Systems

Metam is a goal-oriented framework that queries the downstream task with a candidate dataset, forming a feedback loop that automatically steers the discovery and augmentation process. To select candidates efficiently, Metam leverages properties of the: i) data, ii) utility function, and iii) solution set size.

Solo is a tabular data discovery system that uses self-supervision to automatically assemble training data over a large repostitory of tables withou any human involvement and then trains and deploys the system.

Ver is a data discovery system that identifies project-join views over large repositories of tables that do not contain join path information, and even when input queries are inaccurate. Ver implements a reference architecture to solve both the technical (scale and search) and human (semantic ambiguity, navigating a large number of results) problems of view discovery.