Abstract
Following the success of image recognition, machine learning approaches have recently been proposed to improve the efficiency for such systems as industry operation and maintenance, smart buildings, and smart homes. These applications are beginning to be deployed in pervasive environments. This poses greater stress in maintaining the quality of the applications. To date, there is no architecture and tools developed that can automatically support application quality maintenance. Even worse, there is no clear definition on the requirements. In this paper, we present initial experiments that we conducted with real use cases pertaining to Industry 4.0 and discuss a set of requirements that should be met by pervasive platforms to better support AI-based applications running in the edge.
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Lalanda, P., Wang, D., Vega, G., Cervantes, H., Khalid, M.A. (2020). Service-Oriented Pervasive Platform Supporting Machine Learning Applications in Smart Buildings. In: Yangui, S., et al. Service-Oriented Computing – ICSOC 2019 Workshops. ICSOC 2019. Lecture Notes in Computer Science(), vol 12019. Springer, Cham. https://doi.org/10.1007/978-3-030-45989-5_1
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