On the Costs and Benefits of Learned Indexing for Dynamic
High-Dimensional Data

D 2025

On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data

SLANINÁKOVÁ, Terézia; Jaroslav OĽHA; David PROCHÁZKA; Matej ANTOL; Vlastislav DOHNAL et. al.

Basic information

Original name

On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data

Authors

SLANINÁKOVÁ, Terézia; Jaroslav OĽHA; David PROCHÁZKA; Matej ANTOL and Vlastislav DOHNAL

Edition

1. vyd. Cham, Big Data Analytics and Knowledge Discovery 27th International Conference, DaWaK 2025, Bangkok, Thailand, August 25–27, 2025, Proceedings, p. 251-258, 8 pp. 2025

Publisher

Springer Cham

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

10200 1.2 Computer and information sciences

Country of publisher

Switzerland

Confidentiality degree

is not subject to a state or trade secret

Publication form

printed version "print"

References:

URL

Organization unit

Faculty of Informatics

ISBN

978-3-032-02214-1

DOI

http://dx.doi.org/10.1007/978-3-032-02215-8_20

Keywords in English

Learned indexing; Dynamization; Dynamic datasets; kNN search ; ANN search

Abstract

In the original language

One of the main challenges within the growing research area of learned indexing is the lack of adaptability to dynamically expanding datasets . This paper explores the dynamization of a static learned index for complex data through operations such as node splitting and broadening, enabling efficient adaptation to new data. Furthermore, we evaluate the trade-offs between static and dynamic approaches by introducing an amortized cost model to assess query performance in tandem with the build costs of the index structure, enabling experimental determination of when a dynamic learned index outperforms its static counterpart. We apply the dynamization method to a static learned index and demonstrate that its superior scaling quickly surpasses the static implementation in terms of overall costs as the database grows.

Links

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/1638/2024, interní kód MU

Name: Umělá inteligence a správa komplexních rozsáhlých dat

Investor: Masaryk University, Artificial intelligence in large-scale data management

Cite

SLANINÁKOVÁ, Terézia; Jaroslav OĽHA; David PROCHÁZKA; Matej ANTOL and Vlastislav DOHNAL. On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data. In Carson K. Leung, Anton Dignös, Gabriele Kotsis, A. Min Tjoa, Ismail Khalil. Big Data Analytics and Knowledge Discovery 27th International Conference, DaWaK 2025, Bangkok, Thailand, August 25–27, 2025, Proceedings. 1st ed. Cham: Springer Cham, 2025, p. 251-258. ISBN 978-3-032-02214-1. Available from: https://dx.doi.org/10.1007/978-3-032-02215-8_20.

@inproceedings{2512838,
   author = {Slanináková, Terézia and Oľha, Jaroslav and Procházka, David and Antol, Matej and Dohnal, Vlastislav},
   address = {Cham},
   booktitle = {Big Data Analytics and Knowledge Discovery 27th International Conference, DaWaK 2025, Bangkok, Thailand, August 25–27, 2025, Proceedings},
   doi = {http://dx.doi.org/10.1007/978-3-032-02215-8_20},
   edition = {1},
   editor = {Carson K. Leung, Anton Dignös, Gabriele Kotsis, A. Min Tjoa, Ismail Khalil},
   keywords = {Learned indexing; Dynamization; Dynamic datasets; kNN search ; ANN search},
   howpublished = {tištěná verze "print"},
   language = {eng},
   location = {Cham},
   isbn = {978-3-032-02214-1},
   pages = {251-258},
   publisher = {Springer Cham},
   title = {On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data},
   url = {https://link.springer.com/chapter/10.1007/978-3-032-02215-8_20},
   year = {2025}
}

TY  - CONF
ID  - 2512838
AU  - Slanináková, Terézia - Oľha, Jaroslav - Procházka, David - Antol, Matej - Dohnal, Vlastislav
PY  - 2025
TI  - On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data
PB  - Springer Cham
CY  - Cham
SN  - 9783032022141
KW  - Learned indexing
KW  - Dynamization
KW  - Dynamic datasets
KW  - kNN search
KW  - ANN search
UR  - https://link.springer.com/chapter/10.1007/978-3-032-02215-8_20
N2  - One of the main challenges within the growing research area of learned indexing is the lack of adaptability to dynamically expanding datasets . This paper explores the dynamization of a static learned index for complex data through operations such as node splitting and broadening, enabling efficient adaptation to new data. Furthermore, we evaluate the trade-offs between static and dynamic approaches by introducing an amortized cost model to assess query performance in tandem with the build costs of the index structure, enabling experimental determination of when a dynamic learned index outperforms its static counterpart. We apply the dynamization method to a static learned index and demonstrate that its superior scaling quickly surpasses the static implementation in terms of overall costs as the database grows.
ER  -

SLANINÁKOVÁ, Terézia; Jaroslav OĽHA; David PROCHÁZKA; Matej ANTOL and Vlastislav DOHNAL. On the Costs and Benefits of Learned Indexing for Dynamic High-Dimensional Data. In Carson K. Leung, Anton Dignös, Gabriele Kotsis, A. Min Tjoa, Ismail Khalil. \textit{Big Data Analytics and Knowledge Discovery 27th International Conference, DaWaK 2025, Bangkok, Thailand, August 25–27, 2025, Proceedings}. 1st ed. Cham: Springer Cham, 2025, p.~251-258. ISBN~978-3-032-02214-1. Available from: https://dx.doi.org/10.1007/978-3-032-02215-8\_{}20.

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