Masking Neural Networks Using Reachability Graphs to Predict Process Events
Published in 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021
Recommended citation: J. Theis and H. Darabi, "Masking Neural Networks Using Reachability Graphs to Predict Process Events," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021, pp. 1-6, doi: 10.1109/ICCSI53130.2021.9736237. https://ieeexplore.ieee.org/document/9736237
Decay Replay Mining is a deep learning method that utilizes process model notations to predict the next event. However, this method does not intertwine the neural network with the structure of the process model to its full extent. This paper proposes an approach to further interlock the process model of Decay Replay Mining with its neural network for next event prediction. The approach uses a masking layer which is initialized based on the reachability graph of the process model. Additionally, modifications to the neural network architecture are proposed to increase the predictive performance. Experimental results demonstrate the value of the approach and underscore the importance of discovering precise and generalized process models.
Recommended citation: J. Theis and H. Darabi, "Masking Neural Networks Using Reachability Graphs to Predict Process Events," 2021 International Conference on Cyber-Physical Social Intelligence (ICCSI), 2021, pp. 1-6, doi: 10.1109/ICCSI53130.2021.9736237.