When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints

M. Shoush, M. Dumas
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引用次数: 8

Abstract

. Prescriptive process monitoring approaches leverage histori-cal data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process’s performance. A centerpiece of a prescriptive process monitoring method is its intervention policy: a decision function determining if and when to trigger an intervention on an ongoing case. Previous proposals in this field rely on intervention policies that consider only the current state of a given case. These approaches do not consider the tradeoff between triggering an intervention in the current state, given the level of uncertainty of the underlying predictive models, versus delaying the intervention to a later state. Moreover, they assume that a resource is always available to perform an intervention (infinite capacity). This paper addresses these gaps by introducing a prescriptive process monitoring method that filters and ranks ongoing cases based on prediction scores, prediction uncertainty, and causal ef-fect of the intervention, and triggers interventions to maximize a gain function, considering the available resources. The proposal is evaluated using a real-life event log. The results show that the proposed method outperforms existing baselines regarding total gain.
何时干预?不确定性和资源约束下的规定性过程监控
. 说明性流程监控方法利用历史数据来规定运行时干预措施,这些干预措施可能会防止负面结果或改善流程的性能。规定性流程监控方法的核心是其干预策略:决定是否以及何时对正在进行的案例触发干预的决策函数。这一领域以前的建议依赖于只考虑给定情况的当前状态的干预政策。这些方法没有考虑在当前状态下触发干预与将干预延迟到以后的状态之间的权衡。此外,它们假设资源总是可用来执行干预(无限容量)。本文通过引入一种规范的过程监测方法来解决这些差距,该方法根据预测分数、预测不确定性和干预的因果效应对正在进行的案例进行过滤和排名,并在考虑可用资源的情况下触发干预以最大化增益函数。使用真实事件日志对提案进行评估。结果表明,该方法在总增益方面优于现有基准。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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