Understanding and Using Context
Personal and Ubiquitous Computing
Teaching Context to Applications
Personal and Ubiquitous Computing
Adapting Applications in Mobile Terminals Using Fuzzy Context Information
Mobile HCI '02 Proceedings of the 4th International Symposium on Mobile Human-Computer Interaction
What Shall We Teach Our Pants?
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
An infrastructure for context-awareness based on first order logic
Personal and Ubiquitous Computing
Ontology Based Context Modeling and Reasoning using OWL
PERCOMW '04 Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops
A Framework for Developing Mobile, Context-aware Applications
PERCOM '04 Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom'04)
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Research and implementation of the context-aware middleware based on neural network
AIS'04 Proceedings of the 13th international conference on AI, Simulation, and Planning in High Autonomy Systems
Prediction of indoor movements using bayesian networks
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
A formal model of reliable sensor perception
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Context-aware pervasive service composition and its implementation
Personal and Ubiquitous Computing
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Context-aware computing is a hot area in ubiquitous computing. There are several challenges to be covered. This paper focuses on context reasoning, which means deducing higher context from raw sensor data. The context reasoning problem is discussed on two different levels: context inference/recognition concerning the generation of context atoms from raw sensor data, and context reasoning concerning the composition of context atoms and deduction of higher-level context. In this paper, we discuss some commonly used reasoning technologies in context-aware systems, including rule-based logics and machine learning methods. Besides, a clustering algorithm, the Symbol Clustering Map, is introduced to learn the current context