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arXiv Machine Learning By Sjoerd van Straten, Alessandro Padella, Marwan Hassani

Process-Aware Event Log Augmentation for Process Prediction under Labeled Data Scarcity

Read the original on arXiv Machine Learning →

The paper introduces SiamSA-PPM, a self‑supervised framework that augments event logs for predictive process monitoring by generating realistic trace variants using control‑flow semantics and frequent behavioral patterns. It combines Siamese learning with statistically grounded data augmentation to learn generalizable prefix representations without requiring labeled data. Experiments on diverse real‑world logs show that SiamSA-PPM matches or outperforms state‑of‑the‑art baselines in next‑activity and final‑outcome prediction tasks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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