DevLan/vispubs huggingface dataset:
Procedural Texture Synthesis for Zoom-Independent Visualization of Multivariate Data
Authors: Rostislav Khlebnikov, Bernhard Kainz, Markus Steinberger, Marc Streit, Dieter Schmalstieg
Published at EuroVis, 2012

Simultaneous visualization of multiple continuous data attributes in a single visualization is a task that is important for many application areas. Unsurprisingly, many methods have been proposed to solve this task. However, the behavior of such methods during the exploration stage, when the user tries to understand the data with panning and zooming, has not been given much attention.In this paper, we propose a method that uses procedural texture synthesis to create zoom‐independent visualizations of three scalar data attributes. The method is based on random‐phase Gabor noise, whose frequency is adapted for the visualization of the first data attribute. We ensure that the resulting texture frequency lies in the range that is perceived well by the human visual system at any zoom level. To enhance the perception of this attribute, we also apply a specially constructed transfer function that is based on statistical properties of the noise. Additionally, the transfer function is constructed in a way that it does not introduce any aliasing to the texture. We map the second attribute to the texture orientation. The third attribute is color coded and combined with the texture by modifying the value component of the HSV color model. The necessary contrast needed for texture and color perception was determined in a user study. In addition, we conducted a second user study that shows significant advantages of our method over current methods with similar goals. We believe that our method is an important step towards creating methods that not only succeed in visualizing multiple data attributes, but also adapt to the behavior of the user during the data exploration stage.
Reliable Adaptive Modelling of Vascular Structures with Non-Circular Cross-Sections
Authors: Jan Kretschmer, Thomas Beck 0001, Christian Tietjen, Bernhard Preim, Marc Stamminger
Published at EuroVis, 2012

Accurate visualizations of complex vascular structures are essential for medical applications, such as diagnosis, therapy planning and medical education. Vascular trees are usually described using centerlines, since they capture both the topology and the geometry of the vasculature in an intuitive manner. State‐of‐the‐art vessel segmentation algorithms deliver vascular outlines as free‐form contours along the centerline, since this allows capturing anatomical pathologies. However, existing methods for generating surface representations from centerlines can only cope with circular outlines. We present a novel model‐based technique that is capable of generating intersection‐free surfaces from centerlines with complex outlines. Vascular segments are described by local signed distance functions and combined using Boolean operations. An octree‐based surface generation strategy automatically computes watertight, scale‐adaptive meshes with a controllable quality. In contrast to other approaches, our method generates a reliable representation that guarantees to capture all vessels regardless of their size.
Rolled-out Wordles: A Heuristic Method for Overlap Removal of 2D Data Representatives
Authors: Hendrik Strobelt, Marc Spicker, Andreas Stoffel, Daniel A. Keim, Oliver Deussen
Published at EuroVis, 2012

When representing 2D data points with spacious objects such as labels, overlap can occur. We present a simple algorithm which modifies the (Mani‐) Wordle idea with scan‐line based techniques to allow a better placement. We give an introduction to common placement techniques from different fields and compare our method to these techniques w.r.t. euclidean displacement, changes in orthogonal ordering as well as shape and size preservation. Especially in dense scenarios our method preserves the overall shape better than known techniques and allows a good trade‐off between the other measures. Applications on real world data are given and discussed.
Scalable Detection of Spatiotemporal Encounters in Historical Movement Data
Authors: Peter Bak, Mattias Marder, Sivan Harary, Avi Yaeli, Harold J. Ship
Published at EuroVis, 2012

The widespread adoption of location‐aware devices is resulting in the generation of large amounts of spatiotemporal movement data, collected and stored in digital repositories. This forms a fertile ground for domain experts and scientists to analyze such historical data and discover interesting movement behavioral patterns. Experts in many domains, such as transportation, logistics and retail, are interested in detecting and understanding movement patterns and behavior of objects in relation to each other. Their insights can point to optimization potential and reveal deviations from planned behavior. In this paper, we focus on the detection of the encounter patterns as one possible type in movement behavior. These patterns refer to objects being close to one another in terms of space and time. We define scalability as a core requirement when dealing with historical movement data, in order to allow the domain expert to set parameters of the encounter detection algorithm. Our approach leverages a designated data structure and requires only a single pass over chronological data, thus resulting in highly scalable and fast technique to detect encounters. Consequently, users are able to explore their data by interactively specifying the spatial and temporal windows that define encounters. We evaluate our proposed method as a function of its input parameters and data size. We instantiate the proposed method on urban public transportation data, where we found a large number of encounters. We show that single encounters emerge into higher level patterns that are of particular interest and value to the domain.
Semantic Wordification of Document Collections
Authors: Fernando Vieira Paulovich, Franklina Maria Bragion Toledo, Guilherme P. Telles, Rosane Minghim, Luis Gustavo Nonato
Published at EuroVis, 2012

