Developing a context-aware electronic tourist guide: some issues and experiences
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Using Pervasive Computing to Deliver Elder Care
IEEE Pervasive Computing
Context-Aware Computing: A Test Case
UbiComp '02 Proceedings of the 4th international conference on Ubiquitous Computing
Bayesian Artificial Intelligence
Bayesian Artificial Intelligence
Automated Analysis of Nursing Home Observations
IEEE Pervasive Computing
A Smart Sensor to Detect the Falls of the Elderly
IEEE Pervasive Computing
Middleware Support for Quality of Context in Pervasive Context-Aware Systems
PERCOMW '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
Context-Aware Computing Applications
WMCSA '94 Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications
A practical approach to recognizing physical activities
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
The design of a portable kit of wireless sensors for naturalistic data collection
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Modeling human behavior from simple sensors in the home
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
Using a live-in laboratory for ubiquitous computing research
PERVASIVE'06 Proceedings of the 4th international conference on Pervasive Computing
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Context mining algorithms from sensor data have been researched and successful results have been shown. However, since these existing works are focused on improving the accuracy of context mining, they are established on the assumption that they can acquire a complete set of necessary data. Therefore, the context mining algorithms do not work sufficiently since the data drops easily in the reality. In this paper, to cope with this problem, we propose a middleware named UDS (Uninterruptible Data Supply System). The system compensates the missing data, creates virtually complete dataset and provides upper layer applications. Applications operating over UDS can work sufficiently with some data actually missing. We have defined two types of characteristic data deficit patterns and created a robust model for both patterns utilizing Bayesian Network. In the evaluation, we show UDS can sustain the quality of context over 80% with 40% data missing.