11 citations · 21 across the 10 of their papers we have counts for
11 papers
Computational Complexity of Alignments
Christopher T. Schwanen, Wied Pakusa, Wil M. P. van der Aalst
In process mining, alignments quantify the degree of deviation between an observed event trace and a business process model and constitute one of the most important conformance che…
Hierarchical Decomposition of Separable Workflow-Nets
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. While early versions…
Knowledge-Driven Hallucination in Large Language Models: An Empirical Study on Process Modeling
Humam Kourani, Anton Antonov, Alessandro Berti +1
The utility of Large Language Models (LLMs) in analytical tasks is rooted in their vast pre-trained knowledge, which allows them to interpret ambiguous inputs and infer missing inf…
No AI Without PI! Object-Centric Process Mining as the Enabler for Generative, Predictive, and Prescriptive Artificial Intelligence
Wil M. P. van der Aalst
The uptake of Artificial Intelligence (AI) impacts the way we work, interact, do business, and conduct research. However, organizations struggle to apply AI successfully in industr…
OCPQ: Object-Centric Process Querying & Constraints
Aaron Küsters, Wil M. P. van der Aalst
Process querying is used to extract information and insights from process execution data. Similarly, process constraints can be checked against input data, yielding information on…
CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Alessandro Berti, Wil M. P. van der Aalst
Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they…
ProReco: A Process Discovery Recommender System
Tsung-Hao Huang, Tarek Junied, Marco Pegoraro +1
Process discovery aims to automatically derive process models from historical execution data (event logs). While various process discovery algorithms have been proposed in the last…
Control-flow anomaly detection by process mining-based feature extraction and dimensionality reduction
Francesco Vitale, Marco Pegoraro, Wil M. P. van der Aalst +1
The business processes of organizations may deviate from normal control flow due to disruptive anomalies, including unknown, skipped, and wrongly-ordered activities. To identify th…
Object-Centric Local Process Models
Viki Peeva, Marvin Porsil, Wil M. P. van der Aalst
Process mining is a technology that helps understand, analyze, and improve processes. It has been present for around two decades, and although initially tailored for business proce…
Leveraging Large Language Models for Enhanced Process Model Comprehension
Humam Kourani, Alessandro Berti, Jasmin Hennrich +6
In Business Process Management (BPM), effectively comprehending process models is crucial yet poses significant challenges, particularly as organizations scale and processes become…
Imposing Rules in Process Discovery: an Inductive Mining Approach
Ali Norouzifar, Marcus Dees, Wil van der Aalst
Process discovery aims to discover descriptive process models from event logs. These discovered process models depict the actual execution of a process and serve as a foundational…