Ver: View Discovery in the Wild
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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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Jun 2021.
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Citing papers per year: 2023: 1; 2024: 2; 2025: 0; 2026: 1.
4 papers.
- 2023METAM: Goal-Oriented Data Discoverycited by 4
- 2024Retrieve, Merge, Predict: Augmenting Tables with Data Lakescited by 4
- 2024FeatNavigator: Automatic Feature Augmentation on Tabular Datacited by 3
- 2026Discovery-Driven Integration of Disjoint Tables via Textcited by 0
- 2026Discovery-Driven Integration of Disjoint Tables via Text22 Sep 2026
- 2024FeatNavigator: Automatic Feature Augmentation on Tabular Data13 Jun 2024
- 2024Retrieve, Merge, Predict: Augmenting Tables with Data Lakes9 Feb 2024
- 2023METAM: Goal-Oriented Data Discovery18 Apr 2023