visual feedback in querying large databases
In this paper, we describe a database query system that provides visual relevance feedback in querying large databases. The goal of our system is to support the query specification process by using each pixel of the display to represent one data item of the database. By arranging and coloring the pixels according to their relevance for the query, the user gets a visual impression of the resulting data set. Using sliders for each condition of the query, the user may change the query dynamically and receives immediate feedback by the visual representation of the resulting data set. By using multiple windows for different parts of a complex query, the user gets visual feedback for each part of the query and, therefore, will easier understand the overall result. The system may be used to query any database that contains tens of thousands to millions of data items, but it is especially helpful to explore large data sets with an unknown distribution of values and to find the interesting hot spots in huge amounts of data. The direct feedback allows to visually display the influence of incremental query refinements and, therefore, allows a better, easier and faster query specification.
visualization feedback queries queries data database paper systems relevance support process color user change representation windows complexity help exploration distributed values direct influence incremental refine relevance-feedback query