论文标题
构建特定于域的机器学习工作流程：最先进的概念框架
Building Domain-Specific Machine Learning Workflows: A Conceptual Framework for the State-of-the-Practice
论文作者
论文摘要
领域专家越来越多地利用机器学习来解决其特定领域的问题。本文提出了六个关键挑战，域专家将问题转换为计算工作流程，然后转变为可执行的实现。这些挑战来自我们的概念框架，它列出了域专家在开发解决方案时可以选择采取的选项的“途径”。 为了在最新的实践中理解我们的概念框架，本文讨论了可用的文本和图形工作流程系统的选择及其对这六个挑战的支持。还检查了来自各个领域的文献研究的案例研究，以突出域专家使用的工具以及对域特异性和机器学习的分类，并将其用于其问题，工作流程和实施。 最先进的事物介绍了我们对六个主要挑战的讨论，我们确定哪些挑战未通过可用的工具充分解决。我们还建议针对软件工程研究人员提高这些工具的自动化并在软件工程和各种科学领域之间传播最佳实践技术的可能研究方向。
Domain experts are increasingly employing machine learning to solve their domain-specific problems. This article presents six key challenges that a domain expert faces in transforming their problem into a computational workflow, and then into an executable implementation. These challenges arise out of our conceptual framework which presents the "route" of options that a domain expert may choose to take while developing their solution. To ground our conceptual framework in the state-of-the-practice, this article discusses a selection of available textual and graphical workflow systems and their support for these six challenges. Case studies from the literature in various domains are also examined to highlight the tools used by the domain experts as well as a classification of the domain-specificity and machine learning usage of their problem, workflow, and implementation. The state-of-the-practice informs our discussion of the six key challenges, where we identify which challenges are not sufficiently addressed by available tools. We also suggest possible research directions for software engineering researchers to increase the automation of these tools and disseminate best-practice techniques between software engineering and various scientific domains.
