# Evaluating Predictive Business Process Monitoring Approaches on Small Event Logs

arXiv: https://arxiv.org/abs/2104.00362 · HTML: https://mlsanity.com/paper/2104.00362/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 26 references that are in the map.
- First posted on arXiv 1 Apr 2021.

## Builds on (most cited first, 11 of 11)

- [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)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [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)
- [Predictive Business Process Monitoring via Generative Adversarial Nets: The Case of Next Event Prediction](https://mlsanity.com/paper/2003.11268/) (2020)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Small Sample Learning in Big Data Era](https://mlsanity.com/paper/1808.04572/) (2018)
- [Exploiting Event Log Event Attributes in RNN Based Prediction](https://mlsanity.com/paper/1904.06895/) (2019)
- [Event Stream-Based Process Discovery using Abstract Representations](https://mlsanity.com/paper/1704.08101/) (2017)

## Built on by (most cited first, 6 of 6)

- [Just Tell Me: Prompt Engineering in Business Process Management](https://mlsanity.com/paper/2304.07183/) (2023)
- [Predictive Compliance Monitoring in Process-Aware Information Systems: State of the Art, Functionalities, Research Directions](https://mlsanity.com/paper/2205.05446/) (2022)
- [A Survey on Event Prediction Methods from a Systems Perspective: Bringing Together Disparate Research Areas](https://mlsanity.com/paper/2302.04018/) (2023)
- [Leveraging Data Augmentation for Process Information Extraction](https://mlsanity.com/paper/2404.07501/) (2024)
- [Recent Advances in Data-Driven Business Process Management](https://mlsanity.com/paper/2406.01786/) (2024)
- [A Review of AI and Machine Learning Contribution in Predictive Business Process Management (Process Enhancement and Process Improvement Approaches)](https://mlsanity.com/paper/2407.11043/) (2024)

## Abstract (arXiv)

Predictive business process monitoring is concerned with the prediction how a running process instance will unfold up to its completion at runtime. Most of the proposed approaches rely on a wide number of different machine learning (ML) techniques. In the last years numerous comparative studies, reviews, and benchmarks of such approaches where published and revealed that they can be successfully applied for different prediction targets. ML techniques require a qualitatively and quantitatively sufficient data set. However, there are many situations in business process management (BPM) where only a quantitatively insufficient data set is available. The problem of insufficient data in the context of BPM is still neglected. Hence, none of the comparative studies or benchmarks investigates the performance of predictive business process monitoring techniques in environments with small data sets. In this paper an evaluation framework for comparing existing approaches with regard to their suitability for small data sets is developed and exemplarily applied to state-of-the-art approaches in predictive business process monitoring.