Detection and removal of infrequent behavior from event streams of business processes

作者：

Highlights：

• A new online filter is presented, which filters anomalies from event streams.

• The filter uses an evolving ensemble of probabilistic non-deterministic automata.

• Experiments show that filter parameterization leads to different accuracy levels.

• Additionally, the paper presents a noise-oriented taxonomy of event data.

摘要

•A new online filter is presented, which filters anomalies from event streams.•The filter uses an evolving ensemble of probabilistic non-deterministic automata.•Experiments show that filter parameterization leads to different accuracy levels.•Additionally, the paper presents a noise-oriented taxonomy of event data.

论文关键词：Process mining,Event streams,Filtering,Outlier detection,Anomaly detection

论文评审过程：Received 31 January 2019, Revised 31 May 2019, Accepted 10 September 2019, Available online 9 October 2019, Version of Record 17 March 2020.

论文官网地址：https://doi.org/10.1016/j.is.2019.101451