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

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.