We present Sparse Numerical Array-Based Range Filters (SNARF), a learned range filter
that efficiently supports range queries for numerical data. SNARF creates a model of the …

In recent years, we have seen increased interest in applying machine learning to system
problems. For example, there has been work on applying machine learning to improve …

In-memory join is an essential operator in any database engine. It has been extensively
investigated in the database literature. In this paper, we study whether exploiting the CDF …

External sorting is at the core of many operations in large-scale database systems, such as
ordering and aggregation queries for large result sets, building indexes, sort-merge joins …

Designing algorithms is cumbersome and errorprone. This, among other things, has
increasingly led to efforts to extend or even replace designing algorithms with machine …

This work analyzes and parallelizes LearnedSort, the novel algorithm that sorts using
machine learning models based on the cumulative distribution function. LearnedSort is …

Sorting is one of the most fundamental algorithms in computer science. Recently, Learned
Sorts, which use machine learning to improve sorting speed, have attracted attention. While …

This paper aims to better understand the strengths and limitations of adopting learned-
based approaches in sequential sorting numerical data, via two main research steps. First …