Wil's References for the year 2024!

I try to update my webpage once per year and provide as many papers as possible. Enjoy reading! Click here to return to home page.

  1. P. Ceravolo, S. Barbon Junior, E. Damiani, and W.M.P. van der Aalst. Tuning Machine Learning to Address Process Mining Requirements. IEEE Access, 12:24583–24595, 2024.
  2. M.Y. Wynn, W.M.P. van der Aalst, E. Verbeek, and B.N. Di Stefano. The IEEE XES Standard for Process Mining: Experiences, Adoption, and Revision. IEEE Computational Intelligence Magazine, 19(1):20–23, 2024.
  3. L.L. Mannel and W.M.P. van der Aalst. Discovering Process Models with Long-Term Dependencies while Providing Guarantees and Filtering Infrequent Behavior Patterns. Fundamenta Informaticae, 190(2-4):109–158, 2024.
  4. A. Berti, J. Herforth, M.S. Qafari, and W.M.P. van der Aalst. Graph-Based Feature Extraction on Object-Centric Event Logs. International Journal of Data Science and Analytics, 18(2):1–23, 2024.
  5. D. Schuster, F. Zerbato, S.J. van Zelst, and W.M.P. van der Aalst. Defining and Visualizing Process Execution Variants from Partially Ordered Event Data. Information Sciences, 657:119958, 2024.
  6. A.T. Burke, S.J.J. Leemans, M.T. Wynn, W.M.P. van der Aalst, and A.H.M. ter Hofstede. A Chance For Models To Show Their Quality: Stochastic Process Model-Log Dimensions. Information Systems, 124:102382, 2024.
  7. D. Schuster, E. Benevento, D. Aloini, and W.M.P. van der Aalst. Analyzing Healthcare Processes with Incremental Process Discovery: Practical Insights from a Real-World Application. Journal of Healthcare Informatics Research, 8(3):523–554, 2024.
  8. H.H. Beyel, O. Makke, M. Pourbafrani, O. Gusikhin, and W.M.P. van der Aalst. Analyzing Data Streams from Cyber-Physical-Systems: A Case Study. SN Computer Science, 5(6):706, 2024.
  9. E. Brzychczy, A. Zuber, and W.M.P. van der Aalst. Process Mining of Mining Processes: Analyzing Longwall Coal Excavation Using Event Data. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 54(5):2723–2734, 2024.
  10. C. Weinhardt, J. Fegert, O. Hinz, and W.M.P. van der Aalst. Digital Democracy: A Wake-Up Call. Business and Information Systems Engineering, 66(2):127–134, 2024.
  11. W.M.P. van der Aalst. Matthias Jarke (1952-2024), A Pioneer in Information Systems and Data Management. Business and Information Systems Engineering, 66(2):135, 2024.
  12. B. Hanneke, O. Hinz, J. Pfeiffer, and W.M.P. van der Aalst. The Internet of Value: Unleashing the Blockchain’s Potential with Tokenization. Business and Information Systems Engineering, 66(4):411–419, 2024.
  13. J. Pfeiffer, J.F. Lachenmaier, O. Hinz, and W.M.P. van der Aalst. New Laws and Regulation. Business and Information Systems Engineering, 66(6):653–666, 2024.
  14. C. Rennert and W.M.P. van der Aalst. Improving Precision in Process Trees Using Subprocess Tree Logs. In J. De Smedt and P. Soffer, editors, Fourth International Workshop on Event Data and Behavioral Analytics (EdbA 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 110–122. Springer-Verlag, Berlin, 2024.
  15. M. Rafiei, D. Bayrak, M. Pourbafrani, G. Park, H. Helal, G. Lakemeyer, and W.M.P. van der Aalst. Extracting Rules from Event Data for Study Planning. In J. De Smedt and P. Soffer, editors, Second International Workshop Education Meets Process Mining (EduPM 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 361–374. Springer-Verlag, Berlin, 2024.
  16. T. Li, G. Park, and W.M.P. van der Aalst. Checking Constraints for Object-Centric Process Executions. In J. De Smedt and P. Soffer, editors, Eight International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 392–405. Springer-Verlag, Berlin, 2024.
  17. G. Park, S. Aydin, C. Ugur, and W.M.P. van der Aalst. Analyzing an After-Sales Service Process Using Object-Centric Process Mining: A Case Study. In J. De Smedt and P. Soffer, editors, Eight International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 406–418. Springer-Verlag, Berlin, 2024.
