From Things' Modeling Language (ThingML) to Things' Machine Learning (ThingML2)
This work addresses the need for easier machine learning integration in IoT systems for developers, though it is incremental as it builds on an existing modeling language.
The paper tackles the problem of integrating machine learning into IoT modeling by extending the ThingML language and tool to support data analytics at the modeling level, resulting in a prototype that automatically generates Java and Python code using libraries like Keras and TensorFlow.
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.