Sketch *-metric: Comparing Data Streams via Sketching

Emmanuelle Anceaume 1, 2 Yann Busnel 3
1 CIDER
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE
2 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
IRISA-D1 - SYSTÈMES LARGE ÉCHELLE, Inria Rennes – Bretagne Atlantique , CentraleSupélec
3 GDD - Gestion de Données Distribuées [Nantes]
LINA - Laboratoire d'Informatique de Nantes Atlantique
Abstract : 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
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Emmanuelle Anceaume, Yann Busnel. Sketch *-metric: Comparing Data Streams via Sketching. 12th IEEE International Symposium on Network Computing and Applications (IEEE NCA 2013), Aug 2013, Boston, United States. pp.11, ⟨10.1109/NCA.2013.11⟩. ⟨hal-00926685⟩

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