36-Issue 3

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https://diglib.eg.org/handle/10.2312/2631433

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Cycle Plot Revisited: Multivariate Outlier Detection Using a Distance-Based Abstraction
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Bögl, Markus; Filzmoser, Peter; Gschwandtner, Theresia; Lammarsch, Tim; Leite, Roger A.; Miksch, Silvia; Rind, Alexander; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
The cycle plot is an established and effective visualization technique for identifying and comprehending patterns in periodic time series, like trends and seasonal cycles. It also allows to visually identify and contextualize extreme values and outliers from a different perspective. Unfortunately, it is limited to univariate data. For multivariate time series, patterns that exist across several dimensions are much harder or impossible to explore. We propose a modified cycle plot using a distance-based abstraction (Mahalanobis distance) to reduce multiple dimensions to one overview dimension and retain a representation similar to the original. Utilizing this distance-based cycle plot in an interactive exploration environment, we enhance the Visual Analytics capacity of cycle plots for multivariate outlier detection. To enable interactive exploration and interpretation of outliers, we employ coordinated multiple views that juxtapose a distance-based cycle plot with Cleveland's original cycle plots of the underlying dimensions. With our approach it is possible to judge the outlyingness regarding the seasonal cycle in multivariate periodic time series.
Visualization of Delay Uncertainty and its Impact on Train Trip Planning: A Design Study
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Wunderlich, Marcel; Ballweg, Kathrin; Fuchs, Georg; Landesberger, Tatiana von; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Uncertainty about possible train delays has an impact on train trips, as the exact arrival time is unknown during trip planning. Delays can lead to missing a connecting train at the transfer station, or to coming too late to an appointment at the destination. Facing this uncertainty, the traveler may wish to use an earlier train or a different connection arriving well before the appointment. Currently, train trip planning is based on scheduled times of connections between two stations. Information about approximate delays is only available shortly before train departure. Although several visualization approaches can show temporal uncertainty, we are not aware of any visual design specifically supporting trip planning, which can show delay uncertainty and its impact on the connections. We propose and evaluate a visual design which extends train trip planning with delay uncertainty. It shows the scheduled train connections together with their expected train delays as well as their impacts on both the arrival time, and the potential of missing a transfer. The visualization also includes information about alternative connections in case of these critical transfers. In this way the user is able to judge which train connection is suitable for a trip. We conducted a user study with 76 participants to evaluate our design. We compared it to two alternative presentations that are prominent in Germany. The study showed that our design performs comparably well for tasks concerning train schedules. The additional uncertainty display as well as the visualization of alternative connections was appreciated and well understood. The participants were able to estimate when they would likely arrive at their destination despite possible train delays while they were unable to estimate this with existing presentations. The users would prefer to use the new design for their trip planning.
Graph Layouts by t-SNE
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kruiger, J. F.; Rauber, Paulo E.; Martins, Rafael Messias; Kerren, Andreas; Kobourov, Stephen; Telea, Alexandru C.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We propose a new graph layout method based on a modification of the t-distributed Stochastic Neighbor Embedding (t-SNE) dimensionality reduction technique. Although t-SNE is one of the best techniques for visualizing high-dimensional data as 2D scatterplots, t-SNE has not been used in the context of classical graph layout. We propose a new graph layout method, tsNET, based on representing a graph with a distance matrix, which together with a modified t-SNE cost function results in desirable layouts. We evaluate our method by a formal comparison with state-of-the-art methods, both visually and via established quality metrics on a comprehensive benchmark, containing real-world and synthetic graphs. As evidenced by the quality metrics and visual inspection, tsNET produces excellent layouts.
Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Wan, Yong; Hansen, Charles; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Research on microscopy data from developing biological samples usually requires tracking individual cells over time. When cells are three-dimensionally and densely packed in a time-dependent scan of volumes, tracking results can become unreliable and uncertain. Not only are cell segmentation results often inaccurate to start with, but it also lacks a simple method to evaluate the tracking outcome. Previous cell tracking methods have been validated against benchmark data from real scans or artificial data, whose ground truth results are established by manual work or simulation. However, the wide variety of real-world data makes an exhaustive validation impossible. Established cell tracking tools often fail on new data, whose issues are also difficult to diagnose with only manual examinations. Therefore, data-independent tracking evaluation methods are desired for an explosion of microscopy data with increasing scale and resolution. In this paper, we propose the uncertainty footprint, an uncertainty quantification and visualization technique that examines nonuniformity at local convergence for an iterative evaluation process on a spatial domain supported by partially overlapping bases. We demonstrate that the patterns revealed by the uncertainty footprint indicate data processing quality in two algorithms from a typical cell tracking workflow - cell identification and association. A detailed analysis of the patterns further allows us to diagnose issues and design methods for improvements. A 4D cell tracking workflow equipped with the uncertainty footprint is capable of self diagnosis and correction for a higher accuracy than previous methods whose evaluation is limited by manual examinations.
Internal and External Visual Cue Preferences for Visualizations in Presentations
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kong, Ha-Kyung; Liu, Zhicheng; Karahalios, Karrie; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Presenters, such as analysts briefing to an executive committee, often use visualizations to convey information. In these cases, providing clear visual guidance is important to communicate key concepts without confusion. This paper explores visual cues that guide attention to a particular area of a visualization. We developed a visual cue taxonomy distinguishing internal from external cues, designed a web tool based on the taxonomy, and conducted a user study with 24 participants to understand user preferences in choosing visual cues. Participants perceived internal cues (e.g., transparency, brightness, and magnification) as the most useful visual cues and often combined them with other internal or external cues to emphasize areas of focus for their audience. Interviews also revealed that the choice of visual cues depends on not only the chart type, but also the presentation setting, the audience, and the function cues are serving. Considering the complexity of choosing visual cues, we provide design implications for improving the organization, consistency, and integration of visual cues within existing workflows.
CoreFlow: Extracting and Visualizing Branching Patterns from Event Sequences
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Liu, Zhicheng; Kerr, Bernard; Dontcheva, Mira; Grover, Justin; Hoffman, Matthew; Wilson, Alan; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Event sequence datasets with high event cardinality and long sequences are difficult to visualize and analyze. In particular, it is hard to generate a high level visual summary of paths and volume of flow. Existing approaches of mining and visualizing frequent sequential patterns look promising, but have limitations in terms of scalability, interpretability and utility. We propose CoreFlow, a technique that automatically extracts and visualizes branching patterns in event sequences. CoreFlow constructs a tree by recursively applying a three-step procedure: rank events, divide sequences into groups, and trim sequences by the chosen event. The resulting tree contains key events as nodes, and links represent aggregated flows between key events. Based on CoreFlow, we have developed an interactive system for event sequence analysis. Our approach can compute branching patterns for millions of events in a few seconds, with improved interpretability of extracted patterns compared to previous work. We also present case studies of using the system in three different domains and discuss success and failure cases of applying CoreFlow to real-world analytic problems. These case studies call forth future research on metrics and models to evaluate the quality of visual summaries of event sequences.
Finding a Clear Path: Structuring Strategies for Visualization Sequences
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Hullman, Jessica; Kosara, Robert; Lam, Heidi; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Little is known about how people structure sets of visualizations to support sequential viewing. We contribute findings from several studies examining visualization sequencing and reception. In our first study, people made decisions between various possible structures as they ordered a set of related visualizations (consisting of either bar charts or thematic maps) into what they considered the clearest sequence for showing the data.We find that most people structure visualization sequences hierarchically: they create high level groupings based on shared data properties like time period, measure, level of aggregation, and spatial region, then order the views within these groupings. We also observe a tendency for certain types of similarities between views, like a common spatial region or aggregation level, to be seen as more appropriate categories for organizing views in a sequence than others, like a common time period or measure. In a second study, we find that viewers' perceptions of the quality and intention of different sequences are largely consistent with the perceptions of the users who created them. The understanding of sequence preferences and perceptions that emerges from our studies has implications for the development of visualization authoring tools and sequence recommendations for guided analysis.
