Work

Event Log Sampling for Predictive Monitoring

Year: 2022

Type: conference-paper

Abstract: 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.

Source: Lecture notes in business information processing

DOI: 10.1007/978-3-030-98581-3_12

Authors: Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M. P. van der Aalst

Institutions: RWTH Aachen University, Eindhoven University of Technology, Fraunhofer Institute for Applied Information Technology

Language: English

Topic: Business Process Modeling and Analysis

Subfield: Management Information Systems

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Funder: Alexander von Humboldt-Stiftung

OpenAlex ID: W4225709018