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» Unsupervised Outlier Detection in Time Series Data
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DMIN
2009
119views Data Mining» more  DMIN 2009»
13 years 6 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
ACCV
2010
Springer
13 years 3 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...
IQ
2007
13 years 10 months ago
Rule-Based Measurement Of Data Quality In Nominal Data
: Sufficiently high data quality is crucial for almost every application. Nonetheless, data quality issues are nearly omnipresent. The reasons for poor quality cannot simply be bla...
Jochen Hipp, Markus Müller, Johannes Hohendor...
PKDD
2005
Springer
159views Data Mining» more  PKDD 2005»
14 years 1 months ago
Fast Burst Correlation of Financial Data
We examine the problem of monitoring and identification of correlated burst patterns in multi-stream time series databases. Our methodology is comprised of two steps: a burst dete...
Michail Vlachos, Kun-Lung Wu, Shyh-Kwei Chen, Phil...
SIGPRO
2008
136views more  SIGPRO 2008»
13 years 8 months ago
Estimation of slowly varying parameters in nonlinear systems via symbolic dynamic filtering
This paper introduces a novel method for real-time estimation of slowly varying parameters in nonlinear dynamical systems. The core concept is built upon the principles of symboli...
Venkatesh Rajagopalan, Subhadeep Chakraborty, Asok...