Visualization of Time-Varying Volumetric Data using Differential Time-Histogram Table

In addition to the overall size of the data set, what makes large time-varying data visualization hard is the need to constantly transfer each time step of the data from disk to

The new visualization technique for time-varying multi- channel EEG data, referred to as tiled parallel coordinates, is based on the parallel coordinate method and additionally

To deploy the multiresolution framework over a PC cluster, we intro- duce an algorithm to partition the WTSP tree into distribu- tion units, and distribute the partitioned data

When the time slider is used to create an animation, on each sequential frame the time window moves and so the tempo- ral uncertainty may change for all data sets.. The data for

Figure 10: Since the turbulent jet data set is statistically static, we can define a single 1D transfer function to clas- sify the entire time series.. We present results from

Three components in our UI are shown including the multivariate view for timestep 29 (a), the temporal view for three variables where x and y axes correspond to timestep and data

Additionally, a time-varying data domain changes continuously, immedi- ately invalidating previously computed results at each data time change.. Even for very simple

As the size of a single time step for typical time-varying data sets is already in the range of what a hard drive can trans- fer per second (compare Table 1), it becomes clear that