automated parameter suggestion.
To reduce the risk of workflow failures and optimize resource utilization.

Main Methods:

FReeP employs three Machine Learning algorithms based on Preference Learning.
Two algorithms focus on recommending values for individual parameters.
A third algorithm is designed to recommend values for multiple parameters simultaneously.

Main Results:

Experimental results using provenance data from two widely used SWfs demonstrate FReeP's effectiveness in recommending single parameter values.
The study indicates FReeP's potential for recommending values for multiple parameters in SWfs.
FReeP successfully utilizes past user preferences to guide recommendations.

Conclusions:

FReeP offers a promising solution for automating parameter configuration in Scientific Workflows.
The method has shown utility in improving the reliability and efficiency of SWf execution.
Further development could enhance its capability for complex, multi-parameter recommendations.