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Process Monitoring Using Maximum Sequence Divergence

Kang, Yihuang and Zadorozhny, Vladimir (2015) Process Monitoring Using Maximum Sequence Divergence. Knowledge and Information Systems Journal. (In Press)

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Process Monitoring involves tracking a system's behaviors, evaluating the current state of the system, and discovering interesting events that require
immediate actions. In this paper, we propose a process monitoring approach that helps detect the changes of dynamic systems, monitor the divergence of the system development, and evaluate the significance of the deviation. We begin with the discussion of the data reduction and symbolic data representation. Timeseries representation methods are also discussed and used as examples in the proposed approach to discretize the raw data into sequences of system states. Markov Chains and stationary state distributions are continuously generated for sequences to represent the snapshots of the system dynamics in different time frames. We use the Generalized Jensen-Shannon Divergence as a measure to monitor the changes of the stationary symbol probability distributions and evaluate the significance of the system deviation. We prove that the proposed approach is able to detect the deviation of the systems we monitor and assess the deviation significance in probabilistic manner


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Item Type: Article
Status: In Press
CreatorsEmailPitt UsernameORCID
Zadorozhny, Vladimirvladimir@sis.pitt.eduVIZ
Date: 14 June 2015
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Knowledge and Information Systems Journal
DOI or Unique Handle: 10.1007/s10115-015-0858-z
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: Yes
Official URL:
Article Type: Research Article
Date Deposited: 04 Aug 2015 13:48
Last Modified: 04 Feb 2021 21:14

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