Next Activity Prediction: An Application of Shallow Learning Techniques Against Deep Learning Over the BPI Challenge 2020

Abstract

Business Process Management is a domain that is composed by different research or application areas. Process Mining is one of them and it is a data-driven approach to analyze and improve processes. In Process Mining “next activity prediction” is one of the most important task. The used data are event logs i.e., a timeseries of recorded events. The event logs are mainly processed using deep learning algorithms. In this study, it was proposed the comparison of prediction performance of shallow learning algorithms with a three block Bidirectional LSTM (Bi-LSTM) architecture in predicting the next activity. The algorithms were applied on all the events logs of the BPI Challenge 2020 dataset. Results show that shallow learning algorithms outperform the three-block architecture from a minimum of 1.5 to a maximum of 6 times. This suggest that simpler algorithms may be more effective than the three-block architecture.

Keywords

• Bi-LSTM
• next-activity prediction
• process mining
• public administration
• shallow learning
• university processes