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

Armin Moin, Stephan Rössler, Marouane Sayih, Stephan Günnemann

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

9 Zitate (Scopus)

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.

OriginalspracheEnglisch
TitelProceedings - 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Companion Proceedings
Herausgeber (Verlag)Association for Computing Machinery, Inc
Seiten82-83
Seitenumfang2
ISBN (elektronisch)9781450381352
DOIs
PublikationsstatusVeröffentlicht - 16 Okt. 2020
Veranstaltung23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020 - Virtual, Online, Kanada
Dauer: 16 Okt. 202023 Okt. 2020

Publikationsreihe

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

Konferenz

Konferenz23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems, MODELS-C 2020
Land/GebietKanada
OrtVirtual, Online
Zeitraum16/10/2023/10/20

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