The proceedings contain 19 papers. The special focus in this conference is on Topology Based Methods in dataanalysis and visualization. The topics include: Glyphs for non-linear vector field singularities;2D asymmetr...
ISBN:
(纸本)9783642543005
The proceedings contain 19 papers. The special focus in this conference is on Topology Based Methods in dataanalysis and visualization. The topics include: Glyphs for non-linear vector field singularities;2D asymmetric tensor field topology;on the elusive concept of Lagrangian coherent structures;ridge concepts for the visualization of Lagrangian coherent structures;Filtering of FTLE for visualizing spatial separation in unsteady 3D flow;A variance based FTLE-like method for unsteady uncertain vector fields;on the finite-time scope for computing lagrangian coherent structures from Lyapunov exponents;Scale-space approaches to FTLE ridges;efficient computation of a hierarchy of discrete 3D gradient vector fields;computing simply-connected cells in three-dimensional Morse-Smale complexes;combinatorial vector field topology in three dimensions;topological cacti: Visualizing contour-based statistics;enhanced topology-sensitive clustering by reeb graph shattering;efficient computation of persistent homology for cubical data;visualizing invariant manifolds in area-preserving maps;understanding quasi-periodic fieldlines and their topology in toroidal magnetic fields;preface.
Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling...
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Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of time-series, e. g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.
We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion a...
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We present a visualization framework for exploring and analyzing data sets from biomechanical and neuromuscular simulations. These data sets describe versatile information related to the different stages of a motion analysis. In studying these data using a 3D visualization approach, interactive exploring is important, especially for supporting spatial analysis. Moreover, as these data contain many various but related elements, numerical analysis of neuromuscular simulations is complicated. visualization techniques enhance the analysis process, thus improving the effectiveness of the experiments. Our approach allows convenient definitions of relationships between numerical data sets and 3D objects. Scientific simulation data sets appropriate for this style of analysis are present everywhere motion analysis is performed and are predominant in many clinical works. In this paper, we outline the functionalities of the framework as well as applications embedded within the OpenSim simulation platform. These functionalities form an effective approach specifically designed for the investigation of neuromuscular simulations. This claim is supported by evaluation experiments where the framework was used to analyze gaits and crouch motions.
The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e. g., being of sparse or dense structure...
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The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e. g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization - a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown - right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.
Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In ...
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Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.
Research in the field of complex fluids such as polymer solutions, particulate suspensions and foams studies how the flow of fluids with different material parameters changes as a result of various constraints. Surfac...
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Research in the field of complex fluids such as polymer solutions, particulate suspensions and foams studies how the flow of fluids with different material parameters changes as a result of various constraints. Surface Evolver, the standard solver software used to generate foam simulations, provides large, complex, time-dependent data sets with hundreds or thousands of individual bubbles and thousands of time steps. However this software has limited visualization capabilities, and no foam specific visualization software exists. We describe the foam research application area where, we believe, visualization has an important role to play. We present a novel application that provides various techniques for visualization, exploration and analysis of time-dependent 2D foam simulation data. We show new features in foam simulation data and new insights into foam behavior discovered using our application.
In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animat...
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In modeling and analysis of longitudinal social networks, visual exploration is used in particular to complement and inform other methods. The most common graphical representations for this purpose appear to be animations and small multiples of intermediate states, depending on the type of media available. We present an alternative approach based on matrix representation of gestaltlines (a combination of Tufte's sparklines with glyphs based on gestalt theory). As a result, we obtain static, compact, yet data-rich diagrams that support specifically the exploration of evolving dyadic relations and persistent group structure, although at the expense of cross-sectional network views and indirect linkages.
Clustering as a fundamental dataanalysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especial...
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Clustering as a fundamental dataanalysis technique has been widely used in many analytic applications. However, it is often difficult for users to understand and evaluate multidimensional clustering results, especially the quality of clusters and their semantics. For large and complex data, high-level statistical information about the clusters is often needed for users to evaluate cluster quality while a detailed display of multidimensional attributes of the data is necessary to understand the meaning of clusters. In this paper, we introduce DICON, an icon-based cluster visualization that embeds statistical information into a multi-attribute display to facilitate cluster interpretation, evaluation, and comparison. We design a treemap-like icon to represent a multidimensional cluster, and the quality of the cluster can be conveniently evaluated with the embedded statistical information. We further develop a novel layout algorithm which can generate similar icons for similar clusters, making comparisons of clusters easier. User interaction and clutter reduction are integrated into the system to help users more effectively analyze and refine clustering results for large datasets. We demonstrate the power of DICON through a user study and a case study in the healthcare domain. Our evaluation shows the benefits of the technique, especially in support of complex multidimensional cluster analysis.
Large observations and simulations in scientific research give rise to high-dimensional data sets that present many challenges and opportunities in dataanalysis and visualization. Researchers in application domains s...
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Large observations and simulations in scientific research give rise to high-dimensional data sets that present many challenges and opportunities in dataanalysis and visualization. Researchers in application domains such as engineering, computational biology, climate study, imaging and motion capture are faced with the problem of how to discover compact representations of high-dimensional data while preserving their intrinsic structure. In many applications, the original data is projected onto low-dimensional space via dimensionality reduction techniques prior to modeling. One problem with this approach is that the projection step in the process can fail to preserve structure in the data that is only apparent in high dimensions. Conversely, such techniques may create structural illusions in the projection, implying structure not present in the original high-dimensional data. Our solution is to utilize topological techniques to recover important structures in high-dimensional data that contains non-trivial topology. Specifically, we are interested in high-dimensional branching structures. We construct local circle-valued coordinate functions to represent such features. Subsequently, we perform dimensionality reduction on the data while ensuring such structures are visually preserved. Additionally, we study the effects of global circular structures on visualizations. Our results reveal never-before-seen structures on real-world data sets from a variety of applications.
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