Process Mining to Discover the Global Process from its Fragments’ Executions

Minh Khoi Nguyen, Hanh Nhi Tran, Ileana Ober

2022

Abstract

Process analysis to improve performance and detect anomalies is important for process management. However, such an analysis requires a global process model that is sometimes hard to get, especially for complex processes involving various teams. This is also an obstacle for BAPE (Bottom-up Artifact-centric Process Environment), an artifact-centric process management environment that allows splitting a process into several fragments which are separately modeled and enacted by different actors. Thus, the knowledge about the whole process is distributed over the involved teams and an overview on the complete process is missing. This paper presents the integration of process mining into BAPE in order to construct the overall process model from the execution data of process fragments.

Paper Citation

in Harvard Style

Nguyen M., Tran H. and Ober I. (2022). Process Mining to Discover the Global Process from its Fragments’ Executions. In Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-568-5, pages 363-370. DOI: 10.5220/0011044800003176

in Bibtex Style

@conference{enase22,
author={Minh Khoi Nguyen and Hanh Nhi Tran and Ileana Ober},
title={Process Mining to Discover the Global Process from its Fragments’ Executions},
booktitle={Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2022},
pages={363-370},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011044800003176},
isbn={978-989-758-568-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Process Mining to Discover the Global Process from its Fragments’ Executions
SN - 978-989-758-568-5
AU - Nguyen M.
AU - Tran H.
AU - Ober I.
PY - 2022
SP - 363
EP - 370
DO - 10.5220/0011044800003176