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

ABSTRACT: Scientific Workflows (SWfs) have revolutionized how scientists in various domains of science conduct their experiments. The management of SWfs is performed by complex tools that provide support for workflow composition, monitoring, execution, capturing, and storage of the data generated during execution. In some cases, they also provide components to ease the visualization and analysis of the generated data. During the workflow's composition phase, programs must be selected to perform the activities defined in the workflow specification. These programs often require additional parameters that serve to adjust the program's behavior according to the experiment's goals. Consequently, workflows commonly have many parameters to be manually configured, encompassing even more than one hundred in

SUBMITTER: Silva Junior D 

PROVIDER: S-EPMC8279147 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature