Rank Aggregation of Candidate Sets for Efficient Similarity Search

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14330%2F14%3A00073743" target="_blank" >RIV/00216224:14330/14:00073743 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-10085-2_4" target="_blank" >http://dx.doi.org/10.1007/978-3-319-10085-2_4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-10085-2_4" target="_blank" >10.1007/978-3-319-10085-2_4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Rank Aggregation of Candidate Sets for Efficient Similarity Search

Popis výsledku v původním jazyce

Many current applications need to organize data with respect to mutual similarity between data objects. Generic similarity retrieval in large data collections is a tough task that has been drawing researchers? attention for two decades. A typical generalstrategy to retrieve the most similar objects to a given example is to access and then refine a candidate set of objects; the overall search costs (and search time) then typically correlate with the candidate set size. We propose a generic approach thatcombines several independent indexes by aggregating their candidate sets in such a way that the resulting candidate set can be one or two orders of magnitude smaller (while keeping the answer quality). This achievement comes at the expense of higher computational costs of the ranking algorithm but experiments on two real-life and one artificial datasets indicate that the overall gain can be significant.

Název v anglickém jazyce

Rank Aggregation of Candidate Sets for Efficient Similarity Search

Popis výsledku anglicky

Many current applications need to organize data with respect to mutual similarity between data objects. Generic similarity retrieval in large data collections is a tough task that has been drawing researchers? attention for two decades. A typical generalstrategy to retrieve the most similar objects to a given example is to access and then refine a candidate set of objects; the overall search costs (and search time) then typically correlate with the candidate set size. We propose a generic approach thatcombines several independent indexes by aggregating their candidate sets in such a way that the resulting candidate set can be one or two orders of magnitude smaller (while keeping the answer quality). This achievement comes at the expense of higher computational costs of the ranking algorithm but experiments on two real-life and one artificial datasets indicate that the overall gain can be significant.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

25th International Conference on Database and Expert Systems Applications (DEXA 2014 )

ISBN

9783319100845

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

17

Strana od-do

42-58

Název nakladatele

Springer International Publishing Switzerland

Místo vydání

Haidelberg

Místo konání akce

Munich, Germany

Datum konání akce

1. 1. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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