Clustering Object-Centric Event Logs

Anahita Ghahfarokhi, Fatemeh Akoochekian, Fareed Zandkarimi, Wil van der Aalst

2023

Abstract

Process mining provides various algorithms to analyze process executions based on event data. Process discovery, the most prominent category of process mining techniques, aims to discover process models from event logs. However, it leads to spaghetti models when working with real-life data. To reduce the complexity of process models, several clustering techniques have been proposed on top of event logs with a single case notion. However, in real-life processes often multiple objects are involved in a process. Recently, Object-Centric Event Logs (OCELs) have been introduced to capture the information of such processes, and several process discovery techniques have been developed on top of OCELs. Yet, the output of the discovery techniques leads to complex models. In this paper, we propose a clustering-based approach to cluster similar objects in OCELs to simplify the obtained process models. Using a case study of a real Business-to-Business (B2B) process, we demonstrate that our approach reduces the complexity of the models and generates coherent subsets of objects which help the end-users gain insights into the process.

Paper Citation

in Harvard Style

Ghahfarokhi A., Akoochekian F., Zandkarimi F. and van der Aalst W. (2023). Clustering Object-Centric Event Logs. In Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-664-4, SciTePress, pages 444-451. DOI: 10.5220/0012123900003541

in Bibtex Style

@conference{data23,
author={Anahita Ghahfarokhi and Fatemeh Akoochekian and Fareed Zandkarimi and Wil van der Aalst},
title={Clustering Object-Centric Event Logs},
booktitle={Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2023},
pages={444-451},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012123900003541},
isbn={978-989-758-664-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Clustering Object-Centric Event Logs
SN - 978-989-758-664-4
AU - Ghahfarokhi A.
AU - Akoochekian F.
AU - Zandkarimi F.
AU - van der Aalst W.
PY - 2023
SP - 444
EP - 451
DO - 10.5220/0012123900003541
PB - SciTePress