# Blend: A Unified Data Discovery System

arXiv: https://arxiv.org/abs/2310.02656 · HTML: https://mlsanity.com/paper/2310.02656/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Oct 2023.

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

- [Generating Skyline Datasets for Data Science Models](https://mlsanity.com/paper/2502.11262/) (2025)
- [A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search](https://mlsanity.com/paper/2604.16394/) (2026)
- [ModelTables: A Corpus of Tables about Models](https://mlsanity.com/paper/2512.16106/) (2025)
- [PAL-Bench: Evidence-Grounded Profile Reconstruction from Longitudinal Personal Albums](https://mlsanity.com/paper/2606.16175/) (2026)

## Abstract (arXiv)

Most research on data discovery has so far focused on improving individual discovery operators such as join, correlation, or union discovery. However, in practice, a combination of these techniques and their corresponding indexes may be necessary to support arbitrary discovery tasks. We propose BLEND, a comprehensive data discovery system that supports existing operators and enables their flexible pipelining. BLEND is based on a set of lower-level operators that serve as fundamental building blocks for more complex and sophisticated user tasks. To reduce the execution runtime of discovery pipelines, we propose a unified index structure and a rule-based optimizer that rewrites SQL statements into low-level operators when possible. We show the superior flexibility and efficiency of our system compared to ad-hoc discovery pipelines and stand-alone solutions.