A Comprehensive Context Modeling Framework for Pervasive Computing Systems

Reichle, Roland; Wagner, Michael; Khan, Mohammad Ullah; Geihs, Kurt; Lorenzo, Juan Alberto Mena; Valla, Massimo; Fra, Cristina; Paspallis, Nearchos; Papadopoulos, George Α.

doi:10.1007/978-3-540-68642-2_23

Cited by 65 publications

(37 citation statements)

References 10 publications

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“…Step 5: Implement the context model Each provided and required context type needs to be modeled as an entityscope pair with associated representation and QoFC metadata according to the MUSIC context model [13]. We illustrate this with the current location context type:…”

Section: Methodological Predictor Component Developmentmentioning

confidence: 99%

A Methodological Approach to Quality of Future Context for Proactive Smart Systems

Vanrompay

Berbers

2012

Internet of Things, Smart Spaces, and Next Generation Networking

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Abstract. Many current context-aware systems only react to the current situation and context changes as the occur. In order to anticipate to future situations and exhibit proactive behavior, these systems should also be aware of their future context. Since predicted context is uncertain and can be wrong, applications need to be able to assess the quality of the predicted context information. This allows applications to make a wellinformed decision whether to act on the prediction or not. In this paper, we present prediction quality metrics to evaluate the probability of future situations. These metrics are integrated in a structured prediction component development methodology, which is illustrated by a health care application scenario. The metrics and the methodology address the needs of the developer aiming to build context-aware applications that realize proactive behavior with regard to past, present and future context.

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Section: Methodological Predictor Component Developmentmentioning

confidence: 99%

A Methodological Approach to Quality of Future Context for Proactive Smart Systems

Vanrompay

Berbers

2012

Internet of Things, Smart Spaces, and Next Generation Networking

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“…Furthermore, the notion of context is central to context-aware services that use it for adaptation purposes. Context information can stand for a plethora of information, from user"s location, device resources (Reichle et al, 2008), up to user"s agenda and other high level information (Kirsch-Pinheiro et al, 2004). Nevertheless, in order to perform such adaptation processes, context should be modelled appropriately.…”

Section: Contextual Descriptionmentioning

confidence: 99%

“…According to Najar et al (2009), different reasons motivate the use of ontologies, such as their capability of enabling knowledge sharing in a non-ambiguous manner and their reasoning possibilities. A formal context representation was proposed by Reichle et al (2008).They represent context information based on three main concepts: 1) the entityspecifying the element to which the context information refers; 2) the scope identifying the exact attribute of the selected entity that it characterizes; and 3) the representation used to specify the internal representation used to encode context information in datastructures. According to this context model, we directly associate the scope that we observe with the entity that the context element refers to.…”

Section: Contextual Descriptionmentioning

confidence: 99%

Intention Prediction Mechanism in an Intentional Pervasive Information System

Najar

Pinheiro

Vanrompay

et al. 2013

Intelligent Technologies and Techniques for Pervasive Computing

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Nowadays, the development of pervasive technologies has allowed the improvement of services availability. These services, offeredby information systems (IS), are becoming more pervasive, i.e., accessed anytime, anywhere. However, those pervasive information systems (PIS) remain too complex for the user, who just wants a service satisfying his needs. This complexity requires considerable efforts from the user in order to select the most appropriate service. Thus, an important challenge in PIS is to reduce user"s understanding effort. In this chapter, we propose to enhance PIS transparency and productivity through a user-centred vision based on an intentional approach. We propose an intention prediction approach. This approach allows anticipating user"s future requirements, offering the most suitable service in a transparent and discrete way. This intention prediction approach is guided by the user"s context.Itis based on the analysis of the user"s previous situations in order to learn user"s behaviour in a dynamic environment.

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“…For example "<Entity >user|Bob</Entity>" refers to Bob as a user. The entities in CPPL are expressed using the MUSIC context model [12]. The MUSIC context model enables context parameters to be specified in a hierarchical manner by noting all the parent concepts in the inheritance chain to which the context parameter is assigned to (e.g., "#healthInfo.bloodPressure.systolic").…”

Section: Cppl Modelmentioning

confidence: 99%

A Context-Aware Privacy Policy Language for Controlling Access to Context Information of Mobile Users

Behrooz

Devlić

2012

Security and Privacy in Mobile Information and Communication Systems

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Abstract. This paper introduces a Context-aware Privacy Policy Language (CPPL) that enables mobile users to control who can access their context information, at what detail, and in which situation by specifying their contextaware privacy rules. Context-aware privacy rules map a set of privacy rules to one or more user's situations, in which these rules are valid. Each time a user's situation changes, a list of valid rules is updated, leaving only a subset of the specified rules to be evaluated by a privacy framework upon arrival of a context query. In the existing context-dependent privacy policy languages a user's context is used as an additional condition parameter in a privacy rule, thus all the specified privacy rules have to be evaluated when a request to access a user's context arrives. Keeping the number of rules that need to be evaluated small is important because evaluation of a large number of privacy rules can potentially increase the response time to a context query. CPPL also enables rules to be defined based on a user's social relationship with a context requestor, which reduces the number of rules that need to be defined by a user and that consequently need to be evaluated by a privacy mechanism. This paper shows that when compared to the existing context-dependent privacy policy languages, this number of rules (that are encoded using CPPL) decreases with an increasing number of user-defined situations and requestors that are represented by a small number of social relationship groups.

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A Comprehensive Context Modeling Framework for Pervasive Computing Systems

Cited by 65 publications

References 10 publications

A Methodological Approach to Quality of Future Context for Proactive Smart Systems

A Methodological Approach to Quality of Future Context for Proactive Smart Systems

Intention Prediction Mechanism in an Intentional Pervasive Information System

A Context-Aware Privacy Policy Language for Controlling Access to Context Information of Mobile Users

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