Event Log Sampling for Predictive Monitoring

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, state-of-the-art methods for predictive monitoring require the training of comp...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2022-04
Main Authors: Sani, Mohammadreza Fani, Vazifehdoostirani, Mozhgan, Park, Gyunam, Pegoraro, Marco, van Zelst, Sebastiaan J, Wil M P van der Aalst
Format: Article
Language:English
Subjects:
Machine learning
Monitoring
Prediction models
Sampling methods
Training
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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, state-of-the-art methods for predictive monitoring require the training of complex machine learning models, which is often inefficient. This paper proposes an instance selection procedure that allows sampling training process instances for prediction models. We show that our sampling method allows for a significant increase of training speed for next activity prediction methods while maintaining reliable levels of prediction accuracy.
ISSN:2331-8422
DOI:10.48550/arxiv.2204.01470