Adjacency Matrix Deep Learning Prediction Model for Prognosis of the Next Event in a Process

Abstract

Prediction of the next event is important for organizations to improve and optimize their system process to achieve organizational goals. Existing predictive models are limited since they use discovery algorithms that might not be able to conserve the sequences of events as reported in the event log. Discovery algorithms alter the sequence of events in two ways, either the algorithms generate an additional sequence of events not found in the event logs or remove the order of events. Since prediction relies on these process algorithms, the prediction model can suffer and produce underperforming results. Models that do not use discovery algorithms, such as deep learning models, ignore completely the sequence of events. To overcome these limitations, we propose a new algorithm called AXDP (Adjacency Matrix Deep Learning Prediction Model). AXDP predicts the next event of a process using graph theory techniques, specifically adjacency matrices and predicts using the power of deep learning models. AXDP has a major advantage, in that sequence of events is conserved, resulting in better prediction of the next event. When testing AXDP on eight publicly available datasets, AXDP outperforms what we believe to be the most recent and best predictive models that exist for the prediction of the next event for six of the eight datasets.

Keywords

• Deep learning
• process mining
• event logs
• adjacency matrix
• max eigenvalues