Seong-Hun Ham, Hyun Ahn, Kwanghoon Pio Kim, Journal of Internet Computing and Services, Vol. 21, No. 3, pp. 83-92, Jun. 2020
10.7472/jksii.2020.21.3.83, Full Text:  HTML
Keywords: predictive process monitoring, remaining time prediction, LSTM model, Deep Learning, Process Mining

Abstract

Recently, many companies and organizations are interested in predictive process monitoring for the efficient operation of business process models. Traditional process monitoring focused on the elapsed execution state of a particular process instance. On the other hand, predictive process monitoring focuses on predicting the future execution status of a particular process instance. In this paper, we implement the function of the business process remaining time prediction, which is one of the predictive process monitoring functions. In order to effectively model the remaining time, normalization by activity is proposed and applied to the predictive model by taking into account the difference in the distribution of time feature values according to the properties of each activity. In order to demonstrate the superiority of the predictive performance of the proposed model in this paper, it is compared with previous studies through event log data of actual companies provided by 4TU.Centre for Research Data.

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