A dataset search interface with LLM-generated query reformulations, semantic column and granularity filters, and task-specific relevance indicators that helped 12 study participants explore and make sense of dataset search results.
Auctus: A Dataset Search Engine for Data Augmentation
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abstract
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.
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Rethinking Dataset Discovery with DataScout
A dataset search interface with LLM-generated query reformulations, semantic column and granularity filters, and task-specific relevance indicators that helped 12 study participants explore and make sense of dataset search results.