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arXiv 2307.04495cs.SEcs.AI
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基于模型驱动的工程方法支持使用SysML形式化机器学习

Model-Driven Engineering Method to Support the Formalization of Machine Learning using SysML

Technical University of Munich(慕尼黑工业大学);Technical University of Vienna(维也纳工业大学)

Simon Raedler, Juergen Mangler, Stefanie Rinderle-Ma

AI总结:

本文提出一种基于模型驱动工程的方法,利用SysML形式化机器学习任务,整合领域与ML专家知识,支持数据源集成、语义连接和数据处理定义,并通过两个用例和用户研究验证其有效性,促进机器学习在工业中的集成。

摘要:

方法:本文提出了一种方法,通过利用基于模型的工程在系统建模语言SysML的形式化过程中,支持机器学习任务的协作定义。该方法支持识别和集成各种数据源、定义数据属性之间所需的语义连接,以及在机器学习支持范围内定义数据处理步骤。结果:通过整合领域专家和机器学习专家的知识,引入了一种强大的工具,通过使用系统建模语言SysML形式化知识来描述机器学习任务。该方法基于两个用例进行评估,即一个智能天气系统,可根据传感器数据预测天气预报;以及一个3D打印机耗材的废物预防案例,在无法实现预期结果时取消打印(图像处理)。此外,还进行了一项用户研究,以收集潜在用户对所阐述方法的感知工作量和可用性的见解。结论:将机器学习特定属性集成到系统工程技术中,使非数据科学家能够理解形式化知识并定义机器学习问题的特定方面,记录数据相关知识,并进一步支持数据科学家将形式化知识用作使用(半)自动代码生成进行实现的输入。在这方面,本文通过整合来自各个领域的知识做出贡献,从而通过让多个利益相关者参与,促进机器学习在工业中的集成。

英文摘要:

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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