Filter co-ordinations for exploring multi-dimensional data

Many interface designs have been developed for the exploration of multi-dimensional data sets which are based on finding subsets by filtering attribute values. Systems such as dynamic queries use a collection of independent filters to interactively query by restricting attribute values. However, for large data sets there is a need for an alternative style of filtering that better supports stepwise query refinement. This article introduces a new filter coordination which supports both stepwise query refinement and independent filters. Our filter visualization also supports the visualization of attribute value hierarchies enabling multi-level data distribution overviews to be given. Our coordination design is implemented in our SGViewer query tool which we demonstrate with a multi-dimensional web log data set. An evaluation of SGViewer showed that after a short learning period users were able to use it to read trends and proportions and make drill-down queries.