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Process Discovery using Classification Tree Hidden Semi-Markov Model

Kang, Yihuang and Zadorozhny, Vladimir (2016) Process Discovery using Classification Tree Hidden Semi-Markov Model. In: The Fifth IEEE International Workshop on Data Integration and Mining (DIM-2016). In conjunction with IRI'16, 28 July 2016 - 30 July 2016, Pittsburgh, United States of America.

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Abstract

Various and ubiquitous information systems are being used in monitoring, exchanging, and collecting information. These systems are generating massive amount of event sequence logs that may helps us understand underlying phenomenon. By analyzing these logs, we can learn process models that describe system procedures, predict the development of the system, or check whether the changes are expected. In this paper, we consider a novel technique that models these sequences of events in temporal-probabilistic manners. Specifically, we propose a probabilistic process model that combines hidden semi-Markov model and classification trees learning. Our experimental result shows that the proposed approach can answer a kind of question–“what are the most frequent sequence of system dynamics relevant to a given sequence of observable events?”. For example, “Given a series of medical treatments, what are the most relevant patients’ health condition pattern changes in different times?”


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Details

Item Type: Conference or Workshop Item (Paper)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Kang, Yihuang
Zadorozhny, Vladimir
Contributors:
ContributionContributors NameEmailPitt UsernameORCID
ContributorZadorozhny, Vladimirviz@pitt.eduVIZUNSPECIFIED
Date: 2016
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Event Title: The Fifth IEEE International Workshop on Data Integration and Mining (DIM-2016). In conjunction with IRI'16
Event Dates: 28 July 2016 - 30 July 2016
Event Type: Conference
Institution: University of Pittsburgh
Schools and Programs: School of Information Sciences > Information Science
Refereed: No
Related URLs:
Date Deposited: 15 Jul 2016 16:39
Last Modified: 25 Aug 2017 04:57
URI: http://d-scholarship.pitt.edu/id/eprint/28643

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