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 and Pavel ZEZULA

Basic information

Original name

Combining Cache and Priority Queue to Enhance Evaluation of Similarity Search 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

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

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

RIV identification code

RIV/00216224:14330/18:00101090

Organization unit

Faculty of Informatics

ISBN

978-1-5386-8097-1

DOI

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

Keywords in English

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

Abstract

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.

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. 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, p. 956-963. ISBN 978-1-5386-8097-1. Available from: 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 and 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, p.~956-963. ISBN~978-1-5386-8097-1. Available from: https://dx.doi.org/10.1109/FSKD.2018.8687208.

Detailed Information on Publication Record