On Nonmetric Similarity Search Problems in Complex Domains

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F11%3A10045849" target="_blank" >RIV/00216208:11320/11:10045849 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/1978802.1978813" target="_blank" >http://dx.doi.org/10.1145/1978802.1978813</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/1978802.1978813" target="_blank" >10.1145/1978802.1978813</a>

Jazyk výsledku

angličtina

Název v původním jazyce

On Nonmetric Similarity Search Problems in Complex Domains

Popis výsledku v původním jazyce

The task of similarity search is widely used in various areas of computing, including multimedia databases, data mining, bioinformatics, social networks, etc. In fact, retrieval of semantically unstructured data entities requires a form of aggregated qualification that selects entities relevant to a query. A popular type of such a mechanism is similarity querying. For a long time, the database-oriented applications of similarity search employed the definition of similarity restricted to metric distances. Due to its topological properties, metric similarity can be effectively used to index a database which can be then queried efficiently by so-called metric access methods. However, together with the increasing complexity of data entities across variousdomains, in recent years there appeared many similarities that were not metrics -- we call them nonmetric similarity functions. In this paper we survey domains employing nonmetric functions for effective similarity search, and methods for

Název v anglickém jazyce

On Nonmetric Similarity Search Problems in Complex Domains

Popis výsledku anglicky

The task of similarity search is widely used in various areas of computing, including multimedia databases, data mining, bioinformatics, social networks, etc. In fact, retrieval of semantically unstructured data entities requires a form of aggregated qualification that selects entities relevant to a query. A popular type of such a mechanism is similarity querying. For a long time, the database-oriented applications of similarity search employed the definition of similarity restricted to metric distances. Due to its topological properties, metric similarity can be effectively used to index a database which can be then queried efficiently by so-called metric access methods. However, together with the increasing complexity of data entities across variousdomains, in recent years there appeared many similarities that were not metrics -- we call them nonmetric similarity functions. In this paper we survey domains employing nonmetric functions for effective similarity search, and methods for

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>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2011

Kód důvěrnosti údajů

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

Název periodika

ACM Computing Surveys

ISSN

0360-0300

e-ISSN

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Svazek periodika

43

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

50

Strana od-do

1-50

Kód UT WoS článku

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EID výsledku v databázi Scopus

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