The sampling lens: making sense of saturated visualisations
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Measuring Data Abstraction Quality in Multiresolution Visualizations
IEEE Transactions on Visualization and Computer Graphics
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Dealing with visualizations containing large data set is a challenging issue and, in the field of Information Visualization, almost every visual technique reveals its drawback when visualizing large number of items. To deal with this problem we introduce a formal environment, modeling in a virtual space the image features we are interested in (e.g, absolute and relative density, clusters, etc.) and we define some metrics able to characterize the image decay. Such metrics drive our automatic techniques (i.e., not uniform sampling) rescuing the image features and making them visible to the user. In this paper we focus on 2D scatter-plots, devising a novel non uniform data sampling strategy able to preserve in an effective way relative densities.