# Parallel-Structure Deep Learning for Prediction of Remaining Time of Process Instances
###### Abstract  
Event logs generated by Process-Aware Information Systems (PAIS) provide many opportunities for analysis that are expected to help organizations optimize their business processes. The ability to monitor business processes passively can allow an organization to achieve, maintain or enhance competitiveness in the market. Predictive Business Process Monitoring (PBPM) can provide measures such as the prediction of the remaining time of an ongoing process instance (case) by taking past activities in running process instances into account, as based on the event logs of previously completed process instances. With the prediction provided, we expect that organizations can respond quickly to deviations from the desired process. In the context of the growing popularity of deep learning and the need to utilize heterogeneous representation of data; in this study, we derived a new deep-learning approach that utilizes two types of data representation based on a parallel-structure model, which consists of a convolutional neural network (CNN) and a multi-layer perceptron (MLP) with an embedding layer, to predict the remaining time. Conducting experiments with real-world datasets, we compared our proposed method against the existing deep-learning approach to confirm its utility for the provision of more precise prediction (as indicated by error metrics) relative to the baseline method.  
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running, the manager or another person in charge can take action to avoid undesirable situations. One example is a customer's call to his insurance company to ask about when he can claim his insurance. In this case, he can be given an estimate of the remaining time required by the claim process [3]. Another example is when company authorities can be given early notification of a case that has the potential to have adverse outcomes. Then, they can quickly determine the policies that will be carried out.  
Deep learning is a technique that is very popular today due to its ability to solve problems in various fields, especially problems involving images (spatial dependencies) and sequences (Natural Language Processing). Even though deep learning's basic foundation has long been known [4], it has only recently become very popular with the help of the rapid development of hardware and the discovery of various algorithms that can be relied upon to make neural network (NN) performance much better than in the past [5].  
In the context of the growing popularity of deep learning, in this study, we derived and evaluated a new method for solving one of the main problems in PBPM: prediction of the remaining time of process instances based on deep learning and the utilization of the heterogeneous representation of data sources. As evidenced by our experiments, our method could deliver a precise prediction. Additionally, because event logs contain categorical data that are difficult to handle by the FCNN, we propose herein and test in the present study to further strengthen our method, a new encoding technique suitable for event-log-type data and that also utilizes the embedding layer.