Conference Proceedings

Function Interpolation for Learned Index Structures

NF Setiawan, BIP Rubinstein, R Borovica-Gajic

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer | Published : 2020

Abstract

Range indexes such as B-trees are widely recognised as effective data structures for enabling fast retrieval of records by the query key. While such classical indexes offer optimal worst-case guarantees, recent research suggests that average-case performance might be improved by alternative machine learning-based models such as deep neural networks. This paper explores an alternative approach by modelling the task as one of function approximation via interpolation between compressed subsets of keys. We explore the Chebyshev and Bernstein polynomial bases, and demonstrate substantial benefits over deep neural networks. In particular, our proposed function interpolation models exhibit memory f..

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