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Publication Detailed Description
Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review
Journal Title
Sensors
Year (definitive publication)
2023
Language
English
Country
Switzerland
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Abstract
This paper reviews the literature on model-driven engineering (MDE) tools and languages for the internet of things (IoT). Due to the abundance of big data in the IoT, data analytics and machine learning (DAML) techniques play a key role in providing smart IoT applications. In particular, since a significant portion of the IoT data is sequential time series data, such as sensor data, time series analysis techniques are required. Therefore, IoT modeling languages and tools are expected to support DAML methods, including time series analysis techniques, out of the box. In this paper, we study and classify prior work in the literature through the mentioned lens and following the scoping review approach. Hence, the key underlying research questions are what MDE approaches, tools, and languages have been proposed and which ones have supported DAML techniques at the modeling level and in the scope of smart IoT services.
Acknowledgements
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Keywords
Model-driven engineering,Internet of things,Data analytics and machine learning,Time series,Literature review,Scoping review
Fields of Science and Technology Classification
- Computer and Information Sciences - Natural Sciences
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| PT-INNOVATION-0069-Fish2Fork | EEA Grants |
| UIDB/50021/2020 | Fundação para a Ciência e a Tecnologia |
| 101056765 | Comissão Europeia |
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