Combining Cache and Priority Queue to Enhance Evaluation of
Similarity Search Queries

D 2018

Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries

Autoři

NÁLEPA, Filip (203 Česká republika, garant, domácí), Michal BATKO (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, domácí)

Vydání

Neuveden, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, od s. 956-963, 8 s. 2018

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Kód RIV

RIV/00216224:14330/18:00101090

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5386-8097-1

DOI

http://dx.doi.org/10.1109/FSKD.2018.8687208

Klíčová slova anglicky

approximate similarity search; multiple kNN queries; data partitions caching; priority queue based similarity search

Štítky

DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 16. 4. 2019 20:34, RNDr. Filip Nálepa, Ph.D.

Anotace

V originále

A variety of applications have been using content-based similarity search techniques. Higher effectiveness of the search can be, in some cases, achieved by submitting multiple similar queries. We propose new approximation techniques that are specially designed to enhance the trade-off between the effectiveness and the efficiency of multiple k-nearest-neighbors queries. They combine the probability of an indexed object to be a part of the precise query result and the time needed to examine the object. This enables us to improve processing times while maintaining the same query precision as compared to the traditional approximation technique without the proposed optimizations.

Návaznosti

GA16-18889S, projekt VaV

Název: Analytika pro velká nestrukturovaná data (Akronym: Big Data Analytics for Unstructured Data)

Investor: Grantová agentura ČR, Big Data Analytics for Unstructured Data

Citovat

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA. Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries. Online. In Maozhen Li, Xiong Ning, Zheng Xiao, Guoqing Xiao, Kenli Li, and Lipo Wang. 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery. Neuveden: IEEE, 2018, s. 956-963. ISBN 978-1-5386-8097-1. Dostupné z: https://dx.doi.org/10.1109/FSKD.2018.8687208.

@inproceedings{1425798,
   author = {Nálepa, Filip and Batko, Michal and Zezula, Pavel},
   address = {Neuveden},
   booktitle = {2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery},
   doi = {http://dx.doi.org/10.1109/FSKD.2018.8687208},
   editor = {Maozhen Li, Xiong Ning, Zheng Xiao, Guoqing Xiao, Kenli Li, and Lipo Wang},
   keywords = {approximate similarity search; multiple kNN queries; data partitions caching; priority queue based similarity search},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Neuveden},
   isbn = {978-1-5386-8097-1},
   pages = {956-963},
   publisher = {IEEE},
   title = {Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries},
   year = {2018}
}

TY  - JOUR
ID  - 1425798
AU  - Nálepa, Filip - Batko, Michal - Zezula, Pavel
PY  - 2018
TI  - Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries
PB  - IEEE
CY  - Neuveden
SN  - 9781538680971
KW  - approximate similarity search
KW  - multiple kNN queries
KW  - data partitions caching
KW  - priority queue based similarity search
N2  - A variety of applications have been using content-based similarity search techniques. Higher effectiveness of the search can be, in some cases, achieved by submitting multiple similar queries. We propose new approximation techniques that are specially designed to enhance the trade-off between the effectiveness and the efficiency of multiple k-nearest-neighbors queries. They combine the probability of an indexed object to be a part of the precise query result and the time needed to examine the object. This enables us to improve processing times while maintaining the same query precision as compared to the traditional approximation technique without the proposed optimizations.
ER  -

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA. Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search Queries. Online. In Maozhen Li, Xiong Ning, Zheng Xiao, Guoqing Xiao, Kenli Li, and Lipo Wang. \textit{2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery}. Neuveden: IEEE, 2018, s.~956-963. ISBN~978-1-5386-8097-1. Dostupné z: https://dx.doi.org/10.1109/FSKD.2018.8687208.

Podrobný výpis o publikaci