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Auctus: A Dataset Search Engine for Data Augmentation

arXiv 2102.05716 ↗First posted 10 Feb 2021cs.IR cs.DBcited by 16

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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.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 Feb 2021.

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Citing papers per year: 2022: 2; 2023: 2; 2024: 3; 2025: 2; 2026: 7.

16 papers.

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  1. 2023CHORUS: Foundation Models for Unified Data Discovery and Explorationcited by 24
  2. 2025TARGET: Benchmarking Table Retrieval for Generative Taskscited by 17
  3. 2023METAM: Goal-Oriented Data Discoverycited by 4
  4. 2024Retrieve, Merge, Predict: Augmenting Tables with Data Lakescited by 4
  5. 2024FeatNavigator: Automatic Feature Augmentation on Tabular Datacited by 3
  6. 2024MYCROFT: Towards Effective and Efficient External Data Augmentationcited by 1
  7. 2025Something's Fishy In The Data Lake: A Critical Re-evaluation of Table Union Search Benchmarkscited by 1
  8. 2026A Reference Architecture for Agentic Hybrid Retrieval in Dataset Searchcited by 1
  9. 2026LakeQA: An Exploratory QA Benchmark over a Million-Scale Data Lakecited by 1
  10. 2022Russian Web Tables: A Public Corpus of Web Tables for Russian Language Based on Wikipediacited by 0
  11. 2022Active Learning from the Webcited by 0
  12. 2026Hippasus: Effective and Efficient Automatic Feature Augmentation for Machine Learning Tasks on Relational Datacited by 0
4 more
  1. 2026Human-Data Interaction, Exploration, and Visualization in the AI Era: Challenges and Opportunitiescited by 0
  2. 2026SeDa: A Unified System for Dataset Discovery and Multi-Entity Augmented Semantic Explorationcited by 0
  3. 2026MosaicJoin: Compact Semantic Sketches for Value-Level Join Discoverycited by 0
  4. 2026Polaris: Learning to Generate Table Descriptions from Retrieval Feedbackcited by 0