# MATE: Multi-Attribute Table Extraction

arXiv: https://arxiv.org/abs/2110.00318 · HTML: https://mlsanity.com/paper/2110.00318/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Oct 2021.

## Built on by (most cited first, 5 of 5)

- [Tabular Data Augmentation for Machine Learning: Progress and Prospects of Embracing Generative AI](https://mlsanity.com/paper/2407.21523/) (2024)
- [Snoopy: Effective and Efficient Semantic Join Discovery via Proxy Columns](https://mlsanity.com/paper/2502.16813/) (2025)
- [TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes](https://mlsanity.com/paper/2407.01619/) (2024)
- [Alignment-Guided Largest Table Overlap Size Estimation](https://mlsanity.com/paper/2607.03049/) (2026)
- [MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery](https://mlsanity.com/paper/2607.21781/) (2026)

## Abstract (arXiv)

A core operation in data discovery is to find joinable tables for a given table. Real-world tables include both unary and n-ary join keys. However, existing table discovery systems are optimized for unary joins and are ineffective and slow in the existence of n-ary keys. In this paper, we introduce MATE, a table discovery system that leverages a novel hash-based index that enables n-ary join discovery through a space-efficient super key. We design a filtering layer that uses a novel hash, XASH. This hash function encodes the syntactic features of all column values and aggregates them into a super key, which allows the system to efficiently prune tables with non-joinable rows. Our join discovery system is able to prune up to 1000x more false positives and leads to over 60x faster table discovery in comparison to state-of-the-art.