Dr. Sven Weinzierl

Lehrstuhl für Digital Industrial Service Systems

Predictive Recommining: Learning relations between event log characteristics and machine learning approaches for supporting predictive process monitoring (2023) Drodt C, Weinzierl S, Matzner M, Delfmann P Conference contribution, Conference Contribution Best of both worlds: Combining predictive power with interpretable and explainable results for patient pathway prediction (2023) Zilker S, Weinzierl S, Zschech P, Kraus M, Matzner M Conference contribution, Conference Contribution Detecting temporal workarounds in business processes – A deep-learning-based method for analysing event log data (2022) Weinzierl S, Wolf V, Pauli T, Beverungen D, Matzner M Journal article, Original article Predictive end-to-end enterprise process network monitoring (2022) Oberdorf F, Schaschek M, Weinzierl S, Stein N, Matzner M, Flath C Journal article, Original article A method for predicting workarounds in business processes (2022) Weinzierl S, Bartelheimer C, Zilker S, Beverungen D, Matzner M Conference contribution, Conference Contribution Text-aware predictive process monitoring with contextualized word embeddings (2022) Cabrera Pérez L, Weinzierl S, Zilker S, Matzner M Conference contribution, Conference Contribution GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints (2022) Zschech P, Weinzierl S, Hambauer N, Zilker S, Kraus M Conference contribution, Conference Contribution A technique for determining relevance scores of process activities using graph-based neural networks (2021) Stierle M, Weinzierl S, Harl M, Matzner M Journal article, Original article The recomminder: A decision support tool for predictive business process monitoring (2021) Drodt C, Weinzierl S, Matzner M, Delfmann P Conference contribution, Conference Contribution Exploring gated graph sequence neural networks for predicting next process activities (2021) Weinzierl S Conference contribution, Conference Contribution