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Prescriptive process monitoring approaches leverage historical data toprescribe runtime interventions that will likely prevent negative case outcomesor improve a process's performance. A centerpiece of a prescriptive processmonitoring method is its intervention policy: a decision function determiningif and when to trigger an intervention on an ongoing case. Previous proposalsin this field rely on intervention policies that consider only the currentstate of a given case. These approaches do not consider the tradeoff betweentriggering an intervention in the current state, given the level of uncertaintyof the underlying predictive models, versus delaying the intervention to alater state. Moreover, they assume that a resource is always available toperform an intervention (infinite capacity). This paper addresses these gaps byintroducing a prescriptive process monitoring method that filters and ranksongoing cases based on prediction scores, prediction uncertainty, and causaleffect of the intervention, and triggers interventions to maximize a gainfunction, considering the available resources. The proposal is evaluated usinga real-life event log. The results show that the proposed method outperformsexisting baselines regarding total gain.

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