Comparative Visual Analysis of Structure-Performance Relations in Complex Bulk-Heterojunction Morphologies
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Aboulhassan, Amal; Sicat, Ronell; Baum, Daniel; Wodo, Olga; Hadwiger, Markus; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
The structure of Bulk-Heterojunction (BHJ) materials, the main component of organic photovoltaic solar cells, is very complex, and the relationship between structure and performance is still largely an open question. Overall, there is a wide spectrum of fabrication configurations resulting in different BHJ morphologies and correspondingly different performances. Current stateof- the-art methods for assessing the performance of BHJ morphologies are either based on global quantification of morphological features or simply on visual inspection of the morphology based on experimental imaging. This makes finding optimal BHJ structures very challenging. Moreover, finding the optimal fabrication parameters to get an optimal structure is still an open question. In this paper, we propose a visual analysis framework to help answer these questions through comparative visualization and parameter space exploration for local morphology features. With our approach, we enable scientists to explore multivariate correlations between local features and performance indicators of BHJ morphologies. Our framework is built on shape-based clustering of local cubical regions of the morphology that we call patches. This enables correlating the features of clusters with intuition-based performance indicators computed from geometrical and topological features of charge paths.
Understanding Indirect Causal Relationships in Node-Link Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Bae, Juhee; Helldin, Tove; Riveiro, Maria; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
To find correlations and cause and effect relationships in multivariate data sets is central in many data analysis problems. A common way of representing causal relations among variables is to use node-link diagrams, where nodes depict variables and edges show relationships between them. When performing a causal analysis, analysts may be biased by the position of collected evidences, especially when they are at the top of a list. This is of crucial importance since finding a root cause or a derived effect, and searching for causal chains of inferences are essential analytic tasks when investigating causal relationships. In this paper, we examine whether sequential ordering influences understanding of indirect causal relationships and whether it improves readability of multi-attribute causal diagrams. Moreover, we see how people reason to identify a root cause or a derived effect. The results of our design study show that sequential ordering does not play a crucial role when analyzing causal relationships, but many connections from/to a variable and higher strength/certainty values may influence the process of finding a root cause and a derived effect.
Compactly Supported Biorthogonal Wavelet Bases on the Body Centered Cubic Lattice
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Horacsek, Joshua J.; Alim, Usman R.; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
In this work, we present a family of compact, biorthogonal wavelet filter banks that are applicable to the Body Centered Cubic (BCC) lattice. While the BCC lattice has been shown to have superior approximation properties for volumetric data when compared to the Cartesian Cubic (CC) lattice, there has been little work in the way of designing wavelet filter banks that respect the geometry of the BCC lattice. Since wavelets have applications in signal de-noising, compression, and sparse signal reconstruction, these filter banks are an important tool that addresses some of the scalability concerns presented by the BCC lattice. We use these filters in the context of volumetric data compression and reconstruction and qualitatively evaluate our results by rendering images of isosurfaces from compressed data.
Sliceplorer: 1D Slices for Multi-dimensional Continuous Functions
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Torsney-Weir, Thomas; Sedlmair, Michael; Möller, Torsten; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Multi-dimensional continuous functions are commonly visualized with 2D slices or topological views. Here, we explore 1D slices as an alternative approach to show such functions. Our goal with 1D slices is to combine the benefits of topological views, that is, screen space efficiency, with those of slices, that is a close resemblance of the underlying function. We compare 1D slices to 2D slices and topological views, first, by looking at their performance with respect to common function analysis tasks. We also demonstrate 3 usage scenarios: the 2D sinc function, neural network regression, and optimization traces. Based on this evaluation, we characterize the advantages and drawbacks of each of these approaches, and show how interaction can be used to overcome some of the shortcomings.
Visual Verification of Cancer Staging for Therapy Decision Support
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Cypko, Mario A.; Wojdziak, Jan; Stoehr, Matthaeus; Kirchner, Bettina; Preim, Bernhard; Dietz, Andreas; Lemke, Heinz U.; Oeltze-Jafra, Steffen; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
It is generally accepted practice that each cancer patient case should be discussed in a clinical expert meeting, the so-called tumor board. A central role in finding the best therapy options for patients with solid tumors plays the Tumor, lymph Node, and Metastasis staging (TNM staging). Correctness of TNM staging has a significant impact on the therapy choice and hence on the patient's post-therapeutic quality of life or even survival. If inconsistencies in the TNM staging occur, possible explanations and solutions must be found based on the complex patient records, which takes the costly time of (multiple) physicians. We propose a more efficient visual analysis component, which supports a physician in verifying the given TNM staging before forwarding it to the tumor board. Our component comprises a Bayesian network model of the TNM staging process. Using information from the patient records and Bayesian inference, the models computes a patient-specific TNM staging, which is then explored and compared to the given staging by means of a graph-based visualization. Our component is implemented in a research prototype that supports an understanding of the model computations, allows for a fast identification of important influencing factors, and facilitates a quick detection of differences between two TNM stagings. We evaluated our component with five physicians, each studying 20 cases of laryngeal cancer.
