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Authors: Eren Esgin 1 and Pinar Karagoz 2

Affiliations: 1 MBIS R&D Center, AI Research, Istanbul, Turkey, Middle East Technical University, Informatics Institute, Ankara and Turkey ; 2 Middle East Technical University, Computer Engineering Department, Ankara and Turkey

Keyword(s): Case Identifier, Process Mining, Process Profile, Synthetic Log Generator (SynLogGen), Unlabeled Event Log.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Intelligence Applications ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Process Mining ; Symbolic Systems

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.

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Paper citation in several formats:
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) - KDIR; ISBN 978-989-758-382-7; ISSN 2184-3228, SciTePress, pages 516-524. DOI: 10.5220/0008363805160524

@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) - KDIR},
year={2019},
pages={516-524},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008363805160524},
isbn={978-989-758-382-7},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) - KDIR
TI - Process Profiling based Synthetic Event Log Generation
SN - 978-989-758-382-7
IS - 2184-3228
AU - Esgin, E.
AU - Karagoz, P.
PY - 2019
SP - 516
EP - 524
DO - 10.5220/0008363805160524
PB - SciTePress