Process Diagnostics at Coarse-grained Levels

Mahsa Pourbafrani, Firas Gharbi, Wil M. P. van der Aalst

2022

Abstract

Process mining enables the discovery of actionable insights from event data of organizations. Process analysis techniques typically focus on process executions at detailed, i.e., fine-grained levels, which might lead to missed insights. For instance, the relation between the waiting time of process instances and the current states of the process including resources workload is hidden at fine-grained level analysis. We propose an approach for coarse-grained diagnostics of processes while decreasing user dependency and ad hoc decisions compared to the current approaches. Our approach begins with the analysis of processes at fine-grained levels focusing on performance and compliance and proceeds with an automated translation of processes to the time series format, i.e., coarse-grained process logs. We exploit time series analysis techniques to uncover the underlying patterns and potential causes and effects in processes. The evaluation using real and synthetic event logs indicates the efficiency of our approach to discover overlooked insights at fine-grained levels.

Paper Citation

in Harvard Style

Pourbafrani M., Gharbi F. and van der Aalst W. (2022). Process Diagnostics at Coarse-grained Levels. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-569-2, pages 484-491. DOI: 10.5220/0011035000003179

in Bibtex Style

@conference{iceis22,
author={Mahsa Pourbafrani and Firas Gharbi and Wil M. P. van der Aalst},
title={Process Diagnostics at Coarse-grained Levels},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2022},
pages={484-491},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011035000003179},
isbn={978-989-758-569-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Process Diagnostics at Coarse-grained Levels
SN - 978-989-758-569-2
AU - Pourbafrani M.
AU - Gharbi F.
AU - van der Aalst W.
PY - 2022
SP - 484
EP - 491
DO - 10.5220/0011035000003179