Bagging Recurrent Event Imputation for Repair of Imperfect Event Log With Missing Categorical Events

In most computing services, imperfect event logs with missing events are generated for a variety of reasons. Because missing events in imperfect event logs adversely affect the results of process mining analysis, it is essential to handle them effectively. Most existing process mining studies focus on methodologies for generation of good process models, very few methodologies, in fact, have been developed to deal with missing events. To the best of our knowledge, there is a lack of high performance methods for restoration of missing events in actual event log data. In this paper, we propose a new categorical event imputation method that can restore missing categorical events by learning the structural features between observed events in the event log. We evaluated the proposed method by way of comparative experiments with previous studies using six real datasets, and the results demonstrate that the restoration performance was greatly improved and that thereby, our proposed method can significantly improve both the quality of event logs (specifically by restoring missing events in imperfect event logs) and the overall quality of process mining analysis.