Ver: View Discovery in the Wild
Yue Gong, Zhiru Zhu, Sainyam Galhotra, Raul Castro Fernandez

TL;DR
Ver is a data discovery system designed to identify project-join views over large, unannotated repositories, effectively handling scale, search, and semantic ambiguity issues, demonstrated through user studies and large-scale experiments.
Contribution
It introduces a novel architecture for view discovery that addresses both technical scalability and human semantic challenges in large datasets.
Findings
Users successfully find desired views using Ver
Ver performs efficiently on datasets with tens of millions of join paths
The system effectively handles inaccurate input queries
Abstract
We present Ver, 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. We demonstrate users find the view they want when using Ver with a user study and we demonstrate its performance with large-scale end-to-end experiments on real-world datasets containing tens of millions of join paths.
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Taxonomy
TopicsData Visualization and Analytics · Data Quality and Management · Time Series Analysis and Forecasting
