# Ver: View Discovery in the Wild

arXiv: https://arxiv.org/abs/2106.01543 · HTML: https://mlsanity.com/paper/2106.01543/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2021.

## Built on by (most cited first, 4 of 4)

- [Retrieve, Merge, Predict: Augmenting Tables with Data Lakes](https://mlsanity.com/paper/2402.06282/) (2024)
- [METAM: Goal-Oriented Data Discovery](https://mlsanity.com/paper/2304.09068/) (2023)
- [FeatNavigator: Automatic Feature Augmentation on Tabular Data](https://mlsanity.com/paper/2406.09534/) (2024)
- [Discovery-Driven Integration of Disjoint Tables via Text](https://mlsanity.com/paper/2609.26658/) (2026)

## Abstract (arXiv)

We present Ver, a data discovery system that identifies project-join views over large repositories of tables that do not contain join path information, and even when input queries are inaccurate. Ver implements a reference architecture to solve both the technical (scale and search) and human (semantic ambiguity, navigating a large number of results) problems of view discovery. We demonstrate users find the view they want when using Ver with a user study and we demonstrate its performance with large-scale end-to-end experiments on real-world datasets containing tens of millions of join paths.