Algorithmic exploration of axiom spaces for efficient similarity search at large scale

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F12%3A10131930" target="_blank" >RIV/00216208:11320/12:10131930 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007/978-3-642-32153-5_4" target="_blank" >http://link.springer.com/chapter/10.1007/978-3-642-32153-5_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-642-32153-5_4" target="_blank" >10.1007/978-3-642-32153-5_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Algorithmic exploration of axiom spaces for efficient similarity search at large scale

Popis výsledku v původním jazyce

Similarity search is becoming popular in even more disciplines, such as multimedia databases, bioinformatics, social networks, to name a few. The existing indexing techniques often assume the metric space model that could be too restrictive from the domain point of view. Hence, many modern applications that involve complex similarities do not use any indexing and use just sequential search, so they are applicable only to small databases. In this paper we revisit the assumptions which persist in the mainstream research of content-based retrieval. Leaving the traditional indexing paradigms such as the metric space model, our goal is to propose alternative methods for indexing that shall lead to high-performance similarity search. We introduce the designof the algorithmic framework SIMDEX for exploration of analytical properties (axioms) useful for indexing that hold in a given complex similarity space but were not discovered so far. Consequently, the known axioms will be localized as a

Název v anglickém jazyce

Algorithmic exploration of axiom spaces for efficient similarity search at large scale

Popis výsledku anglicky

Similarity search is becoming popular in even more disciplines, such as multimedia databases, bioinformatics, social networks, to name a few. The existing indexing techniques often assume the metric space model that could be too restrictive from the domain point of view. Hence, many modern applications that involve complex similarities do not use any indexing and use just sequential search, so they are applicable only to small databases. In this paper we revisit the assumptions which persist in the mainstream research of content-based retrieval. Leaving the traditional indexing paradigms such as the metric space model, our goal is to propose alternative methods for indexing that shall lead to high-performance similarity search. We introduce the designof the algorithmic framework SIMDEX for exploration of analytical properties (axioms) useful for indexing that hold in a given complex similarity space but were not discovered so far. Consequently, the known axioms will be localized as a

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GAP202%2F11%2F0968" target="_blank" >GAP202/11/0968: Podobnostní nemetrické vyhledávání v rozsáhlých komplexních databázích</a><br>

Návaznosti

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

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

Lecture Notes in Computer Science

ISSN

0302-9743

e-ISSN

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Svazek periodika

7404

Číslo periodika v rámci svazku

2012

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

14

Strana od-do

40-53

Kód UT WoS článku

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EID výsledku v databázi Scopus

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