We propose a general technique to visualize multivariate data sequences. It is based on a symbiotic combination of three powerful concepts from information visualization: sparklines, glyphs and gestalt theory. By visualizing several well-known data sets in new ways we first demonstrate how explicit

Combined volume registration and visualization online submission 108 We present a method for combining and visualizing a set of overlapping volume images with high resolution but limited spatial extent. Our system combines the calculation of a registration metric with ray casting for direct volume

independently to visualize all variables of the dataset. Therefore, existing axis-ordering algorithms, rendering techniques, and interaction methods can easily be applied to the individual parallel-coordinates plots. We demonstrate the value of the PCM in two case studies and show how it can serve

Figure 1: arcs.fm, a backdrop visualization for discussions about music, compares two personal music listening histories. Visualizations usually completely capture our attention or disappear into the ambient background. In this paper we explore a middle ground, with visualizations

, for instance, in social network analysis. There is a need to visualize those relational data structures in such a way that a better understanding of the relationships between vertices as well as their interactive exploration is supported. This paper describes a new radial visualization technique to layout

and experience. We argue for rich narratives that encapsulate both explicit data and implicit knowledge, supported by three levels of provenance: data, analytical and reasoning. Our hypotheses is that visual analytics tools and methods can help to provide a valuable means to make sense of these complex data

The well-known Marschner-Lobb (ML) signal has been originally proposed for visually comparing 3D resampling filters applied on the traditional Cartesian Cubic (CC) lattice. Recently, this popular benchmark is also used for evaluating reconstruction schemes designed for the optimal Body

temporal trends. However, as the number of line graphs increases, the overlap makes it difficult to perform comparison and other analyses. In this paper, we introduce DiffMatrix, a matrix-based interactive visualization designed to support effective analyses of a large number of time series data

methods and uncertainty visualizations. However end-users are not familiar with these uncertainty visualizations, and underlying error measures. Simpli-fied visualization designs were proposed to address this issue. Machine Learning experts showed interest in using such designs to communicate with end

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