Business Process Management

Wir entwickeln Theorien, Modelle, Methoden und Softwarewerkzeuge für das Geschäftsprozessmanagement (Business Process Management, BPM) der Zukunft. Unser Fokus liegt auf analytischen Anwendungen des BPM.

Beispielthemen:

Adaptive Case Management
Business Process Automation
Process Analytics
Process Mining
Predictive Business Process Monitoring

Projekte:

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RISE_BPM networks world-leading research institutions and corporate innovators to develop new horizons for Business Process Management (BPM). BPM is a boundary-spanning discipline focused on division and re-integration of day-to-day work in organisations and on analysis of process data for organisational decision-making. Recent break-through innovations in Social Computing, Smart Devices, Real-Time Computing, and Big Data Technology create a strong impetus for propelling BPM into a pervasive corporate topic that enables design of entirely new products and services.

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Dieses Vorhaben dient der Vorbereitung eines Antrags zur Einzelprojektförderung. Es basiert auf wissenschaftlichen Vorarbeiten des Antragstellers und einer existierenden wissenschaftlichen Kooperation mit Forschern im brasilianischen Bundesstaat Santa Catarina. Das Projekt soll federführend durch das Institut für Wirtschaftsinformatik, European Research Center for Information Systems (ERCIS), an der Westfälischen Wilhelms-Universität Münster und die Universidade para…

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Das vorausschauende Monitoring von Geschäftsprozessen ist eine der maßgeblichen Herausforderungen im Process Mining. Ein Anwendungsfeld stellt die Vorhersage von zukünftigen Prozessschritten dar, die es erlaubt mögliche Probleme frühzeitig zu erkennen. In der Regel enthält das Vorhersagemodell Informationen über die Prozessschritte. Diese sind jedoch in vielen Fällen ungenügend für eine verlässliche Vorhersage. Kontextinformationen über die Prozessinstanz haben sich in vielen Anwendungsg…

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Während der Prozessausführung werden durch nahezu alle Anwendungssysteme für stattfindende Ereignisse Daten – so genannte Ereignislogdaten – erfasst. Ein Teilbereich des Process Mining, Predictive Business Process Monitoring, beschäftigt sich mit der Vorhersage zur Evolution von Prozessinstanzen basierend auf diesen historischen Eventlogdaten. Aufgrund der begrenzten prädiktiven Güte derartiger Verfahren, oftmals bedingt dadurch, dass Geschäftsprozess immer komplexer werden, findet bislang kaum ein E…

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Das Projekt "Analyse von Positionsdaten zur Ermittlung von Durchlaufzeiten in der Fertigung(APED-F)" untersucht die Realisierbarkeit der Ermittlung von Durchlaufzeiten inklusive ihrer Bestandtei-le durch die Analyse von Positionsdaten aus einem Real-Time Indoor-Localization-System

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Publikationen:

Oberdorf F., Schaschek M., Weinzierl S., Stein N., Matzner M., Flath C.:
Predictive end-to-end enterprise process network monitoring
In: Business & Information Systems Engineering (2023)
ISSN: 1867-0202
DOI: 10.1007/s12599-022-00778-4
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Weinzierl S., Wolf V., Pauli T., Beverungen D., Matzner M.:
Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data
In: Journal of Business Analytics 5 (2022), S. 76-100
ISSN: 2573-234X
DOI: 10.1080/2573234X.2021.1978337
URL: https://www.tandfonline.com/doi/full/10.1080/2573234X.2021.1978337
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Brunk J., Stierle M., Papke L., Revoredo K., Matzner M., Becker J.:
Cause vs. effect in context-sensitive prediction of business process instances
In: Information Systems 95 (2021), S. 101635
ISSN: 0306-4379
DOI: 10.1016/j.is.2020.101635
URL: https://www.sciencedirect.com/science/article/abs/pii/S0306437920301046
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Stierle M., Weinzierl S., Harl M., Matzner M.:
A technique for determining relevance scores of process activities using graph-based neural networks
In: Decision Support Systems 144 (2021), Art.Nr.: 113511
ISSN: 0167-9236
DOI: 10.1016/j.dss.2021.113511
URL: http://www.sciencedirect.com/science/article/pii/S016792362100021X
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Beverungen D., Buijs JCAM., Becker J., Di Ciccio C., van der Aalst WMP., Bartelheimer C., vom Brocke J., Comuzzi M., Kraume K., Leopold H., Matzner M., Mendling J., Ogonek N., Post T., Resinas M., Revoredo K., del-Río-Ortega A., La Rosa M., Santoro F., Solti A., Song M., Stein A., Stierle M., Wolf V.:
Seven Paradoxes of Business Process Management in a Hyper-Connected World
In: Business & Information Systems Engineering 63 (2021), S. 145–156
ISSN: 1867-0202
DOI: 10.1007/s12599-020-00646-z
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Brunk J., Stottmeister J., Weinzierl S., Matzner M., Becker J.:
Exploring the effect of context information on deep learning business process predictions
In: Journal of Decision Systems (2020), S. 1-16
ISSN: 1246-0125
DOI: 10.1080/12460125.2020.1790183
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Harl M., Weinzierl S., Stierle M., Matzner M.:
Explainable predictive business process monitoring using gated graph neural networks
In: Journal of Decision Systems (2020), S. 1-16
ISSN: 1246-0125
DOI: 10.1080/12460125.2020.1780780
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Baier S., Dunzer S., Fettke P., Houy C., Matzner M., Pfeiffer P., Rehse JR., Scheid M., Stephan S., Stierle M., Willems B.:
The MobIS-Challenge 2019 - A Report on the WI-2019-Workshop on Model-Based Compliance in Information Systems
In: Enterprise Modelling and Information Systems Architectures - International Journal of Conceptual Modeling 15 (2020)
ISSN: 1866-3621
DOI: 10.18417/EMISA.15.5
URL: https://emisa-journal.org/emisa/article/view/247
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Janiesch C., Matzner M.:
BAMN: a modeling method for business activity monitoring systems
In: Journal of Decision Systems 28 (2019), S. 185-223
ISSN: 1246-0125
DOI: 10.1080/12460125.2019.1631682
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Breuker D., Matzner M., Delfmann P., Becker J.:
Comprehensible Predictive Models for Business Processes
In: Mis Quarterly 40 (2016), S. 1009-1034
ISSN: 0276-7783
URL: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=3315&context=misq
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Janiesch C., Matzner M., Müller O.:
Beyond Process Monitoring: A Proof-of-Concept of Event-driven Business Activity Management
In: Business Process Management Journal 18 (2012), S. 625-643
ISSN: 1463-7154
DOI: 10.1108/14637151211253765
URL: http://publications.martin-matzner.de/4/1/2619.pdf
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Cereja J., Santoro F., Gorbacheva E., Matzner M.:
Application of the Design Thinking Approach to Process Redesign at an Insurance Company in Brazil
In: Brocke J., Mendling J. (Hrsg.): Business Process Management Cases, Springer, Cham, 2018, S. pp 205-233 (Management for Professionals Book Series)
ISBN: 978-3-319-58306-8
DOI: 10.1007/978-3-319-58307-5_12
