In this paper, we present the current position of the research project ML-Quadrat, which aims to extend the methodology, modeling language and tool support of ThingML - an open source modeling tool for IoT/CPS - to address Machine Learning needs for the IoT applications. Currently, ThingML offers a modeling language and tool support for modeling the components of the system, their communication interfaces as well as their behaviors. The latter is done through state machines. However, we argue that in many cases IoT/CPS services involve system components and physical processes, whose behaviors are not well understood in order to be modeled using state machines. Hence, quite often a data-driven approach that enables inference based on the observed data, e.g., using Machine Learning is preferred. To this aim, ML-Quadrat integrates the necessary Machine Learning concepts into ThingML both on the modeling level (syntax and semantics of the modeling language) and on the code generators level. We plan to support two target platforms for code generation regarding Stream Processing and Complex Event Processing, namely Apache SAMOA and Apama.

中文翻译：

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ThingML+ 使用机器学习增强物联网的模型驱动软件工程

在本文中，我们介绍了研究项目 ML-Quadrat 的当前位置，该项目旨在扩展 ThingML（物联网/CPS 的开源建模工具）的方法论、建模语言和工具支持，以满足物联网的机器学习需求应用程序。目前，ThingML 提供了一种建模语言和工具支持，用于对系统组件、它们的通信接口以及它们的行为进行建模。后者是通过状态机完成的。然而，我们认为在许多情况下 IoT/CPS 服务涉及系统组件和物理过程，它们的行为不能很好地理解，以便使用状态机进行建模。因此，通常首选基于观察到的数据进行推理的数据驱动方法，例如，使用机器学习。为了这个目标，ML-Quadrat 在建模级别（建模语言的语法和语义）和代码生成器级别将必要的机器学习概念集成到 ThingML 中。我们计划支持两个目标平台来生成关于流处理和复杂事件处理的代码，即 Apache SAMOA 和 Apama。