Anomaly Detection: Its Real-Life Uses and the Latest Advances - insideBIGDATA

Anomaly detection in the context of data science is detecting a data sample that is out of the ordinary and does not fit into the general data pattern (or an outlier). This deviation can result from a rare occurrence or an outlying event. Identifying these samples, called anomaly detection, is an integral part of any monitoring system. Anomaly detection has been traditionally done manually by inspection, which is a tedious process typically done by experts with significant domain knowledge. Anomaly detection is used in a wide variety of applications.