Functional approximation and visual analytics tools

Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non-spherical structures. Therefore, the functional approximation system is extended to using more general basis functions, such as ellipsoidal basis functions(EBFs) that provide greater compression and visually more accurate encodings of volumetric scattered datasets. In addition to static data approximation, temporal data is encoded using results from encoding previous timestep to speed the encoding time. Moreover, as a part of visual analytics, we developed tools for zoonotic syndromic surveillance, linked animal and human visual analytics for healthcare surveillance, network visualization, etc. In this talk, We will introduce these visual analytics tools and discuss their applications.