A hybrid approach to context modelling in large-scale pervasive computing environments

Deirdre Lee, René Meier
<span title="">2009</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uhjc22zzfvffvmhpz7mtdwpzfy" style="color: black;">Proceedings of the Fourth International ICST Conference on COMmunication System softWAre and middlewaRE - COMSWARE &#39;09</a> </i> &nbsp;
Pervasive computing aims to unobtrusively embed computer systems into everyday life environments to enrich the user experience without demanding the user"s explicit attention. For pervasive computing to be minimally invasive, computer systems must be conscious of and ultimately, be able to act according to the context of the user and her intelligent environment. However, such context is often heterogeneous as it is derived from myriads of independent systems and sensors, can be incomplete, and
more &raquo; ... ay even be erroneous. For independent systems to manage, share, correlate, and reason over context, contextual information must be modelled in a homogenous fashion. This paper proposes a hybrid approach to modelling contextual information that incorporates the management and communication benefits of traditional objectoriented context models, while also taking advantage of the semantic and inference benefits of ontology-based context models. Unlike other approaches, our hybrid model has been designed to support a specific large-scale pervasive domain, namely the transportation domain, and promotes exploiting primary context as the key to accessing and correlating distributed knowledge. The Primary-Context Model and the Primary-Context Ontology are part of a pervasive middleware architecture for integrating independent Intelligent Transport Systems and pervasive transportation services and has been applied to a prototypical realisation of such system and service integration. Context modelling, pervasive computing, hybrid context model, ontology, transportation. systems to model their contextual information in a homogenous fashion, where context is any information that can be used to characterise the situation of an entity [2] . If context is modelled homogenously, it can be accessed consistently, shared between systems, and correlated with context from other systems. There are several approaches to modelling context, which differ in the way the context is structured. The most popular of these approaches are object-oriented models and ontology-based models. Object-oriented models offer the benefits of inheritance, modularity, polymorphism, and encapsulation, but are lacking in formal semantics. Ontology-based models provide a formal specification of all entities in a domain and the relationships between those entities, ensuring a shared understanding of the context. Conversely, the complexity of ontology-based models detracts from their manageability. This paper presents a hybrid approach to modelling context: the Primary-Context Model and the Primary-Context Ontology (PCM and PCOnt). The PCM and PCOnt combine the management and communication benefits of traditional object-oriented modelling techniques with the semantic and inference benefits of ontologybased models. This approach has previously been applied to the global domain resulting in all-encompassing, generic models that
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