# LAQP: Learning-based Approximate Query Processing

arXiv: https://arxiv.org/abs/2003.02446 · HTML: https://mlsanity.com/paper/2003.02446/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2020.

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

- [Learned Sorted Table Search and Static Indexes in Small Model Space](https://mlsanity.com/paper/2107.09480/) (2021)
- [GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions](https://mlsanity.com/paper/2212.09015/) (2022)

## Abstract (arXiv)

Querying on big data is a challenging task due to the rapid growth of data amount. Approximate query processing (AQP) is a way to meet the requirement of fast response. In this paper, we propose a learning-based AQP method called the LAQP. The LAQP builds an error model learned from the historical queries to predict the sampling-based estimation error of each new query. It makes a combination of the sampling-based AQP, the pre-computed aggregations and the learned error model to provide high-accurate query estimations with a small off-line sample. The experimental results indicate that our LAQP outperforms the sampling-based AQP, the pre-aggregation-based AQP and the most recent learning-based AQP method.