因果学习:基于因果结构的业务流程监控
Fernando Montoya1,2,3, Hernán Astudillo4, Daniela Díaz5
1Nexus Payment Systems SpA, Santiago 8320123, Chile.
Entropy (Basel, Switzerland)
|October 25, 2024
概括
本研究介绍了CaProM,这是一种用于业务流程的新型因果监控技术. 它通过识别关键因果变量来增强异常的解释性,改善流程管理中的决策.
科学领域:
- 业务流程管理 业务流程管理
- 因果推理因果推理
- 数据科学数据科学数据科学
背景情况:
- 传统的过程监控很难区分异常与相关性.
- 缺乏因果解释阻碍了对操作变量对异常的影响的理解.
研究的目的:
- 介绍CaProM,一种基于因果关系的商业流程监控技术.
- 提高过程异常的解释性和解释性.
主要方法:
- 结合了异常归因和分布变化归因.
- 使用因果学习来构建过程活动的定向环形图 (DAG).
- 在业务流程中应用DAG用于异常检测和关键节点识别.
主要成果:
- 在银行业数据集 (562个活动流程计划) 上验证.
- 成功地确定了与计划值的重大偏差背后的主要因素.
- 证明了异常的增强解释性和解释性.
结论:
- 在业务流程监控方面,CaProM提供了一个因果关系的方法.
- 通过澄清过程中的因果关系来提高决策准确性.
- 采用横截面数据,保留变量关系并减少与时间序列方法相比的偏差.
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