Towards Automated Semantic Grouping in Workflows for Multi-Disciplinary Analysis

TL;DR

This paper proposes an automated method to identify related tool groups in multidisciplinary workflows by transforming workflows into graphs and applying clustering algorithms, improving maintainability.

Contribution

It introduces a novel graph-based approach for automatic semantic grouping of tools in workflows and evaluates multiple clustering algorithms for this purpose.

Findings

Graph transformation effectively identifies related tool groups.

Clustering results vary depending on workflow design style.

Tailored solutions are necessary for different workflow types.

Abstract

When designing multidisciplinary tool workflows in visual development environments, researchers and engineers often combine simulation tools which serve a functional purpose and helper tools that merely ensure technical compatibility by, e.g., converting between file formats. If the development environment does not offer native support for such groups of tools, maintainability of the developed workflow quickly deteriorates with an increase in complexity. We present an approach towards automatically identifying such groups of closely related tools in multidisciplinary workflows implemented in RCE by transforming the workflow into a graph and applying graph clustering algorithms to it. Further, we implement this approach and evaluate multiple clustering algorithms. Our results strongly indicate that this approach can yield groups of closely related tools in RCE workflows, but also that solutions to this problem will have to be tailor-made to each specific style of workflow design.