An Innovative Next Activity Prediction Using Process Entropy and Dynamic Attribute-Wise-Transformer in Predictive Business Process Monitoring

TL;DR

This paper introduces an entropy-based framework and a novel transformer model for next activity prediction in business process monitoring, improving accuracy on complex datasets while maintaining interpretability.

Contribution

It proposes a dataset entropy quantification method and a new Dynamic Attribute-Wise Transformer model for better long-range dependency capture in event logs.

Findings

DAW-Transformer outperforms on high-entropy datasets

Entropy-based model selection guides algorithm choice

Interpretable methods perform well on low-entropy datasets

Abstract

Next activity prediction in predictive business process monitoring is crucial for operational efficiency and informed decision-making. While machine learning and Artificial Intelligence have achieved promising results, challenges remain in balancing interpretability and accuracy, particularly due to the complexity and evolving nature of event logs. This paper presents two contributions: (i) an entropy-based model selection framework that quantifies dataset complexity to recommend suitable algorithms, and (ii) the DAW-Transformer (Dynamic Attribute-Wise Transformer), which integrates multi-head attention with a dynamic windowing mechanism to capture long-range dependencies across all attributes. Experiments on six public event logs show that the DAW-Transformer achieves superior performance on high-entropy datasets (e.g., Sepsis, Filtered Hospital Logs), whereas interpretable methods like Decision Trees perform competitively on low-entropy datasets (e.g., BPIC 2020 Prepaid Travel Costs). These results highlight the importance of aligning model choice with dataset entropy to balance accuracy and interpretability.