# Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction

arXiv: https://arxiv.org/abs/2312.08847 · HTML: https://mlsanity.com/paper/2312.08847/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 14 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Predictive Business Process Monitoring with LSTM Neural Networks](https://mlsanity.com/paper/1612.02130/) (2016)
- [Predicting Process Behaviour using Deep Learning](https://mlsanity.com/paper/1612.04600/) (2016)
- [Automated Discovery of Process Models from Event Logs: Review and Benchmark](https://mlsanity.com/paper/1705.02288/) (2017)
- [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)
- [A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequences](https://mlsanity.com/paper/2102.07298/) (2021)
- [What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learning](https://mlsanity.com/paper/2110.10225/) (2021)
- [A Discussion on Generalization in Next-Activity Prediction](https://mlsanity.com/paper/2309.09618/) (2023)
- [Methodology of Algorithm Engineering](https://mlsanity.com/paper/2310.18979/) (2023)

## Built on by (most cited first, 4 of 4)

- [Neuro-Symbolic Predictive Process Monitoring](https://mlsanity.com/paper/2509.00834/) (2025)
- [RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processes](https://mlsanity.com/paper/2507.02690/) (2025)
- [DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Mining](https://mlsanity.com/paper/2606.14283/) (2026)
- [EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Prediction](https://mlsanity.com/paper/2607.01785/) (2026)

## Abstract (arXiv)

Predictive Process Monitoring (PPM) aims at leveraging historic process execution data to predict how ongoing executions will continue up to their completion. In recent years, PPM techniques for the prediction of the next activities have matured significantly, mainly thanks to the use of Neural Networks (NNs) as a predictor. While their performance is difficult to beat in the general case, there are specific situations where background process knowledge can be helpful. Such knowledge can be leveraged for improving the quality of predictions for exceptional process executions or when the process changes due to a concept drift. In this paper, we present a Symbolic[Neuro] system that leverages background knowledge expressed in terms of a procedural process model to offset the under-sampling in the training data. More specifically, we make predictions using NNs with attention mechanism, an emerging technology in the NN field. The system has been tested on several real-life logs showing an improvement in the performance of the prediction task.