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Breuker D., Delfmann P., Matzner M., Becker J.:
Designing and Evaluating an Interpretable Predictive Modeling Technique for Business Processes
In: Fournier Fabiana MJ, Mendling Jan (Hrsg.): BPM 2014 International Workshops, Berlin: Springer, 2015, S. 541-553 (Lecture Notes in Business Information Processing, Bd.202)
ISBN: 978-3-319-15894-5
DOI: 10.1007/978-3-319-15895-2_46
URL: http://fluxicon.com/blog/wp-content/uploads/2015/01/DeMiMoP-2014-Predictive-BPM.pdf
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Matzner M., Becker J. (Hrsg.):
Promoting Business Process Management Excellence in Russia
Münster: 2013
(ERCIS Working Papers, Bd. 15)
URL: https://www.ercis.org/sites/www.ercis.org/files/pages/research/ercis-working-papers/ercis_wp_15.pdf
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Ließmann A., Wang W., Weinzierl S., Zilker S., Matzner M.:
Transfer learning for predictive process monitoring
European Conference on Information Systems (Paphos, Cyprus)
In: Proceedings of the 32nd European Conference on Information Systems 2024
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Ließmann A., Zilker S., Weinzierl S., Sukhareva M., Matzner M.:
Predicting customer satisfaction in service processes using multilingual large language models
Hawaii International Conference on System Sciences (Waikiki, Honolulu, Hawaii)
In: Proceedings of the 57th Hawaii International Conference on System Sciences 2024
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Drodt C., Weinzierl S., Matzner M., Delfmann P.:
Predictive Recommining: Learning relations between event log characteristics and machine learning approaches for supporting predictive process monitoring
International Conference on Advanced Information Systems Engineering (Zaragoza, 12. Juni 2023 - 16. Juni 2023)
In: Proceedings of the 35th International Conference on Advanced Information Systems Engineering Forum 2023
DOI: 10.1007/978-3-031-34674-3_9
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Dunzer S., Tang W., Höchstädter N., Zilker S., Matzner M.:
Design Principles for Using Business Process Management Systems
Business Process Management 2023 Workshops (Utrecht, The Netherlands, 11. September 2023 - 15. September 2023)
In: Jochen De Weerdt and Luise Pufahl (Hrsg.): Business Process Management Workshops, Cham, Switzerland: 2023
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Weinzierl S., Bartelheimer C., Zilker S., Beverungen D., Matzner M.:
A method for predicting workarounds in business processes
Pacific Asia Conference on Information Systems (Taipei-Sydney, 5. Juli 2022 - 9. Juli 2022)
In: Proceedings of the 25th Pacific Asia Conference on Information Systems 2022
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Zilker S., Marx E., Stierle M., Matzner M.:
Process Mining for Advanced Service Analytics – From Process Efficiency to Customer Encounter and Experience
Hawaii International Conference on System Sciences (Maui, Hawaii, 4. Januar 2022 - 7. Januar 2022)
In: Proceedings of the 55th Hawaii International Conference on System Sciences 2022
DOI: 10.24251/HICSS.2022.239
URL: http://hdl.handle.net/10125/79571
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Joas A., Matzner M.:
Unleashing Digital Process Innovation with Process Mining: Designing a Training Concept with Action Design Research
European Conference on Information Systems (Timișoara, Romania)
In: Proceedings of the 30th European Conference on Information Systems (ECIS) 2022
Open Access: https://aisel.aisnet.org/ecis2022_rp/146/
URL: https://aisel.aisnet.org/ecis2022_rp/146/
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Dunzer S., Zilker S., Marx E., Grundler V., Matzner M.:
The Status Quo of Process Mining in the Industrial Sector
International Conference on Wirtschaftsinformatik (WI) (Duisburg-Essen // Virtual, 9. März 2021 - 11. März 2021)
In: Proceedings of the 16th International Conference on Wirtschaftsinformatik (WI) 2021
DOI: 10.1007/978-3-030-86800-0_43
URL: https://aisel.aisnet.org/wi2021/EManagementofdigitalprocesses20/Track20/2/
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Weinzierl S., Dunzer S., Tenschert J., Zilker S., Matzner M.:
Predictive business process deviation monitoring
European Conference on Information Systems (Marrakesch)
In: Proceedings of the 29th European Conference on Information Systems 2021
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Drodt C., Weinzierl S., Matzner M., Delfmann P.:
The recomminder: A decision support tool for predictive business process monitoring
International Conference on Business Process Management (Rom)
In: Proceedings of the BPM 2021 Demonstration & Resources Track, Best BPM Dissertation Award, and Doctoral Consortium 2021
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Stierle M., Brunk J., Weinzierl S., Zilker S., Matzner M., Becker J.:
Bringing light into the darkness - A systematic literature review on explainable predictive business process monitoring techniques
European Conference on Information Systems (Marrakesch)
In: Proceedings of the 29th European Conference on Information Systems 2021
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Tenschert J., Dunzer S., Matzner M.:
Notation-agnostic Subprocess Modeling for Adaptive Case Management
International Conference on Wirtschaftsinformatik, WI 2021 (Duisburg, DEU, 9. März 2021 - 11. März 2021)
In: Frederik Ahlemann, Reinhard Schütte, Stefan Stieglitz (Hrsg.): Lecture Notes in Information Systems and Organisation 2021
DOI: 10.1007/978-3-030-86800-0_25
URL: https://aisel.aisnet.org/wi2021/DEnterpriseModelling21/Track21/12/
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Weinzierl S.:
Exploring gated graph sequence neural networks for predicting next process activities
International Conference on Business Process Management (Rom)
In: Proceedings of the BPM 2021 International Workshops. 2021
DOI: 10.1007/978-3-030-94343-1_3
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Tang W., Matzner M.:
Creating Humanistic Value with Process Mining for Improving Work Conditions - A Sociotechnical Perspective
Forty-First International Conference on Information Systems (India, 13. Dezember 2020 - 16. Dezember 2020)
In: Association for Information Systems (Hrsg.): ICIS 2020 Proceedings 2020
URL: https://aisel.aisnet.org/icis2020/is_workplace_fow/is_workplace_fow/16/
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Nguyen A., Chatterjee S., Weinzierl S., Schwinn L., Matzner M., Eskofier B.:
Time matters: Time-aware LSTMs for predictive business process monitoring
International Conference on Process Mining (Padua, 4. Oktober 2020 - 9. Oktober 2020)
In: Proceedings of the ICPM 2020 International Workshops 2020
DOI: 10.1007/978-3-030-72693-5_9
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Tenschert J., Marmaridis S.:
Pertuniti: Subprocess Modeling and Hierarchic Case Management
BPM 2020 Demo (Sevilla, Spain, 13. September 2020 - 18. September 2020)
URL: http://ceur-ws.org/Vol-2673/paperDR13.pdf
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Viner D., Stierle M., Matzner M.:
A Process Mining Software Comparison
2nd International Conference on Process Mining (ICPM) (Padua, 4. Oktober 2020 - 9. Oktober 2020)
In: Proceedings of the ICPM Doctoral Consortium and Tool Demonstration Track 2020 co-located with the 2nd International Conference on Process Mining (ICPM} 2020) 2020
URL: http://ceur-ws.org/Vol-2703/paperTD1.pdf
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Weinzierl S., Dunzer S., Zilker S., Matzner M.:
Prescriptive business process monitoring for recommending next best actions
International Conference on Business Process Management (Sevilla, 13. September 2020 - 18. September 2020)
In: Proceedings of the 18th International Conference on Business Process Management Forum 2020
DOI: 10.1007/978-3-030-58638-6_12
URL: https://link.springer.com/content/pdf/10.1007/978-3-030-58638-6_12.pdf
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Tenschert J., Hormesch M.:
Flexibility, Adherence, and Guidance for Regulated Processes with Case Management
BPM 2020 Industry Forum (Sevilla, Spain, 13. September 2020 - 18. September 2020)
In: BPM 2020 Industry Forum Proceedings 2020
URL: https://congreso.us.es/bpm2020/images/industryforum.pdf
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Hindel J., Cabrera Pérez L., Stierle M.:
Robotic Process Automation: Hype or Hope?
