The LDRML-index: a multi-dimensional learned index based on local dimensionality reduction

Jianrong Li; Haixia Xu; Xia Zhou; Zhida Qiu; Sikai Cheng

doi:10.1117/12.3049589

8 November 2024 The LDRML-index: a multi-dimensional learned index based on local dimensionality reduction

Jianrong Li, Haixia Xu, Xia Zhou, Zhida Qiu, Sikai Cheng

Author Affiliations +

Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134161I (2024) https://doi.org/10.1117/12.3049589
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China

Abstract

In a complex data environment, many databases need to have efficient access to multi-dimensional datasets. Therefore, it is important to construct a multi-dimensional index that can effectively support the retrieval of highly multi-dimensional datasets. We propose a new multi-dimensional learned index called LDRML-index, which consists of a dimensionality reduction module and a learned index module. The first module contains a local dimension reduction (LDR) component and a Z-address calculation component that processes the data to make it applicable to later module. The second module includes a learned index component and a dynamic learned index framework, enabling the learned index to better adapt to the data distribution and improve query efficiency. The experimental results show that LDRML-index can effectively reduce space consumption and improve query performance.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jianrong Li, Haixia Xu, Xia Zhou, Zhida Qiu, and Sikai Cheng "The LDRML-index: a multi-dimensional learned index based on local dimensionality reduction", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134161I (8 November 2024); https://doi.org/10.1117/12.3049589

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