  18. V. Peeva and W.M.P. van der Aalst. Grouping Local Process Models. In J. De Smedt and P. Soffer, editors, Eight International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 419–430. Springer-Verlag, Berlin, 2024.
  19. N. Graves, I. Koren, M. Rafiei, and W.M.P. van der Aalst. From Identities to Quantities: Introducing Items and Decoupling Points to Object-Centric Process Mining. In J. De Smedt and P. Soffer, editors, Second International Workshop on Collaboration Mining for Distributed Systems (COMINDS 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 462–474. Springer-Verlag, Berlin, 2024.
  20. W.M.P. van der Aalst. Lifting Process Discovery and Conformance Checking to the Next Level: A General Approach to Object-Centric Process Mining (Invited Talk). In M. Köhler-Bussmeier, D. Moldt, and H. Rölke, editors, Proceedings of the International Workshop on Petri Nets in Software Engineering (PNSE 2024), volume 3720 of CEUR Workshop Proceedings, pages 1–12. CEUR-WS.org, 2024.
  21. T. Brockhoff, M.N. Gose, M.S. Uysal, and W.M.P. van der Aalst. Process Comparison Using Petri Net Decomposition. In L.M. Kristensen and J.M.E.M. van der Werf, editors, Application and Theory of Petri Nets and Concurrency (Petri Nets 2024), Lecture Notes in Computer Science, pages 83–105. Springer-Verlag, Berlin, 2024.
  22. H.H. Beyel, M. Verket, V. Peeva, C. Rennert, M. Pegoraro, K. Schütt, W.M.P. van der Aalst, and N. Marx. Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study. In H. Schlieter, A. Fred, and H. Gamboa, editors, Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2024, Volume 2, pages 506–515. SCITEPRESS, 2024.
  23. W.M.P. van der Aalst and S.J.J. Leemans. Learning Generalized Stochastic Petri Nets From Event Data. In N. Jansen, S. Junges, B.L. Kaminski, C. Matheja, T. Noll, T. Quatmann, M. Stoelinga, and M. Volk, editors, Principles of Verification: Cycling the Probabilistic Landscape - Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part III, volume 15262 of Lecture Notes in Computer Science, pages 3–17. Springer-Verlag, Berlin, 2024.
  24. A. Norouzifar, H. Kourani, M. Dees, and W.M.P. van der Aalst. Bridging Domain Knowledge and Process Discovery Using Large Language Models. In K. Gdowska, M.T. Gómez-López, and J.R. Rehse, editors, International Workshop on Artificial Intelligence for Business Process Management (AI4BPM 2024), Business Process Management Workshops (BPM 2024), volume 534 of Lecture Notes in Business Information Processing, pages 44–56. Springer-Verlag, Berlin, 2024.
  25. Z. Sadeghibogar, A. Berti, M. Pegoraro, and W.M.P. van der Aalst. Applying Process Mining on Scientific Workflows: A Case Study on High Performance Computing Data. In K. Gdowska, M.T. Gómez-López, and J.R. Rehse, editors, International Workshop on Data-Driven Business Process Optimization (BPO 2024), Business Process Management Workshops (BPM 2024), volume 534 of Lecture Notes in Business Information Processing, pages 84–96. Springer-Verlag, Berlin, 2024.
  26. A. Küsters and W.M.P. van der Aalst. Rust4PM: A Versatile Process Mining Library for When Performance Matters. In A. del-Río-Ortega, M. Montali, S. Rinderle-Ma, H.A. Reijers, J. vom Brocke, M. Weske, B. Depaire, M. Indulska, H. van der Aa, W.T. Adrian, L. Genga, S. Leemans, K. Gdowska, M.T. Gómez-López, J.R. Rehse, and S. Agostinelli, editors, Proceedings of the Demonstration and Resources Forum of BPM 2024, volume 3758 of CEUR Workshop Proceedings, pages 91–95. CEUR-WS.org, 2024.
  27. A. Berti and W.M.P. van der Aalst. CSV-PM-LLM-Parsing: Automatic Ingestion of CSV Event Logs for Process Mining using LLMs. In A. del-Río-Ortega, M. Montali, S. Rinderle-Ma, H.A. Reijers, J. vom Brocke, M. Weske, B. Depaire, M. Indulska, H. van der Aa, W.T. Adrian, L. Genga, S. Leemans, K. Gdowska, M.T. Gómez-López, J.R. Rehse, and S. Agostinelli, editors, Proceedings of the Demonstration and Resources Forum of BPM 2024, volume 3758 of CEUR Workshop Proceedings, pages 131–135. CEUR-WS.org, 2024.