In: Proceedings of the 15th International Conference on Wirtschaftsinformatik 2020
DOI: 10.30844/wi_2020_r6-hindel
URL: https://library.gito.de/oa_wi2020-r6.html
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Weinzierl S., Stierle M., Zilker S., Matzner M.:
A next click recommender system for web-based service analytics with context-aware LSTMs
Hawaii International Conference on System Sciences (Grand Wailea, Maui, Hawaii, 7. Januar 2020 - 10. Januar 2020)
In: Proceedings of the 53rd Hawaii International Conference on System Sciences 2020
DOI: 10.24251/HICSS.2020.190
URL: http://hdl.handle.net/10125/63929
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Tenschert J., Tang W., Matzner M.:
Enterprise System Capabilities for Organizational Change in the BPM Life Cycle
Business Process Management 2020 Workshops (Sevilla, Spain, 13. September 2020 - 18. September 2023)
In: Adela del-Río-Ortega, Henrik Leopold and Flavia M. Santoro (Hrsg.): Business Process Management Workshops, Cham, Switzerland: 2020
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Weinzierl S., Zilker S., Stierle M., Park G., Matzner M.:
From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events
Internationale Tagung Wirtschaftsinformatik (Potsdam, 8. März 2020 - 11. März 2020)
In: Proceedings of the 15th International Conference on Wirtschaftsinformatik 2020
DOI: 10.30844/wi_2020_c12-weinzierl
URL: https://library.gito.de/open-access-pdf/C12_Prescriptive_process_monitoring_-_a_technique_for_determining_next_best_actions_resub.pdf
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Stierle M., Zilker S., Dunzer S., Tenschert J., Karagegova G.:
Design Principles for Comprehensible Process Discovery in Process Mining
In: Rowe F, Amrani RE, Limayem M, Newell S, Pouloudi N, Heck E, Quammah AE (Hrsg.): 28th European Conference on Information Systems - Liberty, Equality, and Fraternity in a Digitizing World, ECIS 2020, Marrakech, Morocco, June 15-17, 2020 2020
URL: https://aisel.aisnet.org/ecis2020_rp/42/
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Weinzierl S., Wolf V., Pauli T., Beverungen D., Matzner M.:
Detecting workarounds in business processes — A deep learning method for analyzing event logs
European Conference on Information Systems (Marrakesch, 15. Juni 2020 - 17. Juni 2020)
In: Proceedings of the 28th European Conference on Information Systems 2020
URL: https://www.researchgate.net/publication/341180737_DETECTING_WORKAROUNDS_IN_BUSINESS_PROCESSES_-_A_DEEP_LEARNING_METHOD_FOR_ANALYZING_EVENT_LOGS
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Weinzierl S., Zilker S., Brunk J., Revoredo K., Matzner M., Becker J.:
XNAP: Making LSTM-based next activity predictions explainable by using LRP
International Conference on Business Process Management (Sevilla, 13. September 2020 - 18. September 2020)
In: Proceedings of the BPM 2020 International Workshops. 2020
DOI: 10.1007/978-3-030-66498-5_10
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Wellmann C., Stierle M., Dunzer S., Matzner M.:
A Framework to Evaluate the Viability of Robotic Process Automation for Business Process Activities
BPM2020
In: Asatiani A, García JM, Helander N, Jiménez-Ramírez A, Koschmider A, Mendling J, Meroni G, Reijers HA (Hrsg.): Business Process Management: Blockchain and Robotic Process Automation Forum, Cham: 2020
DOI: 10.1007/978-3-030-58779-6_14
URL: https://link.springer.com/content/pdf/10.1007/978-3-030-58779-6_14.pdf
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Weinzierl S., Revoredo K., Matzner M.:
Predictive business process monitoring with context information from documents
European Conference on Information Systems (Stockholm, 8. Juni 2019 - 14. Juni 2019)
In: Proceedings of the 27th European Conference on Information Systems 2019
URL: https://www.researchgate.net/publication/333245929_PREDICTIVE_BUSINESS_PROCESS_MONITORING_WITH_CONTEXT_INFORMATION_FROM_DOCUMENTS
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Dunzer S., Stierle M., Matzner M., Baier S.:
Conformance checking: A state-of-the-art literature review
S-BPM ONE 2019 (Seville, Spain, 26. Juni 2019 - 28. Juni 2019)
In: Proceedings of the 11th International Conference on Subject-Oriented Business Process Management, S-BPM ONE 2019 2019
