Sketch *-metric: Comparing Data Streams via Sketching

Emmanuelle Anceaume (1, 2) , Yann Busnel (3)

1 CIDER
2 CIDRE - Confidentialité, Intégrité, Disponibilité et Répartition
3 GDD - Gestion de Données Distribuées [Nantes]

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

Mots clés: Data stream; Divergence; Randomized approximation algorithm

Domaines: Algorithme et structure de données [cs.DS]

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⟩