  Event log records all events that occur during the execution of business
processes, so detecting and correcting anomalies in event log can provide
reliable guarantee for subsequent process analysis. The previous works mainly
include next event prediction based methods and autoencoder-based methods.
These methods cannot accurately and efficiently detect anomalies and correct
anomalies at the same time, and they all rely on the set threshold to detect
anomalies. To solve these problems, we propose a business process anomaly
correction method based on Transformer autoencoder. By using self-attention
mechanism and autoencoder structure, it can efficiently process event sequences
of arbitrary length, and can directly output corrected business process
instances, so that it can adapt to various scenarios. At the same time, the
anomaly detection is transformed into a classification problem by means of
selfsupervised learning, so that there is no need to set a specific threshold
in anomaly detection. The experimental results on several real-life event logs
show that the proposed method is superior to the previous methods in terms of
anomaly detection accuracy and anomaly correction results while ensuring high
running efficiency.
