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ML-Quadrat & DriotData: a model-driven engineering tool and a low-code platform for smart IoT services

Moin, A., Mituca, A., Challenger, M., Badii, A. and Günnemann, S. (2022) ML-Quadrat & DriotData: a model-driven engineering tool and a low-code platform for smart IoT services. In: 2022 IEEE/ACM 44th International Conference on Software Engineering, 22-24 May 2022, Pittsburgh, PA, USA, pp. 144-148, https://doi.org/10.1109/ICSE-Companion55297.2022.9793752.

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To link to this item DOI: 10.1109/ICSE-Companion55297.2022.9793752

Abstract/Summary

In this paper, we present ML-Quadrat, an open-source research prototype that is based on the Eclipse Modeling Framework (EMF) and the state of the art in the literature of Model-Driven Software Engineering (MDSE) for smart Cyber-Physical Systems (CPS) and the Internet of Things (IoT). Its envisioned users are mostly software developers who might not have deep knowledge and skills in the heterogeneous IoT platforms and the diverse Artificial Intelligence (AI) technologies, specifically regarding Machine Learning (ML). ML-Quadrat is released under the terms of the Apache 2.0 license on Github1. Additionally, we demonstrate an early tool prototype of DriotData, a web-based Low-Code platform targeting citizen data scientists and citizen/end-user software developers. DriotData exploits and adopts ML-Quadrat in the industry by offering an ex-tended version of it as a subscription-based service to companies, mainly Small- and Medium-Sized Enterprises (SME). The current preliminary version of DriotData has three web-based model editors: text-based, tree-/form-based and diagram-based. The latter is designed for domain experts in the problem or use case domains (namely the IoT vertical domains) who might not have knowledge and skills in the field of IT. Finally, a short video demonstrating the tools is available on YouTube: https://youtu.be/VAuz25w0a5k.

Item Type:Conference or Workshop Item (Paper)
Refereed:Yes
Divisions:Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
ID Code:106224

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