Relative error streaming quantiles

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F21%3A10438016" target="_blank" >RIV/00216208:11320/21:10438016 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1145/3452021.3458323" target="_blank" >https://doi.org/10.1145/3452021.3458323</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/3452021.3458323" target="_blank" >10.1145/3452021.3458323</a>

Result language

angličtina

Original language name

Relative error streaming quantiles

Original language description

Approximating ranks, quantiles, and distributions over streaming data is a central task in data analysis and monitoring. Given a stream of n items from a data universe U equipped with a total order, the task is to compute a sketch (data structure) of size poly (log(n), 1/ϵ). Given the sketch and a query item y in U, one should be able to approximate its rank in the stream, i.e., the number of stream elements smaller than or equal to y. Most works to date focused on additive ϵ n error approximation, culminating in the KLL sketch that achieved optimal asymptotic behavior. This paper investigates multiplicative (1+-ϵ)-error approximations to the rank. Practical motivation for multiplicative error stems from demands to understand the tails of distributions, and hence for sketches to be more accurate near extreme values. The most space-efficient algorithms due to prior work store either O(log(ϵ^2 n) / ϵ^2) or O(log^3(ϵ n) / ϵ) universe items. This paper presents a randomized algorithm storing O(log^{1.5} (ϵ n)/ϵ) items, which is within an O(sqrt{log(ϵ n)}) factor of optimal. The algorithm does not require prior knowledge of the stream length and is fully mergeable, rendering it suitable for parallel and distributed computing environments. (C) 2021 ACM.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2021

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

ISBN

978-1-4503-8381-3

ISSN

—

e-ISSN

—

Number of pages

13

Pages from-to

96-108

Publisher name

Association for Computing Machinery

Place of publication

Neuveden

Event location

Virtual (Xi&apos;an, Shaanxi, China)

Event date

Jun 20, 2021

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—