# Fire Now, Fire Later: Alarm-Based Systems for Prescriptive Process Monitoring

arXiv: https://arxiv.org/abs/1905.09568 · HTML: https://mlsanity.com/paper/1905.09568/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 18 of 18)

- [Prescriptive Process Monitoring: Quo Vadis?](https://mlsanity.com/paper/2112.01769/) (2021)
- [Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach](https://mlsanity.com/paper/2109.02894/) (2021)
- [When to intervene? Prescriptive Process Monitoring Under Uncertainty and Resource Constraints](https://mlsanity.com/paper/2206.07745/) (2022)
- [Outcome-Oriented Prescriptive Process Monitoring Based on Temporal Logic Patterns](https://mlsanity.com/paper/2211.04880/) (2022)
- [Open challenges for Machine Learning based Early Decision-Making research](https://mlsanity.com/paper/2204.13111/) (2022)
- [Learning When to Treat Business Processes: Prescriptive Process Monitoring with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2303.03572/) (2023)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [Intervening With Confidence: Conformal Prescriptive Monitoring of Business Processes](https://mlsanity.com/paper/2212.03710/) (2022)
- [Learning optimal policies from event logs through reinforcement learning: a comparison of deep and MDP-based approaches](https://mlsanity.com/paper/2303.09209/) (2023)
- [Automatically Reconciling the Trade-off between Prediction Accuracy and Earliness in Prescriptive Business Process Monitoring](https://mlsanity.com/paper/2307.05939/) (2023)
- [Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach](https://mlsanity.com/paper/2307.06564/) (2023)
- [Early Classification of Time Series is Meaningful](https://mlsanity.com/paper/2104.13257/) (2021)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Learning to act: a Reinforcement Learning approach to recommend the best next activities](https://mlsanity.com/paper/2203.15398/) (2022)
- [Using Reinforcement Learning to Optimize Responses in Care Processes: A Case Study on Aggression Incidents](https://mlsanity.com/paper/2310.00981/) (2023)
- [Beyond Yes or No: Predictive Compliance Monitoring Approaches for Quantifying the Magnitude of Compliance Violations](https://mlsanity.com/paper/2502.01141/) (2025)
- [PrefixGuard: From LLM-Agent Traces to Online Failure-Warning Monitors](https://mlsanity.com/paper/2605.06455/) (2026)
- [ProcK: Machine Learning for Knowledge-Intensive Processes](https://mlsanity.com/paper/2109.04881/) (2021)

## Abstract (arXiv)

Predictive process monitoring is a family of techniques to analyze events produced during the execution of a business process in order to predict the future state or the final outcome of running process instances. Existing techniques in this field are able to predict, at each step of a process instance, the likelihood that it will lead to an undesired outcome.These techniques, however, focus on generating predictions and do not prescribe when and how process workers should intervene to decrease the cost of undesired outcomes. This paper proposes a framework for prescriptive process monitoring, which extends predictive monitoring with the ability to generate alarms that trigger interventions to prevent an undesired outcome or mitigate its effect. The framework incorporates a parameterized cost model to assess the cost-benefit trade-off of generating alarms. We show how to optimize the generation of alarms given an event log of past process executions and a set of cost model parameters. The proposed approaches are empirically evaluated using a range of real-life event logs. The experimental results show that the net cost of undesired outcomes can be minimized by changing the threshold for generating alarms, as the process instance progresses. Moreover, introducing delays for triggering alarms, instead of triggering them as soon as the probability of an undesired outcome exceeds a threshold, leads to lower net costs.