Towards Efficient Indexing of Arbitrary Similarity

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F13%3A10139516" target="_blank" >RIV/00216208:11320/13:10139516 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.sigmod.org/publications/sigmod-record/1306/pdfs/03.articles.bartos.pdf" target="_blank" >http://www.sigmod.org/publications/sigmod-record/1306/pdfs/03.articles.bartos.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2503792.2503794" target="_blank" >10.1145/2503792.2503794</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Towards Efficient Indexing of Arbitrary Similarity

Popis výsledku v původním jazyce

The popularity of similarity search expanded with the increased interest in multimedia databases, bioinformatics, or social networks, and with the growing number of users trying to find information in huge collections of unstructured data. During the exploration, the users handle database objects in different ways based on the utilized similarity models, ranging from simple to complex models. Efficient indexing techniques for similarity search are required especially for growing databases. In this paper, we study implementation possibilities of the recently announced theoretical framework SIMDEX, the task of which is to algorithmically explore a given similarity space and find possibilities for efficient indexing. Instead of a fixed set of indexing properties, such as metric space axioms, SIMDEX aims to seek for alternative properties that are valid in a particular similarity model (database) and, at the same time, provide efficient indexing. In particular, we propose to implement the

Název v anglickém jazyce

Towards Efficient Indexing of Arbitrary Similarity

Popis výsledku anglicky

The popularity of similarity search expanded with the increased interest in multimedia databases, bioinformatics, or social networks, and with the growing number of users trying to find information in huge collections of unstructured data. During the exploration, the users handle database objects in different ways based on the utilized similarity models, ranging from simple to complex models. Efficient indexing techniques for similarity search are required especially for growing databases. In this paper, we study implementation possibilities of the recently announced theoretical framework SIMDEX, the task of which is to algorithmically explore a given similarity space and find possibilities for efficient indexing. Instead of a fixed set of indexing properties, such as metric space axioms, SIMDEX aims to seek for alternative properties that are valid in a particular similarity model (database) and, at the same time, provide efficient indexing. In particular, we propose to implement the

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

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 periodika

SIGMOD Record

ISSN

0163-5808

e-ISSN

—

Svazek periodika

42

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

5-10

Kód UT WoS článku

000321322500001

EID výsledku v databázi Scopus

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