Many cases make light work for visualization in many eyes

Visualization is among the most powerful of data analysis techniques and is readily available in standalone systems or components of everyday software packages. In recent years much work has been done to design and develop visualization systems with reduced entry and usage barriers in order to make visualization available to the masses. Here we describe a novel application of case-based reasoning techniques to help users visualize complex datasets. We exploit an online visualization service, Many Eyes, and explore how case-based representation of datasets including simple features such as size and content types can produce recommendations of visualization types to assist novice users in the selection of appropriate visualizations.