Modelling and Predictive Monitoring of Business Processes under Uncertainty with Reinforcement Learning.

IF 3.4 3区 综合性期刊 Q2 CHEMISTRY, ANALYTICAL Sensors Pub Date : 2023-08-03 DOI:10.3390/s23156931
Alexandros Bousdekis, Athanasios Kerasiotis, Silvester Kotsias, Georgia Theodoropoulou, Georgios Miaoulis, Djamchid Ghazanfarpour
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Abstract

The analysis of business processes based on their observed behavior recorded in event logs can be performed with process mining. This method can discover, monitor, and improve processes in various application domains. However, the process models produced by typical process discovery methods are difficult for humans to understand due to their high complexity (the so-called "spaghetti-like" process models). Moreover, these methods cannot handle uncertainty or perform predictions because of their deterministic nature. Recently, researchers have been developing predictive approaches for running business cases of processes. This paper focuses on developing a predictive business process monitoring approach using reinforcement learning (RL), which has been successful in other contexts but not yet explored in this area. The proposed approach is evaluated in the banking sector through a use case.

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基于强化学习的不确定性业务过程建模与预测监测。
基于事件日志中记录的观察到的行为的业务流程分析可以通过流程挖掘来执行。该方法可以发现、监视和改进各种应用程序域中的流程。然而,由典型流程发现方法生成的流程模型由于其高度复杂性(所谓的“意大利面式”流程模型)而难以被人类理解。此外,这些方法不能处理不确定性或执行预测,因为它们的确定性性质。最近,研究人员一直在开发用于运行流程业务用例的预测方法。本文着重于开发一种使用强化学习(RL)的预测性业务流程监控方法,该方法在其他环境中取得了成功,但尚未在该领域进行探索。建议的方法通过用例在银行部门中进行评估。
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来源期刊
Sensors
Sensors 工程技术-电化学
CiteScore
7.30
自引率
12.80%
发文量
8430
审稿时长
1.7 months
期刊介绍: Sensors (ISSN 1424-8220) provides an advanced forum for the science and technology of sensors and biosensors. It publishes reviews (including comprehensive reviews on the complete sensors products), regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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