An Empirical Study on the Reliability of Perceiving Correlation Indices using Scatterplots
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Sher, Varshita; Bemis, Karen G.; Liccardi, Ilaria; Chen, Min; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Scatterplots have been in use for about two centuries, primarily for observing the relationship between two variables and commonly for supporting correlation analysis. In this paper, we report an empirical study that examines how humans' perception of correlation using scatterplots relates to the Pearson's product-moment correlation coefficient (PPMCC) - a commonly used statistical measure of correlation. In particular, we study human participants' estimation of correlation under different conditions, e.g., different PPMCC values, different densities of data points, different levels of symmetry of data enclosures, and different patterns of data distribution. As the participants were instructed to estimate the PPMCC of each stimulus scatterplot, the difference between the estimated and actual PPMCC is referred to as an offset. The results of the study show that varying PPMCC values, symmetry of data enclosure, or data distribution does have an impact on the average offsets, while only large variations in density cause an impact that is statistically significant. This study indicates that humans' perception of correlation using scatterplots does not correlate with computed PPMCC in a consistent manner. The magnitude of offsets may be affected not only by the difference between individuals, but also by geometric features of data enclosures. It suggests that visualizing scatterplots does not provide adequate support to the task of retrieving their corresponding PPMCC indicators, while the underlying model of humans' perception of correlation using scatterplots ought to feature other variables in addition to PPMCC. The paper also includes a theoretical discussion on the cost-benefit of using scatterplots.
Visual Analysis of Confocal Raman Spectroscopy Data using Cascaded Transfer Function Design
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Schikora, Christoph Markus; Plack, Markus; Bornemann, Rainer; Bolívar, Peter Haring; Kolb, Andreas; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
2D Confocal Raman Microscopy (CRM) data consist of high dimensional per-pixel spectral data of 1000 bands and allows for complex spectral and spatial-spectral analysis tasks, i.e., in material discrimination, material thickness, and spatial material distributions. Currently, simple integral methods are commonly applied as visual analysis solutions to CRM data which exhibit restricted discrimination power in various regards. In this paper we present a novel approach for the visual analysis of 2D multispectral CRM data using multi-variate visualization techniques. Due to the large amount of data and the demand of an explorative approach without a-priori restriction, our system allows for arbitrary interactive (de)selection of varaibles w/o limitation and an unrestricted online definition/construction of new, combined properties. Our approach integrates CRM specific quantitative measures and handles material-related features for mixed materials in a quantitative manner. Technically, we realize the online definition/construction of new, combined properties as semi-automatic, cascaded, 1D and 2D multidimensional transfer functions (MD-TFs). By interactively incorporating new (raw or derived) properties, the dimensionality of the MD-TF space grows during the exploration procedure and is virtually unlimited. The final visualization is achieved by an enhanced color mixing step which improves saturation and contrast.
Measuring Symmetry in Drawings of Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Welch, Eric; Kobourov, Stephen; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Layout symmetry is an important and desired feature in graph drawing. While there is a substantial body of work in computer vision around the detection and measurement of symmetry in images, there has been little effort to define and validate meaningful measures of the symmetry of graph drawings. In this paper, we evaluate two algorithms that have been proposed for measuring graph drawing symmetry, comparing their judgments to those of human subjects, and investigating the use of stress as an alternative measure of symmetry. We discuss advantages and disadvantages of these measures, possible ways to improve them, and implications for the design of algorithms that optimize the symmetry in the layout.
