activity
20242026
most citedFramework for Grouping Local Process Models

1 citations · 1 across the 1 of their papers we have counts for

collaborators

9 papers

cs.LG20261 cited

Framework for Grouping Local Process Models

Viki Peeva, Wil M. P. van der Aalst

Local Process Models (LPMs) are an underexplored concept in process mining. LPMs describe patterns in event data considering sequence, choice, concurrency, and loop. In recent year…

cs.AI2025

Discriminative Rule Learning for Outcome-Guided Process Model Discovery

Ali Norouzifar, Wil van der Aalst

Event logs extracted from information systems offer a rich foundation for understanding and improving business processes. In many real-world applications, it is possible to disting…

cs.AI2025

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…

cs.SE2025

Online Discovery of Simulation Models for Evolving Business Processes (Extended Version)

Francesco Vinci, Gyunam Park, Wil van der Aalst +1

Business Process Simulation (BPS) refers to techniques designed to replicate the dynamic behavior of a business process. Many approaches have been proposed to automatically discove…

cs.DB2025

eST Miner -- Process Discovery Based on Firing Partial Orders

Sabine Folz-Weinstein, Christian Rennert, Lisa Luise Mannel +2

Process discovery generates process models from event logs. Traditionally, an event log is defined as a multiset of traces, where each trace is a sequence of events. The total orde…

cs.LO2025

Translating Workflow Nets into the Partially Ordered Workflow Language

Humam Kourani, Gyunam Park, Wil van der Aalst

The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. However, its adoptio…