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ORCIDWil M. P. van der Aalst
Willibrordus Martinus Pancratius van der Aalst
Person information
- affiliation: RWTH Aachen University, Chair of Process and Data Science, Germany
- affiliation (former): Eindhoven University of Technology, Department of Mathematics and Computer Science
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2020 – today
- 2022
[j263]Mahsa Pourbafrani
, Wil M. P. van der Aalst:
Discovering System Dynamics Simulation Models Using Process Mining. IEEE Access 10: 78527-78547 (2022)- [j262]Cristina Mihale-Wilson, Oliver Hinz, Wil M. P. van der Aalst, Christof Weinhardt:
Corporate Digital Responsibility. Bus. Inf. Syst. Eng. 64(2): 127-132 (2022) - [j261]Christian Peukert, Christof Weinhardt, Oliver Hinz, Wil M. P. van der Aalst:
Metaverse: How to Approach Its Challenges from a BISE Perspective. Bus. Inf. Syst. Eng. 64(4): 401-406 (2022) - [j260]Wil M. P. van der Aalst:
European leadership in process management. Commun. ACM 65(4): 80-83 (2022) - [j259]Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Utilizing domain knowledge in data-driven process discovery: A literature review. Comput. Ind. 137: 103612 (2022) - [j258]Jing Yang, Chun Ouyang, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede, Yang Yu:
OrdinoR: A framework for discovering, evaluating, and analyzing organizational models using event logs. Decis. Support Syst. 158: 113771 (2022) - [j257]Vincent Bloemen, Sebastiaan J. van Zelst, Wil M. P. van der Aalst, Boudewijn F. van Dongen, Jaco van de Pol:
Aligning observed and modelled behaviour by maximizing synchronous moves and using milestones. Inf. Syst. 103: 101456 (2022) - [j256]Vincenzo Pasquadibisceglie, Annalisa Appice, Giovanna Castellano, Wil M. P. van der Aalst:
PROMISE: Coupling predictive process mining to process discovery. Inf. Sci. 606: 250-271 (2022) - [j255]Jorge Munoz-Gama, Niels Martin, Carlos Fernández-Llatas, Owen A. Johnson, Marcos Sepúlveda, Emmanuel Helm, Victor Galvez-Yanjari, Eric Rojas, Antonio Martinez-Millana, Davide Aloini, Ilaria Angela Amantea, Robert Andrews, Michael Arias, Iris Beerepoot, Elisabetta Benevento, Andrea Burattin, Daniel Capurro, Josep Carmona, Marco Comuzzi, Benjamin Dalmas, Rene de la Fuente, Chiara Di Francescomarino, Claudio Di Ciccio, Roberto Gatta, Chiara Ghidini, Fernanda Gonzalez-Lopez, Gema Ibáñez-Sánchez, Hilda B. Klasky, Angelina Prima Kurniati, Xixi Lu, Felix Mannhardt, Ronny Mans, Mar Marcos, Renata Medeiros de Carvalho, Marco Pegoraro, Simon K. Poon, Luise Pufahl, Hajo A. Reijers, Simon Remy, Stefanie Rinderle-Ma, Lucia Sacchi, Fernando Seoane, Minseok Song, Alessandro Stefanini, Emilio Sulis, Arthur H. M. ter Hofstede, Pieter J. Toussaint, Vicente Traver, Zoe Valero-Ramon, Inge van de Weerd, Wil M. P. van der Aalst, Rob J. B. Vanwersch, Mathias Weske, Moe Thandar Wynn, Francesca Zerbato:
Process mining for healthcare: Characteristics and challenges. J. Biomed. Informatics 127: 103994 (2022) - [j254]Elisabetta Benevento, Davide Aloini, Wil M. P. van der Aalst:
How Can Interactive Process Discovery Address Data Quality Issues in Real Business Settings? Evidence from a Case Study in Healthcare. J. Biomed. Informatics 130: 104083 (2022) - [j253]Philipp Brauner, Manuela Dalibor, Matthias Jarke, Ike Kunze, István Koren, Gerhard Lakemeyer, Martin Liebenberg, Judith Michael, Jan Pennekamp, Christoph Quix, Bernhard Rumpe, Wil M. P. van der Aalst, Klaus Wehrle, Andreas Wortmann, Martina Ziefle:
A Computer Science Perspective on Digital Transformation in Production. ACM Trans. Internet Things 3(2): 15:1-15:32 (2022) - [c554]Jan Niklas Adams, Wil M. P. van der Aalst:
