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Learned metric index - proposition of learned indexing for unstructured data

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F21%3A00118915" target="_blank" >RIV/00216224:14330/21:00118915 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1016/j.is.2021.101774" target="_blank" >http://dx.doi.org/10.1016/j.is.2021.101774</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.is.2021.101774" target="_blank" >10.1016/j.is.2021.101774</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Learned metric index - proposition of learned indexing for unstructured data

  • Original language description

    The main paradigm of similarity searching in metric spaces has remained mostly unchanged for decades - data objects are organized into a hierarchical structure according to their mutual distances, using representative pivots to reduce the number of distance computations needed to efficiently search the data. We propose an alternative to this paradigm, using machine learning models to replace pivots, thus posing similarity search as a classification problem, which stands in for numerous expensive distance computations. Even a relatively naive implementation of this idea is more than competitive with state-of-the-art methods in terms of speed and recall, proving the concept as viable and showing great potential for its future development.

  • 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

    10200 - Computer and information sciences

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2021

  • Confidentiality

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

Data specific for result type

  • Name of the periodical

    Information Systems

  • ISSN

    0306-4379

  • e-ISSN

  • Volume of the periodical

    100

  • Issue of the periodical within the volume

    101774

  • Country of publishing house

    NL - THE KINGDOM OF THE NETHERLANDS

  • Number of pages

    12

  • Pages from-to

    1-12

  • UT code for WoS article

    000649115200005

  • EID of the result in the Scopus database

    2-s2.0-85104454116