  28. A. Berti, U. Jessen, W.M.P. van der Aalst, and D. Fahland. Explainable Object-Centric Anomaly Detection: the Role of Domain Knowledge. In A. del-Río-Ortega, M. Montali, S. Rinderle-Ma, H.A. Reijers, J. vom Brocke, M. Weske, B. Depaire, M. Indulska, H. van der Aa, W.T. Adrian, L. Genga, S. Leemans, K. Gdowska, M.T. Gómez-López, J.R. Rehse, and S. Agostinelli, editors, Workshop Short Paper, BPM 2024, volume 3758 of CEUR Workshop Proceedings, pages 162–168. CEUR-WS.org, 2024.
  29. L. Liss, J.N. Adams, and W.M.P. van der Aalst. TOTeM: Temporal Object Type Model for Object-Centric Process Mining. In A. Marrella, M. Resinas, M. Jans, and M. Rosemann, editors, Business Process Management Forum - BPM 2024 Forum, volume 526 of Lecture Notes in Business Information Processing, pages 107–123. Springer-Verlag, Berlin, 2024.
  30. G. Park, J.N. Adams, and W.M.P. van der Aalst. Conformance Checking and Performance Analysis Using Object-Centric Directly-Follows Graphs. In A. Marrella, M. Resinas, M. Jans, and M. Rosemann, editors, Business Process Management Forum - BPM 2024 Forum, volume 526 of Lecture Notes in Business Information Processing, pages 179–196. Springer-Verlag, Berlin, 2024.
  31. J.N. Adams, E. Hastrup-Kiil, G. Park, and W.M.P. van der Aalst. Super Variants. In A. Marrella, M. Resinas, M. Jans, and M. Rosemann, editors, International Conference on Business Process Management (BPM 2024), volume 14940 of Lecture Notes in Computer Science, pages 111–128. Springer-Verlag, Berlin, 2024.
  32. H.H. Beyel and W.M.P. van der Aalst. Improving Process Discovery Using Translucent Activity Relationships. In A. Marrella, M. Resinas, M. Jans, and M. Rosemann, editors, International Conference on Business Process Management (BPM 2024), volume 14940 of Lecture Notes in Computer Science, pages 146–163. Springer-Verlag, Berlin, 2024.
  33. E.G. Rocha, S.J. van Zelst, and W.M.P. van der Aalst. Mining Behavioral Patterns for Conformance Diagnostics. In A. Marrella, M. Resinas, M. Jans, and M. Rosemann, editors, International Conference on Business Process Management (BPM 2024), volume 14940 of Lecture Notes in Computer Science, pages 291–308. Springer-Verlag, Berlin, 2024.
  34. E.G. Rocha and W.M.P. van der Aalst. Precision-Guided Minimization of Arbitrary Declarative Process Models. In H. van der Aa, D. Bork, R. Schmidt, and A. Sturm, editors, Enterprise, Business-Process and Information Systems Modeling (BPMDS/EMMSAD 2024), volume 511 of Lecture Notes in Business Information Processing, pages 48–56. Springer-Verlag, Berlin, 2024.
  35. A. Norouzifar, M. Rafiei, M. Dees, and W.M.P. van der Aalst. Process Variant Analysis Across Continuous Features: A Novel Framework. In H. van der Aa, D. Bork, R. Schmidt, and A. Sturm, editors, Enterprise, Business-Process and Information Systems Modeling (BPMDS/EMMSAD 2024), volume 511 of Lecture Notes in Business Information Processing, pages 129–142. Springer-Verlag, Berlin, 2024.
  36. H. Kourani, A. Berti, D. Schuster, and W.M.P. van der Aalst. Process Modeling with Large Language Models. In H. van der Aa, D. Bork, R. Schmidt, and A. Sturm, editors, Enterprise, Business-Process and Information Systems Modeling (BPMDS/EMMSAD 2024), volume 511 of Lecture Notes in Business Information Processing, pages 229–244. Springer-Verlag, Berlin, 2024.
  37. T.H. Huang, E. Schneider, M. Pegoraro, and W.M.P. van der Aalst. Fast and Sound: Accelerating Synthesis-Rules-Based Process Discovery. In H. van der Aa, D. Bork, R. Schmidt, and A. Sturm, editors, Enterprise, Business-Process and Information Systems Modeling (BPMDS/EMMSAD 2024), volume 511 of Lecture Notes in Business Information Processing, pages 259–274. Springer-Verlag, Berlin, 2024.