Word clouds have become one of the most widely accepted visual resources for document analysis and visualization, motivating the development of several methods for building layouts of keywords extracted from textual data. Existing methods are effective to demonstrate content, but are not capable of preserving semantic relationships among keywords while still linking the word cloud to the underlying document groups that generated them. Such representation is highly desirable for exploratory analysis of document collections. In this paper we present a novel approach to build document clouds, named ProjCloud that aim at solving both semantical layouts and linking with document sets. ProjCloud generates a semantically consistent layout from a set of documents. Through a multidimensional projection, it is possible to visualize the neighborhood relationship between highly related documents and their corresponding word clouds simultaneously. Additionally, we propose a new algorithm for building word clouds inside polygons, which employs spectral sorting to maintain the semantic relationship among words. The effectiveness and flexibility of our methodology is confirmed when comparisons are made to existing methods. The technique automatically constructs projection based layouts the user may choose to examine in the form of the point clouds or corresponding word clouds, allowing a high degree of control over the exploratory process.
Semi-Supervised Dimensionality Reduction based on Partial Least Squares for Visual Analysis of High Dimensional Data
Authors: Jose Gustavo Paiva, William Robson Schwartz, Hélio Pedrini, Rosane Minghim
Published at EuroVis, 2012

Dimensionality reduction is employed for visual data analysis as a way to obtaining reduced spaces for high dimensional data or to mapping data directly into 2D or 3D spaces. Although techniques have evolved to improve data segregation on reduced or visual spaces, they have limited capabilities for adjusting the results according to user's knowledge. In this paper, we propose a novel approach to handling both dimensionality reduction and visualization of high dimensional data, taking into account user's input. It employs Partial Least Squares (PLS), a statistical tool to perform retrieval of latent spaces focusing on the discriminability of the data. The method employs a training set for building a highly precise model that can then be applied to a much larger data set very effectively. The reduced data set can be exhibited using various existing visualization techniques. The training data is important to code user's knowledge into the loop. However, this work also devises a strategy for calculating PLS reduced spaces when no training data is available. The approach produces increasingly precise visual mappings as the user feeds back his or her knowledge and is capable of working with small and unbalanced training sets.
StratomeX: Visual Analysis of Large-Scale Heterogeneous Genomics Data for Cancer Subtype Characterization
Authors: Alexander Lex, Marc Streit, Hans-Jörg Schulz, Christian Partl, Dieter Schmalstieg, Peter J. Park, Nils Gehlenborg
Published at EuroVis, 2012

Identification and characterization of cancer subtypes are important areas of research that are based on the integrated analysis of multiple heterogeneous genomics datasets. Since there are no tools supporting this process, much of this work is done using ad‐hoc scripts and static plots, which is inefficient and limits visual exploration of the data. To address this, we have developed StratomeX, an integrative visualization tool that allows investigators to explore the relationships of candidate subtypes across multiple genomic data types such as gene expression, DNA methylation, or copy number data. StratomeX represents datasets as columns and subtypes as bricks in these columns. Ribbons between the columns connect bricks to show subtype relationships across datasets. Drill‐down features enable detailed exploration. StratomeX provides insights into the functional and clinical implications of candidate subtypes by employing small multiples, which allow investigators to assess the effect of subtypes on molecular pathways or outcomes such as patient survival. As the configuration of viewing parameters in such a multi‐dataset, multi‐view scenario is complex, we propose a meta visualization and configuration interface for dataset dependencies and data‐view relationships. StratomeX is developed in close collaboration with domain experts. We describe case studies that illustrate how investigators used the tool to explore subtypes in large datasets and demonstrate how they efficiently replicated findings from the literature and gained new insights into the data.
The World's Languages Explorer: Visual Analysis of Language Features in Genealogical and Areal Contexts
Authors: Christian Rohrdantz, Michael Hund, Thomas Mayer 0001, Bernhard Wälchli, Daniel A. Keim
Published at EuroVis, 2012

This paper presents a novel Visual Analytics approach that helps linguistic researchers to explore the world's languages with respect to several important tasks: (1) The comparison of manually and automatically extracted language features across languages and within the context of language genealogy, (2) the exploration of interrelations among several of such features as well as their homogeneity and heterogeneity within subtrees of the language genealogy, and (3) the exploration of genealogical and areal influences on the features. We introduce the World's Languages Explorer, which provides the required functionalities in one single Visual Analytics environment. Contributions are made for different parts of the system: We introduce an extended Sunburst visualization whose so‐called feature‐rings allow for a cross‐comparison of a large number of features at once, within the hierarchical context of the language genealogy. We suggest a mapping of homogeneity measures to all levels of the hierarchy. In addition, we suggest an integration of information from the areal data space into the hierarchical data space. With our approach we bring Visual Analytics research to a new application field, namely Historical Comparative Linguistics, and Linguistic and Areal Typology. Finally, we provide evidence of the good performance of our system in this area through two application case studies conducted by domain experts.
Tracing Tuples Across Dimensions: A Comparison of Scatterplots and Parallel Coordinate Plots
Authors: Xiaole Kuang, Haimo Zhang, Shengdong Zhao, Michael J. McGuffin
Published at EuroVis, 2012

