Similarity Search for an Extreme Application: Experience and
Implementation

D 2021

Similarity Search for an Extreme Application: Experience and Implementation

MÍČ, Vladimír, Tomáš RAČEK, Aleš KŘENEK a Pavel ZEZULA

Základní údaje

Originální název

Similarity Search for an Extreme Application: Experience and Implementation

Autoři

MÍČ, Vladimír (203 Česká republika, domácí), Tomáš RAČEK (203 Česká republika, domácí), Aleš KŘENEK (203 Česká republika, domácí) a Pavel ZEZULA (203 Česká republika, garant, domácí)

Vydání

Cham, Similarity Search and Applications: 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings, od s. 265-279, 15 s. 2021

Nakladatel

Springer

Další údaje

Jazyk

angličtina

Typ výsledku

Stať ve sborníku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Švýcarsko

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

Odkazy

URL

Impakt faktor

Impact factor: 0.402 v roce 2005

Kód RIV

RIV/00216224:14330/21:00122667

Organizační jednotka

Fakulta informatiky

ISBN

978-3-030-89656-0

ISSN

DOI

http://dx.doi.org/10.1007/978-3-030-89657-7_20

UT WoS

000722252200020

Klíčová slova anglicky

Similarity search in metric space;Efficiency;Distance distribution;Dimensionality curse;Extreme distance function

Štítky

core_B, DISA, firank_B

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 23. 11. 2021 14:16, RNDr. Vladimír Míč, Ph.D.

Anotace

V originále

Contemporary challenges for efficient similarity search include complex similarity functions, the curse of dimensionality, and large sizes of descriptive features of data objects. This article reports our experience with a database of protein chains which form (almost) metric space and demonstrate the following extreme properties. Evaluation of the pairwise similarity of protein chains can take even tens of minutes, and has a variance of six orders of magnitude. The minimisation of a number of similarity comparisons is thus crucial, so we propose a generic three stage search engine to solve it. We improve the median searching time 73 times in comparison with the search engine currently employed for the protein database in practice.

Návaznosti

EF16_019/0000822, projekt VaV

Název: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur

Citovat

MÍČ, Vladimír, Tomáš RAČEK, Aleš KŘENEK a Pavel ZEZULA. Similarity Search for an Extreme Application: Experience and Implementation. In Nora Reyes, Richard Connor, Nils Kriege, Daniyal Kazempour, Ilaria Bartolini, Erich Schubert, Jian-Jia Chen. Similarity Search and Applications: 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings. Cham: Springer, 2021, s. 265-279. ISBN 978-3-030-89656-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-89657-7_20.

@inproceedings{1799684,
   author = {Míč, Vladimír and Raček, Tomáš and Křenek, Aleš and Zezula, Pavel},
   address = {Cham},
   booktitle = {Similarity Search and Applications: 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-030-89657-7_20},
   editor = {Nora Reyes, Richard Connor, Nils Kriege, Daniyal Kazempour, Ilaria Bartolini, Erich Schubert, Jian-Jia Chen},
   keywords = {Similarity search in metric space;Efficiency;Distance distribution;Dimensionality curse;Extreme distance function},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-030-89656-0},
   pages = {265-279},
   publisher = {Springer},
   title = {Similarity Search for an Extreme Application: Experience and Implementation},
   url = {https://link.springer.com/chapter/10.1007/978-3-030-89657-7_20},
   year = {2021}
}

TY  - JOUR
ID  - 1799684
AU  - Míč, Vladimír - Raček, Tomáš - Křenek, Aleš - Zezula, Pavel
PY  - 2021
TI  - Similarity Search for an Extreme Application: Experience and Implementation
PB  - Springer
CY  - Cham
SN  - 9783030896560
KW  - Similarity search in metric space;Efficiency;Distance distribution;Dimensionality curse;Extreme distance function
UR  - https://link.springer.com/chapter/10.1007/978-3-030-89657-7_20
N2  - Contemporary challenges for efficient similarity search include complex similarity functions, the curse of dimensionality, and large sizes of descriptive features of data objects. This article reports our experience with a database of protein chains which form (almost) metric space and demonstrate the following extreme properties. Evaluation of the pairwise similarity of protein chains can take even tens of minutes, and has a variance of six orders of magnitude. The minimisation of a number of similarity comparisons is thus crucial, so we propose a generic three stage search engine to solve it. We improve the median searching time 73 times in comparison with the search engine currently employed for the protein database in practice.
ER  -

MÍČ, Vladimír, Tomáš RAČEK, Aleš KŘENEK a Pavel ZEZULA. Similarity Search for an Extreme Application: Experience and Implementation. In Nora Reyes, Richard Connor, Nils Kriege, Daniyal Kazempour, Ilaria Bartolini, Erich Schubert, Jian-Jia Chen. \textit{Similarity Search and Applications: 14th International Conference, SISAP 2021, Dortmund, Germany, September 29 - October 1, 2021, Proceedings}. Cham: Springer, 2021, s.~265-279. ISBN~978-3-030-89656-0. Dostupné z: https://dx.doi.org/10.1007/978-3-030-89657-7\_{}20.

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