Dr. Sven Weinzierl

2025

• Dunzer S., Zilker S., Weinzierl S., Tang W., Dieckmann F., Stenglein S., Rist J., Matzner M.:
  A technology-specific process mining maturity grid for manufacturing and logistics
  In: International Journal of Production Research (2025)
  ISSN: 0020-7543
  DOI: 10.1080/00207543.2025.2451810
  URL: https://www.tandfonline.com/doi/full/10.1080/00207543.2025.2451810
• Herchenbach M., Weinzierl S., Zilker S., Schwulera E., Matzner M.:
  Adaptive AI-based causal control: Toward an autonomous factory in solder paste printing
  In: Computers in Industry (2025)
  ISSN: 0166-3615
  DOI: 10.1016/j.compind.2025.104256
  URL: https://www.sciencedirect.com/science/article/pii/S0166361525000211
• 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
  In: Business & Information Systems Engineering (2025)
  ISSN: 1867-0202
  DOI: 10.1007/s12599-024-00922-2
  URL: https://link.springer.com/article/10.1007/s12599-024-00922-2
• Rosenberger J., Wolfrum L., Weinzierl S., Kraus M., Zschech P.:
  CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations
  In: Expert Systems With Applications 275 (2025), Article No.: 127043
  ISSN: 0957-4174
  DOI: 10.1016/j.eswa.2025.127043
• Weinzierl S., Zilker S., Ließmann A., Käppel M., Wang W., Matzner M.:
  From source to target: Leveraging transfer learning for predictive process monitoring in organizations (Forthcoming)
  In: Business & Information Systems Engineering (2025)
  ISSN: 1867-0202
• Zschech P., Weinzierl S., Kraus M.:
  Inherently interpretable machine learning: A contrasting paradigm to post-hoc explainable AI (Forthcoming)
  In: Business & Information Systems Engineering (2025), p. 1 - 19
  ISSN: 1867-0202
  DOI: 10.1007/s12599-025-00964-0
  URL: https://link.springer.com/article/10.1007/s12599-025-00964-0

2024

• Kraus M., Tschernutter D., Weinzierl S., Zschech P.:
  Interpretable generalized additive neural networks
  In: European Journal of Operational Research (2024)
  ISSN: 0377-2217
  DOI: 10.1016/j.ejor.2023.06.032
  URL: https://www.sciencedirect.com/science/article/pii/S0377221723005027
• 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
  URL: https://www.sciencedirect.com/science/article/pii/S0957417424010479
• 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

• 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
  URL: https://link.springer.com/article/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
  URL: https://www.tandfonline.com/doi/abs/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
  URL: https://www.tandfonline.com/doi/abs/10.1080/12460125.2020.1780780

2025

• Käppel M., Neuberger J., Weinzierl S., Möhrlein F., Matzner M., Jablonski S.:
  A Human-In-The-Loop Approach for Improving Fairness in Predictive Business Process Monitoring
  DOI: 10.1007/978-3-032-02867-9_21
• Möhrlein F., Käppel M., Neuberger J., Weinzierl S., Ackermann L., Matzner M., Jablonski S.:
  FairLoop: Software Support for Human-Centric Fairness in Predictive Business Process Monitoring
• Reinhard P., Ließmann A., Weinzierl S., Zilker S., Li MM., Matzner M., Leimeister JM.:
  Event Log Construction from Multimodel Data - A Reference Architecture for Exploiting Process Mining in IT Service Management
  European Conference on Information Systems (Amman, Jordan)
  In: Proceedings of the 33rd European Conference on Information Systems 2025
  DOI: https://open.fau.de/handle/openfau/36267
  URL: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/5

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

2024

• Ackermann L., Käppel M., Marcus L., Moder L., Dunzer S., Hornsteiner M., Ließmann A., Zisgen Y., Empl P., Herm LV., Neis N., Neuberger J., Schaschek M., Weinzierl S., Wörderhoff N., Jablonski S., Koschmider A., Kratsch W., Matzner M., Rinderle-Ma S., Röglinger M., Schönig S., Winkelmann A.:
  Recent Advances in Data-Driven Business Process Management
  (2024)
  Open Access: https://arxiv.org/abs/2406.01786
  URL: https://arxiv.org/abs/2406.01786
  (online publication)