OCπ: Object-Centric Process Insights. Petri Nets 2022: 139-150 - [c553]Lisa Luise Mannel, Wil M. P. van der Aalst:
Discovering Process Models with Long-Term Dependencies While Providing Guarantees and Handling Infrequent Behavior. Petri Nets 2022: 303-324 - [c552]Viki Peeva, Lisa Luise Mannel, Wil M. P. van der Aalst:
From Place Nets to Local Process Models. Petri Nets 2022: 346-368 - [c551]Wil M. P. van der Aalst:
Discovering Directly-Follows Complete Petri Nets from Event Data. A Journey from Process Algebra via Timed Automata to Model Learning 2022: 539-558 - [c550]Jing Yang, Chun Ouyang, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst:
No Time to Dice: Learning Execution Contexts from Event Logs for Resource-Oriented Process Mining. BPM 2022: 163-180 - [c549]Gyunam Park, Janik-Vasily Benzin, Wil M. P. van der Aalst:
Detecting Context-Aware Deviations in Process Executions. BPM (Forum) 2022: 190-206 - [c548]Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann, Wil M. P. van der Aalst:
Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data. BPMDS/EMMSAD@CAiSE 2022: 173-187 - [c547]Daniel Schuster, Lukas Schade, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Temporal Performance Analysis for Block-Structured Process Models in Cortado. CAiSE Forum 2022: 110-119 - [c546]Majid Rafiei, Gamal Elkoumy, Wil M. P. van der Aalst:
Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing. CoopIS 2022: 75-94 - [c545]Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Conformance Checking for Trace Fragments Using Infix and Postfix Alignments. CoopIS 2022: 299-310 - [c544]Tsung-Hao Huang, Wil M. P. van der Aalst:
Discovering Sound Free-Choice Workflow Nets with Non-block Structures. EDOC 2022: 200-216 - [c543]Gyunam Park, Jan Niklas Adams, Wil M. P. van der Aalst:
OPerA: Object-Centric Performance Analysis. ER 2022: 281-292 - [c542]Mahsa Pourbafrani, Firas Gharbi, Wil M. P. van der Aalst:
Process Diagnostics at Coarse-grained Levels. ICEIS (1) 2022: 484-491 - [c541]Daniel Schuster, Michael Martini, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data. ICSOC 2022: 19-35 - [c540]Jan Niklas Adams, Gyunam Park, Sergej Levich, Daniel Schuster, Wil M. P. van der Aalst:
A Framework for Extracting and Encoding Features from Object-Centric Event Data. ICSOC 2022: 36-53 - [c539]Daniel Schuster, Emanuel Domnitsch, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
A Generic Trace Ordering Framework for Incremental Process Discovery. IDA 2022: 264-277 - [c538]Tobias Brockhoff, Merih Seran Uysal, Wil M. P. van der Aalst:
Modeling Digital Shadows in Manufacturing by Using Process Mining. Modellierung (Workshops) 2022: 133-138 - [c537]Gyunam Park, Marco Comuzzi, Wil M. P. van der Aalst:
Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations. RCIS 2022: 159-176 - [c536]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Hybrid Business Process Simulation: Updating Detailed Process Simulation Models Using High-Level Simulations. RCIS 2022: 177-194 - [c535]Alessandro Berti, Minh Phan Nghia, Wil M. P. van der Aalst:
PM4Py-GPU: A High-Performance General-Purpose Library for Process Mining. RCIS 2022: 727-734 - [c534]Mahsa Pourbafrani, Majid Rafiei, Alessandro Berti, Wil M. P. van der Aalst:
Interactive Business Process Comparison Using Conformance and Performance Insights - A Tool. RCIS 2022: 735-743 - [p27]Wil M. P. van der Aalst:
Process Mining: A 360 Degree Overview. Process Mining Handbook 2022: 3-34 - [p26]Wil M. P. van der Aalst:
Foundations of Process Discovery. Process Mining Handbook 2022: 37-75 - [p25]Eduardo González López de Murillas, Hajo A. Reijers, Wil M. P. van der Aalst:
Data-Aware Process Oriented Query Language. Process Querying Methods 2022: 49-83 - [p24]Wil M. P. van der Aalst, Josep Carmona:
Scaling Process Mining to Turn Insights into Actions. Process Mining Handbook 2022: 495-502 - [e37]Wil M. P. van der Aalst, Josep Carmona:
Process Mining Handbook. Lecture Notes in Business Information Processing 448, Springer 2022, ISBN 978-3-031-08847-6 [contents] - [e36]Alfredo Cuzzocrea, Oleg Gusikhin, Wil M. P. van der Aalst, Slimane Hammoudi:
Proceedings of the 11th International Conference on Data Science, Technology and Applications, DATA 2022, Lisbon, Portugal, July 11-13, 2022. SCITEPRESS 2022, ISBN 978-989-758-583-8 [contents] - [i106]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Interactive Process Improvement using Simulation of Enriched Process Trees. CoRR abs/2201.07755 (2022) - [i105]Marco Pegoraro, Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Wil M. P. van der Aalst, Lukas Martin, Gernot Marx:
Analyzing Medical Data with Process Mining: a COVID-19 Case Study. CoRR abs/2202.04625 (2022) - [i104]Alessandro Berti, Anahita Farhang Ghahfarokhi, Gyunam Park, Wil M. P. van der Aalst:
A Scalable Database for the Storage of Object-Centric Event Logs. CoRR abs/2202.05639 (2022) - [i103]Anahita Farhang Ghahfarokhi, Wil M. P. van der Aalst:
A Python Tool for Object-Centric Process Mining Comparison. CoRR abs/2202.05709 (2022) - [i102]Wil M. P. van der Aalst:
How to Write Beautiful Process-and-Data-Science Papers? CoRR abs/2203.09286 (2022) - [i101]Gyunam Park, Marco Comuzzi, Wil M. P. van der Aalst:
Analyzing Process-Aware Information System Updates Using Digital Twins of Organizations. CoRR abs/2203.12969 (2022) - [i100]Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Marco Pegoraro, Muhammad Abdullah, Rahim Bin Shahid, Qasim Sajid, Muhammad Usman Mansoor, Wil M. P. van der Aalst:
A Web-Based Tool for Comparative Process Mining. CoRR abs/2204.00547 (2022) - [i99]Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Event Log Sampling for Predictive Monitoring. CoRR abs/2204.01470 (2022) - [i98]Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst:
An XES Extension for Uncertain Event Data. CoRR abs/2204.04135 (2022) - [i97]Marco Pegoraro, Merih Seran Uysal, Tom-Hendrik Hülsmann, Wil M. P. van der Aalst:
Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data. CoRR abs/2204.04164 (2022) - [i96]Alessandro Berti, Minh Phan Nghia, Wil M. P. van der Aalst:
PM4Py-GPU: a High-Performance General-Purpose Library for Process Mining. CoRR abs/2204.04898 (2022) - [i95]Gyunam Park, Jan Niklas Adams, Wil M. P. van der Aalst:
OPerA: Object-Centric Performance Analysis. CoRR abs/2204.10662 (2022) - [i94]Wil M. P. van der Aalst:
Six Levels of Autonomous Process Execution Management (APEM). CoRR abs/2204.11328 (2022) - [i93]Gyunam Park, Janik-Vasily Benzin, Wil M. P. van der Aalst:
Detecting Context-Aware Deviations in Process Executions. CoRR abs/2206.05532 (2022) - [i92]Timo Rohrer, Anahita Farhang Ghahfarokhi, Mohamed Behery, Gerhard Lakemeyer, Wil M. P. van der Aalst:
Predictive Object-Centric Process Monitoring. CoRR abs/2207.10017 (2022) - [i91]Anahita Farhang Ghahfarokhi, Fatemeh Akoochekian, Fareed Zandkarimi, Wil M. P. van der Aalst:
Clustering Object-Centric Event Logs. CoRR abs/2207.12764 (2022) - [i90]Majid Rafiei, Gamal Elkoumy, Wil M. P. van der Aalst:
Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing. CoRR abs/2208.01886 (2022) - [i89]Jan Niklas Adams, Daniel Schuster, Seth Schmitz, Günther Schuh, Wil M. P. van der Aalst:
Defining Cases and Variants for Object-Centric Event Data. CoRR abs/2208.03235 (2022) - [i88]Christian Kohlschmidt, Mahnaz Sadat Qafari, Wil M. P. van der Aalst:
Detecting Surprising Situations in Event Data. CoRR abs/2208.13515 (2022) - [i87]Jan Niklas Adams, Gyunam Park, Sergej Levich, Daniel Schuster, Wil M. P. van der Aalst:
A Framework for Extracting and Encoding Features from Object-Centric Event Data. CoRR abs/2209.01219 (2022) - [i86]Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Conformance Checking for Trace Fragments Using Infix and Postfix Alignments. CoRR abs/2209.04290 (2022) - [i85]Alessandro Berti, Wil M. P. van der Aalst:
OC-PM: Analyzing Object-Centric Event Logs and Process Models. CoRR abs/2209.09725 (2022) - [i84]Elisabetta Benevento, Marco Pegoraro, Mattia Antoniazzi, Harry H. Beyel, Viki Peeva, Paul Balfanz, Wil M. P. van der Aalst, Lukas Martin, Gernot Marx:
Process Modeling and Conformance Checking in Healthcare: A COVID-19 Case Study. CoRR abs/2209.10897 (2022) - [i83]Gyunam Park, Wil M. P. van der Aalst:
Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs. CoRR abs/2210.12080 (2022) - [i82]Majid Rafiei, Frederik Wangelik, Wil M. P. van der Aalst:
TraVaS: Differentially Private Trace Variant Selection for Process Mining. CoRR abs/2210.14951 (2022) - [i81]Gyunam Park, Aaron Küsters, Mara Tews, Cameron Pitsch, Jonathan Schneider, Wil M. P. van der Aalst:
Explainable Predictive Decision Mining for Operational Support. CoRR abs/2210.16786 (2022) - [i80]Bianka Bakullari, Wil M. P. van der Aalst:
High-Level Event Mining: A Framework. CoRR abs/2211.00006 (2022) - [i79]Daniel Schuster, Michael Martini, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Control-Flow-Based Querying of Process Executions from Partially Ordered Event Data. CoRR abs/2211.04146 (2022) - [i78]Miriam Wagner, Hayyan Helal, Rene Roepke, Sven Judel, Jens Doveren, Sergej Görzen, Pouya Soudmand, Gerhard Lakemeyer, Ulrik Schroeder, Wil M. P. van der Aalst:
A Combined Approach of Process Mining and Rule-based AI for Study Planning and Monitoring in Higher Education. CoRR abs/2211.12190 (2022) - 2021
- [j252]Kevin Bauer, Oliver Hinz, Wil M. P. van der Aalst, Christof Weinhardt:
Expl(AI)n It to Me - Explainable AI and Information Systems Research. Bus. Inf. Syst. Eng. 63(2): 79-82 (2021) - [j251]Daniel Beverungen, Joos C. A. M. Buijs, Jörg Becker, Claudio Di Ciccio, Wil M. P. van der Aalst, Christian Bartelheimer, Jan vom Brocke, Marco Comuzzi, Karsten Kraume, Henrik Leopold, Martin Matzner, Jan Mendling, Nadine Ogonek, Till Post, Manuel Resinas, Kate Revoredo, Adela del-Río-Ortega, Marcello La Rosa, Flávia Maria Santoro, Andreas Solti, Minseok Song, Armin Stein, Matthias Stierle, Verena Wolf:
Seven Paradoxes of Business Process Management in a Hyper-Connected World. Bus. Inf. Syst. Eng. 63(2): 145-156 (2021) - [j250]Christof Weinhardt, Christian Peukert, Oliver Hinz, Wil M. P. van der Aalst:
Welcome to Economies in IS! Bus. Inf. Syst. Eng. 63(4): 325-328 (2021) - [j249]Niels Martin, Dominik Andreas Fischer, Georgi Dimov Kerpedzhiev, Kanika Goel, Sander J. J. Leemans, Maximilian Röglinger, Wil M. P. van der Aalst, Marlon Dumas, Marcello La Rosa, Moe Thandar Wynn:
Opportunities and Challenges for Process Mining in Organizations: Results of a Delphi Study. Bus. Inf. Syst. Eng. 63(5): 511-527 (2021) - [j248]Wil M. P. van der Aalst, Oliver Hinz, Christof Weinhardt:
Resilient Digital Twins. Bus. Inf. Syst. Eng. 63(6): 615-619 (2021) - [j247]Andrea Burattin, Jochen De Weerdt, Boudewijn F. van Dongen, Jan Claes, Wil M. P. van der Aalst:
Special issue on business process intelligence. Computing 103(1): 1-2 (2021) - [j246]Mohammadreza Fani Sani, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
