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Applied process mining, recommendation, and visual analytics

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TitleInfo
Title
Applied process mining, recommendation, and visual analytics
Name (type = personal)
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Yang
NamePart (type = given)
Sen
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1989-
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Sen Yang
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Marsic
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Ivan
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Ivan Marsic
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Advisory Committee
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chair
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Chen
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Yingying
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Yingying Chen
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Advisory Committee
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internal member
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Sarwate
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Anand D.
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Anand D. Sarwate
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Advisory Committee
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Xiong
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Hui
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Hui Xiong
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Advisory Committee
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Rutgers University
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degree grantor
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School of Graduate Studies
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theses
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2019
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2019-01
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2019
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xx
Language
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eng
Abstract (type = abstract)
Process mining techniques attempt to extract non-trivial knowledge and insights from activity logs and use them for further analyses. The traditional process mining focuses on addressing three different problems: workflow discovery, conformance checking and model enhancement. Although many theoretical studies have been done in the process mining domain, studies that applying process mining on solving real-world problems are limited. This dissertation explores how process mining can be used in real-world process analysis to reveal process insights and help human decision making. Novel algorithms and frameworks were proposed to better model and address the real-world problems. In addition, we introduced the recommender system into the process mining domain to help build a data-driven decision support system. Specifically, this dissertation includes three main contributions: (1) application of process mining techniques in real-world medical process analysis; (2) two different process recommender systems; and (3) a process visual analytic tool.
First, we applied process mining techniques to real-world medical process analysis. To enhance the existing workflow discovery algorithm, we developed a splitting-based workflow discovery method. Our method is able to tackle the duplicate-activity problem by allowing the activity nodes in the model to further split. By comparing our discovered model to hand-made expert workflow model of the same process, we were able to find the discrepancies between work-as-done and work-as-imaged. To further quantify and analyze the discrepancies between work-as-done and work-as-imaged, we invented a framework for automatic process deviation detection. Our framework first compares the observed process traces with knowledge-driven workflow models using a phase-based conformance checking algorithm. The discrepancies (process deviations) were analyzed and false alarms were identified. The false alarms were categorized into three types of causes: (1) model gaps or discrepancies between the model (“work as imagined”) and actual practice (“work as done”), (2) errors in activity trace coding, and (3) algorithm limitations. The deviation detection system was then repaired according to the false alarms. With our framework, the deviation detection accuracy was improved from 66.6% to 98.5%. The output system was then applied on unseen datasets to automatically detect the deviations. We applied our framework to two different medical processes and discovered meaningful medical findings. In addition, to analyze the differences between the medical treatment procedures of different patients, we introduced a framework for analyzing the association between treatment procedures and patient cohorts. The framework works by learning weights of context attributes by best-first search, deciding patient cohorts using clustering algorithms, discovering treatment procedures (or patterns) with process mining techniques, and analyzing the cohort-vs.-procedure through statistical analysis.
Second, existing recommender systems have not been developed based on process mining. Our work presents such a bridge. We designed a data-driven process analysis and recommender system that can provide contemporaneous recommendations of process steps and help with retrospective analyses of the process. We first designed a prototype-based recommender system. This approach relies on mining historic data to uncover the potential association between the way of enacting a process and contextual attributes. If association tests are significant, we train a recommender system to output a prototypical enactment for the given context attributes. The system recommends all steps at once. Although it may not be feasible for the performers to study and follow a long list of steps, this recommendation can be used at runtime to automatically verify the process compliance and detect omitted steps and other process errors. Later, we proposed another recommender system that is able to provide step-by-step recommendations. The system was built on recurrent neural networks. The networks took both environmental and behavioral contextual information as input and output next-step suggestions.
Last, we implemented our methods into a visual analytic tool. The tool was named as VIT-PLA, which is short for Visual Interactive Tool for Process Log Analysis. In this tool, we proposed a prototype-based process data visualization strategy. The strategy works by first clustering process data into clusters and then discovering the prototypical procedure from each cluster. Only such cluster prototypes were visualized and presented to the users. Our strategy can greatly reduce the data amount to visualize but preserve the characteristics of each cluster. Statistical analyses were followed and visualized to help analysts better understand their process data.
Subject (authority = RUETD)
Topic
Electrical and Computer Engineering
Subject (authority = ETD-LCSH)
Topic
Data mining
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Rutgers University Electronic Theses and Dissertations
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ETD_9421
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electronic resource
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Extent
1 online resource (154 pages) : illustrations
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Note (type = statement of responsibility)
by Sen Yang
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School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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Identifier (type = doi)
doi:10.7282/t3-arh6-bh20
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The author owns the copyright to this work.
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Name
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Yang
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Sen
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Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2018-12-11 23:00:54
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Sen Yang
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Rutgers University. School of Graduate Studies
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Author Agreement License
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I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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2019-01-31
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2019-08-02
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after August 2nd, 2019.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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