# Auctus: A Dataset Search Engine for Data Augmentation

arXiv: https://arxiv.org/abs/2102.05716 · HTML: https://mlsanity.com/paper/2102.05716/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2021.

## Built on by (most cited first, 16 of 16)

- [CHORUS: Foundation Models for Unified Data Discovery and Exploration](https://mlsanity.com/paper/2306.09610/) (2023)
- [TARGET: Benchmarking Table Retrieval for Generative Tasks](https://mlsanity.com/paper/2505.11545/) (2025)
- [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)
- [MYCROFT: Towards Effective and Efficient External Data Augmentation](https://mlsanity.com/paper/2410.08432/) (2024)
- [Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarks](https://mlsanity.com/paper/2505.21329/) (2025)
- [A Reference Architecture for Agentic Hybrid Retrieval in Dataset Search](https://mlsanity.com/paper/2604.16394/) (2026)
- [LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lake](https://mlsanity.com/paper/2606.10460/) (2026)
- [Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipedia](https://mlsanity.com/paper/2210.06353/) (2022)
- [Active Learning from the Web](https://mlsanity.com/paper/2210.08205/) (2022)
- [Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Data](https://mlsanity.com/paper/2602.02025/) (2026)
- [Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunities](https://mlsanity.com/paper/2603.05542/) (2026)
- [SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Exploration](https://mlsanity.com/paper/2603.07502/) (2026)
- [MosaicJoin: Compact Semantic Sketches for Value-Level Join Discovery](https://mlsanity.com/paper/2607.21781/) (2026)
- [Polaris: Learning to Generate Table Descriptions from Retrieval Feedback](https://mlsanity.com/paper/2608.17171/) (2026)

## Abstract (arXiv)

The large volumes of structured data currently available, from Web tables to open-data portals and enterprise data, open up new opportunities for progress in answering many important scientific, societal, and business questions. However, finding relevant data is difficult. While search engines have addressed this problem for Web documents, there are many new challenges involved in supporting the discovery of structured data. We demonstrate how the Auctus dataset search engine addresses some of these challenges. We describe the system architecture and how users can explore datasets through a rich set of queries. We also present case studies which show how Auctus supports data augmentation to improve machine learning models as well as to enrich analytics.