Emoji and Chernoff - A Fine Balancing Act or are we Biased?

We seek to answer the question on whether different geometrical attributes within a glyph can bias interpretation of data. We focus on a specific visual encoding, the Emoji, and evaluate its effectiveness at encoding multidimensional features. Given the anthropomorphic nature of the encoding we seek to quantify the amount of bias the encoding itself introduces, and use this to balance the Emoji glyph to remove that bias. We perform our analysis by comparing Emoji with Chernoff faces, of which they can be seen as direct descendant. Results shed light on how this new approach of feature tuning in glyph design can influence overall effectiveness of novel multidimensional encodings

Visual-Interactive Analysis With Self-Organizing Maps - Advances and Research Challenges

Based on the Self-Organizing Map (SOM) algorithm, development of effective solutions for visual analysis and retrieval in complex data is possible. Example application domains include retrieval in multimedia data bases, and analysis in financial, text, and general high-dimensional data sets. While early work defined basic concepts for data representation and visual mappings for SOM-based analysis, recent work contributed advanced visual representations of the output of the SOM algorithm, and explored innovative application concepts

07291 Abstracts Collection -- Scientific Visualization

From 15.07. to 20.07.07, the Dagstuhl Seminar 07291 ``Scientific Visualization\u27\u27 was held in the International Conference and Research Center (IBFI),Schloss Dagstuhl. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

Feature-Based Uncertainty Visualization

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this study, a suite of feature-based techniques is developed to address these issues. First, a framework of feature-level uncertainty visualization is presented to study the uncertainty of the features in scalar and vector data. The uncertainty in the number and locations of features such as sinks or sources of vector fields are referred to as feature-level uncertainty while the uncertainty in the numerical values of the data is referred to as data-level uncertainty. The features of different ensemble members are indentified and correlated. The feature-level uncertainties are expressed as the transitions between corresponding features through new elliptical glyphs. Second, an interactive visualization tool for exploring scalar data with data-level and two types of feature-level uncertainties — contour-level and topology-level uncertainties — is developed. To avoid visual cluttering and occlusion, the uncertainty information is attached to a contour tree instead of being integrated with the visualization of the data. An efficient contour tree-based interface is designed to reduce users’ workload in viewing and analyzing complicated data with uncertainties and to facilitate a quick and accurate selection of prominent contours. This thesis advances the current uncertainty studies with an in-depth investigation of the feature-level uncertainties and an exploration of topology tools for effective and interactive uncertainty visualizations. With quantified representation and interactive capability, feature-based visualization helps people gain new insights into the uncertainties of their data, especially the uncertainties of extracted features which otherwise would remain unknown with the visualization of only data-level uncertainties

Mejorando la calidad de los scatterplots con sugerencias automáticas durante su creación: un caso de estudio de visualización basada en semántica

The process of creating a visualization is a very complex exploration activity and, even for skilled users, it can be difficult to produce an effective visualization. Since the result of such a process depends on the user’s decisions along it, one way to improve the probability of achieving a useful outcome is to assist the user in the configuration and preparation of the visualization. Our proposal consists in live suggestions on how to improve the visualization. These live suggestions are based on the user decisions, and are achieved by the integration of semantic reasoning into the visualization process. In this paper, we present a case study for scatterplots visualization that combines ontologies with a semantic reasoner and helps the user in the generation of an effective visualization.El proceso de creación de una visualización es una actividad de exploración muy compleja e, incluso para usuarios expertos, puede ser difícil obtener como resultado una visualización efectiva. Dado que este resultado depende de las decisiones que el usuario toma a lo largo del proceso de visualización, una forma de mejorar la probabilidad de lograr un buen resultado es ayudar al usuario en la configuración y preparación de la visualización. Nuestra propuesta consiste en ofrecer sugerencias sobre cómo mejorar la visualización mientras el usuario la está creando. Estas sugerencias se basan en las decisiones que el usuario tomó y se logran mediante la integración del razonamiento semántico al proceso de visualización. En este artículo, presentamos un caso de estudio para la visualización de scatterplots (gráficos de dispersión), en el cual se muestra como se asiste al usuario, mediante la combinación de ontologías con un razonador semántico, para logar una visualización efectiva.Facultad de Informátic

An Empirical Evaluation of Visual Cues for 3D Flow Field Perception

Three-dimensional vector fields are common datasets throughout the sciences. They often represent physical phenomena that are largely invisible to us in the real world, like wind patterns and ocean currents. Computer-aided visualization is a powerful tool that can represent data in any way we choose through digital graphics. Visualizing 3D vector fields is inherently difficult due to issues such as visual clutter, self-occlusion, and the difficulty of providing depth cues that adequately support the perception of flow direction in 3D space. Cutting planes are often used to overcome these issues by presenting slices of data that are more cognitively manageable. The existing literature provides many techniques for visualizing the flow through these cutting planes; however, there is a lack of empirical studies focused on the underlying perceptual cues that make popular techniques successful. The most valuable depth cue for the perception of other kinds of 3D data, notably 3D networks and 3D point clouds, is structure-from-motion (also called the Kinetic Depth Effect); another powerful depth cue is stereoscopic viewing, but none of these cues have been fully examined in the context of flow visualization. This dissertation presents a series of quantitative human factors studies that evaluate depth and direction cues in the context of cutting plane glyph designs for exploring and analyzing 3D flow fields. The results of the studies are distilled into a set of design guidelines to improve the effectiveness of 3D flow field visualizations, and those guidelines are implemented as an immersive, interactive 3D flow visualization proof-of-concept application

Visualization and analysis of diffusion tensor fields

technical reportThe power of medical imaging modalities to measure and characterize biological tissue is amplified by visualization and analysis methods that help researchers to see and understand the structures within their data. Diffusion tensor magnetic resonance imaging can measure microstructural properties of biological tissue, such as the coherent linear organization of white matter of the central nervous system, or the fibrous texture of muscle tissue. This dissertation describes new methods for visualizing and analyzing the salient structure of diffusion tensor datasets. Glyphs from superquadric surfaces and textures from reactiondiffusion systems facilitate inspection of data properties and trends. Fiber tractography based on vector-tensor multiplication allows major white matter pathways to be visualized. The generalization of direct volume rendering to tensor data allows large-scale structures to be shaded and rendered. Finally, a mathematical framework for analyzing the derivatives of tensor values, in terms of shape and orientation change, enables analytical shading in volume renderings, and a method of feature detection important for feature-preserving filtering of tensor fields. Together, the combination of methods enhances the ability of diffusion tensor imaging to provide insight into the local and global structure of biological tissue