Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Discovery of activity patterns using topic models
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Modelling situation awareness for Context-aware Decision Support
Expert Systems with Applications: An International Journal
Context-aware systems: A literature review and classification
Expert Systems with Applications: An International Journal
Predicting the location of mobile users: a machine learning approach
Proceedings of the 2009 international conference on Pervasive services
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
Detecting individual activities from video in a smart home
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Constructor: a system for the induction of probabilistic models
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Discovering Activities to Recognize and Track in a Smart Environment
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques
Data Mining: Practical Machine Learning Tools and Techniques
A Bayesian approach to learning causal networks
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Approximating discrete probability distributions with dependence trees
IEEE Transactions on Information Theory
| Hi-index | 12.05 |
Ubiquitous decision support systems require more intelligent mechanism in which more timely and accurate decision support is available. However, conventional context-aware systems, which have been popular in the ubiquitous decision support systems field, cannot provide such agile and proactive decision support. To fill this research void, this paper proposes a new concept of context prediction mechanism by which the ubiquitous decision support devices are able to predict users' future contexts in advance, and provide more timely and proactive decision support that users would be satisfied much more. Especially, location prediction is useful because ubiquitous decision support systems could dynamically adapt their decision support contents for a user based on a user's future location. In this sense, as an alternative for the inference engine mechanism to be used in the ubiquitous decision support systems capable of context-prediction, we propose an inductive approach to recognizing a user's location by learning a dynamic Bayesian network model. The dynamic Bayesian network model has been evaluated with a set of contextual data from undergraduate students. The evaluation result suggests that a dynamic Bayesian network model offers significant predictive power in the location prediction. Besides, we found that the dynamic Bayesian network model has a great potential for the future types of ubiquitous decision support systems.