# Performance-Preserving Event Log Sampling for Predictive Monitoring

arXiv: https://arxiv.org/abs/2301.07624 · HTML: https://mlsanity.com/paper/2301.07624/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 51 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [Dataset Distillation](https://mlsanity.com/paper/1811.10959/) (2018)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Survey and cross-benchmark comparison of remaining time prediction methods in business process monitoring](https://mlsanity.com/paper/1805.02896/) (2018)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Outcome-Oriented Predictive Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/1707.06766/) (2017)
- [Explainable Predictive Process Monitoring](https://mlsanity.com/paper/2008.01807/) (2020)
- [Time and Activity Sequence Prediction of Business Process Instances](https://mlsanity.com/paper/1602.07566/) (2016)
- [LSTM Networks for Data-Aware Remaining Time Prediction of Business Process Instances](https://mlsanity.com/paper/1711.03822/) (2017)
- [Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring](https://mlsanity.com/paper/2010.00889/) (2020)
- [Text-Aware Predictive Monitoring of Business Processes](https://mlsanity.com/paper/2104.09962/) (2021)
- [Conformance Checking Approximation using Subset Selection and Edit Distance](https://mlsanity.com/paper/1912.05022/) (2019)
- [Brief Announcement: On the Limits of Parallelizing Convolutional Neural Networks on GPUs](https://mlsanity.com/paper/2005.13823/) (2020)
- [Explainable Predictive Decision Mining for Operational Support](https://mlsanity.com/paper/2210.16786/) (2022)
- [Uncertain Case Identifiers in Process Mining: A User Study of the Event-Case Correlation Problem on Click Data](https://mlsanity.com/paper/2204.04164/) (2022)
- [Resolving Uncertain Case Identifiers in Interaction Logs: A User Study](https://mlsanity.com/paper/2212.00009/) (2022)
- [Event Log Sampling for Predictive Monitoring](https://mlsanity.com/paper/2204.01470/) (2022)

## Built on by (most cited first, 3 of 3)

- [Timing Process Interventions with Causal Inference and Reinforcement Learning](https://mlsanity.com/paper/2306.04299/) (2023)
- [On the Simplification of Neural Network Architectures for Predictive Process Monitoring](https://mlsanity.com/paper/2509.17145/) (2025)
- [Marginal Log-Likelihood Increments under Dirichlet-Smoothed Markov Estimation](https://mlsanity.com/paper/2609.25675/) (2026)

## Abstract (arXiv)

Predictive process monitoring is a subfield of process mining that aims to estimate case or event features for running process instances. Such predictions are of significant interest to the process stakeholders. However, most of the state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. Moreover, most of these methods require a hyper-parameter optimization that requires several repetitions of the training process which is not feasible in many real-life applications. In this paper, we propose an instance selection procedure that allows sampling training process instances for prediction models. We show that our instance selection procedure allows for a significant increase of training speed for next activity and remaining time prediction methods while maintaining reliable levels of prediction accuracy.