ProcessTransformer: Predictive Business Process Monitoring with Transformer Network

Abstract

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

References in corpus (3)

• Sequence to Sequence Learning with Neural Networks
• An empirical comparison of deep-neural-network architectures for next activity prediction using context-enriched process event logs
• Time Matters: Time-Aware LSTMs for Predictive Business Process Monitoring

Cited by in corpus (6)

• Building Interpretable Models for Business Process Prediction using Shared and Specialised Attention Mechanisms
• Can recurrent neural networks learn process model structure?
• Can deep neural networks learn process model structure? An assessment framework and analysis
• Explainable Artificial Intelligence for Improved Modeling of Processes
• Extending predictive process monitoring for collaborative processes
• What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning