Detecting distance-based outliers in streams of data

In this work a method for detecting distance-based outliers in data streams is presented. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. Two algorithms are presented. The first one exactly answers outlier queries, but has larger space requirements. The second algorithm is directly derived from the exact one, has limited memory requirements and returns an approximate answer based on accurate estimations with a statistical guarantee. Several experiments have been accomplished, confirming the effectiveness of the proposed approach and the high quality of approximate solutions. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications— Data mining General Terms Algorithms, Performance Keywords Anomaly detection, data streams, distance-based outliers

• Type: Conference
• Year: 2007
• Where: CIKM
• Authors: Fabrizio Angiulli, Fabio Fassetti
• Keywords: CIKM 2007, Data Streams, Distance-based Outliers, Outlier Queries