Universal Indexing of Arbitrary Similarity Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139514" target="_blank" >RIV/00216208:11320/13:10139514 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.vldb.org/pvldb/vol6/p1392-bartos.pdf" target="_blank" >http://www.vldb.org/pvldb/vol6/p1392-bartos.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Universal Indexing of Arbitrary Similarity Models

Popis výsledku v původním jazyce

The increasing amount of available unstructured content together with the growing number of large non-relational databases put more emphasis on the emph{content-based retrieval} and precisely on the area of similarity searching. Although there exist several indexing methods for efficient querying, not all of them are best-suited for arbitrary similarity models. Having a metric space, we can easily apply metric access methods but for nonmetric models which typically better describe similarities betweengenerally unstructured objects the situation is a little bit more complicated. To address this challenge, we introduce {bf SIMDEX}, the universal framework that is capable of finding alternative indexing methods that will serve for efficient yet effective similarity searching for any similarity model. Using trivial or more advanced methods for the incremental exploration of possible indexing techniques, we are able to find alternative methods to the widely used metric space model paradi

Název v anglickém jazyce

Universal Indexing of Arbitrary Similarity Models

Popis výsledku anglicky

The increasing amount of available unstructured content together with the growing number of large non-relational databases put more emphasis on the emph{content-based retrieval} and precisely on the area of similarity searching. Although there exist several indexing methods for efficient querying, not all of them are best-suited for arbitrary similarity models. Having a metric space, we can easily apply metric access methods but for nonmetric models which typically better describe similarities betweengenerally unstructured objects the situation is a little bit more complicated. To address this challenge, we introduce {bf SIMDEX}, the universal framework that is capable of finding alternative indexing methods that will serve for efficient yet effective similarity searching for any similarity model. Using trivial or more advanced methods for the incremental exploration of possible indexing techniques, we are able to find alternative methods to the widely used metric space model paradi

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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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

VLDB Endowment, Vol. 6, No. 12.

ISBN

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ISSN

2150-8097

e-ISSN

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Počet stran výsledku

6

Strana od-do

1-6

Název nakladatele

VLDB Endowment

Místo vydání

Neuveden

Místo konání akce

Riva del Garda, Trento, Italy

Datum konání akce

26. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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