Towards Faster Similarity Search by Dynamic Reordering of
Streamed Queries

D 2018

Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA

Basic information

Original name

Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries

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

Berlin, Heidelberg, Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII, p. 61-88, 28 pp. 2018

Publisher

Springer

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/00216224:14330/18:00101119

Organization unit

Faculty of Informatics

ISBN

978-3-662-58383-8

ISSN

DOI

http://dx.doi.org/10.1007/978-3-662-58384-5_3

Keywords in English

stream processing; similarity search

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 propose a technique that dynamically reorders the queries coming from the stream in order to use our caching mechanism in huge data spaces more effectively. We were able to achieve significantly higher throughput compared to the baseline when no reordering and no caching were used. Moreover, our proposal does not incur any additional precision loss of the similarity search, as opposed to some other caching techniques. In addition to the throughput maximization, we also study the potential of trading off the throughput for low delays (waiting times). The proposed technique allows to be parameterized by the amount of the throughput that can be sacrificed.

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 Faster Similarity Search by Dynamic Reordering of Streamed Queries. In Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII. Berlin, Heidelberg: Springer, 2018, p. 61-88. ISBN 978-3-662-58383-8. Available from: https://dx.doi.org/10.1007/978-3-662-58384-5_3.

@inproceedings{1430396,
   author = {Nálepa, Filip and Batko, Michal and Zezula, Pavel},
   address = {Berlin, Heidelberg},
   booktitle = {Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII},
   doi = {http://dx.doi.org/10.1007/978-3-662-58384-5_3},
   keywords = {stream processing; similarity search},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Berlin, Heidelberg},
   isbn = {978-3-662-58383-8},
   pages = {61-88},
   publisher = {Springer},
   title = {Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries},
   year = {2018}
}

TY  - JOUR
ID  - 1430396
AU  - Nálepa, Filip - Batko, Michal - Zezula, Pavel
PY  - 2018
TI  - Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries
PB  - Springer
CY  - Berlin, Heidelberg
SN  - 9783662583838
KW  - stream processing
KW  - similarity search
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 propose a technique that dynamically reorders the queries coming from the stream in order to use our caching mechanism in huge data spaces more effectively. We were able to achieve significantly higher throughput compared to the baseline when no reordering and no caching were used. Moreover, our proposal does not incur any additional precision loss of the similarity search, as opposed to some other caching techniques. In addition to the throughput maximization, we also study the potential of trading off the throughput for low delays (waiting times). The proposed technique allows to be parameterized by the amount of the throughput that can be sacrificed.
ER  -

NÁLEPA, Filip, Michal BATKO and Pavel ZEZULA. Towards Faster Similarity Search by Dynamic Reordering of Streamed Queries. In \textit{Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXVIII}. Berlin, Heidelberg: Springer, 2018, p.~61-88. ISBN~978-3-662-58383-8. Available from: https://dx.doi.org/10.1007/978-3-662-58384-5\_{}3.

Detailed Information on Publication Record