Generating customized low-code development platforms for digital twins

A digital twin improves our use of a cyber–physical system and understanding of its emerging behavior. To this effect, a digital twin is to be developed and configured and potentially also operated by domain experts, who rarely have a professional software engineering background and for whom easy ac...

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Vydané v:Journal of computer languages (Online) Ročník 70; s. 101117
Hlavní autori: Dalibor, Manuela, Heithoff, Malte, Michael, Judith, Netz, Lukas, Pfeiffer, Jérôme, Rumpe, Bernhard, Varga, Simon, Wortmann, Andreas
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.06.2022
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ISSN:2590-1184, 2590-1184
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Shrnutí:A digital twin improves our use of a cyber–physical system and understanding of its emerging behavior. To this effect, a digital twin is to be developed and configured and potentially also operated by domain experts, who rarely have a professional software engineering background and for whom easy access and support, e.g., in form of low-code platforms are missing. In this paper, we report on an integrated method for the model-driven engineering of low-code development platforms for digital twins that enables domain experts to create and operate digital twins for cyber–physical systems using the most appropriate modeling languages. The foundation of this method is (1) a code generation infrastructure for information systems combined with (2) an extensible base architecture for self-adaptive digital twins and (3) reusable language components for their configuration. Using this method, software engineers first configure the information system with the required modeling languages to generate the low-code development platform for digital twins before domain experts leverage the generated platform to create digital twins. This two-step method facilitates creating tailored low-code development platforms as well as creating and operating customized digital twins for a variety of applications.
ISSN:2590-1184
2590-1184
DOI:10.1016/j.cola.2022.101117