3 citations · 9 across the 13 of their papers we have counts for
16 papers
Inductive Process Discovery from Partially Ordered Event Data
Humam Kourani, Tom Breuer, Gyunam Park +1
The Inductive Miner (IM) family is a prominent class of process discovery techniques, combining efficient recursive decomposition with soundness-by-construction guarantees. However…
Beyond Control-Flow: Integrating the Resource Perspective into Multi-Collaborative Process Modeling from Text
Anton Antonov, Humam Kourani, Alessandro Berti +1
Process modeling is a sub-domain of Business Process Management (BPM) focused on the translation of process artifacts into formal models. This task traditionally requires extensive…
PMAx: An Agentic Framework for AI-Driven Process Mining
Anton Antonov, Humam Kourani, Alessandro Berti +2
Process mining provides powerful insights into organizational workflows, but extracting these insights typically requires expertise in specialized query languages and data science…
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…
Integrating Domain Knowledge into Process Discovery Using Large Language Models
Ali Norouzifar, Humam Kourani, Marcus Dees +1
Process discovery aims to derive process models from event logs, providing insights into operational behavior and forming a foundation for conformance checking and process improvem…
Revealing Inherent Concurrency in Event Data: A Partial Order Approach to Process Discovery
Humam Kourani, Gyunam Park, Wil M. P. van der Aalst
Process discovery algorithms traditionally linearize events, failing to capture the inherent concurrency of real-world processes. While some techniques can handle partially ordered…