SISAP 2023 Indexing Challenge – Learned Metric Index

SLANINÁKOVÁ, Terézia, David PROCHÁZKA, Matej ANTOL, Jaroslav OĽHA and Vlastislav DOHNAL. SISAP 2023 Indexing Challenge – Learned Metric Index. In Pedreira, O., Estivill-Castro, V. Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289. Cham: Springer, 2023, p. 282-290. ISBN 978-3-031-46993-0. Available from: https://dx.doi.org/10.1007/978-3-031-46994-7_24.

Basic information

Original name

SISAP 2023 Indexing Challenge – Learned Metric Index

Authors

SLANINÁKOVÁ, Terézia (703 Slovakia, belonging to the institution), David PROCHÁZKA (203 Czech Republic, belonging to the institution), Matej ANTOL (703 Slovakia, belonging to the institution), Jaroslav OĽHA (703 Slovakia, belonging to the institution) and Vlastislav DOHNAL (203 Czech Republic, guarantor, belonging to the institution)

Edition

Cham, Similarity Search and Applications. SISAP 2023. Lecture Notes in Computer Science, vol 14289, p. 282-290, 9 pp. 2023

Publisher

Springer

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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/00216224:14330/23:00132045

Organization unit

Faculty of Informatics

ISBN

978-3-031-46993-0

ISSN

DOI

http://dx.doi.org/10.1007/978-3-031-46994-7_24

Keywords in English

sisap indexing challenge; learned metric index; similarity search; machine learning for indexing; performance benchmarking

Tags

LMI

Tags

International impact, Reviewed

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Abstract

V originále

This submission into the SISAP Indexing Challenge examines the experimental setup and performance of the Learned Metric Index, which uses an architecture of interconnected learned models to answer similarity queries. An inherent part of this design is a great deal of flexibility in the implementation, such as the choice of particular machine learning models, or their arrangement in the overall architecture of the index. Therefore, for the sake of transparency and reproducibility, this report thoroughly describes the details of the specific Learned Metric Index implementation used to tackle the challenge.

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Links

EF16_019/0000822, research and development project	Name: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur
GF23-07040K, research and development project	Name: Naučené indexy pro podobností hledání Investor: Czech Science Foundation, Learned Indexing for Similarity Searching, Lead Agency
LM2018131, research and development project	Name: Česká národní infrastruktura pro biologická data (Acronym: ELIXIR-CZ) Investor: Ministry of Education, Youth and Sports of the CR, Czech National Infrastructure for Biological Data
LM2018140, research and development project	Name: e-Infrastruktura CZ (Acronym: e-INFRA CZ) Investor: Ministry of Education, Youth and Sports of the CR
MUNI/A/1339/2022, interní kód MU	Name: Rozvoj technik pro zpracování dat pro podporu vyhledávání, analýz a vizualizací rozsáhlých datových souborů s využitím umělé inteligence Investor: Masaryk University, Development of data processing techniques to support search, analysis and visualization of large datasets using artificial intelligence
MUNI/A/1590/2023, interní kód MU	Name: Využití technik umělé inteligence pro zpracování dat, komplexní analýzy a vizualizaci rozsáhlých dat Investor: Masaryk University, Using artificial intelligence techniques for data processing, complex analysis and visualization of large-scale data