In this paper, we illustrate how to enhance an existing state-of-the-art modeling language and tool for the Internet of Things (IoT), called ThingML, to support machine learning on the modeling level. To this aim, we extend the Domain-Specific Language (DSL) of ThingML, as well as its code generation framework. Our DSL allows one to define things, which are in charge of carrying out data analytics. Further, our code generators can automatically produce the complete implementation in Java and Python. The generated Python code is responsible for data analytics and employs APIs of machine learning libraries, such as Keras, Tensorflow and Scikit Learn. Our prototype is available as open source software on Github.

中文翻译：

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从事物的建模语言 (ThingML) 到事物的机器学习 (ThingML2)

在本文中，我们说明了如何增强现有的最先进的物联网 (IoT) 建模语言和工具 ThingML，以支持建模级别的机器学习。为此，我们扩展了 ThingML 的领域特定语言 (DSL) 及其代码生成框架。我们的 DSL 允许人们定义负责执行数据分析的事物。此外，我们的代码生成器可以自动生成 Java 和 Python 的完整实现。生成的 Python 代码负责数据分析，并使用机器学习库的 API，如 Keras、Tensorflow 和 Scikit Learn。我们的原型在 Github 上作为开源软件提供。