Towards High Similarity Search Throughput by Dynamic Query
Reordering and Parallel Processing

D 2017

Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA

Základní údaje

Originální název

Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing

Autoři

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

Vydání

Cham, Advances in Databases and Information Systems : 21st European Conference, ADBIS 2017, Nicosia, Cyprus, September 24-27, 2017, Proceedings, od s. 262-277, 16 s. 2017

Nakladatel

Springer International Publishing

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

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/00216224:14330/17:00094944

Organizační jednotka

Fakulta informatiky

ISBN

978-3-319-66916-8

ISSN

DOI

http://dx.doi.org/10.1007/978-3-319-66917-5_18

UT WoS

000463611400018

Klíčová slova anglicky

stream processing; similarity search; parallel processing

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

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

Anotace

V originále

Current era of digital data explosion calls for employment of content-based similarity search techniques since traditional searchable metadata like annotations are not always available. In our work, we focus on a scenario where the similarity search is used in the context of stream processing, which is one of the suitable approaches to deal with huge amounts of data. Our goal is to maximize the throughput of processed queries while a slight delay is acceptable. We extend our previously published technique that dynamically reorders the incoming queries in order to use our caching mechanism more effectively. The extension lies in adoption of a parallel computing environment which allows us to process multiple queries simultaneously.

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. Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing. In Mārīte Kirikova, Kjetil Nørvåg, George Angelos Papadopoulos. Advances in Databases and Information Systems : 21st European Conference, ADBIS 2017, Nicosia, Cyprus, September 24-27, 2017, Proceedings. Cham: Springer International Publishing, 2017, s. 262-277. ISBN 978-3-319-66916-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-66917-5_18.

@inproceedings{1389907,
   author = {Nálepa, Filip and Batko, Michal and Zezula, Pavel},
   address = {Cham},
   booktitle = {Advances in Databases and Information Systems : 21st European Conference, ADBIS 2017, Nicosia, Cyprus, September 24-27, 2017, Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-319-66917-5_18},
   editor = {Mārīte Kirikova, Kjetil Nørvåg, George Angelos Papadopoulos},
   keywords = {stream processing; similarity search; parallel processing},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-319-66916-8},
   pages = {262-277},
   publisher = {Springer International Publishing},
   title = {Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing},
   year = {2017}
}

TY  - JOUR
ID  - 1389907
AU  - Nálepa, Filip - Batko, Michal - Zezula, Pavel
PY  - 2017
TI  - Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing
PB  - Springer International Publishing
CY  - Cham
SN  - 9783319669168
KW  - stream processing
KW  - similarity search
KW  - parallel processing
N2  - Current era of digital data explosion calls for employment of content-based similarity search techniques since traditional searchable metadata like annotations are not always available. In our work, we focus on a scenario where the similarity search is used in the context of stream processing, which is one of the suitable approaches to deal with huge amounts of data. Our goal is to maximize the throughput of processed queries while a slight delay is acceptable. We extend our previously published technique that dynamically reorders the incoming queries in order to use our caching mechanism more effectively. The extension lies in adoption of a parallel computing environment which allows us to process multiple queries simultaneously.
ER  -

NÁLEPA, Filip, Michal BATKO a Pavel ZEZULA. Towards High Similarity Search Throughput by Dynamic Query Reordering and Parallel Processing. In Mārīte Kirikova, Kjetil Nørvåg, George Angelos Papadopoulos. \textit{Advances in Databases and Information Systems : 21st European Conference, ADBIS 2017, Nicosia, Cyprus, September 24-27, 2017, Proceedings}. Cham: Springer International Publishing, 2017, s.~262-277. ISBN~978-3-319-66916-8. Dostupné z: https://dx.doi.org/10.1007/978-3-319-66917-5\_{}18.

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