37-Issue 3

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

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SetCoLa: High-Level Constraints for Graph Layout
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Hoffswell, Jane; Borning, Alan; Heer, Jeffrey; Jeffrey Heer and Heike Leitte and Timo Ropinski
Constraints enable flexible graph layout by combining the ease of automatic layout with customizations for a particular domain. However, constraint-based layout often requires many individual constraints defined over specific nodes and node pairs. In addition to the effort of writing and maintaining a large number of similar constraints, such constraints are specific to the particular graph and thus cannot generalize to other graphs in the same domain. To facilitate the specification of customized and generalizable constraint layouts, we contribute SetCoLa: a domain-specific language for specifying high-level constraints relative to properties of the backing data. Users identify node sets based on data or graph properties and apply high-level constraints within each set. Applying constraints to node sets rather than individual nodes reduces specification effort and facilitates reapplication of customized layouts across distinct graphs. We demonstrate the conciseness, generalizability, and expressiveness of SetCoLa on a series of real-world examples from ecological networks, biological systems, and social networks.
A General Illumination Model for Molecular Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Casajus, Pedro Hermosilla; Vázquez, Pere-Pau; Vinacua, Àlvar; Ropinski, Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
Several visual representations have been developed over the years to visualize molecular structures, and to enable a better understanding of their underlying chemical processes. Today, the most frequently used atom-based representations are the Space-filling, the Solvent Excluded Surface, the Balls-and-Sticks, and the Licorice models. While each of these representations has its individual benefits, when applied to large-scale models spatial arrangements can be difficult to interpret when employing current visualization techniques. In the past it has been shown that global illumination techniques improve the perception of molecular visualizations; unfortunately existing approaches are tailored towards a single visual representation. We propose a general illumination model for molecular visualization that is valid for different representations. With our illumination model, it becomes possible, for the first time, to achieve consistent illumination among all atom-based molecular representations. The proposed model can be further evaluated in real-time, as it employs an analytical solution to simulate diffuse light interactions between objects. To be able to derive such a solution for the rather complicated and diverse visual representations, we propose the use of regression analysis together with adapted parameter sampling strategies as well as shape parametrization guided sampling, which are applied to the geometric building blocks of the targeted visual representations. We will discuss the proposed sampling strategies, the derived illumination model, and demonstrate its capabilities when visualizing several dynamic molecules.
Towards User-Centered Active Learning Algorithms
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Bernard, Jürgen; Zeppelzauer, Matthias; Lehmann, Markus; Müller, Martin; Sedlmair, Michael; Jeffrey Heer and Heike Leitte and Timo Ropinski
The labeling of data sets is a time-consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual-interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual-interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data-based user strategies (clusters, dense areas) work considerably well in early phases, while model-based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.
EuroVis 2018: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Heer, Jeffrey; Leitte, Heike; Ropinski, Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
Visual Analysis of Protein-ligand Interactions
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Vázquez, Pere-Pau; Casajus, Pedro Hermosilla; Guallar, Victor; Estrada, Jorge; Vinacua, Àlvar; Jeffrey Heer and Heike Leitte and Timo Ropinski
The analysis of protein-ligand interactions is complex because of the many factors at play. Most current methods for visual analysis provide this information in the form of simple 2D plots, which, besides being quite space hungry, often encode a low number of different properties. In this paper we present a system for compact 2D visualization of molecular simulations. It purposely omits most spatial information and presents physical information associated to single molecular components and their pairwise interactions through a set of 2D InfoVis tools with coordinated views, suitable interaction, and focus+context techniques to analyze large amounts of data. The system provides a wide range of motifs for elements such as protein secondary structures or hydrogen bond networks, and a set of tools for their interactive inspection, both for a single simulation and for comparing two different simulations. As a result, the analysis of protein-ligand interactions of Molecular Simulation trajectories is greatly facilitated.
CFGExplorer: Designing a Visual Control Flow Analytics System around Basic Program Analysis Operations
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Devkota, Sabin; Isaacs, Katherine E.; Jeffrey Heer and Heike Leitte and Timo Ropinski
To develop new compilation and optimization techniques, computer scientists frequently consult program analysis artifacts such as control flow graphs (CFGs) and traces of executed instructions. A CFG is a directed graph representing possible execution paths in a program. CFGs are commonly visualized as node-link diagrams while traces are commonly viewed in raw text format. Visualizing and exploring CFGs and traces is challenging because of the complexity and specificity of the operations researchers perform. We present a design study where we collaborate with computer scientists researching dynamic binary analysis and compilation techniques. The research group primarily employs CFGs and traces to reason about and develop new algorithms for program optimization and parallelization. Through questionnaires, interviews, and a year-long observation, we analyzed their use of visualization, noting that the tasks they perform match common subroutines they employ in their techniques. Based on this task analysis, we designed CFGExplorer, a visual analytics system that supports computer scientists with interactions that are integrated with the program structure. We developed a domain-specific graph modification to generate graph layouts that reflect program structure. CFGExplorer incorporates structures such as functions and loops, and uses the correspondence between CFGs and traces to support navigation. We further augment the system to highlight the output of program analysis techniques, facilitating exploration at a higher level. We evaluate the tool through guided sessions and semi-structured interviews as well as deployment. Our collaborators have integrated CFGExplorer into their workflow and use it to reason about programs, develop and debug new algorithms, and share their findings.
