From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)

TL;DR

This paper extends the ThingML modeling language to support machine learning by enhancing its DSL and code generation, enabling automatic generation of data analytics implementations in Java and Python with ML library integration.

Contribution

It introduces new DSL features and code generators for ThingML, facilitating machine learning integration at the modeling level for IoT applications.

Findings

Successfully extended ThingML with ML capabilities

Generated code integrates with Keras, Tensorflow, Scikit Learn

Prototype available as open source on Github

Abstract

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.