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arXiv · 2404.06267

PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances

Keyvan Amiri Elyasi·Han van der Aa·Heiner Stuckenschmidt
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Abstract

We present PGTNet, an approach that transforms event logs into graph datasets and leverages graph-oriented data for training Process Graph Transformer Networks to predict the remaining time of business process instances. PGTNet consistently outperforms state-of-the-art deep learning approaches across a diverse range of 20 publicly available real-world event logs. Notably, our approach is most promising for highly complex processes, where existing deep learning approaches encounter difficulties stemming from their limited ability to learn control-flow relationships among process activities and capture long-range dependencies. PGTNet addresses these challenges, while also being able to consider multiple process perspectives during the learning process.

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Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt. 2024-04-09. PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances. https://arxiv.org/abs/2404.06267

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