Comparative Visualizations through Parameterization and Variability

Comparative visualizations and the comparison tasks they support constitute a crucial part of visual data analysis on complex data sets. Existing approaches are ad hoc and often require significant effort to produce comparative visualizations, which is impractical especially in cases where visualizations have to be amended in response to changes in the underlying data. We show that the combination of parameterized visualizations and variations yields an effective model for comparative visualizations. Our approach supports data exploration and automatic visualization updates when the underlying data changes. We provide a prototype implementation and demonstrate that our approach covers most of existing comparative visualizations.