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Relative Error Streaming Quantiles

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3A10475244" target="_blank" >RIV/00216208:11320/23:10475244 - isvavai.cz</a>

  • Result on the web

    <a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=JDxgW_EUIg" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=JDxgW_EUIg</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3617891" target="_blank" >10.1145/3617891</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Relative Error Streaming Quantiles

  • Original language description

    Estimating 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 equipped with a total order, the task is to compute a sketch (data structure) of size polylogarithmic in n. Given the sketch and a query item y, 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 en error approximation, culminating in the KLL sketch that achieved optimal asymptotic behavior. This article investigates multiplicative (1 +/- epsilon)-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(epsilon(2)n)/epsilon 2) or O(log(3) (epsilon n)/epsilon) universe items. We present a randomized sketch storing O(log(1.5) (epsilon n)/epsilon) items that can (1 +/- epsilon)-approximate the rank of each universe item with high constant probability; this space bound is within an O(root log(epsilon n)) factor of optimal. Our algorithm does not require prior knowledge of the stream length and is fully mergeable, rendering it suitable for parallel and distributed computing environments.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    <a href="/en/project/GX19-27871X" target="_blank" >GX19-27871X: Efficient approximation algorithms and circuit complexity</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2023

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Journal of the ACM

  • ISSN

    0004-5411

  • e-ISSN

    1557-735X

  • Volume of the periodical

    70

  • Issue of the periodical within the volume

    5

  • Country of publishing house

    US - UNITED STATES

  • Number of pages

    48

  • Pages from-to

    30

  • UT code for WoS article

    001091490700003

  • EID of the result in the Scopus database