DistillFlow: removing redundancy in scientific workflows - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

DistillFlow: removing redundancy in scientific workflows

Abstract

Scientific workflows management systems are increasingly used by scientists to specify complex data processing pipelines. Workflows are represented using a graph structure, where nodes represent tasks and links represent the dataflow. However, the complexity of workflow structures is increasing over time, reducing the rate of scientific workflows reuse. Here, we introduce DistillFlow, a tool based on effective methods for workflow design, with a focus on the Taverna model. DistillFlow is able to detect "anti-patterns" in the structure of workflows (idiomatic forms that lead to over-complicated design) and replace them with different patterns to reduce the workflow's overall structural complexity. Rewriting workflows in this way is beneficial both in terms of user experience and workflow maintenance.
Fichier principal
Vignette du fichier
distillflowdemo.pdf (1.39 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01091033 , version 1 (04-12-2014)

Identifiers

Cite

Jiuqiang Chen, Sarah Cohen-Boulakia, Christine Froidevaux, Carole Goble, Paolo Missier, et al.. DistillFlow: removing redundancy in scientific workflows. SSDBM '14 Proceedings of the 26th International Conference on Scientific and Statistical Database Management, Jun 2014, Aalborg, Denmark. ⟨10.1145/2618243.2618287⟩. ⟨hal-01091033⟩
334 View
143 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More