37-Issue 3

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https://diglib.eg.org/handle/10.2312/2632394

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Analyzing Residue Surface Proximity to Interpret Molecular Dynamics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Lichtenberg, Nils; Menges, Raphael; Ageev, Vladimir; George, Ajay Abisheck Paul; Heimer, Pascal; Imhof, Diana; Lawonn, Kai; Jeffrey Heer and Heike Leitte and Timo Ropinski
The surface of a molecule holds important information about the interaction behavior with other molecules. In dynamic folding or docking processes, residues of amino acids with different properties change their position within the molecule over time. The atoms of the residues that are accessible to the solvent can directly contribute to binding interactions, while residues buried within the molecular structure contribute to the stability of the molecule. Understanding patterns and causality of structural changes is important for experts in the pharmaceutical domain, e.g., in the process of drug design. We apply an iterative computation of the Solvent Accessible Surface in order to extract virtual layers of a molecule. The extraction allows to track the movement of residues in the body of the molecule, with respect to the distance of the residue to the surface or the core during dynamics simulations. We visualize the obtained layer information for the complete time span of the molecular dynamics simulation as a 2D-map and for individual time-steps as a 3D-representation of the molecule. The data acquisition has been implemented alongside with further analysis functionality in a prototypical application, which is available to the public domain. We underline the feasibility of our approach with a study from the pharmaceutical domain, where our approach has been used for novel insights into the folding behavior of μ-conotoxins.
Visual and Quantitative Analysis of Great Arteries' Blood Flow Jets in Cardiac 4D PC-MRI Data
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Köhler, Benjamin; Grothoff, Matthias; Gutberlet, Matthias; Preim, Bernhard; Jeffrey Heer and Heike Leitte and Timo Ropinski
Flow in the great arteries (aorta, pulmonary artery) is normally laminar with a parabolic velocity profile. Eccentric flow jets are linked to various diseases like aneurysms. Cardiac 4D PC-MRI data provide spatio-temporally resolved blood flow information for the whole cardiac cycle. In this work, we establish a time-dependent visualization and quantification of flow jets. For this purpose, equidistant measuring planes are automatically placed along the vessel's centerline. The flow jet position and region with highest velocities are extracted for every plane in each time step. This is done during pre-processing and without user-defined parameters. We visualize the main flow jet as geometric tube. High-velocity areas are depicted as a net around this tube. Both geometries are time-dependent and can be animated. Quantitative values are provided during cross-sectional measuring plane-based evaluation. Moreover, we offer a plot visualization as summary of flow jet characteristics for the selected plane. Our physiologically plausible results are in accordance with medical findings. Our clinical collaborators appreciate the possibility to view the flow jet in the whole vessel at once, which normally requires repeated pathline filtering due to varying velocities along the vessel course. The overview plots are considered as valuable for documentation purposes.
Landscaper: A Modeling System for 3D Printing Scale Models of Landscapes
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Allahverdi, Kamyar; Djavaherpour, Hessam; Mahdavi-Amiri, Ali; Samavati, Faramarz; Jeffrey Heer and Heike Leitte and Timo Ropinski
Landscape models of geospatial regions provide an intuitive mechanism for exploring complex geospatial information. However, the methods currently used to create these scale models require a large amount of resources, which restricts the availability of these models to a limited number of popular public places, such as museums and airports. In this paper, we have proposed a system for creating these physical models using an affordable 3D printer in order to make the creation of these models more widely accessible. Our system retrieves GIS relevant to creating a physical model of a geospatial region and then addresses the two major limitations of affordable 3D printers, namely the limited number of materials and available printing volume. This is accomplished by separating features into distinct extruded layers and splitting large models into smaller pieces, allowing us to employ different methods for the visualization of different geospatial features, like vegetation and residential areas, in a 3D printing context. We confirm the functionality of our system by printing two large physical models of relatively complex landscape regions.
DimSUM: Dimension and Scale Unifying Map for Visual Abstraction of DNA Origami Structures
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Miao, Haichao; Llano, Elisa De; Isenberg, Tobias; Gröller, Eduard; Barišic, Ivan; Viola, Ivan; Jeffrey Heer and Heike Leitte and Timo Ropinski
We present a novel visualization concept for DNA origami structures that integrates a multitude of representations into a Dimension and Scale Unifying Map (DimSUM). This novel abstraction map provides means to analyze, smoothly transition between, and interact with many visual representations of the DNA origami structures in an effective way that was not possible before. DNA origami structures are nanoscale objects, which are challenging to model in silico. In our holistic approach we seamlessly combine three-dimensional realistic shape models, two-dimensional diagrammatic representations, and ordered alignments in one-dimensional arrangements, with semantic transitions across many scales. To navigate through this large, two-dimensional abstraction map we highlight locations that users frequently visit for certain tasks and datasets. Particularly interesting viewpoints can be explicitly saved to optimize the workflow. We have developed DimSUM together with domain scientists specialized in DNA nanotechnology. In the paper we discuss our design decisions for both the visualization and the interaction techniques. We demonstrate two practical use cases in which our approach increases the specialists' understanding and improves their effectiveness in the analysis. Finally, we discuss the implications of our concept for the use of controlled abstraction in visualization in general.
Hypersliceplorer: Interactive Visualization of Shapes in Multiple Dimensions
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Torsney-Weir, Thomas; Möller, Torsten; Sedlmair, Michael; Kirby, R. Mike; Jeffrey Heer and Heike Leitte and Timo Ropinski
In this paper we present Hypersliceplorer, an algorithm for generating 2D slices of multi-dimensional shapes defined by a simplical mesh. Often, slices are generated by using a parametric form and then constraining parameters to view the slice. In our case, we developed an algorithm to slice a simplical mesh of any number of dimensions with a two-dimensional slice. In order to get a global appreciation of the multi-dimensional object, we show multiple slices by sampling a number of different slicing points and projecting the slices into a single view per dimension pair. These slices are shown in an interactive viewer which can switch between a global view (all slices) and a local view (single slice). We show how this method can be used to study regular polytopes, differences between spaces of polynomials, and multi-objective optimization surfaces.
Hunting High and Low: Visualising Shifting Correlations in Financial Markets
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Simon, Peter M.; Turkay, Cagatay; Jeffrey Heer and Heike Leitte and Timo Ropinski
The analysis of financial assets' correlations is fundamental to many aspects of finance theory and practice, especially modern portfolio theory and the study of risk. In order to manage investment risk, in-depth analysis of changing correlations is needed, with both high and low correlations between financial assets (and groups thereof) important to identify. In this paper, we propose a visual analytics framework for the interactive analysis of relations and structures in dynamic, high-dimensional correlation data. We conduct a series of interviews and review the financial correlation analysis literature to guide our design. Our solution combines concepts from multi-dimensional scaling, weighted complete graphs and threshold networks to present interactive, animated displays which use proximity as a visual metaphor for correlation and animation stability to encode correlation stability. We devise interaction techniques coupled with context-sensitive auxiliary views to support the analysis of subsets of correlation networks. As part of our contribution, we also present behaviour profiles to help guide future users of our approach. We evaluate our approach by checking the validity of the layouts produced, presenting a number of analysis stories, and through a user study. We observe that our solutions help unravel complex behaviours and resonate well with study participants in addressing their needs in the context of correlation analysis in finance.
Rendering and Extracting Extremal Features in 3D Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kindlmann, Gordon L.; Chiw, Charisee; Huynh, Tri; Gyulassy, Attila; Reppy, John; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
Visualizing and extracting three-dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain-specific language, Diderot, we compute direct volume renderings and particlebased feature samplings for a range of mathematical features. Non-expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high-quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.
Visualization of 4D Vector Field Topology
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Hofmann, Lutz; Rieck, Bastian; Sadlo, Filip; Jeffrey Heer and Heike Leitte and Timo Ropinski
In this paper, we present an approach to the topological analysis of four-dimensional vector fields. In analogy to traditional 2D and 3D vector field topology, we provide a classification and visual representation of critical points, together with a technique for extracting their invariant manifolds. For effective exploration of the resulting four-dimensional structures, we present a 4D camera that provides concise representation by exploiting projection degeneracies, and a 4D clipping approach that avoids self-intersection in the 3D projection. We exemplify the properties and the utility of our approach using specific synthetic cases.
Visualizing Expanded Query Results
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Mazurek, Michael; Waldner, Manuela; Jeffrey Heer and Heike Leitte and Timo Ropinski
When performing queries in web search engines, users often face difficulties choosing appropriate query terms. Search engines therefore usually suggest a list of expanded versions of the user query to disambiguate it or to resolve potential term mismatches. However, it has been shown that users find it difficult to choose an expanded query from such a list. In this paper, we describe the adoption of set-based text visualization techniques to visualize how query expansions enrich the result space of a given user query and how the result sets relate to each other. Our system uses a linguistic approach to expand queries and topic modeling to extract the most informative terms from the results of these queries. In a user study, we compare a common text list of query expansion suggestions to three set-based text visualization techniques adopted for visualizing expanded query results - namely, Compact Euler Diagrams, Parallel Tag Clouds, and a List View - to resolve ambiguous queries using interactive query expansion. Our results show that text visualization techniques do not increase retrieval efficiency, precision, or recall. Overall, users rate Parallel Tag Clouds visualizing key terms of the expanded query space lowest. Based on the results, we derive recommendations for visualizations of query expansion results, text visualization techniques in general, and discuss alternative use cases of set-based text visualization techniques in the context of web search.
Hierarchical Correlation Clustering in Multiple 2D Scalar Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Liebmann, Tom; Weber, Gunther H.; Scheuermann, Gerik; Jeffrey Heer and Heike Leitte and Timo Ropinski
Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time-dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2-dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.
Interactive Visual Exploration of Local Patterns in Large Scatterplot Spaces
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Chegini, Mohammad; Shao, Lin; Gregor, Robert; Lehmann, Dirk Joachim; Andrews, Keith; Schreck, Tobias; Jeffrey Heer and Heike Leitte and Timo Ropinski
Analysts often use visualisation techniques like a scatterplot matrix (SPLOM) to explore multivariate datasets. The scatterplots of a SPLOM can help to identify and compare two-dimensional global patterns. However, local patterns which might only exist within subsets of records are typically much harder to identify and may go unnoticed among larger sets of plots in a SPLOM. This paper explores the notion of local patterns and presents a novel approach to visually select, search for, and compare local patterns in a multivariate dataset. Model-based and shape-based pattern descriptors are used to automatically compare local regions in scatterplots to assist in the discovery of similar local patterns. Mechanisms are provided to assess the level of similarity between local patterns and to rank similar patterns effectively. Moreover, a relevance feedback module is used to suggest potentially relevant local patterns to the user. The approach has been implemented in an interactive tool and demonstrated with two real-world datasets and use cases. It supports the discovery of potentially useful information such as clusters, functional dependencies between variables, and statistical relationships in subsets of data records and dimensions.
Visualizing Multidimensional Data with Order Statistics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Raj, Mukund; Whitaker, Ross T.; Jeffrey Heer and Heike Leitte and Timo Ropinski
Multidimensional data sets are common in many domains, and dimensionality reduction methods that determine a lower dimensional embedding are widely used for visualizing such data sets. This paper presents a novel method to project data onto a lower dimensional space by taking into account the order statistics of the individual data points, which are quantified by their depth or centrality in the overall set. Thus, in addition to conveying relative distances in the data, the proposed method also preserves the order statistics, which are often lost or misrepresented by existing visualization methods. The proposed method entails a modification of the optimization objective of conventional multidimensional scaling (MDS) by introducing a term that penalizes discrepancies between centrality structures in the original space and the embedding. We also introduce two strategies for visualizing lower dimensional embeddings of multidimensional data that takes advantage of the coherent representation of centrality provided by the proposed projection method. We demonstrate the effectiveness of our visualization with comparisons on different kinds of multidimensional data, including categorical and multimodal, from a variety of domains such as botany and health care.
