TY - GEN
T1 - Online latent variable detection in sensor networks
AU - Sun, Jimeng
AU - Papadimitriou, Spiros
AU - Faloutsos, Christos
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2005/12/12
Y1 - 2005/12/12
N2 - Sensor networks attract increasing interest, for a broad range of applications. Given a sensor network, one key issue becomes how to utilize it efficiently and effectively. In particular, how can we detect the underlying correlations (latent variables) among many co-evolving sensor measurements? Can we do it incrementally? We present a system that can (1) collect the measurements from the real wireless sensors; (2) process them in real-time; and (3) determine the correlations (latent variables) among the sensor streams on the fly.
AB - Sensor networks attract increasing interest, for a broad range of applications. Given a sensor network, one key issue becomes how to utilize it efficiently and effectively. In particular, how can we detect the underlying correlations (latent variables) among many co-evolving sensor measurements? Can we do it incrementally? We present a system that can (1) collect the measurements from the real wireless sensors; (2) process them in real-time; and (3) determine the correlations (latent variables) among the sensor streams on the fly.
UR - http://www.scopus.com/inward/record.url?scp=28444476165&partnerID=8YFLogxK
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U2 - 10.1109/ICDE.2005.100
DO - 10.1109/ICDE.2005.100
M3 - Conference contribution
AN - SCOPUS:28444476165
SN - 0769522858
T3 - Proceedings - International Conference on Data Engineering
SP - 1126
EP - 1127
BT - Proceedings - 21st International Conference on Data Engineering, ICDE 2005
T2 - 21st International Conference on Data Engineering, ICDE 2005
Y2 - 5 April 2005 through 8 April 2005
ER -