A nonparametric adaptive sampling strategy for online monitoring of big data streams

Xiaochen Xian, Andi Wang, Kaibo Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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).

Original languageEnglish (US)
Title of host publication2017 13th IEEE Conference on Automation Science and Engineering, CASE 2017
PublisherIEEE Computer Society
Pages844-846
Number of pages3
ISBN (Electronic)9781509067800
DOIs
StatePublished - Jul 1 2017
Externally publishedYes
Event13th IEEE Conference on Automation Science and Engineering, CASE 2017 - Xi'an, China
Duration: Aug 20 2017Aug 23 2017

Publication series

NameIEEE International Conference on Automation Science and Engineering
Volume2017-August
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089

Other

Other13th IEEE Conference on Automation Science and Engineering, CASE 2017
Country/TerritoryChina
CityXi'an
Period8/20/178/23/17

Keywords

  • Distribution-free
  • Multivariate CUSUM Procedure
  • Partial Observations
  • Process Change Detection
  • Statistical Process Control

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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