Predictive Business Process Monitoringwith Context Information from Documents. - Sven Weinzierl; Kate Cerqueira Revoredo; Martin Matzner (ECIS, 2019)

Prescriptive Business Process Monitoring for Recommending Next Best Actions. - Sven Weinzierl; Sebastian Dunzer; Sandra Zilker; Martin Matzner (BPM (Forum), 2020)

XNAP: Making LSTM-Based Next Activity Predictions Explainable by Using LRP. - Sven Weinzierl; Sandra Zilker; Jens Brunk; Kate Revoredo; Martin Matzner; Jörg Becker 001 (Business Process Management Workshops, 2020)

Detecting Workarounds in Business Processes - a Deep Learning method for Analyzing Event Logs. - Sven Weinzierl; Verena Wolf; Tobias Pauli; Daniel Beverungen; Martin Matzner (ECIS, 2020)

A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs. - Sven Weinzierl; Matthias Stierle; Sandra Zilker; Martin Matzner (HICSS, 2020)

Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring. - An Nguyen; Srijeet Chatterjee; Sven Weinzierl; Leo Schwinn; Martin Matzner; Bjoern M. Eskofier (ICPM Workshops, 2020)

Closing the Gap between Smart Manufacturing Applications and Data Management. - Emanuel Marx; Matthias Stierle; Sven Weinzierl; Martin Matzner (Wirtschaftsinformatik (Community Tracks), 2020)

From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events. - Sven Weinzierl; Sandra Zilker; Matthias Stierle; Martin Matzner; Gyunam Park (Wirtschaftsinformatik (Zentrale Tracks), 2020)

An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs. - Sven Weinzierl; Sandra Zilker; Jens Brunk; Kate Revoredo; An Nguyen; Martin Matzner; Jörg Becker 001; Björn M. Eskofier (CoRR, 2020)

Exploring the effect of context information on deep learning business process predictions. - Jens Brunk; Johannes Stottmeister; Sven Weinzierl; Martin Matzner; Jörg Becker 001 (J. Decis. Syst., 2020)

Explainable predictive business process monitoring using gated graph neural networks. - Maximilian Harl; Sven Weinzierl; Matthias Stierle; Martin Matzner (J. Decis. Syst., 2020)

The Recomminder: A decision support tool for Predictive Business Process Monitoring. - Christoph Drodt; Sven Weinzierl; Martin Matzner; Patrick Delfmann (BPM (PhD/Demos), 2021)

Exploring Gated Graph Sequence Neural Networks for Predicting Next Process Activities. - Sven Weinzierl (Business Process Management Workshops, 2021)

Bringing Light Into the Darkness - A Systematic Literature Review on Explainable Predictive Business Process Monitoring Techniques. - Matthias Stierle; Jens Brunk; Sven Weinzierl; Sandra Zilker; Martin Matzner; Jörg Becker 001 (ECIS, 2021)

Predictive Business Process Deviation Monitoring. - Sven Weinzierl; Sebastian Dunzer; Johannes Christian Tenschert; Sandra Zilker; Martin Matzner (ECIS, 2021)

A technique for determining relevance scores of process activities using graph-based neural networks. - Matthias Stierle; Sven Weinzierl; Maximilian Harl; Martin Matzner (Decis. Support Syst., 2021)

Text-Aware Predictive Process Monitoring with Contextualized Word Embeddings. - Lena M. Cabrera; Sven Weinzierl; Sandra Zilker; Martin Matzner (Business Process Management Workshops, 2022)

GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints. - Patrick Zschech; Sven Weinzierl; Nico Hambauer; Sandra Zilker; Mathias Kraus (ECIS, 2022)

A Method for Predicting Workarounds in Business Processes. - Sven Weinzierl; Christian Bartelheimer; Sandra Zilker; Daniel Beverungen; Martin Matzner (PACIS, 2022)

Predictive Recommining: Learning Relations Between Event Log Characteristics and Machine Learning Approaches for Supporting Predictive Process Monitoring. - Christoph Drodt; Sven Weinzierl; Martin Matzner; Patrick Delfmann (CAiSE Forum, 2023)

