PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929307536850944 |
|---|---|
| author | Elyasi, Keyvan Amiri van der Aa, Han Stuckenschmidt, Heiner |
| author_facet | Elyasi, Keyvan Amiri van der Aa, Han Stuckenschmidt, Heiner |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06267 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances Elyasi, Keyvan Amiri van der Aa, Han Stuckenschmidt, Heiner Machine Learning Artificial Intelligence 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. |
| title | PGTNet: A Process Graph Transformer Network for Remaining Time Prediction of Business Process Instances |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2404.06267 |