# Hands-off Model Integration in Spatial Index Structures

arXiv: https://arxiv.org/abs/2006.16411 · HTML: https://mlsanity.com/paper/2006.16411/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [A Survey of Learned Indexes for the Multi-dimensional Space](https://mlsanity.com/paper/2403.06456/) (2024)
- [The "AI+R"-tree: An Instance-optimized R-tree](https://mlsanity.com/paper/2207.00550/) (2022)

## Abstract (arXiv)

Spatial indexes are crucial for the analysis of the increasing amounts of spatial data, for example generated through IoT applications. The plethora of indexes that has been developed in recent decades has primarily been optimised for disk. With increasing amounts of memory even on commodity machines, however, moving them to main memory is an option. Doing so opens up the opportunity to use additional optimizations that are only amenable to main memory. In this paper we thus explore the opportunity to use light-weight machine learning models to accelerate queries on spatial indexes. We do so by exploring the potential of using interpolation and similar techniques on the R-tree, arguably the most broadly used spatial index. As we show in our experimental analysis, the query execution time can be reduced by up to 60% while simultaneously shrinking the index's memory footprint by over 90%