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 proactively 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.
Highlights
Nowadays, Process-Aware Information Systems (PAIS) are utilized more and more by companies or organizations to help their operations run better
Our parallel structure is similar to that of the method proposed by Yao et al [18], which consists of convolutional neural network (CNN) and recurrent neural networks (RNN) to classify hematoxylin–eosin-stained breast biopsy images into four classes
With the help of the validation test, we can stop the training before converging when the validation test loss does not decrease after several epochs, which parameter is called “patience.” For our proposed model, we set it at Patience 50, while for the baseline method, which is based on the long short-term memory (LSTM) model, we set it at Patience 100
Summary
Process-Aware Information Systems (PAIS) are utilized more and more by companies or organizations to help their operations run better. The availability of event records, commonly called event logs, is increasing [1] These event logs, as accompanied by attribute data, can store all information about any activities that have been executed at particular times. The availability of these event logs provides opportunities for the optimization of business processes through data mining and process mining. Process mining techniques enable the extraction of insights contained in event log systems [1]. These insights can help company authorities to improve their business processes. The increasing number of event logs makes process mining currently the most attractive area in Business Process Management (BPM)
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
