The role of machine learning in scientific workflows
The role of machine learning in scientific workflows
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DOI：
10.1177/1094342019852127
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发表时间：
2019-05
期刊：
影响因子：
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通讯作者：
E. Deelman;A. Mandal;M. Jiang;R. Sakellariou
中科院分区：
文献类型：
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作者：
E. Deelman;A. Mandal;M. Jiang;R. Sakellariou
机器学习（ML）正被应用于许多日常环境中，从图像识别到自然语言处理，再到自动驾驶汽车，再到产品推荐。在科学领域，ML被用于医疗诊断、新材料开发、智能农业、DNA分类等。在这篇文章中，我们描述了在科学工作流管理领域使用ML的机会。科学工作流是当今计算科学的关键，它支持在异构且通常是分布式的环境中定义和执行复杂的应用程序。我们描述了组成和执行科学工作流的挑战，并确定了应用ML技术的机会，通过增强当前的工作流管理系统的功能来应对这些挑战。我们预见，随着ML领域的发展，工作流管理系统提供的自动化将大大增加，并导致科学生产力的显着提高。
Machine learning (ML) is being applied in a number of everyday contexts from image recognition, to natural language processing, to autonomous vehicles, to product recommendation. In the science realm, ML is being used for medical diagnosis, new materials development, smart agriculture, DNA classification, and many others. In this article, we describe the opportunities of using ML in the area of scientific workflow management. Scientific workflows are key to today’s computational science, enabling the definition and execution of complex applications in heterogeneous and often distributed environments. We describe the challenges of composing and executing scientific workflows and identify opportunities for applying ML techniques to meet these challenges by enhancing the current workflow management system capabilities. We foresee that as the ML field progresses, the automation provided by workflow management systems will greatly increase and result in significant improvements in scientific productivity.