# Approximate Query Processing for Group-By Queries based on Conditional Generative Models

arXiv: https://arxiv.org/abs/2101.02914 · HTML: https://mlsanity.com/paper/2101.02914/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 8 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Approximate Query Processing using Deep Generative Models](https://mlsanity.com/paper/1903.10000/) (2019)
- [Rapid Sampling for Visualizations with Ordering Guarantees](https://mlsanity.com/paper/1412.3040/) (2014)
- [Database Learning: Toward a Database that Becomes Smarter Every Time](https://mlsanity.com/paper/1703.05468/) (2017)
- [Random Sampling for Group-By Queries](https://mlsanity.com/paper/1909.02629/) (2019)
- [EntropyDB: A Probabilistic Approach to Approximate Query Processing](https://mlsanity.com/paper/1911.04948/) (2019)

## Abstract (arXiv)

The Group-By query is an important kind of query, which is common and widely used in data warehouses, data analytics, and data visualization. Approximate query processing is an effective way to increase the querying efficiency on big data. The answer to a group-by query involves multiple values, which makes it difficult to provide sufficiently accurate estimations for all the groups. Stratified sampling improves the accuracy compared with the uniform sampling, but the samples chosen for some special queries cannot work for other queries. Online sampling chooses samples for the given query at query time, but it requires a long latency. Thus, it is a challenge to achieve both accuracy and efficiency at the same time. Facing such challenge, in this work, we propose a sample generation framework based on a conditional generative model. The sample generation framework can generate any number of samples for the given query without accessing the data. The proposed framework based on the lightweight model can be combined with stratified sampling and online aggregation to improve the estimation accuracy for group-by queries. The experimental results show that our proposed methods are both efficient and accurate.