Towards Artificial Priority Queues for Similarity Query
Execution

D 2018

Towards Artificial Priority Queues for Similarity Query Execution

ANTOL, Matej a Vlastislav DOHNAL

Základní údaje

Originální název

Towards Artificial Priority Queues for Similarity Query Execution

Autoři

ANTOL, Matej (703 Slovensko, domácí) a Vlastislav DOHNAL (203 Česká republika, garant, domácí)

Vydání

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

Nakladatel

IEEE

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

elektronická verze "online"

Odkazy

URL

Kód RIV

RIV/00216224:14330/18:00101035

Organizační jednotka

Fakulta informatiky

ISBN

978-1-5386-6306-6

ISSN

DOI

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

UT WoS

000440300600014

Klíčová slova anglicky

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

Štítky

approximate search, DISA, index structures, performance evaluation

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 13. 5. 2020 19:16, RNDr. Pavel Šmerk, Ph.D.

Anotace

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.

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

MUNI/A/1213/2017, interní kód MU

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

Investor: Masarykova univerzita, Aplikovaný výzkum na FI: bezpečnost počítačových systémů, SW architektury kritických infrastruktur, zpracování velkých dat, vizualizace dat a virtuální realita, DO R. 2020_Kategorie A - Specifický výzkum - Studentské výzkumné projekty

Citovat

ANTOL, Matej a 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, s. 78-83. ISBN 978-1-5386-6306-6. Dostupné z: 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 a 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, s.~78-83. ISBN~978-1-5386-6306-6. Dostupné z: https://dx.doi.org/10.1109/ICDEW.2018.00020.

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