DOI: 10.1145/3329007.3329014
URL: https://dl.acm.org/citation.cfm?id=3329014
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Brunk J., Revoredo K., Stierle M., Matzner M., Delfmann P., Becker J.:
Prediction of business process instances with dynamic Bayesian networks
8th International Symposium on Data-Driven Process Discovery and Analysis, SIMPDA 2018 (Sevilla)
In: Paolo Ceravolo and López M. Teresa Gómez and Keulen Maurice Van (Hrsg.): Proceedings of the 8th International Symposium on Data-driven Process Discovery and Analysis (SIMPDA 2018) 2018
URL: http://ceur-ws.org/Vol-2270/short1.pdf
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Marx E., Matzner M.:
Optimization of production processes using Process Mining
Netzwerkkongress der ZD.B Initiativen für die Wissenschaft (Kloster Frauenchiemsee, 24. Oktober 2018 - 26. Oktober 2018)
In: Netzwerkkongress der ZD.B Initiativen für die Wissenschaft - Abstractband 2018
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Becker J., Breuker B., Delfmann P., Matzner M.:
Designing and Implementing a Framework for Event-based Predictive Modelling of Business Processes
International Workshop: Evolution of Information Systems and their Design Methods (EMISA 2014) (Luxembourg)
Open Access: http://cs.emis.de/LNI/Proceedings/Proceedings234/71.pdf
URL: http://cs.emis.de/LNI/Proceedings/Proceedings234/71.pdf
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Matzner M., Breuker D.:
Performances of Business Processes and Organizational Routines: Similar Research Problems, Different Research Methods - A Literature Review
European Conference on Information Systems (ECIS 2014) (Tel Aviv)
Open Access: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1242&context=ecis2014
URL: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1242&context=ecis2014
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Matzner M., Scholta H.:
Process Mining Approaches to Detect Organizational Properties in Cyber-Physical Systems
European Conference on Information Systems (ECIS 2014) (Tel Aviv)
Open Access: http://publications.martin-matzner.de/8/1/Ecis2014_1.pdf
URL: http://publications.martin-matzner.de/8/1/Ecis2014_1.pdf
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Matzner M., Schwegmann B., Janiesch C.:
preCEP: Facilitating Predictive Event-Driven Process Analytics
International Conference on Design Science Research in Information Systems and Technology (DESRIST) (Helsinki)
In: Brocke Jan vom, Hekkala Riitta, Ram Sudha, Rossi Matti (Hrsg.): Design Science at the Intersection of Physical and Virtual Design: Proceedings of the 8th International Conference DESRIST 2013, Heidelberg: 2013
DOI: 10.1007/978-3-642-38827-9_36
URL: http://publications.martin-matzner.de/2/2/Desrist2013.pdf
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Breuker D., Matzner M.:
Statistical Sequence Analysis for Business Process Mining and Organizational Routines
European Conference on Information Systems (ECIS) (Utrecht, Netherlands)
In: Proceedings of the 21st European Conference on Information Systems (ECIS 2013) 2013
Open Access: http://publications.martin-matzner.de/3/1/Ecis2013.pdf
URL: http://publications.martin-matzner.de/3/1/Ecis2013.pdf
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Matzner M., Schwegmann B., Janiesch C.:
A Method and Tool for Predictive Event-Driven Process Analytics
Internationale Konferenz Wirtschaftsinformatik (Leipzig)
In: Alt Rainer, Franczyk Bogdan (Hrsg.): Proceedings of the 11th International Conference on Wirtschaftsinformatik (WI2013) 2013
Open Access: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1045&context=wi2013
URL: http://aisel.aisnet.org/cgi/viewcontent.cgi?article=1045&context=wi2013
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Becker J., Matzner M., Oliver M., Walter M.:
A Review of Event Formats as Enablers of event-driven BPM
International Workshop on Event-driven Business Process Management (edBPM 2011) (Clermont-Ferrand, France)
In: Aalst Wil van der, Mylopoulos John, Rosemann Michael, Shaw Michael J, Szyperski Clemens (Hrsg.): International Workshop on Event-driven Business Process Management (edBPM 2011), Berlin: 2012
DOI: 10.1007/978-3-642-28108-2_42
URL: http://udoo.uni-muenster.de/downloads/publications/2526.pdf
