Modelling Context Information with ORM

Henricksen, Karen; Indulska, Jadwiga; McFadden, T.

doi:10.1007/11575863_82

Cited by 15 publications

(8 citation statements)

References 11 publications

(15 reference statements)

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“…Features depicted in Fig. 8 [21], [23]. Moreover, the framework proposed by Zimmermann et al includes a set of tools for supporting the design of context-aware applications and the implementation of fundamental context management components [24].…”

Section: Representation Of Context Entities and Situationsmentioning

confidence: 99%

“…Logic-based and ontology-based models are used to define formal specifications of context entities and the relations among them in a particular domain [19], [20]. Finally, fact-based models, such as the object-role approach applied by Henricksen et al, emerged from the necessity of providing formal models that are able to support query processing and reasoning [21]. …”

Section: Context Modelingmentioning

confidence: 99%

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Managing Dynamic Context to Optimize Smart Interactions and Services

Villegas

Müller

2010

Lecture Notes in Computer Science

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Abstract. With the rapid growth of socio-technical ecosystems, smart interactions and services are permeating every walk of life. As smart interactions must managed automatically and interactively in response to evolving user's matters of concern, the smart Internet requires creative approaches where services and interactions are implemented with awareness of, and dynamic adaptation to, users, computational environments, changing policies and unknown requirements. Consequently, modeling and managing dynamic context is critical for implementing smart services and smart interactions effectively. Thus, smart interactions need infrastructure to acquire, compose, and distribute context information to multiple execution endpoints. Moreover, context management must be controlled and governed to optimize system properties. This chapter surveys context modeling and management approaches intended for the optimization of smart interactions and services, discusses the main challenges and requirements of context-awareness in the smart Internet, and provides a feature-based framework useful for the evaluation and implementation of context modeling and management mechanisms.

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Section: Representation Of Context Entities and Situationsmentioning

confidence: 99%

Section: Context Modelingmentioning

confidence: 99%

Managing Dynamic Context to Optimize Smart Interactions and Services

Villegas

Müller

2010

Lecture Notes in Computer Science

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“…Strang and Linnhoff-Popien [4] and Henricksen et al [5] set out several requirements for a context model, covering technical requirements of the modelling technique, capturing the quality of data, and supporting the representation of past and future states. Building on this work, we present an updated set of requirements for modelling context.…”

Section: Modelling Contextmentioning

confidence: 99%

Ontonym

Stevenson

Knox

Dobson

et al. 2009

Proceedings of the 1st Workshop on Context, Information and Ontologies

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Pervasive systems present the need to interpret large quantities of data from many sources. Context models support developers working with such data by providing a shared representation of the environment on which to base this interpretation. This paper presents a set of requirements for a context model that addresses uncertainty, provenance, sensing and temporal properties of context. Based on these requirements, we describe Ontonym, a set of ontologies that represent core concepts in pervasive computing. We propose a framework for evaluating ontologies in the pervasive computing domain by combining recognised techniques from the literature, and present a preliminary evaluation of Ontonym using these criteria.

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“…A composite activity contains all of its ground activities [35]. For example, if we define an "actively watching TV while drinking" activity on {"actively watching TV", "drinking"} [39], then these activities form part of the composite activity, but only when we recognise all do we recognise the composite activity.…”

Section: Abstract Activitiesmentioning

confidence: 99%

“…Developers or users can profile context, or acquire it from physical or virtual sensors [35]. A piece of context asserts a state of reality, which we model as a relation between two abstract values, each belonging to a dimension of information [36].…”

Section: Context Modelmentioning

confidence: 99%

A top-level ontology for smart environments

Stevenson

Dobson

2011

Pervasive and Mobile Computing

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Recognising human activities is a problem characteristic of a wider class of systems in which algorithms interpret multi-modal sensor data to extract semantically meaningful classifications. Machine learning techniques have demonstrated progress, but the lack of underlying formal semantics impedes the potential for sharing and re-using classifications across systems. We present a top-level ontology model that facilitates the capture of domain knowledge. This model serves as a conceptual backbone when designing ontologies, linking the meaning implicit in elementary information to higher-level information that is of interest to applications. In this way it provides the common semantics for information at different levels of granularity that supports the communication, re-use and sharing of ontologies between systems.

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Modelling Context Information with ORM

Cited by 15 publications

References 11 publications

Managing Dynamic Context to Optimize Smart Interactions and Services

Managing Dynamic Context to Optimize Smart Interactions and Services

Ontonym

A top-level ontology for smart environments

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