Event Log Sampling for Predictive Monitoring

Autor:	Sani, Mohammadreza Fani, Vazifehdoostirani, Mozhgan, Park, Gyunam, Pegoraro, Marco, van Zelst, Sebastiaan J., van der Aalst, Wil M. P.
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Zdroj:	ICPM Workshops (2021) 154-166
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-98581-3_12
Popis:	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. Comment: 7 pages, 1 figure, 4 tables, 34 references
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.01470 Zobrazit plný text záznamu View this record from Arxiv