Understanding and Using Context
Personal and Ubiquitous Computing
Unsupervised Clustering of Symbol Strings and Context Recognition
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
An ontology for context-aware pervasive computing environments
The Knowledge Engineering Review
Simulation of a distributed recommendation system for pervasive networks
Proceedings of the 2005 ACM symposium on Applied computing
Social Serendipity: Mobilizing Social Software
IEEE Pervasive Computing
Handbook on Ontologies
An ontology for mobile device sensor-based context awareness
CONTEXT'03 Proceedings of the 4th international and interdisciplinary conference on Modeling and using context
Unsupervised clustering of context data and learning user requirements for a mobile device
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
DigiDress: a field trial of an expressive social proximity application
UbiComp'05 Proceedings of the 7th international conference on Ubiquitous Computing
A Human Activity Aware Learning Mobile Music Player
Proceedings of the 2007 conference on Advances in Ambient Intelligence
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
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Several approaches to context awareness have been proposed ranging from unsupervised learning to ontologies. Independent of the type of context awareness used a consistent approach to naming contexts is required. A novel paradigm for labeling contexts is described based on close range wireless connections between devices and a very simple, unsupervised learning algorithm. It is shown by simulation analysis that it is possible to achieve a labeling of different contexts which allows context related information to be communicated in a consistent manner between devices. As the learning is unsupervised no user input is required for it to work. Furthermore this approach requires no extra infrastructure or resources to manage the names assigned to the contexts.