| Prescriptive business process monitoring for recommending next best actions S Weinzierl, S Dunzer, S Zilker, M Matzner International conference on business process management, 193-209, 2020 | 57 | 2020 |
| XNAP: making LSTM-based next activity predictions explainable by using LRP S Weinzierl, S Zilker, J Brunk, K Revoredo, M Matzner, J Becker International Conference on Business Process Management, 129-141, 2020 | 34 | 2020 |
| The influence of algorithm aversion and anthropomorphic agent design on the acceptance of AI-based job recommendations. J Ochmann, L Michels, S Zilker, V Tiefenbeck, S Laumer ICIS, 2020 | 25 | 2020 |
| A Next Click Recommender System for Web-based Service Analytics with Context-aware LSTMs. S Weinzierl, M Stierle, S Zilker, M Matzner HICSS, 1-10, 2020 | 22 | 2020 |
| Bringing light into the darkness-A systematic literature review on explainable predictive business process monitoring techniques M Stierle, J Brunk, S Weinzierl, S Zilker, M Matzner, J Becker | 18 | 2021 |
| The evaluation of the black box problem for AI-based recommendations: An interview-based study J Ochmann, S Zilker, S Laumer Innovation Through Information Systems: Volume II: A Collection of Latest …, 2021 | 18 | 2021 |
| An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs S Weinzierl, S Zilker, J Brunk, K Revoredo, A Nguyen, M Matzner, ... arXiv preprint arXiv:2005.01194, 2020 | 18 | 2020 |
| From predictive to prescriptive process monitoring: Recommending the next best actions instead of calculating the next most likely events. S Weinzierl, S Zilker, M Stierle, M Matzner, G Park Wirtschaftsinformatik (Zentrale Tracks), 364-368, 2020 | 17 | 2020 |
| GAM (e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints P Zschech, S Weinzierl, N Hambauer, S Zilker, M Kraus arXiv preprint arXiv:2204.09123, 2022 | 11 | 2022 |
| The status quo of process mining in the industrial sector S Dunzer, S Zilker, E Marx, V Grundler, M Matzner Innovation Through Information Systems: Volume III: A Collection of Latest …, 2021 | 9 | 2021 |
| Text-aware predictive process monitoring with contextualized word embeddings L Cabrera, S Weinzierl, S Zilker, M Matzner International Conference on Business Process Management, 303-314, 2022 | 7 | 2022 |
| Design principles for comprehensible process discovery in process mining M Stierle, S Zilker, S Dunzer, JC Tenscher, G Karagegova | 7 | 2020 |
| Predictive business process deviation monitoring S Weinzierl, S Dunzer, J Tenschert, S Zilker, M Matzner Proceedings of the 29th European Conference on Information Systems (ECIS), 2021 | 6 | 2021 |
| Job seekers' artificial intelligence-related black box concerns J Ochmann, S Zilker, S Laumer Proceedings of the 2020 on Computers and People Research Conference, 101-102, 2020 | 4 | 2020 |
| Best of both worlds: combining predictive power with interpretable and explainable results for patient pathway prediction S Zilker, S Weinzierl, P Zschech, M Kraus, M Matzner | 2 | 2023 |
| Designing a Method for Resource-specific Next Activity Prediction S Zilker PACIS 2022 Proceedings, 2022 | 2 | 2022 |
| Process Mining for Advanced Service Analytics–From Process Efficiency to Customer Encounter and Experience S Zilker, E Marx, M Stierle, M Matzner | 2 | 2022 |
| Predicting Customer Satisfaction in Service Processes Using Multilingual Large Language Models A Liessmann, S Zilker, S Weinzierl, M Sukhareva, M Matzner | | 2024 |
| Context-aware Explanations of Accurate Predictions in Service Processes S Weinzierl, S Zilker, J Brunk, K Revoredo, M Matzner, J Becker | | 2024 |
| TOWARDS AUTOMATED BUSINESS PROCESS REDESIGN IN RUNTIME USING GENERATIVE MACHINE LEARNING MV Harl, S Zilker, S Weinzierl | | 2024 |
Martin MatznerFriedrich-Alexander-Universität Erlangen-Nürnberg (FAU)Dirección de correo verificada de fau.de
