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ORCIDSven Weinzierl
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2020 – today
- 2024
[j6]Mathias Kraus
, Daniel Tschernutter, Sven Weinzierl, Patrick Zschech:
Interpretable generalized additive neural networks. Eur. J. Oper. Res. 317(2): 303-316 (2024)- [j5]Sven Weinzierl, Sandra Zilker, Sebastian Dunzer, Martin Matzner:
Machine learning in business process management: A systematic literature review. Expert Syst. Appl. 253: 124181 (2024) - [c22]Maximilian Victor Harl, Sandra Zilker, Sven Weinzierl:
Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning. ECIS 2024 - [c21]Annina Liessmann, Weixin Wang, Sven Weinzierl, Sandra Zilker, Martin Matzner:
Transfer Learning for Predictive Process Monitoring. ECIS 2024 - [c20]Annina Liessmann, Sandra Zilker, Sven Weinzierl, Maria Sukhareva, Martin Matzner:
Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models. HICSS 2024: 1488-1497 - [c19]Sven Weinzierl, Sandra Zilker, Jens Brunk, Kate Revoredo, Martin Matzner, Jörg Becker:
Context-aware Explanations of Accurate Predictions in Service Processes. HICSS 2024: 1498-1507 - [c18]Sven Weinzierl, Sandra Zilker, Patrick Zschech, Mathias Kraus, Tobias Leibelt, Martin Matzner:
How Risky is my AI System? A Method for Transparent Classification of AI System Descriptions by Regulated AI Risk Categories. ICIS 2024 - [i13]Sandra Zilker, Sven Weinzierl, Mathias Kraus, Patrick Zschech, Martin Matzner:
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. CoRR abs/2405.13187 (2024) - [i12]Sven Weinzierl, Sandra Zilker, Sebastian Dunzer, Martin Matzner:
Machine learning in business process management: A systematic literature review. CoRR abs/2405.16396 (2024) - [i11]Lars Ackermann, Martin Käppel, Laura Marcus, Linda Moder, Sebastian Dunzer, Markus Hornsteiner, Annina Liessmann, Yorck Zisgen, Philip Empl, Lukas-Valentin Herm, Nicolas Neis, Julian Neuberger, Leo Poss, Myriam Schaschek, Sven Weinzierl, Niklas Wördehoff, Stefan Jablonski, Agnes Koschmider, Wolfgang Kratsch, Martin Matzner, Stefanie Rinderle-Ma, Maximilian Röglinger, Stefan Schönig, Axel Winkelmann:
Recent Advances in Data-Driven Business Process Management. CoRR abs/2406.01786 (2024) - [i10]Stefan Arnold, Dilara Yesilbas, Rene Gröbner, Dominik Riedelbauch, Maik Horn, Sven Weinzierl:
Documentation Practices of Artificial Intelligence. CoRR abs/2406.18620 (2024) - [i9]Sven Kruschel, Nico Hambauer, Sven Weinzierl, Sandra Zilker, Mathias Kraus, Patrick Zschech:
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. CoRR abs/2409.14429 (2024) - 2023
- [j4]Felix Oberdorf, Myriam Schaschek, Sven Weinzierl, Nikolai Stein, Martin Matzner, Christoph M. Flath:
Predictive End-to-End Enterprise Process Network Monitoring. Bus. Inf. Syst. Eng. 65(1): 49-64 (2023) - [c17]Christoph Drodt, Sven Weinzierl, Martin Matzner, Patrick Delfmann:
Predictive Recommining: Learning Relations Between Event Log Characteristics and Machine Learning Approaches for Supporting Predictive Process Monitoring. CAiSE Forum 2023: 69-76 - [c16]Sandra Zilker, Sven Weinzierl, Patrick Zschech, Mathias Kraus, Martin Matzner:
Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction. ECIS 2023 - [i8]Stefan Arnold, Dilara Yesilbas, Sven Weinzierl:
Driving Context into Text-to-Text Privatization. CoRR abs/2306.01457 (2023) - [i7]Stefan Arnold, Dilara Yesilbas, Sven Weinzierl:
Guiding Text-to-Text Privatization by Syntax. CoRR abs/2306.01471 (2023) - 2022
- [c15]Lena M. Cabrera, Sven Weinzierl, Sandra Zilker, Martin Matzner:
Text-Aware Predictive Process Monitoring with Contextualized Word Embeddings. Business Process Management Workshops 2022: 303-314 - [c14]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. ECIS 2022 - [c13]Sven Weinzierl, Christian Bartelheimer, Sandra Zilker, Daniel Beverungen, Martin Matzner:
A Method for Predicting Workarounds in Business Processes. PACIS 2022: 108 - [i6]Patrick Zschech, Sven Weinzierl, Nico Hambauer, Sandra Zilker, Mathias Kraus:
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. CoRR abs/2204.09123 (2022) - 2021
- [j3]Matthias Stierle, Sven Weinzierl, Maximilian Harl, Martin Matzner:
A technique for determining relevance scores of process activities using graph-based neural networks. Decis. Support Syst. 144: 113511 (2021) - [c12]Sven Weinzierl:
Exploring Gated Graph Sequence Neural Networks for Predicting Next Process Activities. Business Process Management Workshops 2021: 30-42 - [c11]Christoph Drodt, Sven Weinzierl, Martin Matzner, Patrick Delfmann:
The Recomminder: A decision support tool for Predictive Business Process Monitoring. BPM (PhD/Demos) 2021: 131-135 - [c10]Matthias Stierle, Jens Brunk, Sven Weinzierl, Sandra Zilker, Martin Matzner, Jörg Becker:
Bringing Light Into the Darkness - A Systematic Literature Review on Explainable Predictive Business Process Monitoring Techniques. ECIS 2021 - [c9]Sven Weinzierl, Sebastian Dunzer, Johannes Christian Tenschert, Sandra Zilker, Martin Matzner:
Predictive Business Process Deviation Monitoring. ECIS 2021 - 2020
- [j2]Maximilian Harl, Sven Weinzierl, Matthias Stierle, Martin Matzner:
Explainable predictive business process monitoring using gated graph neural networks. J. Decis. Syst. 29(Supplement): 312-327 (2020) - [j1]Jens Brunk, Johannes Stottmeister, Sven Weinzierl, Martin Matzner, Jörg Becker:
Exploring the effect of context information on deep learning business process predictions. J. Decis. Syst. 29(Supplement): 328-343 (2020) - [c8]Sven Weinzierl, Sandra Zilker, Jens Brunk, Kate Revoredo, Martin Matzner, Jörg Becker:
XNAP: Making LSTM-Based Next Activity Predictions Explainable by Using LRP. Business Process Management Workshops 2020: 129-141 - [c7]Sven Weinzierl, Sebastian Dunzer, Sandra Zilker, Martin Matzner:
Prescriptive Business Process Monitoring for Recommending Next Best Actions. BPM (Forum) 2020: 193-209 - [c6]Sven Weinzierl, Verena Wolf, Tobias Pauli, Daniel Beverungen, Martin Matzner:
Detecting Workarounds in Business Processes - a Deep Learning method for Analyzing Event Logs. ECIS 2020 - [c5]Sven Weinzierl, Matthias Stierle, Sandra Zilker, Martin Matzner:
A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs. HICSS 2020: 1-10 - [c4]An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern M. Eskofier:
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring. ICPM Workshops 2020: 112-123 - [c3]Emanuel Marx, Matthias Stierle, Sven Weinzierl, Martin Matzner:
Closing the Gap between Smart Manufacturing Applications and Data Management. Wirtschaftsinformatik (Community Tracks) 2020: 120-136 - [c2]Sven Weinzierl, Sandra Zilker, Matthias Stierle, Martin Matzner, Gyunam Park:
From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events. Wirtschaftsinformatik (Zentrale Tracks) 2020: 364-368 - [i5]Sven Weinzierl, Sandra Zilker, Jens Brunk, Kate Revoredo, An Nguyen, Martin Matzner, Jörg Becker, Björn M. Eskofier:
An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs. CoRR abs/2005.01194 (2020) - [i4]Matthias Stierle, Sven Weinzierl, Maximilian Harl, Martin Matzner:
A Technique for Determining Relevance Scores of Process Activities using Graph-based Neural Networks. CoRR abs/2008.03110 (2020) - [i3]Sven Weinzierl, Sandra Zilker, Jens Brunk, Kate Revoredo, Martin Matzner, Jörg Becker:
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP. CoRR abs/2008.07993 (2020) - [i2]Sven Weinzierl, Sebastian Dunzer, Sandra Zilker, Martin Matzner:
Prescriptive Business Process Monitoring for Recommending Next Best Actions. CoRR abs/2008.08693 (2020) - [i1]An Nguyen, Srijeet Chatterjee, Sven Weinzierl, Leo Schwinn, Martin Matzner, Bjoern M. Eskofier:
Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring. CoRR abs/2010.00889 (2020)
2010 – 2019
- 2019
- [c1]Sven Weinzierl, Kate Cerqueira Revoredo, Martin Matzner:
Predictive Business Process Monitoringwith Context Information from Documents. ECIS 2019
Coauthor Index
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