One of the fundamental tasks for analytic activity is retrieving (i.e., reading) the value of a particular quantity in an information visualization. However, few previous studies have compared user performance in such value retrieval tasks for different visualizations. We present an experimental comparison of user performance (time and error distance) across four multivariate data visualizations. Three variants of scatterplot (SCP) visualizations, namely SCPs with common vertical axes (SCP‐common), SCPs with a staircase layout (SCP‐staircase), and SCPs with rotated axes between neighboring cells (SCP‐rotated), and a baseline parallel coordinate plots (PCP) were compared. Results show that the baseline PCP is better than SCP‐rotated and SCP‐staircase under all conditions, while the difference between SCP‐common and PCP depends on the dimensionality and density of the dataset. PCP shows advantages over SCP‐common when the dimensionality and density of the dataset are low, but SCP‐common eventually outperforms PCP as data dimensionality and density increase. The results suggest guidelines for the use of SCPs and PCPs that can benefit future researchers and practitioners.
Travel-Route-Centered Metro Map Layout and Annotation
Authors: Hsiang-Yun Wu, Shigeo Takahashi, Chun-Cheng Lin, Hsu-Chun Yen
Published at EuroVis, 2012

When providing travel guides for a specific route in a metro network, we often place the route around the center of the map and annotate stations on the route with thumbnail photographs. Nonetheless, existing methods do not offer an effective means of customizing the network layout in order to accommodate such large annotation labels while preserving its planar embedding. This paper presents a new approach for designing the metro map layout in order to annotate stations on a specific travel route with large annotation labels. Our idea is to elongate the travel route to be straight along the centerline of the map so that we can systematically annotate such stations with external labels. This is accomplished by extending the conventional mixed‐integer programming technique for computing octilinear layouts where orientations inherent to the metro line segments are plausibly rearranged. The stations are then connected with external labels through leaders while minimizing intersections with metro lines for enhancing visual clarity. We present several design examples of metro maps and user studies to demonstrate that the proposed aesthetic criteria successfully direct viewers’ attention to specific travel routes.
Using Signposts for Navigation in Large Graphs
Authors: Thorsten May, Martin Steiger, James Davey, Jörn Kohlhammer
Published at EuroVis, 2012

In this paper we present a new Focus &amp; Context technique for the exploration of large, abstract graphs. Most Focus &amp; Context techniques present context in a visual way. In contrast, our technique uses a symbolic representation: while the focus is a set of visible nodes, labelled signposts provide cues for the context — off‐screen regions of the graph — and indicate the direction of the shortest path linking the visible nodes to these regions. We show how the regions are defined and how they are selected dynamically, depending on the visible nodes. To define the set of visible nodes we use an approach developed by van Ham and Perer that dynamically extracts a subgraph based on an initial focal node and a degree‐of‐interest function. This approach is extended to support multiple focal nodes. With the symbolic visualization, potentially interesting regions of a graph may be represented with a very small visual footprint. We conclude the paper with an initial user study to evaluate the effectiveness of the signposts for navigation tasks.
Vismon: Facilitating Analysis of Trade-Offs, Uncertainty, and Sensitivity In Fisheries Management Decision Making
Authors: Maryam Booshehrian, Torsten Möller, Randall M. Peterman, Tamara Munzner
Published at EuroVis, 2012

In this design study, we present an analysis and abstraction of the data and task in the domain of fisheries management, and the design and implementation of the Vismon tool to address the identified requirements. Vismon was designed to support sophisticated data analysis of simulation results by managers who are highly knowledgeable about the fisheries domain but not experts in simulation software and statistical data analysis. The previous workflow required the scientists who built the models to spearhead the analysis process. The features of Vismon include sensitivity analysis, comprehensive and global trade‐offs analysis, and a staged approach to the visualization of the uncertainty of the underlying simulation model. The tool was iteratively refined through a multi‐year engagement with fisheries scientists with a two‐phase approach, where an initial diverging experimentation phase to test many alternatives was followed by a converging phase where the set of multiple linked views that proved effective were integrated together in a useable way. Several fisheries scientists have used Vismon to communicate with policy makers, and it is scheduled for deployment to policy makers in Alaska.
Visualization of 4D Blood-Flow Fields by Spatiotemporal Hierarchical Clustering
Authors: Roy van Pelt, S. S. A. M. Jacobs, Bart M. ter Haar Romeny, Anna Vilanova
Published at EuroVis, 2012

