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» Visually mining and monitoring massive time series
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KDD
2000
ACM
101views Data Mining» more  KDD 2000»
13 years 10 months ago
Incremental quantile estimation for massive tracking
Data--call records, internet packet headers, or other transaction records--are coming down a pipe at a ferocious rate, and we need to monitor statistics of the data. There is no r...
Fei Chen, Diane Lambert, José C. Pinheiro
ACCV
2010
Springer
13 years 1 months ago
Automatic Workflow Monitoring in Industrial Environments
Robust automatic workflow monitoring using visual sensors in industrial environments is still an unsolved problem. This is mainly due to the difficulties of recording data in work ...
Galina V. Veres, Helmut Grabner, Lee Middleton, Lu...
DMIN
2009
119views Data Mining» more  DMIN 2009»
13 years 4 months ago
Abnormal Process State Detection by Cluster Center Point Monitoring in BWR Nuclear Power Plant
This paper proposes a new method to detect abnormal process state. The method is based on cluster center point monitoring in time and is demonstrated in its application to data fro...
Jaakko Talonen, Miki Sirola
KDD
2009
ACM
172views Data Mining» more  KDD 2009»
13 years 11 months ago
Learning dynamic temporal graphs for oil-production equipment monitoring system
Learning temporal graph structures from time series data reveals important dependency relationships between current observations and histories. Most previous work focuses on learn...
Yan Liu, Jayant R. Kalagnanam, Oivind Johnsen
DAWAK
2008
Springer
13 years 8 months ago
Mining Serial Episode Rules with Time Lags over Multiple Data Streams
The problem of discovering episode rules from static databases has been studied for years due to its wide applications in prediction. In this paper, we make the first attempt to st...
Tung-Ying Lee, En Tzu Wang, Arbee L. P. Chen