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.
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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
Other information
Original language English
Type of outcome Proceedings paper
Field of Study 10201 Computer sciences, information science, bioinformatics
Country of publisher Switzerland
Confidentiality degree is not subject to a state or trade secret
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 0302-9743
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
Changed by Changed by: RNDr. Pavel Šmerk, Ph.D., učo 3880. Changed: 7/4/2024 23:35.
Abstract
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.
Links
EF16_019/0000822, research and development projectName: Centrum excelence pro kyberkriminalitu, kyberbezpečnost a ochranu kritických informačních infrastruktur
GF23-07040K, research and development projectName: Naučené indexy pro podobností hledání
Investor: Czech Science Foundation, Learned Indexing for Similarity Searching, Lead Agency
LM2018131, research and development projectName: 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 projectName: e-Infrastruktura CZ (Acronym: e-INFRA CZ)
Investor: Ministry of Education, Youth and Sports of the CR
MUNI/A/1339/2022, interní kód MUName: 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
PrintDisplayed: 19/7/2024 16:30