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

arXiv: https://arxiv.org/abs/2502.10573 · HTML: https://mlsanity.com/paper/2502.10573/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 57 references that are in the map.
- First posted on arXiv 14 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [ProcessTransformer: Predictive Business Process Monitoring with Transformer Network](https://mlsanity.com/paper/2104.00721/) (2021)
- [Predictive Process Monitoring Methods: Which One Suits Me Best?](https://mlsanity.com/paper/1804.02422/) (2018)
- [Time and Activity Sequence Prediction of Business Process Instances](https://mlsanity.com/paper/1602.07566/) (2016)
- [Prescriptive Business Process Monitoring for Recommending Next Best Actions](https://mlsanity.com/paper/2008.08693/) (2020)
- [Measures of Complexity for Large Scale Image Datasets](https://mlsanity.com/paper/2008.04431/) (2020)
- [XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRP](https://mlsanity.com/paper/2008.07993/) (2020)
- [DeepProcess: Supporting business process execution using a MANN-based recommender system](https://mlsanity.com/paper/1802.00938/) (2018)
- [Learning policies for resource allocation in business processes](https://mlsanity.com/paper/2304.09970/) (2023)
- [A Discussion on Generalization in Next-Activity Prediction](https://mlsanity.com/paper/2309.09618/) (2023)
- [Predicting User-specific Future Activities using LSTM-based Multi-label Classification](https://mlsanity.com/paper/2211.03100/) (2022)

## Built on by (most cited first, 2 of 2)

- [RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes](https://mlsanity.com/paper/2507.02690/) (2025)
- [D-TAIA: Domain-Aware LLM Adaptation for Multi-Task Predictive Process Monitoring](https://mlsanity.com/paper/2608.28236/) (2026)

## Abstract (arXiv)

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.