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DEXA
2010
Springer

DBOD-DS: Distance Based Outlier Detection for Data Streams

9 years 5 months ago
DBOD-DS: Distance Based Outlier Detection for Data Streams
Data stream is a newly emerging data model for applications like environment monitoring, Web click stream, network traffic monitoring, etc. It consists of an infinite sequence of data points accompanied with timestamp coming from external data source. Typically data sources are located onsite and very vulnerable to external attacks and natural calamities, thus outliers are very common in the datasets. Existing techniques for outlier detection are inadequate for data streams because of its metamorphic data distribution and uncertainty. In this paper we propose an outlier detection technique, called Distance-Based Outline Detection for Data Streams (DBOD-DS) based on a novel continuously adaptive probability density function that addresses all the new issues of data streams. Extensive experiments on a real dataset for meteorology applications show the supremacy of DBOD-DS over existing techniques in terms of accuracy.
Md. Shiblee Sadik, Le Gruenwald
Added 08 Nov 2010
Updated 08 Nov 2010
Type Conference
Year 2010
Where DEXA
Authors Md. Shiblee Sadik, Le Gruenwald
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