CIM-CSS: A Formal Modeling Approach to Context Identification and Management for Intelligent Context-Sensitive Systems

Abstract

Context modeling is often used to relate the context in which a system will operate to the entities of interest in the problem domain. It remains the case that context models are inadequate in emerging computing paradigms (e.g., smart spaces and the Internet of Things), in which the relevance of context is shaped dynamically by the changing needs of users. Formal models are required to fuse and interpret contextual information obtained from the heterogeneous sources. In this paper, we propose an integrated and formal context modeling approach for intelligent systems operating in the context-sensitive environments. We introduce a goal-driven, entity-centered identification method for determining which context elements are influential in adapting the system behavior. We then describe a four-layered framework for metamodeling the identification and management of context. First, the framework presents a formal metamodel of context. A formalization of context using the first-order logic with relational operators is then presented to specify formally the context information at different abstraction levels. The metamodel, therefore, prepares the ground for building a formal modeling language and automated support tool (https://github.com/metamodeler/ CIM-CSS/). The proposed model is then evaluated using an application scenario in the smart meeting rooms domain, and the results are analyzed qualitatively.

Applying Machine Learning and Model-Driven Approach for the Identification and Diagnosis Of Covid-19

“…Some are inspired by the works that have been reviewed, while others have been identified based on the requirements of our problem (Table 1; Moradiet al, 2020). Develop a framework called CaaSSET, which stands for Context as a Service Set of Engineering Tools (Baddour et al, 2019). Propose an integrated and formal context modeling approach for intelligent systems operating in context-sensitive environments (Dorndorfer et al, 2019).…”