Enhancing Similarity Search Throughput by Dynamic Query
Reordering

D 2016

Enhancing Similarity Search Throughput by Dynamic Query Reordering

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

Enhancing Similarity Search Throughput by Dynamic Query Reordering

Autoři

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

Vydání

Cham, Database and Expert Systems Applications: 27th International Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Proceedings, Part II, od s. 185-200, 16 s. 2016

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Portugalsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/16:00088102

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-44405-5

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-44406-2_14

UT WoS

000389020200014

Klíčová slova anglicky

Stream processing; Similarity Search

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2020 19:24, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

A lot of multimedia data are being created nowadays, which can only be searched by content since no searching metadata are available for them. To make the content search efficient, similarity indexing structures based on the metric-space model can be used. In our work, we focus on a scenario where the similarity search is used in the context of stream processing. In particular, there is a potentially infinite sequence (stream) of query objects, and a query needs to be executed for each of them. The goal is to maximize the throughput of processed queries while maintaining an acceptable delay. We propose an approach based on dynamic reordering of the incoming queries combined with caching of recent results. We were able to achieve up to 3.7 times higher throughput compared to the base case when no reordering and caching is used.

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. Enhancing Similarity Search Throughput by Dynamic Query Reordering. In Hartmann, Sven and Ma, Hui. Database and Expert Systems Applications: 27th International Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Proceedings, Part II. Cham: Springer International Publishing, 2016, s. 185-200. ISBN 978-3-319-44405-5. Dostupné z: https://dx.doi.org/10.1007/978-3-319-44406-2_14.

@inproceedings{1352385,
   author = {Nálepa, Filip and Batko, Michal and Zezula, Pavel},
   address = {Cham},
   booktitle = {Database and Expert Systems Applications: 27th International Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Proceedings, Part II},
   doi = {http://dx.doi.org/10.1007/978-3-319-44406-2_14},
   editor = {Hartmann, Sven and Ma, Hui},
   keywords = {Stream processing; Similarity Search},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-319-44405-5},
   pages = {185-200},
   publisher = {Springer International Publishing},
   title = {Enhancing Similarity Search Throughput by Dynamic Query Reordering},
   year = {2016}
}

TY  - JOUR
ID  - 1352385
AU  - Nálepa, Filip - Batko, Michal - Zezula, Pavel
PY  - 2016
TI  - Enhancing Similarity Search Throughput by Dynamic Query Reordering
PB  - Springer International Publishing
CY  - Cham
SN  - 9783319444055
KW  - Stream processing
KW  - Similarity Search
N2  - A lot of multimedia data are being created nowadays, which can only be searched by content since no searching metadata are available for them. To make the content search efficient, similarity indexing structures based on the metric-space model can be used. In our work, we focus on a scenario where the similarity search is used in the context of stream processing. In particular, there is a potentially infinite sequence (stream) of query objects, and a query needs to be executed for each of them. The goal is to maximize the throughput of processed queries while maintaining an acceptable delay. We propose an approach based on dynamic reordering of the incoming queries combined with caching of recent results. We were able to achieve up to 3.7 times higher throughput compared to the base case when no reordering and caching is used.
ER  -

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA. Enhancing Similarity Search Throughput by Dynamic Query Reordering. In Hartmann, Sven and Ma, Hui. \textit{Database and Expert Systems Applications: 27th International Conference, DEXA 2016, Porto, Portugal, September 5-8, 2016, Proceedings, Part II}. Cham: Springer International Publishing, 2016, s.~185-200. ISBN~978-3-319-44405-5. Dostupné z: https://dx.doi.org/10.1007/978-3-319-44406-2\_{}14.

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