Flexible navigation through a multi-dimensional parameter space using Berkeley DB snapshots

The concept of a visualization pipeline is central to many applications providing scientific visualization. In practical usage scenarios, when the pipelines fuse multiple datasets and combine various visualization methods they can easily evolve into complex visualization networks directing data flow. Creating and managing complex visualization networks, especially when data itself is time-dependent and requires time-dependent adjustment of multiple visualization parameters, is a tedious manual task with potential for improvement. Here we discuss the benefits of using Berkeley Database (BDB) snapshots to make it easier to create and manage visualization networks for timedependent data. The idea is to represent visualization network states as BDB snapshots accessed via the widely used Hierarchical Data Format (HDF5), and exploit the snapshot indexing system to flexibly navigate through the high-dimensional space of visualization parameters. This enables us to support useful visualization system features, such as dynamic interpolation of visualization parameters between time points and flexible adjustments of camera parameters per time point. The former allows fast continuous navigation of the parameter space to increase animation frame rate and the latter supports multi-viewpoint renderings when generating Virtual Reality panorama movies. The paper describes how the snapshot approach and the new features can be conveniently integrated into modern visualization systems, such as the Visualization Shell (Vish), and presents an evaluation study indicating that the performance penalty of this convenience compared to maintaining visualization networks in HDF5 files is negligible.