Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
People, places, things: web presence for the real world
Mobile Networks and Applications
Modeling Context Information in Pervasive Computing Systems
Pervasive '02 Proceedings of the First International Conference on Pervasive Computing
Ubiquitous computing: the impact on future interaction paradigms and HCI research
CHI EA '97 CHI '97 Extended Abstracts on Human Factors in Computing Systems
The DYNAMOS approach to support context-aware service provisioning in mobile environments
Journal of Systems and Software
The Bayeslet Concept for Modular Context Inference
UBICOMM '08 Proceedings of the 2008 The Second International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies
CroCo: Ontology-Based, Cross-Application Context Management
SMAP '08 Proceedings of the 2008 Third International Workshop on Semantic Media Adaptation and Personalization
Contory: a middleware for the provisioning of context information on smart phones
Proceedings of the ACM/IFIP/USENIX 2006 International Conference on Middleware
Federation and sharing in the context marketplace
LoCA'07 Proceedings of the 3rd international conference on Location-and context-awareness
The Bayesian structural EM algorithm
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Issues and requirements for bayesian approaches in context aware systems
LoCA'05 Proceedings of the First international conference on Location- and Context-Awareness
Context management for the provision of adaptive services to roaming users
IEEE Wireless Communications
| Hi-index | 0.00 |
This work gives an overview over the challenges for context management systems in Ubiquitous Computing frameworks or Personal Smart Spaces. Focused on the integration of context inference in today's context management systems (CMSs) we address important design decisions for future frameworks. The inference system we have in mind is probabilistic and relies on the concept of Bayeslets, special inference rules extending Bayesian networks. We show that for inference rule creation, storage, inference scheduling and update frequency the best solutions are hybrid, allowing for high flexibility and performance while reducing resource costs. We also see that human expert knowledge cannot be substituted completely in an efficient context-aware system.