Dr. Sven Weinzierl

Lehrstuhl für Digital Industrial Service Systems

Towards automated business process redesign in runtime using generative machine learning (2024) Harl M, Zilker S, Weinzierl S Conference contribution, Conference Contribution Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models (2024) Ließmann A, Zilker S, Weinzierl S, Sukhareva M, Matzner M Conference contribution, Conference Contribution Context-aware explanations of accurate predictions in service processes (2024) Weinzierl S, Zilker S, Brunk J, Revoredo K, Matzner M, Becker J Conference contribution, Conference Contribution Interpretable generalized additive neural networks (2024) Kraus M, Tschernutter D, Weinzierl S, Zschech P Journal article, Original article Guiding text-to-text privatization by syntax (2023) Arnold S, Yesilbas D, Weinzierl S Conference contribution, Conference Contribution Driving context into text-to-text privatization (2023) Arnold S, Yesilbas D, Weinzierl S Conference contribution, Conference Contribution 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