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- W2622709708 abstract "In this thesis, we propose a collaborative model driven methodology for designing Autonomic Cognitive IoT systems to deal with IoT design complexity. We defined within this methodology a set of autonomic cognitive design patterns that aim at (1) delineating the dynamic coordination of the autonomic processes to deal with the system's context changeability and requirements evolution at run-time, and (2) adding cognitive abilities to IoT systems to understand big data and generate new insights. To address challenges related to big data and scalability, we propose a generic semantic big data platform that aims at integrating heterogeneous distributed data sources deployed on the cloud and generating knowledge that will be exposed as a service (Knowledge as a Service--KaaS). As an application of the proposed contributions, we instantiated and combined a set of patterns for the development of prescriptive cognitive system for the patient treatment management. Thus, we elaborated two ontological models describing the wearable devices and the patient context as well as the medical knowledge for decision-making. The proposed system is evaluated from the clinical prescriptive through collaborating with medical experts, and from the performance perspective through deploying the system within the KaaS following different configurations" @default.
- W2622709708 created "2017-06-15" @default.
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- W2622709708 date "2016-12-15" @default.
- W2622709708 modified "2023-09-25" @default.
- W2622709708 title "Towards Autonomic and Cognitive IoT Systems, Application to Patients’ Treatments Management" @default.
- W2622709708 hasPublicationYear "2016" @default.
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