TY - GEN
T1 - ML-Quadrat & DriotData
T2 - 44th ACM/IEEE International Conference on Software Engineering: Companion, ICSE-Companion 2022
AU - Moin, Armin
AU - Mituca, Andrei
AU - Challenger, Moharram
AU - Badii, Atta
AU - Gunnemann, Stephan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - domain-specific modeling
KW - iot
KW - low-code
KW - machine learning
KW - model-driven software engineering
UR - http://www.scopus.com/inward/record.url?scp=85132381181&partnerID=8YFLogxK
U2 - 10.1109/ICSE-Companion55297.2022.9793752
DO - 10.1109/ICSE-Companion55297.2022.9793752
M3 - Conference contribution
AN - SCOPUS:85132381181
T3 - Proceedings - International Conference on Software Engineering
SP - 144
EP - 148
BT - Proceedings - 2022 ACM/IEEE 44th International Conference on Software Engineering
PB - IEEE Computer Society
Y2 - 22 May 2022 through 27 May 2022
ER -