WorkflowHub: a registry for computational workflows

• URL: http://arxiv.org/abs/2410.06941v1
• Date: Wed, 9 Oct 2024 14:36:27 GMT
• Title: WorkflowHub: a registry for computational workflows
• Authors: Ove Johan Ragnar Gustafsson, Sean R. Wilkinson, Finn Bacall, Luca Pireddu, Stian Soiland-Reyes, Simone Leo, Stuart Owen, Nick Juty, José M. Fernández, Björn Grüning, Tom Brown, Hervé Ménager, Salvador Capella-Gutierrez, Frederik Coppens, Carole Goble,
• Abstract summary: As both combined records of analysis and descriptions of processing steps should be reusable, reusable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. Hub provides a unified registry for all computational registries that links to community repositories. The registry has a global reach, with hundreds of research organisations involved, and more than 700 registered.
• Score: 0.34864924310198164
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The rising popularity of computational workflows is driven by the need for repetitive and scalable data processing, sharing of processing know-how, and transparent methods. As both combined records of analysis and descriptions of processing steps, workflows should be reproducible, reusable, adaptable, and available. Workflow sharing presents opportunities to reduce unnecessary reinvention, promote reuse, increase access to best practice analyses for non-experts, and increase productivity. In reality, workflows are scattered and difficult to find, in part due to the diversity of available workflow engines and ecosystems, and because workflow sharing is not yet part of research practice. WorkflowHub provides a unified registry for all computational workflows that links to community repositories, and supports both the workflow lifecycle and making workflows findable, accessible, interoperable, and reusable (FAIR). By interoperating with diverse platforms, services, and external registries, WorkflowHub adds value by supporting workflow sharing, explicitly assigning credit, enhancing FAIRness, and promoting workflows as scholarly artefacts. The registry has a global reach, with hundreds of research organisations involved, and more than 700 workflows registered.

Related papers

• AFlow: Automating Agentic Workflow Generation [36.61172223528231]
  Large language models (LLMs) have demonstrated remarkable potential in solving complex tasks across diverse domains. We introduce AFlow, an automated framework that efficiently explores this space using Monte Carlo Tree Search. Empirical evaluations across six benchmark datasets demonstrate AFlow's efficacy, yielding a 5.7% average improvement over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2024-10-14T17:40:40Z)
• Benchmarking Agentic Workflow Generation [80.74757493266057]
  We introduce WorFBench, a unified workflow generation benchmark with multi-faceted scenarios and intricate graph workflow structures. We also present WorFEval, a systemic evaluation protocol utilizing subsequence and subgraph matching algorithms. We observe that the generated can enhance downstream tasks, enabling them to achieve superior performance with less time during inference.
  arXiv  Detail & Related papers  (2024-10-10T12:41:19Z)
• Applying the FAIR Principles to Computational Workflows [0.0]
  computational tools are increasingly recognized as tools for productivity, trends, and democratized access to platforms and processing know-how. As digital objects to be shared, discovered, and reused, computational benefit from the FAIR principles, which stand for Findable, Accessible, Interoperable, and Reusable. We present our recommendations with commentary that reflects our discussions and justifies our choices and adaptations.
  arXiv  Detail & Related papers  (2024-10-04T15:00:17Z)
• ComfyGen: Prompt-Adaptive Workflows for Text-to-Image Generation [87.39861573270173]
  We introduce the novel task of prompt-adaptive workflow generation, where the goal is to automatically tailor a workflow to each user prompt. We propose two LLM-based approaches to tackle this task: a tuning-based method that learns from user-preference data, and a training-free method that uses the LLM to select existing flows. Our work shows that prompt-dependent flow prediction offers a new pathway to improving text-to-image generation quality, complementing existing research directions in the field.
  arXiv  Detail & Related papers  (2024-10-02T16:43:24Z)
• Agent Workflow Memory [71.81385627556398]
  We introduce Agent Memory, a method for inducing commonly reused routines. AWM substantially improves the baseline results by 24.6% and 51.1% relative success rate. Online AWM robustly generalizes in cross-task, website, and domain evaluations.
  arXiv  Detail & Related papers  (2024-09-11T17:21:00Z)
• The Hidden Costs of Automation: An Empirical Study on GitHub Actions Workflow Maintenance [45.53834452021771]
  GitHub Actions (GA) is an orchestration platform that streamlines the automatic execution of engineering tasks. Human intervention is necessary to correct defects, update dependencies, or existing workflow files.
  arXiv  Detail & Related papers  (2024-09-04T01:33:16Z)
• GenAgent: Build Collaborative AI Systems with Automated Workflow Generation -- Case Studies on ComfyUI [64.57616646552869]
  This paper explores collaborative AI systems that use to enhance performance to integrate models, data sources, and pipelines to solve complex and diverse tasks. We introduce GenAgent, an LLM-based framework that automatically generates complex, offering greater flexibility and scalability compared to monolithic models. The results demonstrate that GenAgent outperforms baseline approaches in both run-level and task-level evaluations.
  arXiv  Detail & Related papers  (2024-09-02T17:44:10Z)
• Couler: Unified Machine Learning Workflow Optimization in Cloud [6.769259207650922]
  Couler is a system designed for unified ML workflow optimization in the cloud. We integrate Large Language Models (LLMs) into workflow generation, and provide a unified programming interface for various workflow engines. Couer has successfully improved the CPU/Memory utilization by more than 15% and the workflow completion rate by around 17%.
  arXiv  Detail & Related papers  (2024-03-12T12:47:32Z)
• Reusability Challenges of Scientific Workflows: A Case Study for Galaxy [56.78572674167333]
  This study examined the reusability of existing and exposed several challenges. The challenges preventing reusability include tool upgrading, tool support, design flaws, incomplete, failure to load a workflow, etc.
  arXiv  Detail & Related papers  (2023-09-13T20:17:43Z)
• Power-up! What Can Generative Models Do for Human Computation Workflows? [13.484359389266864]
  Investigation into large language models (LLMs) as part of crowdsourcing remains an under-explored space. From an empirical standpoint, little is currently understood about how LLMs can improve the effectiveness of crowdsourcing.
  arXiv  Detail & Related papers  (2023-07-05T12:35:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.