Wadjet: Finding Outliers in Multiple Multi-Dimensional Heterogeneous Data Streams

Abstract

Data streams are sequences of data points that have the properties of transiency, infiniteness, concept drift, uncertainty, multi-dimensionality, cross-correlation among different streams, asynchronous arrival, and heterogeneity. In this paper we propose a new outlier detection technique for multiple multi-dimensional data streams, called Wadjet, that addresses all the issues of outlier detection in multiple data streams. Wadjet exploits the temporal correlations to identify outliers in each individual data stream, and after this, it exploits the cross-correlations between data streams to identify points that do not conform with these cross-correlations. Experiments comparing Wadjet against existing techniques on real and synthetic datasets show that Wadjet achieves 18.8X higher precision, and competitive execution time and recall.