A data-oriented survey of context models

Bolchini, Cristiana; Curino, Carlo; Quintarelli, Elisa; Schreiber, Fabio Alberto; Tanca, Letizia

doi:10.1145/1361348.1361353

Cited by 242 publications

(162 citation statements)

References 39 publications

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“…While the community of computer science professionals initially perceived the context only as a matter of user location and time, this notion has been extended including, in the idea of context, other personalization aspects like current user interests, current role of the user in the system, the company the user keeps at the moment, and possibly other situational dimensions that may depend on the specific application at hand [6].…”

Section: Context Awarenessmentioning

confidence: 99%

On the Role of Context in the Design of Mobile Mashups

Cassani

Gianelli

Matera

et al. 2017

Communications in Computer and Information Science

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Abstract. This paper presents a design methodology and an accompanying platform for the design and fast development of Context-Aware Mobile mashUpS (CAMUS). The approach is characterized by the role given to context as a first-class modeling dimension used to support i) the identification of the most adequate resources that can satisfy the users' situational needs and ii) the consequent tailoring at runtime of the provided data and functions. Context-based abstractions are exploited to generate models specifying how data returned by the selected services have to be merged and visualized by means of integrated views. Thanks to the adoption of Model-Driven Engineering (MDE) techniques, these models drive the flexible execution of the final mobile app on target mobile devices. A prototype of the platform, making use of novel and advanced Web and mobile technologies, is also illustrated.

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Section: Context Awarenessmentioning

confidence: 99%

On the Role of Context in the Design of Mobile Mashups

Cassani

Gianelli

Matera

et al. 2017

Communications in Computer and Information Science

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“…There are a number of surveys which review the literature relevant to context modeling in general [4], [5] or focusing on ontology-based models [6], [7]. In addition, Costabello [8] presented and compared a number of ontology-based context models against a set of requirements.…”

Section: A Context Representationmentioning

confidence: 99%

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

Karpus

Vagliano

Goczyła

et al. 2016

Annals of Computer Science and Information Systems

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Abstract-Context-aware Recommender Systems aim to provide users with the most adequate recommendations for their current situation. However, an exact context obtained from a user could be too specific and may not have enough data for accurate rating prediction. This is known as the data sparsity problem. Moreover, often user preference representation depends on the domain or the specific recommendation approach used. Therefore, a big effort is required to change the method used. In this paper we present a new approach for contextual pre-filtering (i.e. using the current context to select a relevant subset of data). Our approach can be used with existing recommendation algorithms. It is based on two ontologies: Recommender System Context ontology, which represents the context, and Contextual Ontological User Profile ontology, which represents user preferences. We evaluated our approach through an offline study which showed that when used with well-known recommendation algorithms it can significantly improve the accuracy of prediction.

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“…Context needs an interdisciplinary and holistic research approach for understanding the interdependence between context and technology [6,5]. A context model should be developed according to its application domain since the generality of a context model in terms of expressiveness and powerfulness is inversely proportional to its practical applicability and usability [7]. Replacing the prevailing system-centric view of context-awareness by a user-centric view is suggested by [6], since context-awareness is an activity-driven process, centered around user activities.…”

Section: Context and Context-awarenessmentioning

confidence: 99%

A Context-Aware Approach for Integrating Semantic Web Technologies onto Mobile Devices

Zander

2009

Lecture Notes in Computer Science

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Abstract. Semantic Web technologies such as RDF are usually incorporated in the infrastructure of desktop and web applications and can currently not be entirely deployed on mobile devices. Therefore, the unique opportunities and novel features the Semantic Web offers are not amenable in mobile application scenarios, in which context and contextawareness is essential. In this thesis, we propose a context-sensitive Semantic Web framework for mobile devices by which contextual data can be processed semantically and integrated with data sets and services from other communities. Mobile devices augmented with those capabilities can operate autonomously and independent in different environments.

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A data-oriented survey of context models

Cited by 242 publications

References 39 publications

On the Role of Context in the Design of Mobile Mashups

On the Role of Context in the Design of Mobile Mashups

An Ontology-based Contextual Pre-filtering Technique for Recommender Systems

A Context-Aware Approach for Integrating Semantic Web Technologies onto Mobile Devices

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