# PairwiseHist: Fast, Accurate and Space-Efficient Approximate Query Processing with Data Compression

arXiv: https://arxiv.org/abs/2401.12018 · HTML: https://mlsanity.com/paper/2401.12018/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jan 2024.

## Built on by (most cited first, 1 of 1)

- [SemBench: A Benchmark for Semantic Query Processing Engines](https://mlsanity.com/paper/2511.01716/) (2025)

## Abstract (arXiv)

Exponential growth in data collection is creating significant challenges for data storage and analytics latency.Approximate Query Processing (AQP) has long been touted as a solution for accelerating analytics on large datasets, however, there is still room for improvement across all key performance criteria. In this paper, we propose a novel histogram-based data synopsis called PairwiseHist that uses recursive hypothesis testing to ensure accurate histograms and can be built on top of data compressed using Generalized Deduplication (GD). We thus show that GD data compression can contribute to AQP. Compared to state-of-the-art AQP approaches, PairwiseHist achieves better performance across all key metrics, including 2.6 ×  higher accuracy, 3.5 ×  lower latency, 24 ×  smaller synopses and 1.5--4 ×  faster construction time.