Knowledge-Driven Modulation of Neural Networks with Attention Mechanism for Next Activity Prediction
This work addresses predictive process monitoring for business processes, offering an incremental improvement by combining symbolic knowledge with neural networks.
The paper tackles the problem of predicting next activities in process executions by integrating background process knowledge with neural networks to improve prediction quality for exceptional cases or concept drift, resulting in performance improvements on real-life logs.
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.