Animated Edge Textures in Node-Link Diagrams: a Design Space and Initial Evaluation

International audienceNetwork edge data attributes are usually encoded using color, opacity, stroke thickness and stroke pattern, or some combination thereof. In addition to these static variables, it is also possible to animate dynamic particles flowing along the edges. This opens a larger design space of animated edge textures, featuring additional visual encodings that have potential not only in terms of visual mapping capacity but also playfulness and aesthetics. Such animated edge textures have been used in several commercial and design-oriented visualizations, but to our knowledge almost always in a relatively ad hoc manner. We introduce a design space and Web-based framework for generating animated edge textures, and report on an initial evaluation of particle properties – particle speed, pattern and frequency – in terms of visual perception

Dispersive and absorptive properties of a

The dispersion and the absorption properties of a driven four-level Λ-type atomic system is investigated. It is found that the interaction of double-dark states lead to controllable group velocity of the weak probe field by the intensity of driving field and the relative phase between applied fields. Moreover, the transient dispersion, absorption and the group index are also discussed. The required switching time for switching the group velocity of a weak probe field from subluminal to superluminal pulse propagation is then discussed

Leveraging eye-gaze and time-series features to predict user interests and build a recommendation model for visual analysis

We developed a new concept to improve the efficiency of visual analysis through visual recommendations. It uses a novel eye-gaze based recommendation model that aids users in identifying interesting time-series patterns. Our model combines time-series features and eye-gaze interests, captured via an eye-tracker. Mouse selections are also considered. The system provides an overlay visualization with recommended patterns, and an eye-history graph, that supports the users in the data exploration process. We conducted an experiment with 5 tasks where 30 participants explored sensor data of a wind turbine. This work presents results on pre-attentive features, and discusses the precision/recall of our model in comparison to final selections made by users. Our model helps users to efficiently identify interesting time-series patterns

A Framework for Exploring Multidimensional Data with 3D Projections

Visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework`s applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.Deutscher Akademischer Austauschdienst (DAAD)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)CAPES/DAAD PROBRAL[344/10]Deutscher Akademischer Austauschdienst (DAAD)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)CAPES/DAAD PROBRAL[415-br-probral]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)CNPq[305079/2009-3]CNPq[301295/2008-5]Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)DFG[LI 1530/6-1]Deutsche Forschungsgemeinschaf (DFG)VisComX Center at Jacobs UniversityVisComX Center at Jacobs Universit