Explorative Blood Flow Visualization using Dynamic Line Filtering based on Surface Features
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Behrendt, Benjamin; Berg, Philipp; Beuing, Oliver; Preim, Bernhard; Saalfeld, Sylvia; Jeffrey Heer and Heike Leitte and Timo Ropinski
Rupture risk assessment is a key to devise patient-specific treatment plans of cerebral aneurysms. To understand and predict the development of aneurysms and other vascular diseases over time, both hemodynamic flow patterns and their effect on the vessel surface need to be analyzed. Flow structures close to the vessel wall often correlate directly with local changes in surface parameters, such as pressure or wall shear stress. Yet, in many existing applications, the analyses of flow and surface features are either somewhat detached from one another or only globally available. Especially for the identification of specific blood flow characteristics that cause local startling parameters on the vessel surface, like elevated pressure values, an interactive analysis tool is missing. The explorative visualization of flow data is challenging due to the complexity of the underlying data. In order to find meaningful structures in the entirety of the flow, the data has to be filtered based on the respective explorative aim. In this paper, we present a combination of visualization, filtering and interaction techniques for explorative analysis of blood flow with a focus on the relation of local surface parameters and underlying flow structures. Coherent bundles of pathlines can be interactively selected based on their relation to features of the vessel wall and further refined based on their own hemodynamic features. This allows the user to interactively select and explore flow structures locally affecting a certain region on the vessel wall and therefore to understand the cause and effect relationship between these entities. Additionally, multiple selected flow structures can be compared with respect to their quantitative parameters, such as flow speed. We confirmed the usefulness of our approach by conducting an informal interview with two expert neuroradiologists and an expert in flow simulation. In addition, we recorded several insights the neuroradiologists were able to gain with the help of our tool.
Design Factors for Summary Visualization in Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Sarikaya, Alper; Gleicher, Michael; Szafir, Danielle Albers; Jeffrey Heer and Heike Leitte and Timo Ropinski
Data summarization allows analysts to explore datasets that may be too complex or too large to visualize in detail. Designers face a number of design and implementation choices when using summarization in visual analytics systems. While these choices influence the utility of the resulting system, there are no clear guidelines for the use of these summarization techniques. In this paper, we codify summarization use in existing systems to identify key factors in the design of summary visualizations. We use quantitative content analysis to systematically survey examples of visual analytics systems and enumerate the use of these design factors in data summarization. Through this analysis, we expose the relationship between design considerations, strategies for data summarization in visualization systems, and how different summarization methods influence the analyses supported by systems. We use these results to synthesize common patterns in real-world use of summary visualizations and highlight open challenges and opportunities that these patterns offer for designing effective systems. This work provides a more principled understanding of design practices for summary visualization and offers insight into underutilized approaches.
Core Lines in 3D Second-Order Tensor Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Oster, Timo; Rössl, Christian; Theisel, Holger; Jeffrey Heer and Heike Leitte and Timo Ropinski
Vortices are important features in vector fields that show a swirling behavior around a common core. The concept of a vortex core line describes the center of this swirling behavior. In this work, we examine the extension of this concept to 3D second-order tensor fields. Here, a behavior similar to vortices in vector fields can be observed for trajectories of the eigenvectors. Vortex core lines in vector fields were defined by Sujudi and Haimes to be the locations where stream lines are parallel to an eigenvector of the Jacobian. We show that a similar criterion applied to the eigenvector trajectories of a tensor field yields structurally stable lines that we call tensor core lines. We provide a formal definition of these structures and examine their mathematical properties. We also present a numerical algorithm for extracting tensor core lines in piecewise linear tensor fields. We find all intersections of tensor core lines with the faces of a dataset using a simple and robust root finding algorithm. Applying this algorithm to tensor fields obtained from structural mechanics simulations shows that it is able to effectively detect and visualize regions of rotational or hyperbolic behavior of eigenvector trajectories.
Chart Constellations: Effective Chart Summarization for Collaborative and Multi-User Analyses
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Xu, Shenyu; Bryan, Chris; Li, Jianping Kelvin; Zhao, Jian; Ma, Kwan-Liu; Jeffrey Heer and Heike Leitte and Timo Ropinski
Many data problems in the real world are complex and require multiple analysts working together to uncover embedded insights by creating chart-driven data stories. How, as a subsequent analysis step, do we interpret and learn from these collections of charts? We present Chart Constellations, a system to interactively support a single analyst in the review and analysis of data stories created by other collaborative analysts. Instead of iterating through the individual charts for each data story, the analyst can project, cluster, filter, and connect results from all users in a meta-visualization approach. Constellations supports deriving summary insights about prior investigations and supports the exploration of new, unexplored regions in the dataset. To evaluate our system, we conduct a user study comparing it against data science notebooks. Results suggest that Constellations promotes the discovery of both broad and high-level insights, including theme and trend analysis, subjective evaluation, and hypothesis generation.