  38. I. Koren, M. Jarke, J. Michael, M. Heithoff, L. Tacke genannt Unterberg, M. Stachon, B. Rumpe, and W.M.P. van der Aalst. Navigating the Data Model Divide in Smart Manufacturing: An Empirical Investigation for Enhanced AI Integration. In H. van der Aa, D. Bork, R. Schmidt, and A. Sturm, editors, Enterprise, Business-Process and Information Systems Modeling (BPMDS/EMMSAD 2024), volume 511 of Lecture Notes in Business Information Processing, pages 275–290. Springer-Verlag, Berlin, 2024.
  39. T. Brockhoff, M.S. Uysal, and W.M.P. van der Aalst. Process Comparison Based on Selection-Projection Structures. In G. Guizzardi, F. Maria Santoro, H. Mouratidis, and P. Soffer, editors, Advanced Information Systems Engineering (CAiSE 2024), volume 14663 of Lecture Notes in Computer Science, pages 20–35. Springer-Verlag, Berlin, 2024.
  40. G. Park, M. Rafiei, H. Helal, G. Lakemeyer, and W.M.P. van der Aalst. Incorporating Behavioral Recommendations Mined from Event Logs into AI Planning. In S. Islam and A. Sturm, editors, Intelligent Information Systems (CAiSE Forum 2024), volume 520 of Lecture Notes in Business Information Processing, pages 20–28. Springer-Verlag, Berlin, 2024.
  41. T.H. Huang, T. Junied, M. Pegoraro, and W.M.P. van der Aalst. ProReco: A Process Discovery Recommender System. In S. Islam and A. Sturm, editors, Intelligent Information Systems (CAiSE Forum 2024), volume 520 of Lecture Notes in Business Information Processing, pages 93–101. Springer-Verlag, Berlin, 2024.
  42. D. Kretzschmann, G. Park, A. Berti, and W.M.P. van der Aalst. Overstock Problems in a Purchase-to-Pay Process: An Object-Centric Process Mining Case Study. In J.P.A. Almeida, C. Di Ciccio, and C. Kalloniatis, editors, Advanced Information Systems Engineering Workshops (CAiSE 2024), volume 521 of Lecture Notes in Business Information Processing, pages 347–359. Springer-Verlag, Berlin, 2024.
  43. H.H. Beyel, S. Manuel, and W.M.P. van der Aalst. ActivityGen: Extracting Enabled Activities from Screenshots. In U. Endriss, F. Melo, K. Bach, A. Diz, J. Alonso-Moral, S. Barro, and F. Heintz, editors, European Conference on Artificial Intelligence (ECAI 2024), volume 392 of Frontiers in Artificial Intelligence and Applications, pages 712–720. IOS Press, 2024.
  44. J.N. Adams, H. Drescher, A. Swoboda, N. Günnemann, G. Park, and W.M.P. van der Aalst. Improving Predictive Process Monitoring Using Object-Centric Process Mining. In M. Avital, E. Karahanna, M. Themistocleous, I.D. Constantiou, B. Fitzgerald, and S. Seidel, editors, European Conference on Information Systems (ECIS 2024), 2024.
  45. C.T. Schwanen, W. Pakusa, and W.M.P. van der Aalst. Process Tree Alignments. In J. Borbinha, T.P. Sales, M. Mira da Silva, H.A. Proper, and M. Schnellmann, editors, Enterprise Design, Operations, and Computing (EDOC 2024), volume 15409 of Lecture Notes in Computer Science, pages 300–317. Springer-Verlag, Berlin, 2024.
  46. H. Heidemeyer, L. Auhagen, R.W. Majeed, M. Pegoraro, J. Bienzeisler, V. Peeva, H.H. Beyel, R. Röhrig, W.M.P. van der Aalst, and B. Puladi. A Pipeline for the Usage of the Core Data Set of the Medical Informatics Initiative for Process Mining: A Technical Case Report. In R. Röhrig, N. Grabe, U. Hübner, K. Jung, U. Sax, S. Oliver, M. Sedlmayr, and A. Zapf, editors, German Medical Data Sciences (GMDS 2024), volume 317 of Studies in Health Technology and Informatics, pages 30–39. IOS Press, 2024.
  47. E.G. Rocha, S.J.J. Leemans, and W.M.P. van der Aalst. Stochastic Conformance Checking Based on Expected Subtrace Frequency. In X. Lu, L. Pufahl, and M. Song, editors, International Conference on Process Mining (ICPM 2024), pages 73–80. IEEE Computer Society, 2024.
