Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Dynamic network models for forecasting
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Fundamental challenges in mobile computing
PODC '96 Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing
The context toolkit: aiding the development of context-enabled applications
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Bayesian Networks and Decision Graphs
Bayesian Networks and Decision Graphs
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Survey of Context-Aware Mobile Computing Research
A Survey of Context-Aware Mobile Computing Research
Agent Intelligence Through Data Mining (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Leveraging data about users in general in the learning of individual user models
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Hybrid predictors for next location prediction
UIC'06 Proceedings of the Third international conference on Ubiquitous Intelligence and Computing
| Hi-index | 0.00 |
Context-aware applications and middleware platforms are evolving into major driving factors for pervasive systems. The ability to also make accurate assumptions about future contexts further enables such systems to proactively adapt to upcoming situations. However, the provision of a reusable system component to facilitate the development of such future-context-aware applications is still challenging - as it requires to be generic but, at the same time, as efficient and accurate as possible. To address these requirements, this paper presents the approach of Structured Context Prediction which constitutes a framework to facilitate the application of existing prediction methods. It allows application developers to integrate domain-specific knowledge by creating a customized prediction model at design time and to select, implement and combine prediction methods for the intended purpose. Feasibility is evaluated by applying a prototype system component to two mobile application scenarios, showing that both high accuracy and efficiency are possible.