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Efficient Indexing of Similarity Models with Inequality Symbolic Regression

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139515" target="_blank" >RIV/00216208:11320/13:10139515 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1145/2463372.2463487" target="_blank" >http://dx.doi.org/10.1145/2463372.2463487</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/2463372.2463487" target="_blank" >10.1145/2463372.2463487</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Efficient Indexing of Similarity Models with Inequality Symbolic Regression

  • Original language description

    The increasing amount of available unstructured content introduced a new concept of searching for information - the content-based retrieval. The principle behind is that the objects are compared based on their content which is far more complex than simple text or metadata based searching. Many indexing techniques arose to provide an efficient and effective similarity searching. However, these methods are restricted to a specific domain such as the metric space model. If this prerequisite is not fulfilled, indexing cannot be used, while each similarity search query degrades to sequential scanning which is unacceptable for large datasets. Inspired by previous successful results, we decided to apply the principles of genetic programming to the area of database indexing. We developed the GP-SIMDEX which is a universal framework that is capable of finding precise and efficient indexing methods for similarity searching for any given similarity data. For this purpose, we introduce the inequal

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2013

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    GECCO'13: PROCEEDINGS OF THE 2013 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE

  • ISBN

    978-1-4503-1963-8

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    901-908

  • Publisher name

    ACM

  • Place of publication

    NEW YORK

  • Event location

    Amsterdam

  • Event date

    Jul 6, 2013

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000321981300113