Filtering Out Infrequent Events by Expectation from Business Process Event Logs

Process discovery, one of the key steps in process management, aims at discovering process models from process execution data stored in event logs. Most discovery algorithms assume that all data in an event log fully comply with the process execution specification. However, in real event logs, noise and irrelevant infrequent behaviour are often present. In this paper, we propose a novel filtering method that the removal of infrequent behavior from event logs. The method has been evaluated in detail and it is shown that its application in existing process discovery algorithms significantly improves the quality of the discovered process models and that it scales well to large datasets.