Missing activity log repair method based on image data using CNN
Business process systems may produce low-quality event logs due to the disturbance of outliers and missing values.For the problem of missing activity log repairing,existing log repair research mainly focuses on the recon-struction of missing activity,whereas few work is carried out from the perspective of predicting missing activi-ty.Based on Convolutional Neural Networks(CNN)model incorporating the behavioral features of the trace,an ap-proach of repair missing activity in the event logs was investigated.Its core idea was to transform event logs of busi-ness processes into spatial data in terms of both temporal properties and activity properties,and also depending on the behavioral relationship between the activities.The spatial data was further transformed into image matrices and trained by CNN models,which could achieve the aim of predicting missing activity.The purposed method did not depend on any prior knowledge about the business process model except its event logs.The purposed method was compared with the existing research by using two kinds of event logs,named real and artificially generated event logs.Experimental results showed that the purposed method was superior to the existing research results in activity repair accuracy.
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