Abstract-We present Glimmer, a new multilevel algorithm for multidimensional scaling designed to exploit modern graphics processing unit (GPU) hardware. We also present GPU-SF, a parallel, force-based subsystem used by Glimmer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm makes local minima less likely while the GPU parallelism improves speed of computation. We propose a robust termination condition for GPU-SF based on a filtered approximation of the normalized stress function. We demonstrate the benefits of Glimmer in terms of speed, normalized stress, and visual quality against several previous algorithms for a range of synthetic and real benchmark datasets. We also show that the performance of Glimmer on GPUs is substantially faster than a CPU implementation of the same algorithm.
DimStiller is a system for dimensionality reduction and analysis. It frames the task of understanding and transforming input dimensions as a series of analysis steps where users transform data tables by chaining together different techniques, called operators, into pipelines of expressions. The individual operators have controls and views that are linked together based on the structure of the expression. Users interact with the operator controls to tune parameter choices, with immediate visual feedback guiding the exploration of local neighborhoods of the space of possible data tables. DimStiller also provides global guidance for navigating data-table space through expression templates called workflows, which permit re-use of common patterns of analysis.
For an investigative journalist, a large collection of documents obtained from a Freedom of Information Act request or a leak is both a blessing and a curse: such material may contain multiple newsworthy stories, but it can be difficult and time consuming to find relevant documents. Standard text search is useful, but even if the search target is known it may not be possible to formulate an effective query. In addition, summarization is an important non-search task. We present Overview, an application for the systematic analysis of large document collections based on document clustering, visualization, and tagging. This work contributes to the small set of design studies which evaluate a visualization system "in the wild", and we report on six case studies where Overview was voluntarily used by self-initiated journalists to produce published stories. We find that the frequently-used language of "exploring" a document collection is both too vague and too narrow to capture how journalists actually used our application. Our iterative process, including multiple rounds of deployment and observations of real world usage, led to a much more specific characterization of tasks. We analyze and justify the visual encoding and interaction techniques used in Overview's design with respect to our final task abstractions, and propose generalizable lessons for visualization design methodology.
Patterns on real-world objects are often due to variations in geometry across the surface. Height fields and other common parametric methods cannot synthesize many forms of geometric surface texture such as thorns, scales, and bark. We present an example-based technique for synthesizing a variety of geometric textures on a model's surface. The applied textures can be from models specifically created for this purpose, or may be drawn from user-specified regions of an example model. We extend existing neighborhood-based texture synthesis algorithms to operate on volumetric models. Similar to image analogies [11], given a training pair of unfiltered and filtered source models and an unfiltered destination model (all volumetric grids), we synthesize a filtered fourth model that exhibits the desired geometric texture. The user defines vector fields to specify the direction of texture anisotropy on the source and destination models. The vector field defines a coordinate frame on the destination object's surface that is used to sample the voxel density values in the neighborhood near a given voxel, which then gives a feature vector that is matched to the neighborhoods in the source model. Destination voxels are visited in an order that is dictated by the vector field. We show geometric synthesis results on a variety of models using textures such as pits, grooves, thru-holes and thorns.
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