Towards Artificial Priority Queues for Similarity Query
Execution

D 2018

Towards Artificial Priority Queues for Similarity Query Execution

ANTOL, Matej and Vlastislav DOHNAL

Basic information

Original name

Towards Artificial Priority Queues for Similarity Query Execution

Authors

ANTOL, Matej (703 Slovakia, belonging to the institution) and Vlastislav DOHNAL (203 Czech Republic, guarantor, belonging to the institution)

Edition

Paris, France, 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW), p. 78-83, 6 pp. 2018

Publisher

IEEE

Other information

Language

English

Type of outcome

Stať ve sborníku

Field of Study

10201 Computer sciences, information science, bioinformatics

Country of publisher

Czech Republic

Confidentiality degree

není předmětem státního či obchodního tajemství

Publication form

electronic version available online

References:

URL

RIV identification code

RIV/00216224:14330/18:00101035

Organization unit

Faculty of Informatics

ISBN

978-1-5386-6306-6

ISSN

DOI

http://dx.doi.org/10.1109/ICDEW.2018.00020

UT WoS

000440300600014

Keywords in English

similarity search;index structure;knn algorithm evaluation;query processing optimization;metric space

Abstract

V originále

Content-based retrieval in large collections of unstructured data is challenging not only from the difficulty of the defining similarity between data images where the phenomenon of semantic gap appears, but also the efficiency of execution of similarity queries. Search engines providing similarity search typically organize various multimedia data, e.g. images of a photo stock, and support k-nearest neighbor query. Users accessing such systems then look for data items similar to their specific query object and refine results by re-running the search with an object from the previous query results. This paper is motivated by unsatisfactory query execution performance of indexing structures that use metric space as a convenient data model. We present performance behavior of two state-of-the-art representatives and propose a new universal technique for ordering priority queue of data partitions to be accessed during kNN query evaluation. We verify it in experiments on real-life data-sets.

Links

GA16-18889S, research and development project

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

Investor: Czech Science Foundation

MUNI/A/1213/2017, interní kód MU

Name: Aplikovaný výzkum na FI: bezpečnost počítačových systémů, SW architektury kritických infrastruktur, zpracování velkých dat, vizualizace dat a virtuální realita

Investor: Masaryk University, Applied research at FI: computer systems security, SW architecture of critical infrastructure, big data processing, data visualization and virtual reality, Category A

Citovat

ANTOL, Matej and Vlastislav DOHNAL. Towards Artificial Priority Queues for Similarity Query Execution. Online. In 2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW). Paris, France: IEEE, 2018, p. 78-83. ISBN 978-1-5386-6306-6. Available from: https://dx.doi.org/10.1109/ICDEW.2018.00020.

@inproceedings{1422496,
   author = {Antol, Matej and Dohnal, Vlastislav},
   address = {Paris, France},
   booktitle = {2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)},
   doi = {http://dx.doi.org/10.1109/ICDEW.2018.00020},
   keywords = {similarity search;index structure;knn algorithm evaluation;query processing optimization;metric space},
   howpublished = {elektronická verze "online"},
   language = {eng},
   location = {Paris, France},
   isbn = {978-1-5386-6306-6},
   pages = {78-83},
   publisher = {IEEE},
   title = {Towards Artificial Priority Queues for Similarity Query Execution},
   url = {https://ieeexplore.ieee.org/document/8402023/},
   year = {2018}
}

TY  - JOUR
ID  - 1422496
AU  - Antol, Matej - Dohnal, Vlastislav
PY  - 2018
TI  - Towards Artificial Priority Queues for Similarity Query Execution
PB  - IEEE
CY  - Paris, France
SN  - 9781538663066
KW  - similarity search;index structure;knn algorithm evaluation;query processing optimization;metric space
UR  - https://ieeexplore.ieee.org/document/8402023/
L2  - https://ieeexplore.ieee.org/document/8402023/
N2  - Content-based retrieval in large collections of unstructured data is challenging not only from the difficulty of the defining similarity between data images where the phenomenon of semantic gap appears, but also the efficiency of execution of similarity queries. Search engines providing similarity search typically organize various multimedia data, e.g. images of a photo stock, and support k-nearest neighbor query. Users accessing such systems then look for data items similar to their specific query object and refine results by re-running the search with an object from the previous query results. This paper is motivated by unsatisfactory query execution performance of indexing structures that use metric space as a convenient data model. We present performance behavior of two state-of-the-art representatives and propose a new universal technique for ordering priority queue of data partitions to be accessed during kNN query evaluation. We verify it in experiments on real-life data-sets.
ER  -

ANTOL, Matej and Vlastislav DOHNAL. Towards Artificial Priority Queues for Similarity Query Execution. Online. In \textit{2018 IEEE 34th International Conference on Data Engineering Workshops (ICDEW)}. Paris, France: IEEE, 2018, p.~78-83. ISBN~978-1-5386-6306-6. Available from: https://dx.doi.org/10.1109/ICDEW.2018.00020.

Detailed Information on Publication Record