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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Mardani, Z., Moin, A., Silva, A. R. & Ferreira, J. (2023). Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review. Sensors. 23 (3)
Exportar Referência (IEEE) 
Z. M. Korani et al.,  "Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review", in Sensors, vol. 23, no. 3, 2023
Exportar BibTeX 
@null{korani2023_1715181415391,
	year = "2023",
	url = "https://www.mdpi.com/1424-8220/23/3/1458"
}
Exportar RIS 
TY  - GEN
TI  - Model-driven engineering techniques and tools for machine learning-enabled IoT applications: A scoping review
T2  - Sensors
VL  - 23
AU  - Mardani, Z.
AU  - Moin, A.
AU  - Silva, A. R.
AU  - Ferreira, J.
PY  - 2023
SN  - 1424-8220
DO  - 10.3390/s23031458
UR  - https://www.mdpi.com/1424-8220/23/3/1458
AB  - 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.
ER  -