Work

Learned Indexes From the One-dimensional to the Multi-dimensional Spaces: Challenges, Techniques, and Opportunities

Year: 2025

Type: conference-paper

Abstract: Recently, the class of learned index structures has emerged as one form of database indexes that make use of Machine Learning (ML) techniques. The learned indexes designed for the one-dimensional space have demonstrated improvements in both the query processing time and the index size. Observing the advantages of one-dimensional learned indexes, various learned indexes have been proposed for the multi-dimensional space. This class of learned indexes is termed ''Learned Multi-dimensional Indexes.'' This tutorial on learned indexes is designed based on our long survey article on the subject [4]. In this tutorial, we use a taxonomy to categorize over 100 learned one- and multi-dimensional indexes with more focus on the class of learned multi-dimensional indexes. The goal of this tutorial is to explain the fundamental techniques behind state-of-the-art learned one- and multi-dimensional indexes with emphasis on the latter, and identify the ongoing challenges and future opportunities for research in this domain.

Source: Proceedings - ACM-SIGMOD International Conference on Management of Data

DOI: 10.1145/3722212.3725639

Authors: Abdullah Al-Mamun, Jianguo Wang, Walid G. Aref

Institution: Purdue University West Lafayette

Language: English

Topic: Data Management and Algorithms

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: Universitas Brawijaya

Award: G114319075