Samenvatting
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 But we identified several challenges, i.e., many scientific workflows contain errors, outdated tools, invalid tools connections, improper tagging, improper annotation, and so on. Also, in the future, many workflow
tools can be obsoleted. Then the existing recommendation systems will fail to recommend appropriate workflow construction 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 by analyzing existing workflows in various workflow repositories.
tools/workflow recommendation systems. But we identified several challenges, i.e., many scientific workflows contain errors, outdated tools, invalid tools connections, improper tagging, improper annotation, and so on. Also, in the future, many workflow
tools can be obsoleted. Then the existing recommendation systems will fail to recommend appropriate workflow construction 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 by analyzing existing workflows in various workflow repositories.
Originele taal-2 | Engels |
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Status | Gepubliceerd - 15 nov. 2022 |
Evenement | Workflows in Support of Large-Scale Science - Dallas, Verenigde Staten van Amerika Duur: 14 nov. 2022 → 14 nov. 2022 Congresnummer: 17 https://works-workshop.org/ |
Workshop
Workshop | Workflows in Support of Large-Scale Science |
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Verkorte titel | WORKS |
Land/Regio | Verenigde Staten van Amerika |
Stad | Dallas |
Periode | 14/11/22 → 14/11/22 |
Internet adres |