Abstract (click to expand)
Predictive business process monitoring focuses on predicting future characteristics of a running process using event logs. The foresight into process execution promises great potentials for efficient operations, better resource management, and effective customer services. Deep learning-based approaches have been widely adopted in process mining to address the limitations of classical algorithms for solving multiple problems, especially the next event and remaining-time prediction tasks. Nevertheless, designing a deep neural architecture that performs competitively across various tasks is challenging as existing methods fail to capture long-range dependencies in the input sequences and perform poorly for lengthy process traces. In this paper, we propose ProcessTransformer, an approach for learning high-level representations from event logs with an attention-based network. Our model incorporates long-range memory and relies on a self-attention mechanism to establish dependencies between a multitude of event sequences and corresponding outputs. We evaluate the applicability of our technique on nine real event logs. We demonstrate that the transformer-based model outperforms several baselines of prior techniques by obtaining on average above 80% accuracy for the task of predicting the next activity. Our method also perform competitively, compared to baselines, for the tasks of predicting event time and remaining time of a running case.
- Next Activity Prediction
- Time Prediction of Next Activity
- Remaining Time Prediction
pip install processtransformer
We provide the necessary code to use ProcessTransformer with the event logs of your choice. We illustrate the examples using the helpdesk dataset.
For the data preprocessing, run:
python data_processing.py --dataset=helpdesk --task=next_activity
python data_processing.py --dataset=helpdesk --task=next_time
python data_processing.py --dataset=helpdesk --task=remaining_timeTo train and evaluate the model, run:
python next_activity.py --dataset=helpdesk --epochs=100
python next_time.py --dataset=helpdesk --epochs=100
python remaining_time.py --dataset=helpdesk --epochs=100The events log for the predictive busienss process monitoring can be found at 4TU Research Data
Please consider citing our paper if you use code or ideas from this project:
Zaharah A. Bukhsh, Aaqib Saeed, & Remco M. Dijkman. (2021). "ProcessTransformer: Predictive Business Process Monitoring with Transformer Network". arXiv preprint arXiv:2104.00721
@misc{bukhsh2021processtransformer,
title={ProcessTransformer: Predictive Business Process Monitoring with Transformer Network},
author={Zaharah A. Bukhsh and Aaqib Saeed and Remco M. Dijkman},
year={2021},
eprint={2104.00721},
archivePrefix={arXiv},
primaryClass={cs.LG}
}