Sketch ⋆-Metric: Comparing Data Streams via Sketching

In this paper, we consider the problem of estimating the distance between any two large data streams in small-space constraint. This problem is of utmost importance in data intensive monitoring applications where input streams are generated rapidly. These streams need to be processed on the fly and...

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Bibliographic Details
Published in:	2013 IEEE 12th International Symposium on Network Computing and Applications pp. 25 - 32
Main Authors:	Anceaume, Emmanuelle, Busnel, Yann
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01.08.2013
Subjects:	Approximation algorithms Approximation methods Context Data models Data stream Measurement metric Partitioning algorithms randomized approximation algorithm Robustness
Online Access:	Get full text
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Summary:	In this paper, we consider the problem of estimating the distance between any two large data streams in small-space constraint. This problem is of utmost importance in data intensive monitoring applications where input streams are generated rapidly. These streams need to be processed on the fly and accurately to quickly determine any deviance from nominal behavior. We present a new metric, the Sketch ⋆-metric, which allows to define a distance between updatable summaries (or sketches) of large data streams. An important feature of the Sketch ⋆-metric is that, given a measure on the entire initial data streams, the Sketch ⋆-metric preserves the axioms of the latter measure on the sketch. Extensive experiments conducted on both synthetic traces and real data sets allow us to validate the robustness and accuracy of the Sketch ⋆-metric.
DOI:	10.1109/NCA.2013.11