Article

Fuzzy mining: adaptive process simplification based on multi-perspective metrics

Authors:

Christian W. Günther,

Wil M. P. Van Der AalstAuthors Info & Claims

BPM'07: Proceedings of the 5th international conference on Business process management

Pages 328 - 343

Published: 24 September 2007 Publication History

Abstract

Process Mining is a technique for extracting process models from executionlogs. This is particularly useful in situations where people have an idealizedview of reality. Real-life processes turn out to be less structured than peopletend to believe. Unfortunately, traditional process mining approaches haveproblems dealing with unstructured processes. The discovered models are often"spaghetti-like", showing all details without distinguishing what is important andwhat is not. This paper proposes a new process mining approach to overcome thisproblem. The approach is configurable and allows for different faithfully simplifiedviews of a particular process. To do this, the concept of a roadmap is used asa metaphor. Just like different roadmaps provide suitable abstractions of reality,process models should provide meaningful abstractions of operational processesencountered in domains ranging from healthcare and logistics to web servicesand public administration.

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Information

Published In

BPM'07: Proceedings of the 5th international conference on Business process management

September 2007

417 pages

ISBN:3540751823

Editors:
Gustavo Alonso
ETH Zürich, Department of Computer Science, Zürich, Switzerland
,
Peter Dadam
Universität Ulm, Institut für Datenbanken und Informations systeme, Ulm, Germany
,
Michael Rosemann
Queensland University of Technology, BPM Group, Faculty of Information Technology, Brisbane, QLD, Australia

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 24 September 2007

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Lugaresi GMatta A(2020)Generation and tuning of discrete event simulation models for manufacturing applicationsProceedings of the Winter Simulation Conference10.5555/3466184.3466495(2707-2718)Online publication date: 14-Dec-2020
https://dl.acm.org/doi/10.5555/3466184.3466495
Winter JAniche MCito JDeursen ADumas MPfahl DApel SRusso A(2019)Monitoring-aware IDEsProceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering10.1145/3338906.3338926(420-431)Online publication date: 12-Aug-2019
https://dl.acm.org/doi/10.1145/3338906.3338926
Terragni AHassani MHung CPapadopoulos G(2019)Optimizing customer journey using process mining and sequence-aware recommendationProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297288(57-65)Online publication date: 8-Apr-2019
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Schipper DAniche Mvan Deursen AStorey MAdams BHaiduc S(2019)Tracing back log data to its log statementProceedings of the 16th International Conference on Mining Software Repositories10.1109/MSR.2019.00081(545-549)Online publication date: 26-May-2019
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Porouhan P(2018)Process Mining and Interaction Data Analytics in a Web-Based Multi-Tabletop Collaborative Learning and Teaching EnvironmentInternational Journal of Web-Based Learning and Teaching Technologies10.4018/IJWLTT.201810010313:4(34-61)Online publication date: 1-Oct-2018
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Delias PLakiotaki K(2018)Discovering Process Horizontal Boundaries to Facilitate Process ComprehensionInternational Journal of Operations Research and Information Systems10.4018/IJORIS.20180401019:2(1-31)Online publication date: 1-Apr-2018
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Messaoudi SPanichella ABianculli DBriand LSasnauskas RKhomh FRoy CSiegmund J(2018)A search-based approach for accurate identification of log message formatsProceedings of the 26th Conference on Program Comprehension10.1145/3196321.3196340(167-177)Online publication date: 28-May-2018
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Process Mining: Overview and Opportunities

Mining fuzzy specific rare itemsets for education data

Semantic fuzzy mining: Enhancement of process models and event logs analysis from syntactic to conceptual level

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