TY - GEN
T1 - A nonparametric adaptive sampling strategy for online monitoring of big data streams
AU - Xian, Xiaochen
AU - Wang, Andi
AU - Liu, Kaibo
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - With the rapid development of sensor techniques, we often face the challenges of monitoring big data streams in modern quality control, which consist of massive series of real-time, continuously and sequentially ordered observations. For example, in manufacturing industries, hundreds or thousands of variables are observed during online production for quality insurance. Also, smart grid infrastructure needs to simultaneously monitor massive access points for intrusion and threat detection. As another example, an image sensing device continuously collects high-resolution images at high frequency for video surveillance and object movement tracking. Ideally, in those applications, it is preferable to detect assignable causes as early as possible, while maintaining a prespecified in-control Average Run Length (ARL).
AB - With the rapid development of sensor techniques, we often face the challenges of monitoring big data streams in modern quality control, which consist of massive series of real-time, continuously and sequentially ordered observations. For example, in manufacturing industries, hundreds or thousands of variables are observed during online production for quality insurance. Also, smart grid infrastructure needs to simultaneously monitor massive access points for intrusion and threat detection. As another example, an image sensing device continuously collects high-resolution images at high frequency for video surveillance and object movement tracking. Ideally, in those applications, it is preferable to detect assignable causes as early as possible, while maintaining a prespecified in-control Average Run Length (ARL).
KW - Distribution-free
KW - Multivariate CUSUM Procedure
KW - Partial Observations
KW - Process Change Detection
KW - Statistical Process Control
UR - http://www.scopus.com/inward/record.url?scp=85044974165&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85044974165&partnerID=8YFLogxK
U2 - 10.1109/COASE.2017.8256208
DO - 10.1109/COASE.2017.8256208
M3 - Conference contribution
AN - SCOPUS:85044974165
T3 - IEEE International Conference on Automation Science and Engineering
SP - 844
EP - 846
BT - 2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PB - IEEE Computer Society
T2 - 13th IEEE Conference on Automation Science and Engineering, CASE 2017
Y2 - 20 August 2017 through 23 August 2017
ER -