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 and Pavel ZEZULA

Basic information

Original name

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

Authors

NÁLEPA, Filip (203 Czech Republic, guarantor, belonging to the institution), Michal BATKO (203 Czech Republic, belonging to the institution) and Pavel ZEZULA (203 Czech Republic, belonging to the institution)

Edition

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

Publisher

Springer International Publishing

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Switzerland

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

printed version "print"

Impact factor

Impact factor: 0.402 in 2005

RIV identification code

RIV/00216224:14330/17:00094944

Organization unit

Faculty of Informatics

ISBN

978-3-319-66916-8

ISSN

DOI

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

UT WoS

000463611400018

Keywords in English

stream processing; similarity search; parallel processing

Abstract

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.

Links

GA16-18889S, research and development project

Name: Analytika pro velká nestrukturovaná data (Acronym: Big Data Analytics for Unstructured Data)

Investor: Czech Science Foundation

Citovat

NÁLEPA, Filip, Michal BATKO and 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, p. 262-277. ISBN 978-3-319-66916-8. Available from: 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 and 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, p.~262-277. ISBN~978-3-319-66916-8. Available from: https://dx.doi.org/10.1007/978-3-319-66917-5\_{}18.

Detailed Information on Publication Record