The Case for Learned Index Structures

This paper introduces the concept of 'learned index structures' as a revolutionary approach to optimizing data access in database systems. By leveraging machine learning models, particularly deep learning models, the authors propose a new paradigm for replacing traditional index structures like B-trees, hash indexes, and Bloom filters.

Learned indexes offer significant performance gains and memory savings compared to traditional structures across various datasets. The Recursive Model Index (RMI) architecture helps improve prediction accuracy, and the potential for hybrid indexing combining neural networks and traditional techniques showcases a promising future for enhancing database systems' efficiency and scalability.

Read full paper: https://arxiv.org/abs/1712.01208

Tags: Machine Learning, Systems and Performance, AI for Science