  48. T. Brockhoff, M.S. Uysal, and W.M.P. van der Aalst. Wasserstein Weight Estimation for Stochastic Petri Nets. In X. Lu, L. Pufahl, and M. Song, editors, International Conference on Process Mining (ICPM 2024), pages 81–88. IEEE Computer Society, 2024.
  49. H. Kämmerling, E.G. Rocha, and W.M.P. van der Aalst. ProM4Py - A Python Wrapper For The ProM Framework. In J. De Weerdt, G. Meroni, H. van der Aa, and K. Winter, editors, Doctoral Consortium and Demo Track ICPM 2024, volume 3783 of CEUR Workshop Proceedings. CEUR-WS.org, 2024.
  50. H. Kourani, A. Berti, D. Schuster, and W.M.P. van der Aalst. ProMoAI: Process Modeling with Generative AI. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (IJCAI 2024), pages 8708–8712. ijcai.org, 2024.
  51. L. Tacke genannt Unterberg, I. Koren, and W.M.P. van der Aalst. Maximizing Reuse and Interoperability in Industry 4.0 with a Minimal Data Exchange Format for Machine Data. In J. Michael and M. Weske, editors, Modellierung 2024, volume P-348 of LNI, pages 103–118. Gesellschaft für Informatik e.V., 2024.
  52. H.H. Beyel and W.M.P. van der Aalst. Translucent Precision: Exploiting Enabling Information to Evaluate the Quality of Process Models. In J. Araújo, J.L. de la Vara, M.Y. Santos, and S. Assar, editors, International Conference on Research Challenges in Information Science (RCIS 2024), Part II, volume 514 of Lecture Notes in Business Information Processing, pages 29–37. Springer-Verlag, Berlin, 2024.
  53. A. Norouzifar, M. Dees, and W.M.P. van der Aalst. Imposing Rules in Process Discovery: An Inductive Mining Approach. In J. Araújo, J.L. de la Vara, M.Y. Santos, and S. Assar, editors, International Conference on Research Challenges in Information Science (RCIS 2024), Part I, volume 513 of Lecture Notes in Business Information Processing, pages 220–236. Springer-Verlag, Berlin, 2024.
  54. N. Elbert, L. Liss, W.M.P. van der Aalst, and C.M. Flath. Game Data Event Log from Age of Empire Interactions (Version 1.0.0). https://doi.org/10.5281/zenodo.11060884, May 2024.
  55. L. Liss, N. Elbert, C.M. Flath, and W.M.P. van der Aalst. Object-Centric Event Log for Age of Empires Game Interactions (Version 1.1.0). https://doi.org/10.5281/zenodo.11506366, June 2024.
  56. A. Küsters and W.M.P. van der Aalst. Developing a High-Performance Process Mining Library with Java and Python Bindings in Rust. Computing Research Repository (CoRR) in arXiv, abs/2401.14149, 2024.
  57. A. Berti, I. Koren, J.N. Adams, G. Park, B. Knopp, N. Graves, M. Rafiei, L. Liß, L. Tacke genannt Unterberg, Y. Zhang, C.T. Schwanen, M. Pegoraro, and W.M.P. van der Aalst. OCEL (Object-Centric Event Log) 2.0 Specification. Computing Research Repository (CoRR) in arXiv, abs/2403.01975, 2024.
  58. H. Kourani, A. Berti, D. Schuster, and W.M.P. van der Aalst. ProMoAI: Process Modeling with Generative AI. Computing Research Repository (CoRR) in arXiv, abs/2403.04327, 2024.
  59. H. Kourani, A. Berti, D. Schuster, and W.M.P. van der Aalst. Process Modeling With Large Language Models. Computing Research Repository (CoRR) in arXiv, abs/2403.07541, 2024.
  60. H.H. Beyel, M. Verket, V. Peeva, C. Rennert, M. Pegoraro, K. Schütt, W.M.P. van der Aalst, and N. Marx. Process-Aware Analysis of Treatment Paths in Heart Failure Patients: A Case Study. Computing Research Repository (CoRR) in arXiv, abs/2403.10544, 2024.
  61. B. Bakullari and W.M.P. van der Aalst. High-Level Event Mining: Overview and Future Work. Computing Research Repository (CoRR) in arXiv, abs/2405.14435, 2024.
