Workshop: The 17th Workshop on Workflows in Support of Large-Scale Science (WORKS22)
Authors: Khairul Alam and Banani Roy (University of Saskatchewan) and Alexander Serebrenik (Eindhoven University of Technology, Netherlands)
Abstract: Scientists construct scientific workflows in Scientific Workflow Management Systems (SWfMSs) to analyze scientific data. However, these scientific workflows can be complex and challenging to create for new and expert users due to the significant growth of tools, the heterogeneous nature of data, and the complexity of the tasks. To overcome these obstacles, scientists started to share their designed workflow in the community in the interest of open science, and many researchers constructed several tools/workflow recommendation systems. But we identified several challenges, i.e., many scientific workflows contain errors, outdated tools, invalid tools connections, improper tagging, etc. Also, in the future, many workflow tools can be obsoleted. Then the existing recommendation systems will fail to recommend appropriate tools, eventually creating a less optimal and error-containing workflow. Considering all these challenges, we propose a recommendation system to recommend tools/sub-workflow using machine learning approaches to help scientists create optimal, error-free, and efficient workflows.