Abstract

Scientific workflow systems play a major role in supporting scientists to design, document and execute their computational experiments, automatically tracking provenance during the workflow execution [11; 1]. Scientists follow an iterative exploratory cycle where they often create an initial workflow, and then explore variations of it using different data, replacing some of the software steps, or adding new steps. Sometimes workflows have to be modified because of changes in data (eg when datasets are updated with new formats) or software (eg, software is no longer available, a newer version is better). In current workflow systems, scientists manage this exploratory process manually. Updating a workflow is a complex and timeconsuming task that may involve several steps, and may require tracking down information about different versions of the software used in the workflow.

Date

March 16, 2026

Authors

LAMC Carvalho, Bakinam T Essawy, Daniel Garijo, Claudia Bauzer Medeiros, Yolanda Gil

Journal

Proceedings of the Workshop on Capturing Scientific Knowledge (SciKnow), Austin, Texas

Volume

2017