TY - GEN
T1 - UDS
T2 - 1st International Workshop on Quality of Context, QuaCon 2009
AU - Namatame, Naoya
AU - Nakazawa, Jin
AU - Takashio, Kazunori
AU - Tokuda, Hideyuki
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Context Inference
KW - Data Compensation
KW - Reliable System
UR - http://www.scopus.com/inward/record.url?scp=70549091356&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70549091356&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-04559-2_10
DO - 10.1007/978-3-642-04559-2_10
M3 - Conference contribution
AN - SCOPUS:70549091356
SN - 3642045588
SN - 9783642045585
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 109
EP - 119
BT - Quality of Context - First International Workshop, QuaCon 2009, Revised Papers
Y2 - 25 June 2009 through 26 June 2009
ER -