Process Profiling based Synthetic Event Log Generation

Eren Esgin, Pinar Karagoz

2019

Abstract

The goal of process mining is to discover the process behavior from the runtime information of process executions. Having labeled process logs is crucial for process mining research. However, real life event logs at process-aware information systems are mostly partially assigned to case identifiers, known as unlabeled event log problem. As a remedy to labeled data need in process mining research, we propose an approach to generate synthetic event logs according to the provided process profile, which outlines the activity vocabulary and structure of the corresponding business process. We evaluate the performance of our prototypical implementation in term of compatible log generation under varying parameter setting complexities.

Paper Citation

in Harvard Style

Esgin E. and Karagoz P. (2019). Process Profiling based Synthetic Event Log Generation. In Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR; ISBN 978-989-758-382-7, SciTePress, pages 516-524. DOI: 10.5220/0008363805160524

in Bibtex Style

@conference{kdir19,
author={Eren Esgin and Pinar Karagoz},
title={Process Profiling based Synthetic Event Log Generation},
booktitle={Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR},
year={2019},
pages={516-524},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008363805160524},
isbn={978-989-758-382-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - Volume 1: KDIR
TI - Process Profiling based Synthetic Event Log Generation
SN - 978-989-758-382-7
AU - Esgin E.
AU - Karagoz P.
PY - 2019
SP - 516
EP - 524
DO - 10.5220/0008363805160524
PB - SciTePress