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Wil 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,