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Raffelsieper T., Becker J., Matzner M., Janiesch C.:
Requirements for a Pattern Language for Event-driven Business Activity Monitoring
Multikonferenz der Wirtschaftsinformatik (MKWI 2012) (Braunschweig, Germany)
URL: http://udoo.uni-muenster.de/downloads/publications/2571.pdf
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Friedenstab Jan-Philipp JP., Janiesch C., Matzner M., Oliver M.:
Extending BPMN for Business Activity Monitoring
Hawaii International Conference on System Sciences (HICSS-45) (Wailea, HI, USA)
In: Proceedings of the 45th Annual Hawaii International Conference on System Sciences 2012
DOI: 10.1109/HICSS.2012.276
URL: http://udoo.uni-muenster.de/downloads/publications/2536.pdf
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Janiesch C., Matzner M., Oliver M.:
A Blueprint for Event-driven Business Activity Management
9th International Conference on Business Process Management (BPM 2011) (Clermont-Ferrand, France)
In: Rinderle-Ma Stefanie, Toumani Farouk, Wolf Karsten (Hrsg.): Proceedings of the 9th International Conference Business Process Management (BPM 2011), Berlin: 2011
DOI: 10.1007/978-3-642-23059-2_4
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Janiesch C., Matzner M., Oliver M.:
Slipstream: A BAM Proof of Concept using Standard Software
9th International Conference on Business Process Management (BPM 2011), Demo Track (Clermont-Ferrand, France)
In: 9th International Conference on Business Process Management (BPM) Demo Track 2011
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Janiesch C., Fischer R., Matzner M., Müller O.:
Business Activity Management for Service Networks in Cloud Environments
6th Workshop on Middleware for Service Oriented Computing (Lisboa, Portugal)
In: Proceedings of the 6th Workshop on Middleware for Service-oriented Computing (MW4SOC 2011) 2011
DOI: 10.1145/2093185.2093187
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Janiesch C., Matzner M., Müller O., Vollmer R., Becker J.:
Slipstream: Architecture Options for Real-Time Process Analytics
ACM Symposium on Applied Computing (SAC2011) (Taichung, Taiwan)
In: Proceedings of the ACM Symposium on Applied Computing (SAC 2011) 2011
DOI: 10.1145/1982185.1982252
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Matzner M., Voigt M., Alexandrini F., Araújo TS., Becker J.:
Process Modelling in Brazilian Public Administrations: The domain-specific PICTURE approach
Proceedings of the 15th Americas Conference on Information Systems (AMCIS2009) (San Francisco, CA, USA)
In: Proceedings of the 15th Americas Conference on Information Systems (AMCIS) 2009
URL: http://udoo.uni-muenster.de/downloads/publications/2313.pdf
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Tenschert J.:
Speech-Act-Based Adaptive Case Management (Dissertation, 2019)
URL: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/12543
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Stierle M., Matzner M.:
Customer Service - Device Repair Process (sp2020)
(2020)
DOI: 10.5281/zenodo.3928487
BibTeX: Download

Business Process Management

Projekte:

PropelleR: Deutsch-Russisches Innovationsforum - Promoting business process management excellence in Russia

RISE_BPM: Propelling Business Process Management by Research and Innovation Staff Exchange

SIPI: Bilaterale Kooperationsanbahnung Brasilien - Evaluating Standards for Interorganizational Process Integration in Brazilian-German Value Networks

Bayessche Vorhersagemodelle auf Basis von Kontextinformationen für das Monitoring von Geschäftsprozessen

IdAP: Identifikation von Automatisierungspotentialen mit Process Mining (IdAP)

AISA: Künstliche Intelligenz zur Erschließung von Potentialen zur Semi-Prozessautomatisierung

APED-F: Analyse von Positonsdaten zur Ermittlung von Durchlaufzeiten in der Fertigung

DeLePred: Deep Learning im Kontext von Predictive Maintenance

BuBLe: Ein Brückenschlag: Reinforcement Learning auf Molekül- und Prozessgraphen

CoPro: Multidimensionales Conformance Checking für Prozesse

3PMIPP: Prädiktives und Präskriptives Process Monitoring für Produktionsprozesse in der Industrie

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