Process mining aims at gaining insights into business processes by analyzing the event data that is generated and recorded during process execution. The vast majority of existing process mining techniques works offline, i.e. using static, historical data, stored in event logs. Recently, the notion of online process mining has emerged, in which techniques are applied on live event streams, i.e. as the process executions unfold. Analyzing event streams allows us to gain instant insights into business processes. However, most online process mining techniques assume the input stream to be completely free of noise and other anomalous behavior. Hence, applying these techniques to real data leads to results of inferior quality. In this paper, we propose an event processor that enables us to filter out infrequent behavior from live event streams. Our experiments show that we are able to effectively filter out events from the input stream and, as such, improve online process mining results.

流程挖掘旨在通过分析在流程执行期间生成和记录的事件数据来获得对业务流程的见解。现有的绝大多数过程挖掘技术都是脱机工作的，即使用存储在事件日志中的静态历史数据。最近，出现了在线过程挖掘的概念，其中将技术应用于实时事件流，即随着过程执行的展开。分析事件流使我们能够获得对业务流程的即时见解。但是，大多数在线过程挖掘技术都假定输入流完全没有噪音和其他异常行为。因此，将这些技术应用于实际数据会导致质量下降。在本文中，我们提出了一个事件处理器，该处理器使我们能够从实时事件流中滤除不常见的行为。