Much of the previous work in the large data visualization area has solely focused on handling the scale of the data. This task is clearly a great challenge and necessary, but it is not sufficient. Applying standard visualization techniques to large scale data sets often creates complicated pictures where meaningful trends are lost. A second challenge, then, is to also provide algorithms that simplify what an analyst must understand, using either visual or quantitative means. This challenge can be summarized as improving the legibility or reducing the complexity of massive data sets. Fully meeting both of these challenges is the work of many, many PhD dissertations. In this dissertation, we describe some new techniques to address both the scale and legibility challenges, in hope of contributing to the larger solution.
In addition to our assumption of simultaneously addressing both scale and legibility, we add an additional requirement that the solutions considered fit well within an interoperable framework for diverse algorithms, because a large suite of algorithms is often necessary to fully understand complex data sets.
For scale, we present a general architecture for handling large data, as well as details of a contract-based system for integrating advanced optimizations into a data flow network design. We also describe techniques for volume rendering and performing comparisons at the extreme scale.
For legibility, we present several techniques. Most noteworthy are equivalence class functions, a technique to drive visualizations using statistical methods, and line-scan based techniques for characterizing shape.