Advancements in the acquisition and modeling of flow fields result in unsteady volumetric flow fields of unprecedented quality. An important example is found in the analysis of unsteady blood‐flow data. Preclinical research strives for a better understanding of correlations between the hemodynamics and the progression of cardiovascular diseases. Modern‐day computer models and MRI acquisition provide time‐resolved volumetric blood‐flow velocity fields. Unfortunately, these fields often remain unexplored, as high‐dimensional data are difficult to conceive. We present a spatiotemporal, i.e., four‐dimensional, hierarchical clustering, yielding a sparse representation of the velocity data. The clustering results underpin an illustrative visualization approach, facilitating visual analysis. The hierarchy allows an intuitive level‐of‐detail selection, largely retaining important flow patterns. The clustering employs dissimilarity measures to construct the hierarchy. We have adapted two existing measures for steady vector fields for use in the spacetime domain. Because of the inherent computational complexity of the multidimensional clustering, we introduce a coarse hierarchical clustering approach, which closely approximates the full hierarchy generation, and considerably improves the performance. The resulting clusters are visualized by representative patharrows, in combination with an illustrative anatomical context. We present various seeding approaches and visualization styles, providing sparse overviews of the unsteady behavior of volumetric flow fields.
Visualization of Advection-Diffusion in Unsteady Fluid Flow
Authors: Grzegorz Karol Karch, Filip Sadlo, Daniel Weiskopf, Claus-Dieter Munz, Thomas Ertl
Published at EuroVis, 2012

Advection has been the standard transport mechanism in flow visualization. Diffusion, in contrast, has not been considered important in visual flow field analysis so far, although it is inherent to many physical processes. We present a novel technique that allows for interactive 3D visualization of both advection and diffusion in unsteady fluid flow. We extend texture‐based flow visualization, which is advection‐oriented, by diffusion. Our finite volume approach based on WENO (weighted essentially non‐oscillatory) reconstruction is well parallelizable and features low numerical diffusion at interactive rates. Our scheme contributes to three different applications: (a) high‐quality dye advection at low numerical diffusion, (b) physically‐based dye advection accounting for diffusivity of virtual media, and (c) visualization of advection‐diffusion fluxes in physical media where the velocity field is accompanied by a concentration field. Interactive rendering of the virtual dye is accomplished by ray casting. We apply our GPU implementation to CFD examples of thermal convection and evaporation phenomena.
Visualization of Global Correlation Structures in Uncertain 2D Scalar Fields
Authors: Tobias Pfaffelmoser, Rüdiger Westermann
Published at EuroVis, 2012

Visualizing correlations, i.e., the tendency of uncertain data values at different spatial positions to change contrarily or according to each other, allows inferring on the possible variations of structures in the data. Visualizing global correlation structures, however, is extremely challenging, since it is not clear how the visualization of complicated long‐range dependencies can be integrated into standard visualizations of spatial data. Furthermore, storing correlation information imposes a memory requirement that is quadratic in the number of spatial sample positions. This paper presents a novel approach for visualizing both positive and inverse global correlation structures in uncertain 2D scalar fields, where the uncertainty is modeled via a multivariate Gaussian distribution. We introduce a new measure for the degree of dependency of a random variable on its local and global surroundings, and we propose a spatial clustering approach based on this measure to classify regions of a particular correlation strength. The clustering performs a correlation filtering, which results in a representation that is only linear in the number of spatial sample points. Via cluster coloring the correlation information can be embedded into visualizations of other statistical quantities, such as the mean and the standard deviation. We finally propose a hierarchical cluster subdivision scheme to further allow for the simultaneous visualization of local and global correlations.
Visualizing Motion Data in Virtual Reality: Understanding the Roles of Animation, Interaction, and Static Presentation
Authors: Dane M. Coffey, Fedor Korsakov, Marcus Ewert, Haleh Hagh-Shenas, Lauren Thorson, Arin M. Ellingson, David J. Nuckley, Daniel F. Keefe
Published at EuroVis, 2012

We present a study of interactive virtual reality visualizations of scientific motions as found in biomechanics experiments. Our approach is threefold. First, we define a taxonomy of motion visualizations organized by the method (animation, interaction, or static presentation) used to depict both the spatial and temporal dimensions of the data. Second, we design and implement a set of eight example visualizations suggested by the taxonomy and evaluate their utility in a quantitative user study. Third, together with biomechanics collaborators, we conduct a qualitative evaluation of the eight example visualizations applied to a current study of human spinal kinematics. Results suggest that visualizations in this style that use interactive control for the time dimension of the data are preferable to others. Within this category, quantitative results support the utility of both animated and interactive depictions for space; however, qualitative feedback suggest that animated depictions for space should be avoided in biomechanics applications.