Best of Both Worlds: Combining Predictive Power with Interpretable and Explainable Results for Patient Pathway Prediction. - Sandra Zilker; Sven Weinzierl; Patrick Zschech; Mathias Kraus; Martin Matzner (ECIS, 2023)

Driving Context into Text-to-Text Privatization. - Stefan Arnold; Dilara Yesilbas; Sven Weinzierl (CoRR, 2023)

Guiding Text-to-Text Privatization by Syntax. - Stefan Arnold; Dilara Yesilbas; Sven Weinzierl (CoRR, 2023)

Predictive End-to-End Enterprise Process Network Monitoring. - Felix Oberdorf; Myriam Schaschek; Sven Weinzierl; Nikolai Stein; Martin Matzner; Christoph M. Flath (Bus. Inf. Syst. Eng., 2023)

Towards Automated Business Process Redesign in Runtime Using Generative Machine Learning. - Maximilian Victor Harl; Sandra Zilker; Sven Weinzierl (ECIS, 2024)

Transfer Learning for Predictive Process Monitoring. - Annina Liessmann; Weixin Wang; Sven Weinzierl; Sandra Zilker; Martin Matzner (ECIS, 2024)

Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models. - Annina Liessmann; Sandra Zilker; Sven Weinzierl; Maria Sukhareva; Martin Matzner (HICSS, 2024)

Context-aware Explanations of Accurate Predictions in Service Processes. - Sven Weinzierl; Sandra Zilker; Jens Brunk; Kate Revoredo; Martin Matzner; Jörg Becker 001 (HICSS, 2024)

How Risky is my AI System? A Method for Transparent Classification of AI System Descriptions by Regulated AI Risk Categories. - Sven Weinzierl; Sandra Zilker; Patrick Zschech; Mathias Kraus; Tobias Leibelt; Martin Matzner (ICIS, 2024)

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis. - Sandra Zilker; Sven Weinzierl; Mathias Kraus; Patrick Zschech; Martin Matzner (CoRR, 2024)

Recent Advances in Data-Driven Business Process Management. - 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; et al. (CoRR, 2024)

Documentation Practices of Artificial Intelligence. - Stefan Arnold; Dilara Yesilbas; Rene Gröbner; Dominik Riedelbauch; Maik Horn; Sven Weinzierl (CoRR, 2024)

Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models. - Sven Kruschel; Nico Hambauer; Sven Weinzierl; Sandra Zilker; Mathias Kraus; Patrick Zschech (CoRR, 2024)

Interpretable generalized additive neural networks. - Mathias Kraus; Daniel Tschernutter; Sven Weinzierl; Patrick Zschech (Eur. J. Oper. Res., 2024)

Machine learning in business process management: A systematic literature review. - Sven Weinzierl; Sandra Zilker; Sebastian Dunzer; Martin Matzner (Expert Syst. Appl., 2024)

A Human-in-the-Loop Approach for Improving Fairness in Predictive Business Process Monitoring. - Martin Käppel; Julian Neuberger; Felix Möhrlein; Sven Weinzierl; Martin Matzner; Stefan Jablonski (BPM, 2025)

CareerBERT: Matching resumes to ESCO jobs in a shared embedding space for generic job recommendations. - Julian Rosenberger; Lukas Wolfrum; Sven Weinzierl; Mathias Kraus; Patrick Zschech (Expert Syst. Appl., 2025)

A technology-specific process mining maturity grid for manufacturing and logistics. - Sebastian Dunzer; Sandra Zilker; Sven Weinzierl; Willi Tang; Felix Dieckmann; Simon Stenglein; Johannes Rist; Martin Matzner (Int. J. Prod. Res., 2025)

(Neural-Symbolic) Machine Learning for Inconsistency Measurement. - Sven Weinzierl; Carl Cora (CoRR, 2025)

From Source to Target: Leveraging Transfer Learning for Predictive Process Monitoring in Organizations. - Sven Weinzierl; Sandra Zilker; Annina Liessmann; Martin Käppel; Weixin Wang; Martin Matzner (CoRR, 2025)