Nugget discovery in visual exploration environments by query consolidation

Queries issued by casual users or specialists exploring a dataset often point us to important subsets of the data, be it clusters, outliers or other meaningful features. Capturing and caching such queries (henceforth called nuggets) has many potential benefits, including the optimization of the system performance and the search experience of users. Unfortunately, current visual exploration systems have not yet tapped into this potential resource of identifying and sharing important queries. In this paper, we introduce a query consolidation strategy aimed at solving the general problem of isolating important queries from the potentially huge amount of queries submitted. Our solution clusters redundant queries caused by exploration-style query specification, which is prevalent in data exploration systems. To measure the similarity between queries, we designed an effective distance metric that incorporates both the query specification and the actual query result. To overcome its high complexity when comparing queries with large result sets, we designed an approximation method, which is efficient while still providing excellent accuracy. A user study conducted on multivariate data sets comparing our proposed technique to others in the literature confirms that the proposed distance metric indeed matches well with users' intuition. As proof of feasibility, we integrated our proposed query consolidation solution into the Nugget Management System (NMS) framework [22], which is based on a visual exploration system XmdvTool. A second user study indicates that both the efficiency and accuracy of users' visual exploration are enhanced when supported by NMS.