# Approximate Query Processing using Deep Generative Models

arXiv: https://arxiv.org/abs/1903.10000 · HTML: https://mlsanity.com/paper/1903.10000/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2019.

## Built on by (most cited first, 8 of 8)

- [MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining](https://mlsanity.com/paper/2110.08009/) (2021)
- [Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data](https://mlsanity.com/paper/2210.05508/) (2022)
- [GAN-based Tabular Data Generator for Constructing Synopsis in Approximate Query Processing: Challenges and Solutions](https://mlsanity.com/paper/2212.09015/) (2022)
- [Electra: Conditional Generative Model based Predicate-Aware Query Approximation](https://mlsanity.com/paper/2201.12420/) (2022)
- [CardOOD: Robust Query-driven Cardinality Estimation under Out-of-Distribution](https://mlsanity.com/paper/2412.05864/) (2024)
- [Approximate Query Processing for Group-By Queries based on Conditional Generative Models](https://mlsanity.com/paper/2101.02914/) (2021)
- [Short-length SSVEP data extension by a novel generative adversarial networks based framework](https://mlsanity.com/paper/2301.05599/) (2023)
- [Denoising Refinement Diffusion Models for Simultaneous Generation of Multi-scale Mobile Network Traffic](https://mlsanity.com/paper/2511.17532/) (2025)

## Abstract (arXiv)

Data is generated at an unprecedented rate surpassing our ability to analyze them. The database community has pioneered many novel techniques for Approximate Query Processing (AQP) that could give approximate results in a fraction of time needed for computing exact results. In this work, we explore the usage of deep learning (DL) for answering aggregate queries specifically for interactive applications such as data exploration and visualization. We use deep generative models, an unsupervised learning based approach, to learn the data distribution faithfully such that aggregate queries could be answered approximately by generating samples from the learned model. The model is often compact - few hundred KBs - so that arbitrary AQP queries could be answered on the client side without contacting the database server. Our other contributions include identifying model bias and minimizing it through a rejection sampling based approach and an algorithm to build model ensembles for AQP for improved accuracy. Our extensive experiments show that our proposed approach can provide answers with high accuracy and low latency.