Optimal Tracking of Distributed Heavy Hitters and Quantiles
Ke Yi, Qin Zhang

TL;DR
This paper presents optimal algorithms for tracking heavy hitters and quantiles in distributed data streams with minimal communication, significantly improving previous methods and establishing matching lower bounds.
Contribution
The authors introduce the first optimal algorithms for distributed heavy hitters and quantiles tracking, achieving worst-case communication costs that match lower bounds.
Findings
Achieved $O(k/ ext{ε} imes ext{log} n)$ communication complexity
Provided matching lower bounds proving optimality
Extended results to tracking all quantiles simultaneously
Abstract
We consider the the problem of tracking heavy hitters and quantiles in the distributed streaming model. The heavy hitters and quantiles are two important statistics for characterizing a data distribution. Let be a multiset of elements, drawn from the universe . For a given , the -heavy hitters are those elements of whose frequency in is at least ; the -quantile of is an element of such that at most elements of are smaller than and at most elements of are greater than . Suppose the elements of are received at remote {\em sites} over time, and each of the sites has a two-way communication channel to a designated {\em coordinator}, whose goal is to track the set of -heavy hitters and the -quantile of approximately at all times with minimum…
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Taxonomy
TopicsData Management and Algorithms · Advanced Database Systems and Queries · Distributed and Parallel Computing Systems
