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

• URL: http://arxiv.org/abs/2206.07745v1
• Date: Wed, 15 Jun 2022 18:18:33 GMT
• Title: When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints
• Authors: Mahmoud Shoush, Marlon Dumas
• Abstract summary: Prescriptive process monitoring approaches leverage historical data to prescribe runtime interventions that will likely prevent negative case outcomes or improve a process's performance. Previous proposals in this field rely on intervention policies that consider only the current state of a given case. 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 effect of the intervention, and triggers interventions to maximize a gain function.
• Score: 0.7487718119544158
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Prescriptive process monitoring approaches leverage historical 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 effect 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.

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