Several decision points exist in business processes (e.g., whether a purchase order needs a manager's approval or not), and different decisions are made for different process instances based on their characteristics (e.g., a purchase order higher than $500 needs a manager approval). Decision mining in process mining aims to describe/predict the routing of a process instance at a decision point of the process. By predicting the decision, one can take proactive actions to improve the process. For instance, when a bottleneck is developing in one of the possible decisions, one can predict the decision and bypass the bottleneck. However, despite its huge potential for such operational support, existing techniques for decision mining have focused largely on describing decisions but not on predicting them, deploying decision trees to produce logical expressions to explain the decision. In this work, we aim to enhance the predictive capability of decision mining to enable proactive operational support by deploying more advanced machine learning algorithms. Our proposed approach provides explanations of the predicted decisions using SHAP values to support the elicitation of proactive actions. We have implemented a Web application to support the proposed approach and evaluated the approach using the implementation.
翻译:在业务流程中存在若干决定点(例如,定购单是否需要管理人员的批准),根据不同流程的特点(例如,定购单超过500美元,需要管理人员的批准),对不同流程的情况做出不同决定(例如,定购单是否需要管理人员的批准);在采矿过程中,决定旨在描述/预先判断流程实例在流程决定点的路线;通过预测决定,可以采取积极主动的行动来改进流程;例如,当可能作出的决定之一出现瓶颈时,人们可以预测该决定并绕过瓶颈;然而,尽管决定采矿的现有技术具有巨大的业务支持潜力,但主要侧重于描述决定,而不是预测决定,部署决策树来解释决定;在这项工作中,我们的目标是提高决定采矿的预测能力,以便通过部署更先进的机器学习算法来提供积极的业务支持;我们提出的办法解释了使用SHAP值来预测的决定,以支持主动采取行动。我们实施了网络应用程序,以支持拟议的办法,并评价了采用执行的方法。