CoCo: dynamic composition of context information

Buchholz, Thomas A.; Krause, Michael; Linnhoff‐Popien, Claudia; Schiffers, Michael

doi:10.1109/mobiq.2004.1331740

Cited by 29 publications

(16 citation statements)

References 9 publications

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“…Schillit [18] captures the situational context using vectors that describe "the condition of situation, the sensing device, the required accuracy and update rate". In an object-oriented way, CoCo [19] represents context and situations using a graphical language and abstractions such as class, object, scales and factory. In the Context Modeling Language (CML) model, Henricksen [20][21] defines situations using predicate logic.…”

Section: Related Workmentioning

confidence: 99%

Reasoning about Context in Uncertain Pervasive Computing Environments

Haghighi

Krishnaswamy

Zaslavsky

et al. 2008

Lecture Notes in Computer Science

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Abstract.Context-awareness is a key to enabling intelligent adaptation in pervasive computing applications that need to cope with dynamic and uncertain environments. Addressing uncertainty is one of the major issues in contextbased situation modeling and reasoning approaches. Uncertainty can be caused by inaccuracy, ambiguity or incompleteness of sensed context. However, there is another aspect of uncertainty that is associated with human concepts and realworld situations. In this paper we propose and validate a Fuzzy Situation Inference (FSI) technique that is able to represent uncertain situations and reflect delta changes of context in the situation inference results. The FSI model integrates fuzzy logic principles into the Context Spaces (CS) model, a formal and general context reasoning and modeling technique for pervasive computing environments. The strengths of fuzzy logic for modeling and reasoning of imperfect context and vague situations are combined with the CS model's underlying theoretical basis for supporting context-aware pervasive computing scenarios. An implementation and evaluation of the FSI model are presented to highlight the benefits of the FSI technique for context reasoning under uncertainty.

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Section: Related Workmentioning

confidence: 99%

Reasoning about Context in Uncertain Pervasive Computing Environments

Haghighi

Krishnaswamy

Zaslavsky

et al. 2008

Lecture Notes in Computer Science

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“…This requires the ability to describe such compositions. Therefore, we have developed a language that describes the request for composed context information in so-called CoCo graphs [1]. The graph-like structures are expressed in XML.…”

Section: The Coco Infrastructurementioning

confidence: 99%

“…In previous works on those topics, existing approaches to context provisioning (cp. [1]) and context information models (cp. [7] and [8]) have been evaluated thoroughly.…”

Section: Related Workmentioning

confidence: 99%

“…On the other hand, context information about any entity, as for example the user's location can be used in many different context-aware services. To relieve context-aware services of the intricacies of context retrieval and composition as well as to facilitate the reuse of context information at the same time, in [1] we proposed infra-structural services, namely the CoCo Infrastructure. Context information is exchanged over domain boundaries, thus all involved actors have to agree on how to express and interpret context information.…”

Section: Introductionmentioning

confidence: 99%

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A Context Middleware Using an Ontology-Based Information Model

Hochstatter

Duergner

Krause

2007

Dependable and Adaptable Networks and Services

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Abstract. For the adaptation of services to the current situation of a user, the services are in need of specific context information. The acquisition of context in highly dynamic environments is a complex process as the appropriate context sources are not known in advance. Moreover, to realize Mark Weiser's vision of ubiquitous computing, many services on the one hand and a good deal of context information on the other hand have to be combined. Hence, we follow a middleware approach to automate context retrieval for services. For the exchange over domain boundaries, services in need of and services offering context information have to agree on a common description of the information. Therefore, a flexible and extensible information model is a basic requirement. This paper describes in detail the integration of those two important foundations of context-aware computing.

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“…Ontologies fulfills all those requirements in contrast to the other solutions that mostly lack in support for richness and quality of information as well as assistance with incompleteness and ambiguity of information. Our modeling approach was driven by requirements we collected during the work with and development of a reference context brokering and processing ar-chitecture (see Context Composition infrastructure in [4]). We identified the following additional requirements:…”

Section: Context Metamodeling Approachmentioning

confidence: 99%