Event Log Sampling for Predictive Monitoring

• URL: http://arxiv.org/abs/2204.01470v1
• Date: Mon, 4 Apr 2022 13:36:48 GMT
• Title: Event Log Sampling for Predictive Monitoring
• Authors: Mohammadreza Fani Sani, Mozhgan Vazifehdoostirani, Gyunam Park, Marco Pegoraro, Sebastiaan J. van Zelst, Wil M.P. van der Aalst
• Abstract summary: 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.
• Score: 0.3425341633647624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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