Fast and Accurate Mining of Correlated Heavy Hitters

TL;DR

This paper introduces a new counter-based algorithm for mining correlated heavy hitters in data streams, demonstrating improved accuracy, speed, and space efficiency over previous methods through theoretical proofs and extensive experiments.

Contribution

The paper presents a novel counter-based algorithm for correlated heavy hitter mining, with proven error bounds and superior performance compared to existing algorithms.

Findings

Outperforms Misra--Gries based algorithm in accuracy and speed

Requires significantly less space

Proven correctness and error bounds

Abstract

The problem of mining Correlated Heavy Hitters (CHH) from a two-dimensional data stream has been introduced recently, and a deterministic algorithm based on the use of the Misra--Gries algorithm has been proposed by Lahiri et al. to solve it. In this paper we present a new counter-based algorithm for tracking CHHs, formally prove its error bounds and correctness and show, through extensive experimental results, that our algorithm outperforms the Misra--Gries based algorithm with regard to accuracy and speed whilst requiring asymptotically much less space.