Key Time Steps Selection for Large-Scale Time-Varying Volume Datasets Using an Information-Theoretic Storyboard
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Zhou, Bo; Chiang, Yi-Jen; Jeffrey Heer and Heike Leitte and Timo Ropinski
Key time steps selection is essential for effective and efficient scientific visualization of large-scale time-varying datasets. We present a novel approach that can decide the number of most representative time steps while selecting them to minimize the difference in the amount of information from the original data.We use linear interpolation to reconstruct the data of intermediate time steps between selected time steps.We propose an evaluation of selected time steps by computing the difference in the amount of information (called information difference) using variation of information (VI) from information theory, which compares the interpolated time steps against the original data. In the one-time preprocessing phase, a dynamic programming is applied to extract the subset of time steps that minimize the information difference. In the run-time phase, a novel chart is used to present the dynamic programming results, which serves as a storyboard of the data to guide the user to select the best time steps very efficiently. We extend our preprocessing approach to a novel out-of-core approximate algorithm to achieve optimal I/O cost, which also greatly reduces the in-core computing time and exhibits a nice trade-off between computing speed and accuracy. As shown in the experiments, our approximate method outperforms the previous globally optimal DTW approach [TLS12] on out-of-core data by significantly improving the running time while keeping similar qualities, and is our major contribution.
Maps and Globes in Virtual Reality
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Yang, Yalong; Jenny, Bernhard; Dwyer, Tim; Marriott, Kim; Chen, Haohui; Cordeil, Maxime; Jeffrey Heer and Heike Leitte and Timo Ropinski
This paper explores different ways to render world-wide geographic maps in virtual reality (VR). We compare: (a) a 3D exocentric globe, where the user's viewpoint is outside the globe; (b) a flat map (rendered to a plane in VR); (c) an egocentric 3D globe, with the viewpoint inside the globe; and (d) a curved map, created by projecting the map onto a section of a sphere which curves around the user. In all four visualisations the geographic centre can be smoothly adjusted with a standard handheld VR controller and the user, through a head-tracked headset, can physically move around the visualisation. For distance comparison exocentric globe is more accurate than egocentric globe and flat map. For area comparison more time is required with exocentric and egocentric globes than with flat and curved maps. For direction estimation, the exocentric globe is more accurate and faster than the other visual presentations. Our study participants had a weak preference for the exocentric globe. Generally the curved map had benefits over the flat map. In almost all cases the egocentric globe was found to be the least effective visualisation. Overall, our results provide support for the use of exocentric globes for geographic visualisation in mixed-reality.
Towards Easy Comparison of Local Businesses Using Online Reviews
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Wang, Yong; Haleem, Hammad; Shi, Conglei; Wu, Yanhong; Zhao, Xun; Fu, Siwei; Qu, Huamin; Jeffrey Heer and Heike Leitte and Timo Ropinski
With the rapid development of e-commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E-Comp, a carefully-designed visual analytics system based on online reviews, to help customers compare local businesses at different levels of details. More specifically, intuitive glyphs overlaid on maps are designed for quick candidate selection. Grouped Sankey diagram visualizing the rating difference by common customers is chosen for more reliable comparison of two businesses. Augmented word cloud showing adjective-noun word pairs, combined with a temporal view, is proposed to facilitate in-depth comparison of businesses in terms of different time periods, rating scores and features. The effectiveness and usability of E-Comp are demonstrated through a case study and in-depth user interviews.
PixelSNE: Pixel-Aligned Stochastic Neighbor Embedding for Efficient 2D Visualization with Screen-Resolution Precision
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Kim, Minjeong; Choi, Minsuk; Lee, Sunwoong; Tang, Jian; Park, Haesun; Choo, Jaegul; Jeffrey Heer and Heike Leitte and Timo Ropinski
Embedding and visualizing large-scale high-dimensional data in a two-dimensional space is an important problem, because such visualization can reveal deep insights of complex data. However, most of the existing embedding approaches run on an excessively high precision, even when users want to obtain a brief insight from a visualization of large-scale datasets, ignoring the fact that in the end, the outputs are embedded onto a fixed-range pixel-based screen space. Motivated by this observation and directly considering the properties of screen space in an embedding algorithm, we propose Pixel-Aligned Stochastic Neighbor Embedding (PixelSNE), a highly efficient screen resolution-driven 2D embedding method which accelerates Barnes-Hut treebased t-distributed stochastic neighbor embedding (BH-SNE), which is known to be a state-of-the-art 2D embedding method. Our experimental results show a significantly faster running time for PixelSNE compared to BH-SNE for various datasets while maintaining comparable embedding quality.

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