POP-ON: Prediction of Process Using One-Way Language Model Based on NLP Approach

Junhyung Moon · Gyuyoung Park · Jongpil Jeong

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Business Process Modeling and Analysis Time Series Analysis and Forecasting Robotic Process Automation Applications

Abstract

In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”.

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References

1992Process modeling 2012Process Mining Manifesto 2017Predictive Business Process Monitoring with LSTM Neural Networks 2003Business Process Intelligence 2010Time prediction based on process mining 2017Predicting process behaviour using deep learning 2008Fundamentals of Production Logistics 2005Factors and measures of business process modelling: model building through a multiple case study

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