  62. A. Norouzifar, M. Rafiei, M. Dees, and W.M.P. van der Aalst. Process Variant Analysis Across Continuous Features: A Novel Framework. Computing Research Repository (CoRR) in arXiv, abs/2406.04347, 2024.
  63. A. Berti, U. Jessen, W.M.P. van der Aalst, and D. Fahland. Challenges of Anomaly Detection in the Object-Centric Setting: Dimensions and the Role of Domain Knowledge. Computing Research Repository (CoRR) in arXiv, abs/2407.09023, 2024.
  64. A. Berti, H. Kourani, and W.M.P. van der Aalst. PM-LLM-Benchmark: Evaluating Large Language Models on Process Mining Tasks. Computing Research Repository (CoRR) in arXiv, abs/2407.13244, 2024.
  65. H. Kourani, A. Berti, J. Henrich, W. Kratsch, R. Weidlich, C.Y. Li, A. Arslan, D. Schuster, and W.M.P. van der Aalst. Leveraging Large Language Models for Enhanced Process Model Comprehension. Computing Research Repository (CoRR) in arXiv, abs/2408.08892, 2024.
  66. A. Norouzifar, H. Kourani, M. Dees, and W.M.P. van der Aalst. Bridging Domain Knowledge and Process Discovery Using Large Language Models. Computing Research Repository (CoRR) in arXiv, abs/2408.17316, 2024.
  67. A. Norouzifar, M. Dees, and W.M.P. van der Aalst. Imposing Rules in Process Discovery: An Inductive Mining Approach. Computing Research Repository (CoRR) in arXiv, abs/2408.17326, 2024.
  68. C. Rennert, M. Pourbafrani, and W.M.P. van der Aalst. Evaluation of Study Plans using Partial Orders. Computing Research Repository (CoRR) in arXiv, abs/2410.03314, 2024.
  69. D. Fahland, M. Montali, J. Lebherz, W.M.P. van der Aalst, M. van Asseldonk, P. Blank, L. Bosmans, M. Brenscheidt, C. Di Ciccio, A. Delgado, D. Calegari, J. Peeperkorn, E. Verbeek, L. Vugs, and M.T. Wynn. Towards a Simple and Extensible Standard for Object-Centric Event Data (OCED): Core Model, Design Space, and Lessons Learned. Computing Research Repository (CoRR) in arXiv, abs/2410.14495, 2024.
  70. V. Peeva, M. Porsil, and W.M.P. van der Aalst. Object-Centric Local Process Models. Computing Research Repository (CoRR) in arXiv, abs/abs/2411.10468, 2024.
  71. H. Kourani, A. Berti, D. Schuster, and W.M.P. van der Aalst. Evaluating Large Language Models on Business Process Modeling: Framework, Benchmark, and Self-Improvement Analysis. Computing Research Repository (CoRR) in arXiv, abs/2412.00023, 2024.
  72. W.M.P. van der Aalst. We overschatten wat we met AI volgend jaar kunnen doen, maar onderschatten langere termijneffecten. Stichting Beste-ID, Uitgave 2024, www.beste-id.nl, 2024.
  73. M. Pegoraro, E. Benevento, D. Alioni, and W.M.P. van der Aalst. Advances in Computational Methods for Process and Data Mining in Healthcare. Mathematical Biosciences and Engineering, 21(7):6603–6607, 2024.
  74. W.M.P. van der Aalst. How Object-Centric Process Mining Helps to Unleash Predictive and Generative AI. In L. Reinkemeyer, editor, Process Intelligence in Action: Taking Process Mining to the Next Level, pages 219–232. Springer-Verlag, Berlin, 2024.
  75. W.M.P. van der Aalst, H.A. Reijers, and L. Maruster. Process Mining Beyond Workflows. Computers in Industry, 161:104126, 2024.
  76. J. Munoz-Gama, F. Zerbato. G. Janssenswillen, and W.M.P. van der Aalst. Second International Workshop Education meets Process Mining (EduPM 2023). In J. De Smedt and P. Soffer, editors, ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 1–3. Springer-Verlag, Berlin, 2024.
  77. W.M.P. van der Aalst. Unraveling the Fabric of Intertwined Processes: How Object-Centric Process Mining is Changing the Way We Improve Operational Processes. In J. De Smedt and P. Soffer, editors, Second International Workshop on Collaboration Mining for Distributed Systems (COMINDS 2023), ICPM 2023 Workshop Proceedings, volume 503 of Lecture Notes in Business Information Processing, pages 1–5. Springer-Verlag, Berlin, 2024.