Process Mining and Deep Neural Network approach for the Prediction of Business Process Outcome

Abstract

Process mining is a new budding study in recent years which uses event logs and data. It is widely used in business organizations and it helps to improve the understanding of business processes, based on data. When a business process integrated with information systems provides the basis for new approach in data analysis. Process mining has a very good relationship with deep learning models that enables a strong relationship between business process management and business intelligence approach. Since the event data is available for process discovery and conformance checking techniques, process mining focuses on the entire process model. Process mining monitors and improves the real process by mining facts from event logs. While extracting knowledge there is no delay in today's information systems. The mining process models are used in various analysis. By applying deep learning techniques like deep neural networks and long short-term memory, risk management in a business process has been easily tracked and visualizing different activities involved in process prediction is also possible. Long Short-Term Memory of deep neural networks will be used for industrial process optimization, process improvement and process discovery in a Chemical Processes. It is well-suited to classify, process and predict time series given time lags of unknown duration. The revealed process models can be used for a large variety of analysis purposes. Deep fuzzy neural network framework that integrates the hidden feature of the CNN model and the LSTM model is proposed to improve the forecasting accuracy of business process outcome prediction.