Scalable Visual Analytics
Data scientists rely on visualizations to interpret the data returned by queries, but finding the right visualization remains a manual task that is often laborious. We propose a DBMS that partially automates the task of finding the right visualizations for a query. Given a query, the new DBMS optimizer will explore not only the space of physical plans for the query, but also the space of possible visualizations for the results of the query. The output will comprise a recommendation of potentially “interesting” or “useful” visualizations, where each visualization is coupled with a suitable query execution plan. We discuss the technical challenges in building this system and outline an agenda for future research in our vision paper, to be published at VLDB’14.