Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes

  title={Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes},
  author={Mahmoud Shoush and Marlon Dumas},
. Prescriptive process monitoring methods seek to improve the performance of a process by selectively triggering interventions at runtime (e.g., offering a discount to a customer) to increase the probability of a desired case outcome (e.g., a customer making a purchase). The backbone of a prescriptive process monitoring method is an intervention policy, which determines for which cases and when an intervention should be executed. Existing methods in this field rely on predictive models to define… 

Figures and Tables from this paper



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

. 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

Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach

A prescriptive process monitoring technique that triggers interventions to optimize a cost function under fixed resource constraints is proposed that relies on predictive modeling to identify cases that are likely to lead to a negative outcome, in combination with causal inference to estimate the effect of an intervention on the outcome of the case.

Alarm-Based Prescriptive Process Monitoring

A framework for prescriptive process monitoring is proposed, which extends predictive process monitoring approaches with the concepts of alarms, interventions, compensations, and mitigation effects and incorporates a parameterized cost model to assess the cost-benefit tradeoffs of applying prescriptives process monitoring in a given setting.

Prescriptive Process Monitoring for Cost-Aware Cycle Time Reduction

A prescriptive monitoring method that uses orthogonal random forests to estimate the causal effect of triggering a time-reducing intervention for each ongoing case of a process and triggers interventions according to a user-defined policy is proposed.

Prescriptive process monitoring: Quo vadis?

The need to validate existing and new methods in real-world settings, extend the types of interventions beyond those related to the temporal and cost perspectives, and design policies that take into account causality and second-order effects is highlighted.

Fire now, fire later: alarm-based systems for prescriptive process monitoring

A framework for prescriptive process monitoring is proposed, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect and incorporates a parameterized cost model to assess the cost–benefit trade-off of generating alarms.

Outcome-Oriented Predictive Process Monitoring: Review and Benchmark

This article presents a systematic review and taxonomy of outcome-oriented predictive process monitoring methods, and a comparative experimental evaluation of eleven representative methods using a benchmark covering 24 predictive process Monitoring tasks based on nine real-life event logs.

Prescriptive Business Process Monitoring for Recommending Next Best Actions

This work presents a PrBPM technique that transforms the next most likely activities into the next best actions regarding a given KPI, and shows that the technique`s next bestactions can outperform next activity predictions regarding the optimisation of a KPI and the distance from the actual process instances.

Triggering Proactive Business Process Adaptations via Online Reinforcement Learning

Online reinforcement learning is used as an alternative solution to learn when to trigger proactive process adaptations based on the predictions and their reliability at run time and experimental results indicate that this approach may on average lead to 12.2% lower process execution costs compared to empirical thresholding.

Predictive Business Process Monitoring Considering Reliability Estimates

This work experimentally analyzes the effect of considering prediction reliability estimates for proactive business process adaptation using ensemble prediction techniques, which are applied to an industry data set from the transport and logistics domain.