Multi-attribute Reparation of Incomplete Event Logs Based on Bert Model
Process mining automatically constructs process models from event logs and uses them to analyze,enhance,and monitor actual business processes.Reparation of event logs is one of the initiatives to improve the ac-curacy of process mining results because incomplete event logs seriously affect the results of process mining.Mean-while,the current techniques for repairing event logs mainly repair missing activities in event logs but rarely consid-er repairing multiple missing attributes in logs.In reality,logs have missing attributes in addition to missing activi-ties.To address this problem,this research proposes a Bert-based neural network model for repairing multiple miss-ing attributes in events.The method learns the dependencies between attributes in events from a data perspective by per-training tasks of the Bert model,and predicts the missing attribute values based on previous and subsequent contextual information of the attributes.Finally,the researchers experimentally evaluated the accuracy of the pro-posed method by using publicly available real event logs,and the evaluation results show that the proposed method can repair the missing multiple attributes in event logs.
reparation of event logmissing attributeBert modelper-training taskmulti-attribute repara-tion
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