Big data on a few pixels

Data aggregation techniques help to reduce large data volumes in data visualization systems and are particularly effective when incorporating the spatial properties of the final visualization. One such technique is the Visualization-Driven Data Aggregation (VDDA) that models the pixel-level overplotting as data reduction query inside the database. In this paper, we extend VDDA with a novel approach to prepare high-dimensional data for the visualization in chart matrices. Incorporating properties of human perception, we introduce and formalize visual capacity functions for the most common chart types and use these functions for automatically configuring the best-perceivable visualization to contain the acquired data. We demonstrate how the introduced capacity functions can be used for VDDA-precedent pruning using real-world data in a relational database.