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

Keyvan Amiri Elyasi, Han van der Aa, Heiner Stuckenschmidt
4/9/2024
cs.LGcs.AI

No official code found. Best implementation: keyvan-amiri/PGTNet (7 stars, MIT, last commit 2025-11-13).

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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Code Implementations(1)

keyvan-amiri/PGTNetCommunity100%
MIT
72PythonOct 6, 202311 months agoMIT

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Cite this paper

@article{elyasi2024pgtnet,
  title  = {PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances},
  author = {Keyvan Amiri Elyasi and Han van der Aa and Heiner Stuckenschmidt},
  year   = {2024},
  eprint = {2404.06267},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2404.06267v1}
}

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