Rank Aggregation of Candidate Sets for Efficient Similarity
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Rank Aggregation of Candidate Sets for Efficient Similarity Search

NOVÁK, David a Pavel ZEZULA

Základní údaje

Originální název

Rank Aggregation of Candidate Sets for Efficient Similarity Search

Autoři

NOVÁK, David (203 Česká republika, garant, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Haidelberg, 25th International Conference on Database and Expert Systems Applications (DEXA 2014 ), od s. 42-58, 17 s. 2014

Nakladatel

Springer International Publishing Switzerland

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

20201 Electrical and electronic engineering

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/14:00073743

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-10084-5

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-10085-2_4

Klíčová slova anglicky

Similarity Search; Metric Space; Approximation; Scalability

Štítky

core_B, DISA, firank_B

Změněno: 27. 4. 2015 05:47, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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 general strategy 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 that combines 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.

Návaznosti

GBP103/12/G084, projekt VaV

Název: Centrum pro multi-modální interpretaci dat velkého rozsahu

Investor: Grantová agentura ČR, Centrum pro multi-modální interpretaci dat velkého rozsahu

Citovat

NOVÁK, David a Pavel ZEZULA. Rank Aggregation of Candidate Sets for Efficient Similarity Search. In 25th International Conference on Database and Expert Systems Applications (DEXA 2014 ). Haidelberg: Springer International Publishing Switzerland, 2014, s. 42-58. ISBN 978-3-319-10084-5. Dostupné z: https://dx.doi.org/10.1007/978-3-319-10085-2_4.

@inproceedings{1192665,
   author = {Novák, David and Zezula, Pavel},
   address = {Haidelberg},
   booktitle = {25th International Conference on Database and Expert Systems Applications (DEXA 2014 )},
   doi = {http://dx.doi.org/10.1007/978-3-319-10085-2_4},
   keywords = {Similarity Search; Metric Space; Approximation; Scalability},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Haidelberg},
   isbn = {978-3-319-10084-5},
   pages = {42-58},
   publisher = {Springer International Publishing Switzerland},
   title = {Rank Aggregation of Candidate Sets for Efficient Similarity Search},
   year = {2014}
}

TY  - JOUR
ID  - 1192665
AU  - Novák, David - Zezula, Pavel
PY  - 2014
TI  - Rank Aggregation of Candidate Sets for Efficient Similarity Search
PB  - Springer International Publishing Switzerland
CY  - Haidelberg
SN  - 9783319100845
KW  - Similarity Search
KW  - Metric Space
KW  - Approximation
KW  - Scalability
N2  - 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 general strategy 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 that combines 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.
ER  -

NOVÁK, David a Pavel ZEZULA. Rank Aggregation of Candidate Sets for Efficient Similarity Search. In \textit{25th International Conference on Database and Expert Systems Applications (DEXA 2014 )}. Haidelberg: Springer International Publishing Switzerland, 2014, s.~42-58. ISBN~978-3-319-10084-5. Dostupné z: https://dx.doi.org/10.1007/978-3-319-10085-2\_{}4.

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