105 citations · 1.4k across the 94 of their papers we have counts for
19 papers · 1 filter
Process Variant Analysis Across Continuous Features: A Novel Framework
Ali Norouzifar, Majid Rafiei, Marcus Dees +1
Extracted event data from information systems often contain a variety of process executions making the data complex and difficult to comprehend. Unlike current research which only…
Process Modeling With Large Language Models
Humam Kourani, Alessandro Berti, Daniel Schuster +1
In the realm of Business Process Management (BPM), process modeling plays a crucial role in translating complex process dynamics into comprehensible visual representations, facilit…
Developing a High-Performance Process Mining Library with Java and Python Bindings in Rust
Aaron Küsters, Wil M. P. van der Aalst
The most commonly used open-source process mining software tools today are ProM and PM4Py, written in Java and Python, respectively. Such high-level, often interpreted, programming…
Conformance Checking for Trace Fragments Using Infix and Postfix Alignments
Daniel Schuster, Niklas Föcking, Sebastiaan J. van Zelst +1
Conformance checking deals with collating modeled process behavior with observed process behavior recorded in event data. Alignments are a state-of-the-art technique to detect, loc…
A Web-Based Tool for Comparative Process Mining
Madhavi Bangalore Shankara Narayana, Elisabetta Benevento, Marco Pegoraro +5
Process mining techniques enable the analysis of a wide variety of processes using event data. Among the available process mining techniques, most consider a single process perspec…
Trustworthy Artificial Intelligence and Process Mining: Challenges and Opportunities
Andrew Pery, Majid Rafiei, Michael Simon +1
The premise of this paper is that compliance with Trustworthy AI governance best practices and regulatory frameworks is an inherently fragmented process spanning across diverse org…
Model Independent Error Bound Estimation for Conformance Checking Approximation
Mohammadreza Fani Sani, Martin Kabierski, Sebastiaan J. van Zelst +1
Conformance checking techniques allow us to quantify the correspondence of a process's execution, captured in event data, w.r.t., a reference process model. In this context, alignm…
A Python Extension to Simulate Petri nets in Process Mining
M. Pourbafrani, Sandhya Vasudevan, Faizan Zafar +3
The capability of process mining techniques in providing extensive knowledge and insights into business processes has been widely acknowledged. Process mining techniques support di…
Discovering Object-Centric Petri Nets
Wil M. P. van der Aalst, Alessandro Berti
Techniques to discover Petri nets from event data assume precisely one case identifier per event. These case identifiers are used to correlate events, and the resulting discovered…
PMSD: Data-Driven Simulation Using System Dynamics and Process Mining
Mahsa Pourbafrani, Wil M. P. van der Aalst
Process mining extends far beyond process discovery and conformance checking, and also provides techniques for bottleneck analysis and organizational mining. However, these techniq…
An Open-Source Integration of Process Mining Features into the Camunda Workflow Engine: Data Extraction and Challenges
Alessandro Berti, Wil van der Aalst, David Zang +1
Process mining provides techniques to improve the performance and compliance of operational processes. Although sometimes the term "workflow mining" is used, the application in the…
A Novel Token-Based Replay Technique to Speed Up Conformance Checking and Process Enhancement
Alessandro Berti, Wil van der Aalst
Token-based replay used to be the standard way to conduct conformance checking. With the uptake of more advanced techniques (e.g., alignment based), token-based replay got abandone…
Efficient Logging for Blockchain Applications
Christopher Klinkmüller, Ingo Weber, Alexander Ponomarev +2
Second generation blockchain platforms, like Ethereum, can store arbitrary data and execute user-defined smart contracts. Due to the shared nature of blockchains, understanding the…
Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science
Alessandro Berti, Sebastiaan J. van Zelst, Wil van der Aalst
Process mining, i.e., a sub-field of data science focusing on the analysis of event data generated during the execution of (business) processes, has seen a tremendous change over t…
What if Process Predictions are not followed by Good Recommendations? (Technical Report)
Marcus Dees, Massimiliano de Leoni, Wil M. P. van der Aalst +1
Process-aware Recommender systems (PAR systems) are information systems that aim to monitor process executions, predict their outcome, and recommend effective interventions to redu…
An Integrated Framework for Process Discovery Algorithm Evaluation
Toon Jouck, Alfredo Bolt, Benoît Depaire +2
Process mining offers techniques to exploit event data by providing insights and recommendations to improve business processes. The growing amount of algorithms for process discove…
Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)
Maikel Leemans, Wil M. P. van der Aalst, Mark G. J. van den Brand
This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that levera…
Blockchains for Business Process Management - Challenges and Opportunities
Jan Mendling, Ingo Weber, Wil van der Aalst +29
Blockchain technology promises a sizable potential for executing inter-organizational business processes without requiring a central party serving as a single point of trust (and f…
Learning Hybrid Process Models From Events: Process Discovery Without Faking Confidence
Wil M. P. van der Aalst, Riccardo De Masellis, Chiara Di Francescomarino +1
Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a "picture" not allowing for any form of…