The impact of biased sampling of event logs on the performance of process discovery. Computing 103(6): 1085-1104 (2021) - [j245]Majid Rafiei, Wil M. P. van der Aalst:
Group-based privacy preservation techniques for process mining. Data Knowl. Eng. 134: 101908 (2021) - [j244]Wil M. P. van der Aalst:
Free-choice Nets with Home Clusters are Lucent. Fundam. Informaticae 181(4): 273-302 (2021) - [j243]Dirk Fahland, Vadim Denisov, Wil M. P. van der Aalst:
Inferring Unobserved Events in Systems with Shared Resources and Queues. Fundam. Informaticae 183(3-4): 203-242 (2021) - [j242]Sander J. J. Leemans, Wil M. P. van der Aalst, Tobias Brockhoff, Artem Polyvyanyy:
Stochastic process mining: Earth movers' stochastic conformance. Inf. Syst. 102: 101724 (2021) - [j241]Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst:
Conformance checking over uncertain event data. Inf. Syst. 102: 101810 (2021) - [j240]Reiner Hähnle, Wil M. P. van der Aalst:
Automated model analysis tools and techniques presented at FASE 2019. Int. J. Softw. Tools Technol. Transf. 23(3): 285-287 (2021) - [j239]Alessandro Berti, Wil M. P. van der Aalst:
A Novel Token-Based Replay Technique to Speed Up Conformance Checking and Process Enhancement. Trans. Petri Nets Other Model. Concurr. 15: 1-26 (2021) - [c533]Anahita Farhang Ghahfarokhi, Gyunam Park, Alessandro Berti, Wil M. P. van der Aalst:
OCEL: A Standard for Object-Centric Event Logs. ADBIS (Short Papers) 2021: 169-175 - [c532]Wil M. P. van der Aalst:
Reduction Using Induced Subnets to Systematically Prove Properties for Free-Choice Nets. Petri Nets 2021: 208-229 - [c531]Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Cortado - An Interactive Tool for Data-Driven Process Discovery and Modeling. Petri Nets 2021: 465-475 - [c530]Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst:
PROVED: A Tool for Graph Representation and Analysis of Uncertain Event Data. Petri Nets 2021: 476-486 - [c529]Marco Pegoraro, Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Wil M. P. van der Aalst, Lukas Martin, Gernot Marx:
Analyzing Medical Data with Process Mining: A COVID-19 Case Study. BIS (Workshops) 2021: 39-44 - [c528]Merih Seran Uysal, Dominik Hüser, Wil M. P. van der Aalst:
Optimization-Based Business Process Model Matching. BIS 2021: 61-72 - [c527]Marco Pegoraro, Merih Seran Uysal, David Benedikt Georgi, Wil M. P. van der Aalst:
Text-Aware Predictive Monitoring of Business Processes. BIS 2021: 221-232 - [c526]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Forward-Looking Process Mining. BPM (PhD/Demos) 2021: 56-61 - [c525]Wil M. P. van der Aalst, Luis F. R. Santos:
May I Take Your Order? - On the Interplay Between Time and Order in Process Mining. Business Process Management Workshops 2021: 99-110 - [c524]Majid Rafiei, Alexander Schnitzler, Wil M. P. van der Aalst:
PC4PM: A Tool for Privacy/Confidentiality Preservation in Process Mining. BPM (PhD/Demos) 2021: 106-110 - [c523]Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst:
An XES Extension for Uncertain Event Data. BPM (PhD/Demos) 2021: 116-120 - [c522]Herath Mudiyanselage Nelanga Dilum Bandara, Hendrik Bockrath, Richard Hobeck, Christopher Klinkmüller, Luise Pufahl, Martin Rebesky, Wil M. P. van der Aalst, Ingo Weber:
Event Logs of Ethereum-Based Applications: A Collection of Resources for Process Mining on Blockchain Data. BPM (PhD/Demos) 2021: 161-165 - [c521]Majid Rafiei, Wil M. P. van der Aalst:
Privacy-Preserving Continuous Event Data Publishing. BPM (Forum) 2021: 178-194 - [c520]Richard Hobeck, Christopher Klinkmüller, H. M. N. Dilum Bandara, Ingo Weber, Wil M. P. van der Aalst:
Process Mining on Blockchain Data: A Case Study of Augur. BPM 2021: 306-323 - [c519]Jing Yang, Chun Ouyang, Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, Michael Leyer:
Seeing the Forest for the Trees: Group-Oriented Workforce Analytics. BPM 2021: 345-362 - [c518]Jan Niklas Adams, Sebastiaan J. van Zelst, Lara Quack, Kathrin Hausmann, Wil M. P. van der Aalst, Thomas Rose:
A Framework for Explainable Concept Drift Detection in Process Mining. BPM 2021: 400-416 - [c517]Mahnaz Sadat Qafari, Wil M. P. van der Aalst:
Case Level Counterfactual Reasoning in Process Mining. CAiSE Forum 2021: 55-63 - [c516]Sebastiaan J. van Zelst, Luis F. R. Santos, Wil M. P. van der Aalst:
Data-Driven Process Performance Measurement and Prediction: A Process-Tree-Based Approach. CAiSE Forum 2021: 73-81 - [c515]Gyunam Park, Wil M. P. van der Aalst:
Towards Reliable Business Process Simulation: A Framework to Integrate ERP Systems. BPMDS/EMMSAD@CAiSE 2021: 112-127 - [c514]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Extracting Process Features from Event Logs to Learn Coarse-Grained Simulation Models. CAiSE 2021: 125-140 - [c513]Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Freezing Sub-models During Incremental Process Discovery. ER 2021: 14-24 - [c512]Wil M. P. van der Aalst:
Using Free-Choice Nets for Process Mining and Business Process Management. FedCSIS 2021: 9-15 - [c511]Moe Thandar Wynn, Julian Lebherz, Wil M. P. van der Aalst, Rafael Accorsi, Claudio Di Ciccio, Lakmali Jayarathna, H. M. W. Verbeek:
Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop. ICPM Workshops 2021: 3-16 - [c510]Marco Pegoraro, Bianka Bakullari, Merih Seran Uysal, Wil M. P. van der Aalst:
Probability Estimation of Uncertain Process Trace Realizations. ICPM Workshops 2021: 21-33 - [c509]Daniel Schuster, Lukas Schade, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Visualizing Trace Variants from Partially Ordered Event Data. ICPM Workshops 2021: 34-46 - [c508]Tobias Brockhoff, Merih Seran Uysal, Isabelle Terrier, Heiko Göhner, Wil M. P. van der Aalst:
Analyzing Multi-level BOM-Structured Event Data. ICPM Workshops 2021: 47-59 - [c507]Gyunam Park, Wil M. P. van der Aalst:
Realizing A Digital Twin of An Organization Using Action-oriented Process Mining. ICPM 2021: 104-111 - [c506]Jan Niklas Adams, Wil M. P. van der Aalst:
Precision and Fitness in Object-Centric Process Mining. ICPM 2021: 128-135 - [c505]Mahsa Pourbafrani, Shreya Kar, Sebastian Kaiser, Wil M. P. van der Aalst:
Remaining Time Prediction for Processes with Inter-case Dynamics. ICPM Workshops 2021: 140-153 - [c504]Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Event Log Sampling for Predictive Monitoring. ICPM Workshops 2021: 154-166 - [c503]Chiao-Yun Li, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
An Activity Instance Based Hierarchical Framework for Event Abstraction. ICPM 2021: 160-167 - [c502]Alessandro Berti, Gyunam Park, Majid Rafiei, Wil M. P. van der Aalst:
An Event Data Extraction Approach from SAP ERP for Process Mining. ICPM Workshops 2021: 255-267 - [c501]Luciana Barbieri, Edmundo Roberto Mauro Madeira, Kleber Stroeh, Wil M. P. van der Aalst:
Towards a Natural Language Conversational Interface for Process Mining. ICPM Workshops 2021: 268-280 - [c500]Andrew Pery, Majid Rafiei, Michael Simon, Wil M. P. van der Aalst:
Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities. ICPM Workshops 2021: 395-407 - [c499]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Interactive Process Improvement Using Simulation of Enriched Process Trees. ICSOC Workshops 2021: 61-76 - [c498]