An approach to Collaborative Context Prediction

Voigtmann, Christian; Lau, Sian Lun; David, Klaus

doi:10.1109/percomw.2011.5766929

Cited by 9 publications

(2 citation statements)

References 8 publications

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“…To support the future, contexts prediction techniques need to be included. There are many studies regarding context prediction methods (Sigg et al 2012;Voigtmann et al 2011;F0ll et al 2011b). Those approaches predict the contexts that probably will describe the users' future situations based on users' histories and their current contexts.…”

Section: Discussionmentioning

confidence: 99%

A Multi-Temporal Context-aware System for Competences Management

Rosa

Barbosa

Kich

et al. 2015

Int J Artif Intell Educ

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The evolution of computing technology and wireless networks has contributed to the miniaturization of mobile devices and their increase in power, providing services anywhere and anytime. In this scenario, applications have considered the user's contexts to make decisions (Context Awareness). Context-aware applications have enabled new opportunities in different areas, for example, education, games and entertainment, commerce, and competence management. In this article, we present MultCComp, a multi-temporal context-aware system for competences management. The main system contribution is to take advantage of the workers' present and past contexts to help them to develop their competences. We define as multi-temporal context awareness the joint use of workers' present and past contexts to assist them in the development of their competences. We developed a prototype and conducted two experiments with it in an evaluation environment. The first experiment aimed to demonstrate the system functionalities. It consisted of two evaluation scenarios that were followed by two users. The second experiment focused on evaluating the acceptance of the system. It comprised a scenario that was followed by 21 users, who filled out a questionnaire at the end of the test.

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Section: Discussionmentioning

confidence: 99%

A Multi-Temporal Context-aware System for Competences Management

Rosa

Barbosa

Kich

et al. 2015

Int J Artif Intell Educ

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“…In our structure model we specify the components the environment consists of. We assume that the algorithms used to predict a user's next context in a distributed and collaborative manner are Alignment [7], ActiveLeZi [8], and the Collaborative Context Predictor (CCP) [9]. Furthermore, we assume that the knowledge base is not restricted to the user's own context history but also uses additional knowledge in context histories of other users.…”

Section: System Modelmentioning

confidence: 99%

A new approach for distributed and collaborative context prediction

Voigtmann

Schütte

Wacker

et al. 2013

2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops)

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The processing capabilities of current smartphones have increased significantly. We propose a distributed and collaborative context prediction approach that exclusively uses current smartphones to automatically collect, process and predict contexts of users. To predict a user's next context, not only her context history is utilised but also context histories of other users are used. The communication between the smartphones of the users is realised using peer-2-peer. Therefore, no centralised server unit is needed to process the context information of the users externally. We provide a proof-of-concept implementation and present experimental results that demonstrate the practicality of the proposed architecture.

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Predicting Occupant Locations Using Association Rule Mining

Ryan

Brown

2013

Research and Development in Intelligent Systems XXX

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Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. In this paper we present two approaches to occupant location prediction based on association rule mining which allow prediction based on historical occupant movements and any available real time information, or based on recent occupant movements. We show how association rule mining can be adapted for occupant prediction and evaluate both approaches against existing approaches on two sets of real occupants.

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An approach to Collaborative Context Prediction

Cited by 9 publications

References 8 publications

A Multi-Temporal Context-aware System for Competences Management

A Multi-Temporal Context-aware System for Competences Management

A new approach for distributed and collaborative context prediction

Predicting Occupant Locations Using Association Rule Mining

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