Link to the University of Pittsburgh Homepage
Link to the University Library System Homepage Link to the Contact Us Form

Process monitoring using maximum sequence divergence

Kang, Y and Zadorozhny, V (2016) Process monitoring using maximum sequence divergence. Knowledge and Information Systems, 48 (1). 81 - 109. ISSN 0219-1377

This is the latest version of this item.

[img] Plain Text (licence)
Available under License : See the attached license file.

Download (1kB)


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 consider monitoring temporal system state sequences to help detect the changes of dynamic systems, check the divergence of the system development, and evaluate the significance of the deviation. We begin with discussions of data reduction, symbolic data representation, and anomaly detection in temporal discrete sequences. Time-series representation methods are also discussed and used in this paper to discretize raw data into sequences of system states. Markov chains and stationary-state distributions are continuously generated from temporal sequences to represent snapshots of the system dynamics in different time frames. We use generalized Jensen–Shannon divergence as the measure to monitor changes of the stationary symbol probability distributions and evaluate the significance of system deviations. We prove that the proposed approach is able to detect deviations of the systems we monitor and assess the deviation significance in probabilistic manner.


Social Networking:
Share |


Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Kang, Y
Zadorozhny, V
Date: 1 July 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Knowledge and Information Systems
Volume: 48
Number: 1
Page Range: 81 - 109
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
ISSN: 0219-1377
Date Deposited: 15 Jul 2016 17:28
Last Modified: 27 Mar 2021 10:55

Available Versions of this Item


Monthly Views for the past 3 years

Plum Analytics

Actions (login required)

View Item View Item