| Machine learning in business process monitoring: a comparison of deep learning and classical approaches used for outcome prediction W Kratsch, J Manderscheid, M Röglinger, J Seyfried Business & Information Systems Engineering 63, 261-276, 2021 | 124 | 2021 |
| Quality-informed semi-automated event log generation for process mining R Andrews, CGJ van Dun, MT Wynn, W Kratsch, MKE Röglinger, ... Decision Support Systems 132, 113265, 2020 | 78 | 2020 |
| The biggest business process management problems to solve before we die I Beerepoot, C Di Ciccio, HA Reijers, S Rinderle-Ma, W Bandara, ... Computers in Industry 146, 103837, 2023 | 74 | 2023 |
| ProcessGAN: Supporting the creation of business process improvement ideas through generative machine learning C van Dun, L Moder, W Kratsch, M Röglinger Decision Support Systems 165, 113880, 2023 | 33 | 2023 |
| Quantifying chatbots’ ability to learn business processes C Kecht, A Egger, W Kratsch, M Röglinger Information Systems 113, 102176, 2023 | 23 | 2023 |
| Bot log mining: Using logs from robotic process automation for process mining A Egger, AHM ter Hofstede, W Kratsch, SJJ Leemans, M Röglinger, ... International Conference on Conceptual Modeling, 51-61, 2020 | 21 | 2020 |
| Data-driven process prioritization in process networks W Kratsch, J Manderscheid, D Reißner, M Röglinger Decision Support Systems 100, 27-40, 2017 | 20 | 2017 |
| Shedding light on blind spots–developing a reference architecture to leverage video data for process mining W Kratsch, F König, M Röglinger Decision Support Systems 158, 113794, 2022 | 18 | 2022 |
| Event log construction from customer service conversations using natural language inference C Kecht, A Egger, W Kratsch, M Röglinger 2021 3rd International Conference on Process Mining (ICPM), 144-151, 2021 | 16 | 2021 |
| Near-infrared spectroscopy for bladder monitoring: a machine learning approach P Fechner, F König, W Kratsch, J Lockl, M Röglinger ACM transactions on management information systems 14 (2), 1-23, 2023 | 6 | 2023 |
| Automated process (re-) design M Röglinger, C Dun, T Fehrer, DA Fischer, L Moder, W Kratsch | 5 | 2021 |
| Treiberbasierte Simulation im Controlling bei Infineon F Federmann, B Häckel, F Isbruch, W Kratsch, K Möller, C Voit, ... Controlling 32 (2), 28-35, 2020 | 5 | 2020 |
| Analytics pipeline for process mining on video data A Lepsien, A Koschmider, W Kratsch International Conference on Business Process Management, 196-213, 2023 | 4 | 2023 |
| Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction. Business & Information Systems Engineering … W Kratsch, J Manderscheid, M Röglinger, J Seyfried | 4 | 2020 |
| Process Mining for resilient airport operations: A case study of Munich Airport’s turnaround process J Rott, F König, H Häfke, M Schmidt, M Böhm, W Kratsch, H Krcmar Journal of Air Transport Management 112, 102451, 2023 | 3 | 2023 |
| Process Meets Project prioritization-a Decision Model for Developing Process Improvement Roadmaps. L Bitomsky, J Huhn, W Kratsch, M Roeglinger ECIS, 2019 | 3 | 2019 |
| Data-driven Management of Interconnected Business Processes: Contributions to Predictive and Prescriptive Process Mining W Kratsch PQDT-Global, 2020 | 1 | 2020 |
| Customers Like It Hot and Fast–Incorporating Customer Effects into the Meal Delivery Process C van Dun, T Fehrer, W Kratsch, N Wolf | 1 | 2020 |
| Unstrukturierte Daten aus der Serienproduktion mit Process Mining zur Fehleranalyse W Kratsch, G Stengel, A Egger, T Fehrer VDI Mechatroniktagung 2024, 13-18, 2024 | | 2024 |
| Exploring the Interplay of Process Mining and Generative AI: Research and Recommendations for CoEs L Reinkemeyer, M Röglinger, W Kratsch, L Fabri, SJ Schmid, J Wittmann | | 2023 |
