arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2107.02689cs.SEcs.AIcs.LG
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一种模型驱动的机器学习方法和IoT的软件建模

A Model-Driven Approach to Machine Learning and Software Modeling for the IoT

University of Antwerp(安特卫普大学);Flanders Make(法兰德斯制造);University of Reading(雷丁大学);Technical University of Munich(慕尼黑工业大学);Munich Data Science Institute(慕尼黑数据科学研究所)

Armin Moin, Moharram Challenger, Atta Badii, Stephan Günnemann

摘要:

模型用于软件工程(SE)和人工智能(AI)。SE模型可以在不同的抽象层次上指定体系结构,并在软件开发生命周期的各个阶段(从早期概念化和设计到验证、实现、测试和演进)解决不同的问题。然而,人工智能模型可以提供智能功能,例如预测和决策支持。例如,在机器学习(ML)中,这是目前最流行的人工智能子学科,数学模型可以从观察到的数据中学习有用的模式,并能够做出预测。这项工作的目标是通过将所述社区中的模型结合在一起,并为需要ML的智能系统提出一种模型驱动软件开发的整体方法来创造协同效应。我们说明了软件模型如何能够以无缝的方式创建和处理ML模型。主要关注的是物联网(IoT)领域,其中ML和模型驱动的SE都发挥着关键作用。在需要对目标体系结构进行网络物理系统的系统视角的背景下,SE和ML子系统的集成设计环境将最好地支持最终系统实施的优化和整体效率。特别是,我们实现了基于ThingML的ML-Quadrat方法,并使用物联网领域的案例研究以及经验用户评估对其进行了验证。事实证明,所提出的方法不仅是可行的,而且还可能有助于连接到物联网的智能网络物理系统(CPS)的软件开发的性能飞跃,以及使用所提出的建模解决方案的从业者的增强的用户体验。

英文摘要:

Models are used in both Software Engineering (SE) and Artificial Intelligence (AI). SE models may specify the architecture at different levels of abstraction and for addressing different concerns at various stages of the software development life-cycle, from early conceptualization and design, to verification, implementation, testing and evolution. However, AI models may provide smart capabilities, such as prediction and decision-making support. For instance, in Machine Learning (ML), which is currently the most popular sub-discipline of AI, mathematical models may learn useful patterns in the observed data and can become capable of making predictions. The goal of this work is to create synergy by bringing models in the said communities together and proposing a holistic approach to model-driven software development for intelligent systems that require ML. We illustrate how software models can become capable of creating and dealing with ML models in a seamless manner. The main focus is on the domain of the Internet of Things (IoT), where both ML and model-driven SE play a key role. In the context of the need to take a Cyber-Physical System-of-Systems perspective of the targeted architecture, an integrated design environment for both SE and ML sub-systems would best support the optimization and overall efficiency of the implementation of the resulting system. In particular, we implement the proposed approach, called ML-Quadrat, based on ThingML, and validate it using a case study from the IoT domain, as well as through an empirical user evaluation. It transpires that the proposed approach is not only feasible, but may also contribute to the performance leap of software development for smart Cyber-Physical Systems (CPS) which are connected to the IoT, as well as an enhanced user experience of the practitioners who use the proposed modeling solution.

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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