Visualizing Probabilistic Multi-Phase Fluid Simulation Data using a Sampling Approach
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Hummel, Mathias; Jöckel, Lisa; Schäfer, Jan; Hlawitschka, Mark Werner; Garth, Christoph; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Eulerian Method of Moment (MoM) solvers are gaining popularity for multi-phase CFD simulation involving bubbles or droplets in process engineering. Because the actual positions of bubbles are uncertain, the spatial distribution of bubbles is described by scalar fields of moments, which can be interpreted as probability density functions. Visualizing these simulation results and comparing them to physical experiments is challenging, because neither the shape nor the distribution of bubbles described by the moments lend themselves to visual interpretation. In this work, we describe a visualization approach that provides explicit instances of the bubble distribution and produces bubble geometry based on local flow properties. To facilitate animation, the instancing of the bubble distribution provides coherence over time by advancing bubbles between time steps and updating the distribution. Our approach provides an intuitive visualization and enables direct visual comparison of simulation results to physical experiments.
Glyph-Based Comparative Stress Tensor Visualization in Cerebral Aneurysms
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Meuschke, Monique; Voß, Samuel; Beuing, Oliver; Preim, Bernhard; Lawonn, Kai; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We present the first visualization tool that enables a comparative depiction of structural stress tensor data for vessel walls of cerebral aneurysms. Such aneurysms bear the risk of rupture, whereas their treatment also carries considerable risks for the patient. Medical researchers emphasize the importance of analyzing the interaction of morphological and hemodynamic information for the patient-specific rupture risk evaluation and treatment analysis. Tensor data such as the stress inside the aneurysm walls characterizes the interplay between the morphology and blood flow and seems to be an important rupture-prone criterion. We use different glyph-based techniques to depict local stress tensors simultaneously and compare their applicability to cerebral aneurysms in a user study. We thus offer medical researchers an effective visual exploration tool to assess the aneurysm rupture risk.We developed a GPU-based implementation of our techniques with a flexible interactive data exploration mechanism. Our depictions are designed in collaboration with domain experts, and we provide details about the evaluation.
Integrating Visual Analytics Support for Grounded Theory Practice in Qualitative Text Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Chandrasegaran, Senthil; Badam, Sriram Karthik; Kisselburgh, Lorraine; Ramani, Karthik; Elmqvist, Niklas; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts-ofspeech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result.
Sclow Plots: Visualizing Empty Space
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Giesen, Joachim; Kühne, Lars; Lucas, Philipp; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Scatter plots are mostly used for correlation analysis, but are also a useful tool for understanding the distribution of highdimensional point cloud data. An important characteristic of such distributions are clusters, and scatter plots have been used successfully to identify clusters in data. Another characteristic of point cloud data that has received less attention so far are regions that contain no or only very few data points. We show that augmenting scatter plots by projections of flow lines along the gradient vector field of the distance function to the point cloud reveals such empty regions or voids. The augmented scatter plots, that we call sclow plots, enable a much better understanding of the geometry underlying the point cloud than traditional scatter plots, and by that support tasks like dimension inference, detecting outliers, or identifying data points at the interface between clusters. We demonstrate the feasibility of our approach on synthetic and real world data sets.
Graffinity: Visualizing Connectivity in Large Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2017) Kerzner, Ethan; Lex, Alexander; Sigulinsky, Crystal Lynn; Urness, Timothy; Jones, Bryan William; Marc, Robert E.; Meyer, Miriah; Heer, Jeffrey and Ropinski, Timo and van Wijk, Jarke
Multivariate graphs are prolific across many fields, including transportation and neuroscience. A key task in graph analysis is the exploration of connectivity, to, for example, analyze how signals flow through neurons, or to explore how well different cities are connected by flights. While standard node-link diagrams are helpful in judging connectivity, they do not scale to large networks. Adjacency matrices also do not scale to large networks and are only suitable to judge connectivity of adjacent nodes. A key approach to realize scalable graph visualization are queries: instead of displaying the whole network, only a relevant subset is shown. Query-based techniques for analyzing connectivity in graphs, however, can also easily suffer from cluttering if the query result is big enough. To remedy this, we introduce techniques that provide an overview of the connectivity and reveal details on demand.We have two main contributions: (1) two novel visualization techniques that work in concert for summarizing graph connectivity; and (2) Graffinity, an open-source implementation of these visualizations supplemented by detail views to enable a complete analysis workflow. Graffinity was designed in a close collaboration with neuroscientists and is optimized for connectomics data analysis, yet the technique is applicable across domains. We validate the connectivity overview and our open-source tool with illustrative examples using flight and connectomics data.

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