Dr. Sven Weinzierl

2024

• Kruschel S., Hambauer N., Weinzierl S., Zilker S., Kraus M., Zschech P.:
  Challenging the performance-interpretability trade-off: An evaluation of interpretable machine learning models (forthcoming)
  In: Business & Information Systems Engineering (2024)
  ISSN: 1867-0202
• Weinzierl S., Zilker S., Dunzer S., Matzner M.:
  Machine learning in business process management: A systematic literature review
  In: Expert Systems With Applications 253 (2024), Article No.: 124181
  ISSN: 0957-4174
  DOI: 10.1016/j.eswa.2024.124181
• Zilker S., Weinzierl S., Kraus M., Zschech P., Matzner M.:
  A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
  In: Health Care Management Science 27 (2024), p. 136-167
  ISSN: 1386-9620
  DOI: 10.1007/s10729-024-09673-8
  URL: https://link.springer.com/article/10.1007/s10729-024-09673-8#article-info

2023

• Kraus M., Tschernutter D., Weinzierl S., Zschech P.:
  Interpretable generalized additive neural networks
  In: European Journal of Operational Research (2023)
  ISSN: 0377-2217
  DOI: 10.1016/j.ejor.2023.06.032
• 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

2022

• 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), p. 76-100
  ISSN: 2573-234X
  DOI: 10.1080/2573234X.2021.1978337
  URL: https://www.tandfonline.com/doi/full/10.1080/2573234X.2021.1978337

2021

• 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), Article No.: 113511
  ISSN: 0167-9236
  DOI: 10.1016/j.dss.2021.113511
  URL: http://www.sciencedirect.com/science/article/pii/S016792362100021X

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), p. 1-16
  ISSN: 1246-0125
  DOI: 10.1080/12460125.2020.1790183
• Harl M., Weinzierl S., Stierle M., Matzner M.:
  Explainable predictive business process monitoring using gated graph neural networks
  In: Journal of Decision Systems (2020), p. 1-16
  ISSN: 1246-0125
  DOI: 10.1080/12460125.2020.1780780

2024

• Harl M., Zilker S., Weinzierl S.:
  Towards automated business process redesign in runtime using generative machine learning
  European Conference on Information Systems (Paphos, Cyprus)
  In: Proceedings of the 32nd European Conference on Information Systems 2024
• 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
• 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
• Weinzierl S., Zilker S., Brunk J., Revoredo K., Matzner M., Becker J.:
  Context-aware explanations of accurate predictions in service processes
  Hawaii International Conference on System Sciences (Waikiki, Honolulu, Hawaii)
  In: Proceedings of the 57th Hawaii International Conference on System Sciences 2024
• Weinzierl S., Zilker S., Zschech P., Kraus M., Leibelt T., Matzner M.:
  How risky is my AI system? A method for transparent classification of AI system descriptions by regulated AI risk categories
  International Conference on Information Systems (Bangkok, Thailand)
  In: Proceedings of the 45th International Conference on Information Systems 2024
  URL: https://open.fau.de/bitstreams/092b306d-86fb-420d-8ba1-57172d11611a/download

2023

• Arnold S., Yesilbas D., Weinzierl S.:
  Driving context into text-to-text privatization
  Annual Meeting of the Association for Computational Linguistics (Toronto)
  In: Association for Computational Linguistics (ed.): Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing 2023
  DOI: 10.18653/v1/2023.trustnlp-1.2
  URL: https://aclanthology.org/2023.trustnlp-1.2
• Arnold S., Yesilbas D., Weinzierl S.:
  Guiding text-to-text privatization by syntax
  Annual Meeting of the Association for Computational Linguistics (Toronto)
  In: Association for Computational Linguistics (ed.): Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing 2023
  DOI: 10.18653/v1/2023.trustnlp-1.14
  URL: https://aclanthology.org/2023.trustnlp-1.14/
• 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. June 2023 - 16. June 2023)
  In: Proceedings of the 35th International Conference on Advanced Information Systems Engineering Forum 2023
  DOI: 10.1007/978-3-031-34674-3_9
• Zilker S., Weinzierl S., Zschech P., Kraus M., Matzner M.:
  Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction
  European Conference on Information Systems (Kristiansand, 13. June 2023 - 16. June 2023)
  In: Proceedings of the 31st European Conference on Information Systems 2023
  DOI: 10.25593/open-fau-1123
  URL: https://open.fau.de/bitstreams/c8ea3d7e-7fb9-4932-9828-501af75d1f89/download

2022

• Cabrera Pérez L., Weinzierl S., Zilker S., Matzner M.:
  Text-aware predictive process monitoring with contextualized word embeddings
  International Conference on Business Process Management (Münster)
  In: Proceedings of the BPM 2022 International Workshops 2022
  DOI: 10.1007/978-3-031-25383-6_22
• 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. July 2022 - 9. July 2022)
  In: Proceedings of the 25th Pacific Asia Conference on Information Systems 2022
• 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. July 2022 - 9. July 2022)
  In: Proceedings of the 30th European Conference on Information Systems 2022

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
• 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
• 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
• 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

2020

• 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. March 2020 - 11. March 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/
• 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. October 2020 - 9. October 2020)
  In: Proceedings of the ICPM 2020 International Workshops 2020
  DOI: 10.1007/978-3-030-72693-5_9
• 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
• 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. January 2020 - 10. January 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
• 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. June 2020 - 17. June 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
• 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
• 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. March 2020 - 11. March 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

2019

• Weinzierl S., Revoredo K., Matzner M.:
  Predictive business process monitoring with context information from documents
  European Conference on Information Systems (Stockholm, 8. June 2019 - 14. June 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