| PM4Py: A process mining library for Python A Berti, S van Zelst, D Schuster Software Impacts 17, 100556, 2023 | 82 | 2023 |
| Utilizing domain knowledge in data-driven process discovery: A literature review D Schuster, SJ van Zelst, WMP van der Aalst Computers in Industry 137, 2022 | 55 | 2022 |
| Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study A Berti, D Schuster, WMP van der Aalst Business Process Management Workshops. BPM 2023, 427-439, 2024 | 47 | 2024 |
| Defining cases and variants for object-centric event data JN Adams, D Schuster, S Schmitz, G Schuh, WMP van der Aalst 2022 4th International Conference on Process Mining (ICPM), 128-135, 2022 | 35 | 2022 |
| Process Modeling With Large Language Models H Kourani, A Berti, D Schuster, WMP van der Aalst International Conference on Business Process Modeling, Development and …, 2024 | 32 | 2024 |
| Incremental Discovery of Hierarchical Process Models D Schuster, SJ van Zelst, WMP van der Aalst Research Challenges in Information Science. RCIS 2020, 417-433, 2020 | 32 | 2020 |
| A framework for extracting and encoding features from object-centric event data JN Adams, G Park, S Levich, D Schuster, WMP van der Aalst Service-Oriented Computing. ICSOC 2022, 36-53, 2022 | 31 | 2022 |
| Cortado—An interactive tool for data-driven process discovery and modeling D Schuster, SJ Zelst, WMP van der Aalst Application and Theory of Petri Nets and Concurrency. PETRI NETS 2021, 465-475, 2021 | 23 | 2021 |
| Online Process Monitoring Using Incremental State-Space Expansion: An Exact Algorithm D Schuster, SJ van Zelst Business Process Management. BPM 2020, 147-164, 2020 | 21 | 2020 |
| Cortado: A dedicated process mining tool for interactive process discovery D Schuster, SJ van Zelst, WMP van der Aalst SoftwareX 22, 101373, 2023 | 16 | 2023 |
| Alignment Approximation for Process Trees D Schuster, S van Zelst, WMP van der Aalst Process Mining Workshops. ICPM 2020, 247-259, 2021 | 13 | 2021 |
| Scalable online conformance checking using incremental prefix-alignment computation D Schuster, GJ Kolhof Service-Oriented Computing – ICSOC 2020 Workshops., 379-394, 2020 | 11 | 2020 |
| Freezing sub-models during incremental process discovery D Schuster, SJ van Zelst, WMP van der Aalst Conceptual Modeling. ER 2021, 14-24, 2021 | 10 | 2021 |
| Visualizing Trace Variants from Partially Ordered Event Data D Schuster, L Schade, SJ van Zelst, WMP van der Aalst Process Mining Workshops. ICPM 2021, 34-46, 2021 | 10 | 2021 |
| Evaluating Large Language Models in Process Mining: Capabilities, Benchmarks, Evaluation Strategies, and Future Challenges A Berti, H Kourani, H Hafke, CY Li, D Schuster Enterprise, Business-Process and Information Systems Modeling, 13-21, 2024 | 9 | 2024 |
| Conformance Checking for Trace Fragments Using Infix and Postfix Alignments D Schuster, N Föcking, SJ van Zelst, WMP van der Aalst Cooperative Information Systems. CoopIS 2022, 299-310, 2022 | 9 | 2022 |
| The process mining toolkit (pmtk): Enabling advanced process mining in an integrated fashion A Berti, CY Li, D Schuster, SJ van Zelst ICPM 2021 Doctoral Consortium and Demo Track 2021, 43-44, 2021 | 8 | 2021 |
| ProMoAI: Process Modeling with Generative AI H Kourani, A Berti, D Schuster, WMP van der Aalst arXiv preprint arXiv:2403.04327, 2024 | 7 | 2024 |
| Supporting Users in the Continuous Evolution of Automated Routines in Their Smart Spaces E Serral, D Schuster, Y Bertrand Business Process Management Workshops. BPM 2021, 391-402, 2021 | 7 | 2021 |
| Defining and visualizing process execution variants from partially ordered event data D Schuster, F Zerbato, SJ van Zelst, WMP van der Aalst Information Sciences 657, 119958, 2024 | 6 | 2024 |
Wil van der AalstProfessor of Process and Data Science, RWTH Aachen University || Celonis || Fraunhofer FIT || TU/eEmail verificata su pads.rwth-aachen.de
