UC San Diego
Building a foundation for human centric multi-dimensional data analysis
- Author(s): Ponto, Kevin
- et al.
This dissertation introduces a foundation for human centric, large-scale, multi-dimensional data analysis. This research enables collaborative workspaces by utilizing ultra-large, high-resolution display environments, distributed rendering techniques, and new interface modalities. Contributions include interactive visualization of ultra-large layered data sets, real-time distributed large-scale data acquisition, scalable distributed approaches for video playback in tiled display environments, natural exploratory techniques for multi- dimensional data, and multi-user interface technologies for distributed display environments. Presented is a technique for the interactive visualization and interrogation of multi-dimensional gigapixel imagery, allowing several users to simultaneously compare and contrast complex data layers in a collaborative environment. This system is augmented through a distributed data gathering and visualization component, which allows researchers to pull, construct, and interrogate geospatial information from remote servers. Multimedia content can also be configured interactively, and viewed in many side-by-side comparisons using various color and temporal filters. Techniques also allow for the scalable playback of video content through a distributed architecture. Additionally, multi-touch devices allow for hands-on analysis of massive, multi-dimensional data. The presented research introduces a set of natural metaphors, which allow for rapid analysis of global and local characteristics in the data set. The interface modalities can also be used for volumetric data, where position, gesture and pressure information are used for voxel density and depth specific operations. Finally, the combination of multi-touch devices and tiled display environments is presented, enabling multi-user collaborative environments