Comprehensive survey on deep learning approaches in predictive business process monitoring

Abstract

In last few years lot of work is carried out in process mining field by various researchers. Process mining deals with analysis and extraction of process related information from the event logs created by business processes. Predictive monitoring of business process is subfield of process mining which includes activities where event logs are analyzed to make various process specific predictions. The various machine learning and deep learning techniques have been proposed in predictive business process monitoring (BPM). The aim of these techniques is to predict next process event, remaining cycle time, deadline violations etc. of running process instance. The goal of this paper is to discuss the most representative deep learning approaches used for the runtime prediction of business process. The different types of deep learning approaches used in predictive BPM based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM) and Stacked Autoencoders have are highlighted in this paper. Also we are focusing on aspects like type of dataset, predicted values, type of data encoding and quality evaluation metrics for the categorization of these approaches. In this paper we have highlighted various research gaps in mentioned deep learning approaches which can be referred by other researchers in this field to enhance effectiveness of predictive BPM.