Scientific Workflow: A Survey and Research Directions - Springer As an antidote to this common problem, this paper presents a concise survey of existing workflow technology from the business and scientific domain and makes a number of key suggestions towards the future development of scientific workflow systems
Scientific Workflows and Provenance: Introduction and Research . . . Scientific workflows are becoming increasingly popular for compute-intensive and data-intensive scientific applications The vision and promise of scientific workflows includes rapid, easy workflow design, reuse, scalable execution, and other advantages, e g , to facilitate “reproducible science” through provenance (e g , data lineage) support However, as described in the paper, important
WfCommons: A framework for enabling scientific workflow research and . . . Scientific workflows are a cornerstone of modern scientific computing They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored processed on heterogeneous, distributed resources The workflow research and development community has employed a number of methods for the quantitative evaluation of
Scientific Workflows: Moving Across Paradigms: ACM Computing Surveys . . . A common strategy to make these in silico experiments more manageable is to model them as workflows and to use a workflow management system to organize their execution This article looks at the overall challenge posed by a new order of scientific experiments and the systems they need to be run on, and examines how this challenge can be addressed by workflows and workflow management systems
WorkflowHub: Community Framework for Enabling Scientific Workflow . . . Scientific workflows are a cornerstone of modern scientific computing They are used to describe complex computational applications that require efficient and robust management of large volumes of data, which are typically stored processed on heterogeneous, distributed resources The workflow research and development community has employed a number of methods for the quantitative evaluation of
The (R)evolution of Scientific Workflows in the Agentic AI Era: Abstract Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem However, it is