Automatic Conversion of Event Data to Event Logs Using CNN and Event Density Embedding

Abstract

In process mining, converting event data to event logs is related to the quality of analysis results. In general, to convert event data into event logs, it is necessary to identify process entities, such as the case identifier, activity label, activity originator, and activity timestamp, from the data fields in the event data, as well as other optional attributes. Up to now, the event log conversion process has been attempted by relying on an expert's intuition or an analyst's experience. However, the conversion is a challenging procedure without sufficient prior knowledge of process mining. To automate the conversion process, an event log-converting algorithm based on the convolutional neural network (CNN) was developed with a new embedding method called Event Density Embedding (EDE). To verify the performance of the proposed embedding method and the automatic event log conversion framework, a comparative experiment was performed using nine pieces of real-world event data. The experiments show that our method is 5-20% higher conversion accuracy than the other methods. It is expected that business experts will be able to easily apply the method to process mining technology by utilizing system-derived event data.