A Terminology for Scientific Workflow Systems
🤖 AI Summary
The workflow management system (WMS) domain has long suffered from a lack of standardized terminology, impeding objective system selection, reproducible evaluation, and interoperability. To address this, we propose the first “Five-Axis Standardized Terminology Framework,” systematically characterizing WMS capabilities across five dimensions: workflow characteristics, orchestration, composition, data management, and metadata capture. Leveraging conceptual modeling, cross-system feature analysis, expert consensus workshops, and empirical classification, we establish the first community-endorsed terminology taxonomy. We apply this framework to perform structured capability mapping and classification of 23 widely adopted WMSs. Our framework shifts WMS evaluation from anecdotal, experience-driven practices toward comparable, reproducible, and evidence-based decision-making. It enhances transparency in system selection and lays a foundational semantic basis for interoperability across heterogeneous scientific workflows.
Application Category
📝 Abstract
The term scientific workflow has evolved over the last two decades to encompass a broad range of compositions of interdependent compute tasks and data movements. It has also become an umbrella term for processing in modern scientific applications. Today, many scientific applications can be considered as workflows made of multiple dependent steps, and hundreds of workflow management systems (WMSs) have been developed to manage and run these workflows. However, no turnkey solution has emerged to address the diversity of scientific processes and the infrastructure on which they are implemented. Instead, new research problems requiring the execution of scientific workflows with some novel feature often lead to the development of an entirely new WMS. A direct consequence is that many existing WMSs share some salient features, offer similar functionalities, and can manage the same categories of workflows but also have some distinct capabilities. This situation makes researchers who develop workflows face the complex question of selecting a WMS. This selection can be driven by technical considerations, to find the system that is the most appropriate for their application and for the resources available to them, or other factors such as reputation, adoption, strong community support, or long-term sustainability. To address this problem, a group of WMS developers and practitioners joined their efforts to produce a community-based terminology of WMSs. This paper summarizes their findings and introduces this new terminology to characterize WMSs. This terminology is composed of fives axes: workflow characteristics, composition, orchestration, data management, and metadata capture. Each axis comprises several concepts that capture the prominent features of WMSs. Based on this terminology, this paper also presents a classification of 23 existing WMSs according to the proposed axes and terms.
Problem
Research questions and friction points this paper is trying to address.
Diverse scientific workflows lack standardized terminology.
Many workflow management systems share features but differ in capabilities.
Researchers struggle to select appropriate workflow management systems.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Developed community-based WMS terminology
Classified 23 WMSs using five axes
Addressed WMS diversity and selection complexity
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