Dr. Sven Weinzierl
Research Areas
- Process Analytics with Machine Learning
- Business Analytics with Machine Learning
Teaching
- TS410 – Integrated Business Process in SAP S/4HANA (Training)
- Forschungsmethodisches Seminar
- Seminar Wirtschaftsinformatik
CV
Work Experience
Since 08/2018 | Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
Researcher | Chair of Digital Industrial Service Systems |
09/2017 – 12/2017 | DATEV eG
Working Student | Artificial Intelligence / Machine Learning |
05/2017 – 07/2017 | MHP Management- und IT-Beratung GmbH
Working Student | Predictive Analytics |
03/2015 – 07/2015 | MHP Management- und IT-Beratung GmbH
Intern | Business Intelligence |
01/2014 – 10/2014 | DATEV eG
Working Student | Software Engineering |
08/2011 – 12/2013 | Ribe Holding GmbH & Co. KG
Working Student | Software Development in Systems Integration |
Education
Since 08/2018 | Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU)
PhD candidate | 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 |
Research Contributions
Publikationen
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 - 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 - 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 - 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 - 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 - 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 - 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
- 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 - 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 - 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