From things' modeling language (ThingML) to things' machine learning (ThingML2)

Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
PublisherAssociation for Computing Machinery, Inc
Pages82-83
Number of pages2
ISBN (Electronic)9781450381352
DOIs
StatePublished - 16 Oct 2020
Event23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Virtual, Online, Canada
Duration: 16 Oct 202023 Oct 2020

Publication series

NameProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings

Conference

Conference23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020
Country/TerritoryCanada
CityVirtual, Online
Period16/10/2023/10/20

Keywords

  • Domain-specific modeling
  • Internet of things
  • Machine learning

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