论文标题
Thingml+通过机器学习的互联网增强模型驱动的软件工程
ThingML+ Augmenting Model-Driven Software Engineering for the Internet of Things with Machine Learning
论文作者
论文摘要
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的方法，建模语言和工具支持（用于物联网/CPS的开源建模工具），以满足物联网应用程序的机器学习需求。当前，ThingML提供了建模语言和工具支持，用于建模系统的组件，它们的通信界面以及其行为。后者是通过国家机器完成的。但是，我们认为，在许多情况下，物联网/CPS服务涉及系统组件和物理过程，其行为尚未得到充分理解，以便使用状态机进行建模。因此，通常是一种数据驱动的方法，可以基于观察到的数据来启用推理，例如，使用机器学习是首选的。为此，ML-Quadrat将必要的机器学习概念集成到建模级别（建模语言的语法和语义）和代码生成器级别上。我们计划支持有关流处理和复杂事件处理的两个目标平台，即Apache Samoa和Apama。
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.