An Approximate Parallel Vectors Operator for Multiple Vector Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Gerrits, Tim; Rössl, Christian; Theisel, Holger; Jeffrey Heer and Heike Leitte and Timo Ropinski
The Parallel Vectors (PV) Operator extracts the locations of points where two vector fields are parallel. In general, these features are line structures. The PV operator has been used successfully for a variety of problems, which include finding vortex-core lines or extremum lines. We present a new generic feature extraction method for multiple 3D vector fields: The Approximate Parallel Vectors (APV) Operator extracts lines where all fields are approximately parallel. The definition of the APV operator is based on the application of PV for two vector fields that are derived from the given set of fields. The APV operator enables the direct visualization of features of vector field ensembles without processing fields individually and without causing visual clutter. We give a theoretical analysis of the APV operator and demonstrate its utility for a number of ensemble data.
Baseball Timeline: Summarizing Baseball Plays Into a Static Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Ono, Jorge H. Piazentin; Dietrich, Carlos; Silva, Claudio T.; Jeffrey Heer and Heike Leitte and Timo Ropinski
In sports, Play Diagrams are the standard way to represent and convey information. They are widely used by coaches, managers, journalists and fans in general. There are situations where diagrams may be hard to understand, for example, when several actions are packed in a certain region of the field or there are just too many actions to be transformed in a clear depiction of the play. The representation of how actions develop through time, in particular, may be hardly achieved on such diagrams. The time, and the relationship among the actions of the players through time, is critical on the depiction of complex plays. In this context, we present a study on how player actions may be clearly depicted on 2D diagrams. The study is focused on Baseball plays, a sport where diagrams are heavily used to summarize the actions of the players. We propose a new and simple approach to represent spatiotemporal information in the form of a timeline. We designed our visualization with a requirement driven approach, conducting interviews and fulfilling the needs of baseball experts and expert-fans. We validate our approach by presenting a detailed analysis of baseball plays and conducting interviews with four domain experts.
Interactive Investigation of Traffic Congestion on Fat-Tree Networks Using TREESCOPE
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Bhatia, Harsh; Jain, Nikhil; Bhatele, Abhinav; Livnat, Yarden; Domke, Jens; Pascucci, Valerio; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
Parallel simulation codes often suffer from performance bottlenecks due to network congestion, leaving millions of dollars of investments underutilized. Given a network topology, it is critical to understand how different applications, job placements, routing schemes, etc., are affected by and contribute to network congestion, especially for large and complex networks. Understanding and optimizing communication on large-scale networks is an active area of research. Domain experts often use exploratory tools to develop both intuitive and formal metrics for network health and performance. This paper presents TREESCOPE, an interactive, web-based visualization tool for exploring network traffic on large-scale fat-tree networks. TREESCOPE encodes the network topology using a tailored matrix-based representation and provides detailed visualization of all traffic in the network. We report on the design process of TREESCOPE, which has been received positively by network researchers as well as system administrators. Through case studies of real and simulated data, we demonstrate how TREESCOPE's visual design and interactive support for complex queries on network traffic can provide experts with new insights into the occurrences and causes of congestion in the network.
ConcaveCubes: Supporting Cluster-based Geographical Visualization in Large Data Scale
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Li, Mingzhao; Choudhury, Farhana; Bao, Zhifeng; Samet, Hanan; Sellis, Timos; Jeffrey Heer and Heike Leitte and Timo Ropinski
In this paper we study the problem of supporting effective and scalable visualization for the rapidly increasing volumes of urban data. From an extensive literature study, we find that the existing solutions suffer from at least one of the drawbacks below: (i) loss of interesting structures/outliers due to sampling; (ii) supporting heatmaps only, which provides limited information; and (iii) no notion of real-world geography semantics (e.g., country, state, city) is captured in the visualization result as well as the underlying index. Therefore, we propose ConcaveCubes, a cluster-based data cube to support interactive visualization of large-scale multidimensional urban data. Specifically, we devise an appropriate visualization abstraction and visualization design based on clusters. We propose a novel concave hull construction method to support boundary based cluster map visualization, where real-world geographical semantics are preserved without any information loss. Instead of calculating the clusters on demand, ConcaveCubes (re)utilizes existing calculation and visualization results to efficiently support different kinds of user interactions. We conduct extensive experiments using real-world datasets and show the efficiency and effectiveness of ConcaveCubes by comparing with the state-of-the-art cube-based solutions.
ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2018) El-Assady, Mennatallah; Sevastjanova, Rita; Keim, Daniel; Collins, Christopher; Jeffrey Heer and Heike Leitte and Timo Ropinski
We present ThreadReconstructor, a visual analytics approach for detecting and analyzing the implicit conversational structure of discussions, e.g., in political debates and forums. Our work is motivated by the need to reveal and understand single threads in massive online conversations and verbatim text transcripts. We combine supervised and unsupervised machine learning models to generate a basic structure that is enriched by user-defined queries and rule-based heuristics. Depending on the data and tasks, users can modify and create various reconstruction models that are presented and compared in the visualization interface. Our tool enables the exploration of the generated threaded structures and the analysis of the untangled reply-chains, comparing different models and their agreement. To understand the inner-workings of the models, we visualize their decision spaces, including all considered candidate relations. In addition to a quantitative evaluation, we report qualitative feedback from an expert user study with four forum moderators and one machine learning expert, showing the effectiveness of our approach.
Cosine-Weighted B-Spline Interpolation on the Face-Centered Cubic Lattice
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Rácz, Gergely Ferenc; Csébfalvi, Balázs; Jeffrey Heer and Heike Leitte and Timo Ropinski
Cosine-Weighted B-spline (CWB) interpolation [Csé13] has been originally proposed for volumetric data sampled on the Body-Centered Cubic (BCC) lattice. The BCC lattice is well known to be optimal for sampling isotropically band-limited signals above the Nyquist limit. However, the Face-Centered Cubic (FCC) lattice has been recently proven to be optimal for low-rate sampling. The CWB interpolation is a state-of-the-art technique on the BCC lattice, which outperforms, for example, the previously proposed box-spline interpolation in terms of both efficiency and visual quality. In this paper, we show that CWB interpolation can be adapted to the FCC lattice as well, and results in similarly isotropic signal reconstructions as on the BCC lattice.
Illustrative Multivariate Visualization for Geological Modelling
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Rocha, Allan; Mota, Roberta Cabral Ramos; Hamdi, Hamidreza; Alim, Usman R.; Sousa, Mario Costa; Jeffrey Heer and Heike Leitte and Timo Ropinski
In this paper, we present a novel illustrative multivariate visualization for geological modelling to assist geologists and reservoir engineers in visualizing multivariate datasets in superimposed representations, in contrast to the single-attribute visualizations supported by commercial software. Our approach extends the use of decals from a single surface to 3D irregular grids, using the layering concept to represent multiple attributes. We also build upon prior work to augment the design and implementation of different geological attributes (namely, rock type, porosity, and permeability). More specifically, we propose a new sampling strategy to generate decals for porosity on the geological grid, a hybrid visualization for permeability which combines 2D decals and 3D ellipsoid glyphs, and a perceptually-based design that allows us to visualize additional attributes (e.g., oil saturation) while avoiding visual interference between layers. Furthermore, our visual design draws from traditional geological illustrations, facilitating the understanding and communication between interdisciplinary teams. An evaluation by domain experts highlights the potential of our approach for geological modelling and interpretation in this complex domain.
Exploring High-Dimensional Structure via Axis-Aligned Decomposition of Linear Projections
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Thiagarajan, Jayaraman J.; Liu, Shusen; Ramamurthy, Karthikeyan Natesan; Bremer, Peer-Timo; Jeffrey Heer and Heike Leitte and Timo Ropinski
Two-dimensional embeddings remain the dominant approach to visualize high dimensional data. The choice of embeddings ranges from highly non-linear ones, which can capture complex relationships but are difficult to interpret quantitatively, to axis-aligned projections, which are easy to interpret but are limited to bivariate relationships. Linear project can be considered as a compromise between complexity and interpretability, as they allow explicit axes labels, yet provide significantly more degrees of freedom compared to axis-aligned projections. Nevertheless, interpreting the axes directions, which are often linear combinations of many non-trivial components, remains difficult. To address this problem we introduce a structure aware decomposition of (multiple) linear projections into sparse sets of axis-aligned projections, which jointly capture all information of the original linear ones. In particular, we use tools from Dempster-Shafer theory to formally define how relevant a given axis-aligned project is to explain the neighborhood relations displayed in some linear projection. Furthermore, we introduce a new approach to discover a diverse set of high quality linear projections and show that in practice the information of k linear projections is often jointly encoded in ~ k axis-aligned plots. We have integrated these ideas into an interactive visualization system that allows users to jointly browse both linear projections and their axis-aligned representatives. Using a number of case studies we show how the resulting plots lead to more intuitive visualizations and new insights.
Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
(The Eurographics Association and John Wiley & Sons Ltd., 2018) Miranda, Fabio; Lage, Marcos; Doraiswamy, Harish; Mydlarz, Charlie; Salamon, Justin; Lockerman, Yitzchak; Freire, Juliana; Silva, Claudio T.; Jeffrey Heer and Heike Leitte and Timo Ropinski
Advances in technology coupled with the availability of low-cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group-by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube-based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space-efficient cube data structures allow for updates. Thus, the cube must be re-computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory-efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web-based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.
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.

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