Automating Multivariable Workflow Composition for Model-to-Model Integration
Automating Multivariable Workflow Composition for Model-to-Model Integration
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DOI：
10.1109/escience55777.2022.00030
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Raul Alejandro Vargas-Acosta;L. G. Chavira;N. Villanueva-Rosales;D. Pennington
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文献类型：
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作者：
Raul Alejandro Vargas-Acosta;L. G. Chavira;N. Villanueva-Rosales;D. Pennington
许多与社会相关的挑战，包括环境挑战，都需要采用综合方法，将解耦的数据、模型和观点整合在一起。整合数据和模型对于这些方法至关重要，但也可能变得繁琐。计算工作流被广泛用于集成域内或跨域的异构数据和计算过程。然而，创建计算工作流可能需要潜在用户不一定拥有的计算和领域专业知识。本文介绍了我们的努力，使自动化的多变量工作流组合实现的可持续水集成建模（SWIM）平台的工作流作曲家。我们描述了不知情的搜索算法中使用的工作流程作曲家和初步评估的案例研究，需要整合两个水（平衡）模型，覆盖中格兰德河在美国西南地区。初步结果表明，综合模型的评价不仅要考虑技术和科学的角度，而且要考虑用户如何理解和使用这些复杂系统的结果。通过使各种利益相关者能够使用科学模型，使模型到模型集成自动化的努力可以显著支持科学工作和决策。
Many societal-relevant challenges, including environmental ones, require comprehensive approaches that integrate decoupled data, models, and perspectives. Integrating data and models is critical for these approaches but can also become cumbersome. Computational workflows are widely used to integrate heterogeneous data and computational processes within or across domains. However, creating computational workflows may require computational and domain expertise not necessarily possessed by potential users. This paper presents our efforts to enable automated multivariable workflow composition implemented as a workflow composer in the Sustainable Water for Integrated Modeling (SWIM) platform. We describe the uninformed search algorithm used in the workflow composer and an initial evaluation with a case study that requires integrating two water (balance) models that cover the Middle Rio Grande in the U.S. Southwest region. Preliminary results show that the evaluation of integrating models should not only consider the technical and scientific perspective but also how users understand and use the results of these complex systems. Efforts toward automating the model-to-model integration can significantly support scientific endeavors and decision-making by enabling various stakeholders to use scientific models.