Convolutional Neural Networks in Process Mining and Data Analytics for Prediction Accuracy

Abstract

For the reliable prediction and analysis of large amounts of data, big data analytics may be applied in many disciplines. They facilitate the discovery of important information in large amounts of data that would otherwise be obscured. Almost all organizations stored their data in the cloud as event logs over the last few decades. These data can be utilized to extract useful information, which can be used to boost an organization’s productivity and effectiveness by identifying, monitoring, and optimizing its processes. Supporting operations, recognizing faults in running processes, predicting event length, and predicting the next activity are all ways of accomplishing this. As part of our strategy, we provide a data collection and machine learning technique. Process mining can help you achieve these objectives. The major enabler of data-driven approaches in process mining is predictive process monitoring. Deep learning has been used in the realm of predictive monitoring to provide accurate future activity predictions in a running trace by analyzing data from previous events. Using image-based data engineering and convolutional neural networks, the next activity in a business process has been forecast in this paper (CNN). The use of CNN in process mining and data analytics guarantees that the proposed system has high accuracy in predicting the next activity in a business process. The experimental evaluation shows that the proposed CNN algorithm is faster at training and inference than the Long Short-Term Memory (LSTM) approach, even when the process has longer traces.