Title :
Online parallel monitoring via hard-thresholding post-change estimation
Author :
Yuan Wang ; Yajun Mei
Author_Institution :
H. Milton Stewart Sch. of Ind. & Syst. Eng., Georgia Inst. of Technol., Atlanta, GA, USA
fDate :
June 29 2014-July 4 2014
Abstract :
The online parallel monitoring problem is studied when one is monitoring large-scale data streams, and an event occurs at an unknown time and affects an unknown subset of data streams. Efficient online parallel monitoring schemes are developed by combining the standard sequential change-point method with hard-thresholding post-change estimation. Theoretical analysis and simulation study demonstrate the usefulness of hard-thresholding for online parallel monitoring.
Keywords :
Monte Carlo methods; computerised monitoring; data communication; maximum likelihood estimation; data streams; hard-thresholding post-change estimation; online parallel monitoring problem; online parallel monitoring schemes; sequential change-point method; Estimation; Integrated optics; Optical sensors; Optimized production technology; Testing;
Conference_Titel :
Information Theory (ISIT), 2014 IEEE International Symposium on
Conference_Location :
Honolulu, HI
DOI :
10.1109/ISIT.2014.6875423
