Event Log Sampling for Predictive Monitoring
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 12 of its 37 references that are in the map. First posted on arXiv 4 Apr 2022.
Builds on
12 of 37 references.
References · 12
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Citing papers per year: 2023: 1.
1 paper.