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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
- 2023
[j278]Majid Rafiei
, Wil M. P. van der Aalst:
An Abstraction-Based Approach for Privacy-Aware Federated Process Mining. IEEE Access 11: 33697-33714 (2023)- [j277]Wil M. P. van der Aalst, Oliver Hinz, Christof Weinhardt:
Sustainable Systems Engineering. Bus. Inf. Syst. Eng. 65(1): 1-6 (2023) - [j276]Timm Teubner, Christoph M. Flath, Christof Weinhardt, Wil M. P. van der Aalst, Oliver Hinz:
Welcome to the Era of ChatGPT et al. Bus. Inf. Syst. Eng. 65(2): 95-101 (2023) - [j275]Michael Nofer, Kevin Bauer, Oliver Hinz, Wil M. P. van der Aalst, Christof Weinhardt:
Quantum Computing. Bus. Inf. Syst. Eng. 65(4): 361-367 (2023) - [j274]Gyunam Park, Daniel Schuster, Wil M. P. van der Aalst:
Pattern-based action engine: Generating process management actions using temporal patterns of process-centric problems. Comput. Ind. 153: 104020 (2023) - [j273]Jan Niklas Adams, Gyunam Park, Wil M. P. van der Aalst:
Preserving complex object-centric graph structures to improve machine learning tasks in process mining. Eng. Appl. Artif. Intell. 125: 106764 (2023) - [j272]Jan Niklas Adams, Sebastiaan J. van Zelst, Thomas Rose, Wil M. P. van der Aalst:
Explainable concept drift in process mining. Inf. Syst. 114: 102177 (2023) - [j271]Wil M. P. van der Aalst, Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini, Humam Kourani:
Discovering hybrid process models with bounds on time and complexity: When to be formal and when not? Inf. Syst. 116: 102214 (2023) - [j270]Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Performance-preserving event log sampling for predictive monitoring. J. Intell. Inf. Syst. 61(1): 53-82 (2023) - [j269]Luciana Barbieri, Edmundo R. M. Madeira, Kleber Stroeh, Wil M. P. van der Aalst:
A natural language querying interface for process mining. J. Intell. Inf. Syst. 61(1): 113-142 (2023) - [j268]Jan Niklas Adams, Cameron Pitsch, Tobias Brockhoff, Wil M. P. van der Aalst:
An Experimental Evaluation of Process Concept Drift Detection. Proc. VLDB Endow. 16(8): 1856-1869 (2023) - [j267]Michael Martini, Daniel Schuster, Wil M. P. van der Aalst:
Mining Frequent Infix Patterns from Concurrency-Aware Process Execution Variants. Proc. VLDB Endow. 16(10): 2666-2678 (2023) - [j266]Daniel Schuster, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Cortado: A dedicated process mining tool for interactive process discovery. SoftwareX 22: 101373 (2023) - [j265]Alessandro Berti, Wil M. P. van der Aalst:
OC-PM: analyzing object-centric event logs and process models. Int. J. Softw. Tools Technol. Transf. 25(1): 1-17 (2023) - [c601]Wil M. P. van der Aalst:
Twin Transitions Powered By Event Data - Using Object-Centric Process Mining To Make Processes Digital and Sustainable. ATAED/PN4TT@Petri Nets 2023 - [c600]Aaron Küsters, Wil M. P. van der Aalst:
Revisiting the Alpha Algorithm To Enable Real-Life Process Discovery Applications. ATAED/PN4TT@Petri Nets 2023 - [c599]Christian Rennert, Lisa Luise Mannel, Wil M. P. van der Aalst:
Improving the eST-Miner Models by Replacing Imprecise Structures Using Place Projection. ATAED/PN4TT@Petri Nets 2023 - [c598]Yisong Zhang, Wil M. P. van der Aalst:
Explorative Process Discovery Using Activity Projections. Petri Nets 2023: 229-239 - [c597]Chiao-Yun Li, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Event Abstraction for Partial Order Patterns. BPM 2023: 38-54 - [c596]Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Incremental Discovery of Process Models Using Trace Fragments. BPM 2023: 55-73 - [c595]Gal Engelberg, Moshe Hadad, Marco Pegoraro, Pnina Soffer, Ethan Hadar, Wil M. P. van der Aalst:
An Uncertainty-Aware Event Log of Network Traffic. BPM (Demos / Resources Forum) 2023: 67-71 - [c594]Timo Pohl, Alessandro Berti, Mahnaz Sadat Qafari, Wil M. P. van der Aalst:
A Collection of Simulated Event Logs for Fairness Assessment in Process Mining. BPM (Demos / Resources Forum) 2023: 87-91 - [c593]Zahra Sadeghibogar, Alessandro Berti, Marco Pegoraro, Wil M. P. van der Aalst:
SLURMminer: A Tool for SLURM System Analysis with Process Mining. BPM (Demos / Resources Forum) 2023: 97-101 - [c592]Mohammadreza Fani Sani, Juan J. Garza Gonzalez, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Alignment Approximator: A ProM Plug-In to Approximate Conformance Statistics. BPM (Demos / Resources Forum) 2023: 102-106 - [c591]Eduardo Goulart Rocha, Wil M. P. van der Aalst:
Polynomial-Time Conformance Checking for Process Trees. BPM 2023: 109-125 - [c590]Bianka Bakullari, Jules van Thoor, Dirk Fahland, Wil M. P. van der Aalst:
The Interplay Between High-Level Problems and the Process Instances that Give Rise to Them. BPM (Forum) 2023: 145-162 - [c589]Mahsa Pourbafrani, Niels Lücking, Matthieu Lucke, Wil M. P. van der Aalst:
Steady State Estimation for Business Process Simulations. BPM (Forum) 2023: 178-195 - [c588]Tsung-Hao Huang, Wil M. P. van der Aalst:
Unblocking Inductive Miner - While Preserving Desirable Properties. BPMDS/EMMSAD@CAiSE 2023: 327-342 - [c587]Chiao-Yun Li, Aparna Joshi, Nicholas T. L. Tam, Sean Shing Fung Lau, Jinhui Huang, Tejaswini Shinde, Wil M. P. van der Aalst:
Rectify Sensor Data in IoT: A Case Study on Enabling Process Mining for Logistic Process in an Air Cargo Terminal. CoopIS 2023: 293-310 - [c586]Harry H. Beyel, Omar Makke, Fangbo Yuan, Oleg Gusikhin, Wil M. P. van der Aalst:
Analyzing Cyber-Physical Systems in Cars: A Case Study. DATA 2023: 195-204 - [c585]Anahita Farhang Ghahfarokhi, Fatemeh Akoochekian, Fareed Zandkarimi, Wil M. P. van der Aalst:
Clustering Object-Centric Event Logs. DATA 2023: 444-451 - [c584]Wil M. P. van der Aalst:
Toward More Realistic Simulation Models Using Object-Centric Process Mining. ECMS 2023: 5-13 - [c583]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Data-Driven Simulation In Process Mining: Introducing A Reference Model. ECMS 2023: 411-420 - [c582]Lukas Liss, Jan Niklas Adams, Wil M. P. van der Aalst:
Object-Centric Alignments. ER 2023: 201-219 - [c581]Benedikt Knopp, Mahsa Pourbafrani, Wil M. P. van der Aalst:
Discovering Object-Centric Process Simulation Models. ICPM 2023: 81-88 - [c580]Humam Kourani, Daniel Schuster, Wil M. P. van der Aalst:
Scalable Discovery of Partially Ordered Workflow Models with Formal Guarantees. ICPM 2023: 89-96 - [c579]Felix C. Groß, Lisa Luise Mannel, Wil M. P. van der Aalst:
Enhancing the Applicability of the eST-Miner: Efficient Precision-Guided Implicit Place Avoidance. ICPM 2023: 121-128 - [c578]Nina Graves, István Koren, Wil M. P. van der Aalst:
ReThink Your Processes! A Review of Process Mining for Sustainability. ICT4S 2023: 164-175 - [c577]Majid Rafiei, Frederik Wangelik, Mahsa Pourbafrani, Wil M. P. van der Aalst:
TraVaG: Differentially Private Trace Variant Generation Using GANs. RCIS 2023: 415-431 - [c576]Ali Norouzifar, Wil M. P. van der Aalst:
Discovering Process Models that Support Desired Behavior and Avoid Undesired Behavior. SAC 2023: 365-368 - [d2]Benedikt Knopp, Wil M. P. van der Aalst:
Order Management Object-centric Event Log in OCEL 2.0 Standard. Zenodo, 2023
- [d1]Benedikt Knopp, Wil M. P. van der Aalst:
Order Management Object-centric Event Log in OCEL 2.0 Standard. Zenodo, 2023 - [i127]Tsung-Hao Huang, Wil M. P. van der Aalst:
Comparing Ordering Strategies For Process Discovery Using Synthesis Rules. CoRR abs/2301.02182 (2023) - [i126]Tsung-Hao Huang, Wil M. P. van der Aalst:
Discovering Sound Free-choice Workflow Nets With Non-block Structures. CoRR abs/2301.02185 (2023) - [i125]Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
Performance-Preserving Event Log Sampling for Predictive Monitoring. CoRR abs/2301.07624 (2023) - [i124]Ali Norouzifar, Wil M. P. van der Aalst:
Discovering Process Models that Support Desired Behavior and Avoid Undesired Behavior. CoRR abs/2302.10984 (2023) - [i123]Majid Rafiei, Frederik Wangelik, Mahsa Pourbafrani, Wil M. P. van der Aalst:
TraVaG: Differentially Private Trace Variant Generation Using GANs. CoRR abs/2303.16704 (2023) - [i122]Lukas Liss, Jan Niklas Adams, Wil M. P. van der Aalst:
Object-Centric Alignments. CoRR abs/2305.05113 (2023) - [i121]Aaron Küsters, Wil M. P. van der Aalst:
Revisiting the Alpha Algorithm To Enable Real-Life Process Discovery Applications - Extended Report. CoRR abs/2305.17767 (2023) - [i120]Paolo Ceravolo, Sylvio Barbon Junior, Ernesto Damiani, Wil M. P. van der Aalst:
Tailoring Machine Learning for Process Mining. CoRR abs/2306.10341 (2023) - [i119]Timo Pohl, Alessandro Berti, Mahnaz Sadat Qafari, Wil M. P. van der Aalst:
A Collection of Simulated Event Logs for Fairness Assessment in Process Mining. CoRR abs/2306.11453 (2023) - [i118]Alessandro Berti, Daniel Schuster, Wil M. P. van der Aalst:
Abstractions, Scenarios, and Prompt Definitions for Process Mining with LLMs: A Case Study. CoRR abs/2307.02194 (2023) - [i117]Zahra Sadeghibogar, Alessandro Berti, Marco Pegoraro, Wil M. P. van der Aalst:
Applying Process Mining on Scientific Workflows: a Case Study. CoRR abs/2307.02833 (2023) - [i116]Bianka Bakullari, Jules van Thoor, Dirk Fahland, Wil M. P. van der Aalst:
The Interplay Between High-Level Problems and The Process Instances That Give Rise To Them. CoRR abs/2309.01571 (2023) - [i115]Majid Rafiei, Duygu Bayrak, Mahsa Pourbafrani, Gyunam Park, Hayyan Helal, Gerhard Lakemeyer, Wil M. P. van der Aalst:
Extracting Rules from Event Data for Study Planning. CoRR abs/2310.02735 (2023) - [i114]Gyunam Park, Sevde Aydin, Cuneyt Ugur, Wil M. P. van der Aalst:
Analyzing An After-Sales Service Process Using Object-Centric Process Mining: A Case Study. CoRR abs/2310.10174 (2023) - [i113]Jan Niklas Adams, Jari Peeperkorn, Tobias Brockhoff, Isabelle Terrier, Heiko Göhner, Merih Seran Uysal, Seppe vanden Broucke, Jochen De Weerdt, Wil M. P. van der Aalst:
Discovering High-Quality Process Models Despite Data Scarcity. CoRR abs/2310.11332 (2023) - [i112]Viki Peeva, Wil M. P. van der Aalst:
Grouping Local Process Models. CoRR abs/2311.03040 (2023) - [i111]Alessandro Berti, Marco Montali, Wil M. P. van der Aalst:
Advancements and Challenges in Object-Centric Process Mining: A Systematic Literature Review. CoRR abs/2311.08795 (2023) - 2022
- [j264]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Discovering System Dynamics Simulation Models Using Process Mining. IEEE Access 10: 78527-78547 (2022) - [j263]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) - [j262]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) - [j261]Ali Sunyaev, Christof Weinhardt, Wil M. P. van der Aalst, Oliver Hinz:
BISE Student. Bus. Inf. Syst. Eng. 64(6): 701-706 (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]Jan Niklas Adams, Gyunam Park, Wil M. P. van der Aalst:
ocpa: A Python library for object-centric process analysis. Softw. Impacts 14: 100438 (2022) - [j252]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) - [c575]Jan Niklas Adams, Wil M. P. van der Aalst:
OCπ: Object-Centric Process Insights. Petri Nets 2022: 139-150 - [c574]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 - [c573]Viki Peeva, Lisa Luise Mannel, Wil M. P. van der Aalst:
From Place Nets to Local Process Models. Petri Nets 2022: 346-368 - [c572]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 - [c571]Humam Kourani, Chiara Di Francescomarino, Chiara Ghidini, Wil M. P. van der Aalst, Sebastiaan J. van Zelst:
Mining for Long-Term Dependencies in Causal Graphs. Business Process Management Workshops 2022: 117-131 - [c570]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 - [c569]Gyunam Park, Janik-Vasily Benzin, Wil M. P. van der Aalst:
Detecting Context-Aware Deviations in Process Executions. BPM (Forum) 2022: 190-206 - [c568]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 - [c567]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 - [c566]Majid Rafiei, Gamal Elkoumy, Wil M. P. van der Aalst:
Quantifying Temporal Privacy Leakage in Continuous Event Data Publishing. CoopIS 2022: 75-94 - [c565]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 - [c564]Tsung-Hao Huang, Wil M. P. van der Aalst:
Discovering Sound Free-Choice Workflow Nets with Non-block Structures. EDOC 2022: 200-216 - [c563]Gyunam Park, Jan Niklas Adams, Wil M. P. van der Aalst:
OPerA: Object-Centric Performance Analysis. ER 2022: 281-292 - [c562]Mahsa Pourbafrani, Firas Gharbi, Wil M. P. van der Aalst:
Process Diagnostics at Coarse-grained Levels. ICEIS (1) 2022: 484-491 - [c561]Alexandre Goossens, Johannes De Smedt, Jan Vanthienen, Wil M. P. van der Aalst:
Enhancing Data-Awareness of Object-Centric Event Logs. ICPM Workshops 2022: 18-30 - [c560]Julian Weber, Gyunam Park, Majid Rafiei, Wil M. P. van der Aalst:
Interactive Process Identification and Selection from SAP ERP (Extended Abstract). ICPM Doctoral Consortium / Demo 2022: 61-64 - [c559]Adam T. Burke, Sander J. J. Leemans, Moe Thandar Wynn, Wil M. P. van der Aalst, Arthur H. M. ter Hofstede:
Stochastic Process Model-Log Quality Dimensions: An Experimental Study. ICPM 2022: 80-87 - [c558]Timo Pohl, Mahnaz Sadat Qafari, Wil M. P. van der Aalst:
Discrimination-Aware Process Mining: A Discussion. ICPM Workshops 2022: 101-113 - [c557]Majid Rafiei, Frederik Wangelik, Wil M. P. van der Aalst:
TraVaS: Differentially Private Trace Variant Selection for Process Mining. ICPM Workshops 2022: 114-126 - [c556]Jan Niklas Adams, Daniel Schuster, Seth Schmitz, Günther Schuh, Wil M. P. van der Aalst:
Defining Cases and Variants for Object-Centric Event Data. ICPM 2022: 128-135 - [c555]Bianka Bakullari, Wil M. P. van der Aalst:
High-Level Event Mining: A Framework. ICPM 2022: 136-143 - [c554]Christian Kohlschmidt, Mahnaz Sadat Qafari, Wil M. P. van der Aalst:
Detecting Surprising Situations in Event Data. ICPM Workshops 2022: 216-228 - [c553]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 - Case Study. ICPM Workshops 2022: 315-327 - [c552]Harry H. Beyel, Wil M. P. van der Aalst:
Creating Translucent Event Logs to Improve Process Discovery. ICPM Workshops 2022: 435-447 - [c551]Gyunam Park, Wil M. P. van der Aalst:
Monitoring Constraints in Business Processes Using Object-Centric Constraint Graphs. ICPM Workshops 2022: 479-492 - [c550]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. ICPM Workshops 2022: 513-525 - [c549]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 - [c548]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 - [c547]Tsung-Hao Huang, Wil M. P. van der Aalst:
Comparing Ordering Strategies for Process Discovery Using Synthesis Rules. ICSOC Workshops 2022: 40-52 - [c546]Gyunam Park, Aaron Küsters, Mara Tews, Cameron Pitsch, Jonathan Schneider, Wil M. P. van der Aalst:
Explainable Predictive Decision Mining for Operational Support. ICSOC Workshops 2022: 66-79 - [c545]Mahsa Pourbafrani, Firas Gharbi, Wil M. P. van der Aalst:
A Tool for Business Processes Diagnostics. ICSOC Workshops 2022: 350-354 - [c544]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 - [c543]Chiao-Yun Li, Sebastiaan J. van Zelst, Wil M. P. van der Aalst:
A Framework for Automated Abstraction Class Detection for Event Abstraction. ISDA (2) 2022: 126-136 - [c542]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 - [c541]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 - [c540]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 - [c539]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 - [c538]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 - [p28]Wil M. P. van der Aalst:
Process Mining: A 360 Degree Overview. Process Mining Handbook 2022: 3-34 - [p27]Wil M. P. van der Aalst:
Foundations of Process Discovery. Process Mining Handbook 2022: 37-75 - [p26]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 - [p25]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] - [i110]Mahsa Pourbafrani, Wil M. P. van der Aalst:
Interactive Process Improvement using Simulation of Enriched Process Trees. CoRR abs/2201.07755 (2022) - [i109]Marco Pegoraro, Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Wil M. P. van der Aalst, Lukas Martin,