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Probabilistic Process Monitoring in Process-Aware Information Systems

Kang, Yihuang (2014) Probabilistic Process Monitoring in Process-Aware Information Systems. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Complex information systems generate large amount of event logs that represent the states of system dynamics. By monitoring these logs, we can learn the process models that describe the underlying business procedures, predict the future development of the systems, and check whether the process models match the expected ones. Most of the existing process monitoring techniques are derived from the workflow management systems used to cope with the logs generated by systems with deterministic outcomes. In this dissertation, however, I consider novel techniques that handle event log data, monitor system deviations, and infer the development of systems based on probabilistic process models. In particular, I present a novel process monitoring approach based on maximizing the information divergences of the system state dynamics and demonstrate its efficiency in detecting abrupt changes, as well as long-term system deviation. In addition, a new process modeling technique, Classification Tree hidden (semi-) Markov Model (CTHMM), is proposed. I show that CTHMM derived from Classification and Regression Tree and hidden semi-Markov model (HSMM) with hidden system states identified by Classification Tree can help discover and predict relevant system state sequences in temporal-probabilistic manners. The main contributions of this dissertation can be summarized as follows: 1) a new approach used in process monitoring that helps detect anomalies of dynamic systems from the point of views of both system change-point and long-term system deviation; 2) a unique HMM/HSMM learning technique that solves the problem of hidden state splitting and estimates HMM/HSMM parameters simultaneously; 3) a novel temporal-probabilistic process model that generates human-comprehensible IF-THEN system state definitions used to help infer evolutions of discrete dynamic systems.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairZadorozhny , Vladimirvladimir@sis.pitt.eduVIZ
Committee MemberHirtle, Stephen C.hirtle@pitt.eduHIRTLE
Committee MemberDruzdzel , Marek marek@sis.pitt.eduDRUZDZEL
Committee MemberPerera , Subashan ksp9@pitt.eduKSP9
Date: 29 August 2014
Date Type: Publication
Defense Date: 9 June 2014
Approval Date: 29 August 2014
Submission Date: 15 July 2014
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 127
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Temporal Data Mining, Process Monitoring, Process Mining, Probabilistic Model, Markov Models, Hidden Semi-Markov Model, Classification and Regression Tree, Sequence Data Mining
Date Deposited: 29 Aug 2014 19:44
Last Modified: 15 Nov 2016 14:22


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