Name
Abstract | ||
|---|---|---|
AbstractThe 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. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.14778/3476311.3476346 | Hosted Content |
DocType | Volume | Issue |
Journal | 14 | 12 |
ISSN | Citations | PageRank |
2150-8097 | 2 | 0.38 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sonia Castelo | 1 | 7 | 3.84 |
| Rémi Rampin | 2 | 15 | 1.97 |
| Aécio S. R. Santos | 3 | 22 | 4.84 |
| Aline Bessa | 4 | 2 | 0.38 |
| Fernando Seabra Chirigati | 5 | 205 | 16.38 |
| Juliana Freire | 6 | 3956 | 270.89 |