机器学习的需求工程:回顾与反思

Zhong Pei, Lin Liu, Chen Wang, Jianmin Wang
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引用次数: 12

摘要

今天,许多工业流程正在经历数字化转型,这通常需要在业务流程中集成易于理解的领域模型和最先进的机器学习技术。然而,关于何时、何地以及如何将各种领域模型和端到端机器学习技术适当地嵌入到给定的业务工作流中的需求引出和设计决策需要进一步的探索。本文旨在从跨领域协作的角度概述机器学习应用的需求工程过程。我们首先回顾了关于机器学习需求工程的文献,然后一步一步地进行协作需求分析过程。还讨论了与上述步骤相关的工业数据驱动智能应用的示例案例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Requirements Engineering for Machine Learning: A Review and Reflection
Today, many industrial processes are undergoing digital transformation, which often requires the integration of well-understood domain models and state-of-the-art machine learning technology in business processes. However, requirements elicitation and design decision making about when, where and how to embed various domain models and end-to-end machine learning techniques properly into a given business workflow requires further exploration. This paper aims to provide an overview of the requirements engineering process for machine learning applications in terms of cross domain collaborations. We first review the literature on requirements engineering for machine learning, and then go through the collaborative requirements analysis process step-by-step. An example case of industrial data-driven intelligence applications is also discussed in relation to the aforementioned steps.
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