Tracking scalar features in unstructured data sets

3D time-varying unstructured and structured data sets are difficult to visualize and analyze because of the immense amount of data involved. These data sets contain many evolving amorphous regions, and standard visualization techniques provide no facilities to aid the scientist to follow regions of interest. In this paper, we present a basic framework for the visualization of time-varying data sets, and a new algorithm and data structure to track volume features in unstructured scalar data sets. The algorithm and data structure are general and can be used for structured, curvilinear, adaptive and hybrid grids as well. The features tracked can be any type of connected regions. Examples are shown from ongoing research.