Research on real-time outlier detection over big data streams

Abstract

ABSTRACTNowadays technological advances have promoted big data streams common in many applications, including mobile internet applications, internet of things, and industry production process. Outliers should be detected from big data streams in many cases. However, the special characteristics of big data streams, such as transiency, uncertainty, multidimensionality, dynamic distribution, and dynamic relationship make outlier detection more challenging. This paper discusses the key issues, major challenges, and existing most frequently used methods for detecting outliers over big data streams, and then summarizes the directions for further investigation. This research can provide novel theoretical support and technical guidance for outlier detection over big data streams.