Anchoring data quality dimensions in ontological foundations
Communications of the ACM
Space/time trade-offs in hash coding with allowable errors
Communications of the ACM
Distributed mediation of ambiguous context in aware environments
Proceedings of the 15th annual ACM symposium on User interface software and technology
Understanding and Using Context
Personal and Ubiquitous Computing
AIMQ: a methodology for information quality assessment
Information and Management
Modelling and Using Imperfect Context Information
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
Mobility '08 Proceedings of the International Conference on Mobile Technology, Applications, and Systems
Proceedings of the 2010 ACM Symposium on Applied Computing
Geo-social interaction: context-aware help in large scale public spaces
AmI'10 Proceedings of the First international joint conference on Ambient intelligence
AIS'12 Proceedings of the Third international conference on Autonomous and Intelligent Systems
VANET IR-CAS: utilizing IR techniques in developing context aware system for VANET
Proceedings of the second ACM international symposium on Design and analysis of intelligent vehicular networks and applications
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The evolution to ubiquitous information and communication networks is evident. Technology is emerging that connects everyday objects and embeds intelligence in our environment. In the Internet of Things, smart objects collect context information from various sources to turn a static environment into a smart and proactive one. Managing the ambiguous nature of context information will be crucial to select relevant information for the tasks at hand. In this paper we present a vector space model that uses context quality parameters to manage context ambiguity and to identity irrelevant context providers. We also discuss backpropagation applied in the network architecture to filter unused context information in the network as close to the source as possible. Experiments show that our contribution not only reduces the amount of useless information a smart object deals with, but also the distribution of unused context information throughout the network architecture.