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Friedrich-Alexander-Universität Lehrstuhl für Digital Industrial Service Systems WISO
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Friedrich-Alexander-Universität Lehrstuhl für Digital Industrial Service Systems WISO
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  3. Sven Weinzierl

Sven Weinzierl

Bereichsnavigation: Team
  • Dr. Emanuel Marx
  • Matthias Stierle
  • Sandra Zilker
  • Sebastian Dunzer
  • Stellenangebote
  • Sven Weinzierl
  • Tobias Pauli
  • Willi Tang
  • Prof. Dr. Martin Matzner

Sven Weinzierl

Forschungsgebiete

  • Predictive Process Analytics with Deep Learning
  • Prescriptive Process Analytics with Deep Learning

Lehre

  • TS410 – Integrated Business Process in SAP S/4HANA (Training)
  • Forschungsmethodisches Seminar
  • Seminar Wirtschaftsinformatik

CV

Berufserfahrung

Seit 08/2018 Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Forscher | Chair of Digital Industrial Service Systems

09/2017 – 12/2017 DATEV eG

Werkstudent | Artificial Intelligence / Machine Learning

05/2017 – 07/2017 MHP Management- und IT-Beratung GmbH

Werkstudent | Predictive Analytics

03/2015 – 07/2015 MHP Management- und IT-Beratung GmbH

Prakitkant | Business Intelligence

01/2014 – 10/2014 DATEV eG

Werkstudent | Software Engineering

08/2011 – 12/2013 Ribe Holding GmbH & Co. KG

Werkstudent | Software Development in Systems Integration

Education

Seit 08/2018 Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)

Dokotrand | Information Systems

04/2016 – 06/2018 Otto-Friedrich-Universität Bamberg

Master of Science | Information Systems

10/2012 – 03/2016 Hochschule Ansbach

Bachelor of Arts | Information Systems

09/2008 – 07/2011 Ribe Holding GmbH & Co. KG

IHK Professional Training | IT Merchant


Beiträge zur Forschung

Publikationen

2022

  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • Zschech P., Weinzierl S., Hambauer N., Zilker S., Kraus M.:
    GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
    European Conference on Information Systems (Timisoara, 5. Juli 2022 - 9. Juli 2022)
    In: Proceedings of the 30th European Conference on Information Systems 2022
    BibTeX: Download

2021

  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download

2020

  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • Marx E., Stierle M., Weinzierl S., Matzner M.:
    Closing the Gap between Smart Manufacturing Applications and Data Management
    International Conference on Wirtschaftsinformatik (WI) (Potsdam, 9. März 2020 - 11. März 2020)
    In: Proceedings of the 15th International Conference on Wirtschaftsinformatik (WI) 2020
    DOI: 10.30844/wi_2020_u1-marx
    URL: https://library.gito.de/2021/07/wi2020-community-tracks-9/
    BibTeX: Download
  • 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 1st International Workshop on Leveraging Machine Learning in Process Mining 2020
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download
  • 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
    BibTeX: Download

2019

  • 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
    BibTeX: Download
Friedrich-Alexander-Universität Erlangen-Nürnberg
Lehrstuhl für Digital Industrial Service Systems

Fürther Str. 248
90429 Nürnberg
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