Sketch \star-metric: Comparing Data Streams via Sketching

TL;DR

This paper introduces the Sketch ster-metric, a new method for efficiently estimating the distance between large data streams using sketches, preserving key properties and validated through extensive experiments.

Contribution

The paper proposes the Sketch ster-metric, a novel metric that compares data stream sketches while maintaining measure properties, enabling efficient and accurate stream analysis.

Findings

The Sketch ster-metric accurately estimates stream distances.

It preserves measure axioms like non-negativity and symmetry.

Experiments show robustness on synthetic and real data.

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 \star-metric, which allows to define a distance between updatable summaries (or sketches) of large data streams. An important feature of the Sketch \star-metric is that, given a measure on the entire initial data streams, the Sketch \star-metric preserves the axioms of the latter measure on the sketch (such as the non-negativity, the identity, the symmetry, the triangle inequality but also specific properties of the f-divergence). Extensive experiments conducted on both synthetic traces and real data allow us to validate the robustness and accuracy of the Sketch \star-metric.