  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.
