Anomaly Detection Guidelines for Data Streams in Big Data

Abstract

Real time data analysis in data streams is a highly challenging area in big data. The surge in big data techniques has recently attracted considerable interest to the detection of significant changes or anomalies in data streams. There is a variety of literature across a number of fields relevant to anomaly detection. The growing number of techniques, from seemingly disconnected areas, prevents a comprehensive review. Many interesting techniques may therefore remain largely unknown to the anomaly detection community at large. The survey presents a compact, but comprehensive overview of diverse strategies for anomaly detection in evolving data streams. A number of recommendations based performance and applicability to use cases are provided. We expect that our classification and recommendations will provide useful guidelines to practitioners in this rapidly evolving field.