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.
Highlights
IntroductionThe miniaturization of electronic devices and the proliferation of smart objects have allowed advanced information and computing technologies to be used in people’s everyday activities
The ‘‘Ubiquitous computing’’ era envisioned by Weiser [1] is becoming a reality
We developed a context identification method that takes different views into account and addresses: (1) device expert’s concerns through bottom-up decision processes i.e., collecting low-level facts recognizable by devices, which can be abstracted to higher-level states through further analysis, (2) Context-sensitive systems (CSSs) design expert’s concerns through top-down decision processes i.e., collecting high-level states of interest to a CSS, which can be refined to lower-level states, and (3) the concerns of domain experts and end-users/operators through observations and assessments i.e., extracting cognitive knowledge and facts about organizational or operational environments and individual users in a particular domain
Summary
The miniaturization of electronic devices and the proliferation of smart objects have allowed advanced information and computing technologies to be used in people’s everyday activities These developments have transformed desktop computing, and various new paradigms are emerging, including context-aware systems (CAS), ambient intelligence (AmI), self-adaptive systems (SAS), the Internet of Things (IoT), cyber-physical systems (CPS), and ubiquitous information systems (UIS) [2]–[5]. A system should be aware of contextual changes and adapt its behavior (in terms of service and information) to accommodate users’ needs [2]–[5] This requires the system to observe, process, and understand the situation in which it is used, to allow it draw high-level abstract conclusions about it [6]. The Problem Frames (PF) approach [9] represents the context in which a problem
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