ProcessTransformer: Predictive Business Process Monitoring with Transformer Network
This work addresses predictive business process monitoring for efficient operations and resource management, representing an incremental improvement over existing deep learning methods.
The paper tackles the problem of predicting future characteristics in business processes, such as next activity and remaining time, by proposing ProcessTransformer, an attention-based network that captures long-range dependencies in event logs, achieving over 80% accuracy on average for next activity prediction and competitive performance on time-related tasks.
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