Nugget Discovery in Visual Exploration Environments by Query Consolidation ∗ ABSTRACT

Queries issued by casual users or specialists exploring a data set often point us to important subsets of the data, be it clusters, outliers or other features of particular importance. Capturing and caching such queries (henceforth called nuggets) has many potential benefits, including the optimization of both the performance of the underlying system as well as the search experience of users. Unfortunately, current visual exploration systems, while facilitating data exploration by providing graphical depictions of the data, 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. Particularly, our solution clusters redundant queries caused by exploration-style query specification, which is prevalent in data exploration systems. Then, it generates a representative for each group of clustered queries. To measure the similarity between queries, we design a highly effective distance metric that integrates both the query specification and the actual query result. To overcome its high time complexity when comparing queries with large result sets, we also design an approximation method, which offers high time efficiency while still providing excellent accuracy. A user study conducted on real 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 develop a prototype Nugget Management System (NMS) based on our proposed query consolidation solution. A second user study comparing a freeware visual exploration system XMDVTool, both with and without being supplemented by NMS, indicates that both the efficiency and accuracy of users ’ visual exploration are indeed enhanced when supported by our technology. ∗ This work is supported under NSF grant IIS-0119276 and a grant from the NSA