mlsanityData to 14 Oct 2026

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

arXiv 2312.08847 ↗First posted 14 Dec 2023cs.AI cs.LG cs.NEcited by 4

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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.

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

10 of 33 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2016Predictive Business Process Monitoring with LSTM Neural Networkscited by 104
  3. 2016Predicting Process Behaviour using Deep Learningcited by 60
  4. 2017Automated Discovery of Process Models from Event Logs: Review and Benchmarkcited by 28
  5. 2018Predictive Process Monitoring Methods: Which One Suits Me Best?cited by 27
  6. 2016Time and Activity Sequence Prediction of Business Process Instancescited by 24
  7. 2021A Deep Adversarial Model for Suffix and Remaining Time Prediction of Event Sequencescited by 17
  8. 2021What Averages Do Not Tell -- Predicting Real Life Processes with Sequential Deep Learningcited by 7
  9. 2023A Discussion on Generalization in Next-Activity Predictioncited by 4
  10. 2023Methodology of Algorithm Engineeringcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 2.

4 papers.

  1. 2025Neuro-Symbolic Predictive Process Monitoringcited by 7
  2. 2025RLHGNN: Reinforcement Learning-driven Heterogeneous Graph Neural Network for Next Activity Prediction in Business Processescited by 1
  3. 2026DIFF-ERO: A Conformance-Aware Loss for Deep Learning in Process Miningcited by 1
  4. 2026EHHN: An Event-driven Heterogeneous Hypergraph Network for Object-Centric Next Activity Predictioncited by 0