Towards a Conceptual Model for Undesired Situation Detection through Process Mining

Matheus Flores, Denílson Ebling, Jonas Gassen, Vinícius Maran, Alencar Machado

2020

Abstract

As technology advances, recent research propose solutions to monitor and control organizational processes, aiming to maximize efficiency and productivity and minimize the loss of resources involved in the execution of processes, whether human or technological, in addition to maintaining a controlled environment so that the objectives of the organizations are achieved, that is, the satisfaction of their customers. For this, historical information contained in the event log is frequently used, related to the execution of processes in the organizational environment. These information serves as a basis for controlling the environment, preventing the occurrence of unwanted situations. In this context, this paper presents a model for detecting situations of interest in the organizational environment through event logs, making it possible to initiate proactive actions in the face of these situations, resulting in a Web application provided by interfaces that validate the purpose of the article. Beyond the scenario, an event log related to the execution of a real process was tested. By means of control charts, it is possible to view (using time parameters) the delay in the execution of the process, which may be related to a situation of interest.

Paper Citation

in Harvard Style

Flores M., Ebling D., Gassen J., Maran V. and Machado A. (2020). Towards a Conceptual Model for Undesired Situation Detection through Process Mining.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-423-7, pages 809-816. DOI: 10.5220/0009564408090816

in Bibtex Style

@conference{iceis20,
author={Matheus Flores and Denílson Ebling and Jonas Gassen and Vinícius Maran and Alencar Machado},
title={Towards a Conceptual Model for Undesired Situation Detection through Process Mining},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2020},
pages={809-816},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009564408090816},
isbn={978-989-758-423-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Towards a Conceptual Model for Undesired Situation Detection through Process Mining
SN - 978-989-758-423-7
AU - Flores M.
AU - Ebling D.
AU - Gassen J.
AU - Maran V.
AU - Machado A.
PY - 2020
SP - 809
EP - 816
DO - 10.5220/0009564408090816