Efficient Indexing of Similarity Models with Inequality Symbolic Regression

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient Indexing of Similarity Models with Inequality Symbolic Regression

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Efficient Indexing of Similarity Models with Inequality Symbolic Regression

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

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

ISBN

978-1-4503-1963-8

ISSN

—

e-ISSN

—

Počet stran výsledku

8

Strana od-do

901-908

Název nakladatele

ACM

Místo vydání

NEW YORK

Místo konání akce

Amsterdam

Datum konání akce

6. 7. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000321981300113