35-Issue 3

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https://diglib.eg.org/handle/10.2312/15270

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BubbleNet: A Cyber Security Dashboard for Visualizing Patterns
(The Eurographics Association and John Wiley & Sons Ltd., 2016) McKenna, Sean; Staheli, Diane; Fulcher, Cody; Meyer, Miriah; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The field of cyber security is faced with ever-expanding amounts of data and a constant barrage of cyber attacks. Within this space, we have designed BubbleNet as a cyber security dashboard to help network analysts identify and summarize patterns within the data. This design study faced a range of interesting constraints from limited time with various expert users and working with users beyond the network analyst, such as network managers. To overcome these constraints, the design study employed a user-centered design process and a variety of methods to incorporate user feedback throughout the design of BubbleNet. This approach resulted in a successfully evaluated dashboard with users and further deployments of these ideas in both research and operational environments. By explaining these methods and the process, it can benefit future visualization designers to help overcome similar challenges in cyber security or alternative domains.
Composite Flow Maps
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Cornel, Daniel; Konev, Artem; Sadransky, Bernhard; Horváth, Zsolt; Brambilla, Andrea; Viola, Ivan; Waser, Jürgen; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Flow maps are widely used to provide an overview of geospatial transportation data. Existing solutions lack the support for the interactive exploration of multiple flow components at once. Flow components are given by different materials being transported, different flow directions, or by the need for comparing alternative scenarios. In this paper, we combine flows as individual ribbons in one composite flow map. The presented approach can handle an arbitrary number of sources and sinks. To avoid visual clutter, we simplify our flow maps based on a force-driven algorithm, accounting for restrictions with respect to application semantics. The goal is to preserve important characteristics of the geospatial context. This feature also enables us to highlight relevant spatial information on top of the flow map such as traffic conditions or accessibility. The flow map is computed on the basis of flows between zones. We describe a method for auto-deriving zones from geospatial data according to application requirements. We demonstrate the method in real-world applications, including transportation logistics, evacuation procedures, and water simulation. Our results are evaluated with experts from corresponding fields.
Visual Analysis of Governing Topological Structures in Excitable Network Dynamics
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Ngo, Quynh Quang; Hütt, Marc-Thorsten; Linsen, Lars; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
To understand how topology shapes the dynamics in excitable networks is one of the fundamental problems in network science when applied to computational systems biology and neuroscience. Recent advances in the field discovered the influential role of two macroscopic topological structures, namely hubs and modules. We propose a visual analytics approach that allows for a systematic exploration of the role of those macroscopic topological structures on the dynamics in excitable networks. Dynamical patterns are discovered using the dynamical features of excitation ratio and co-activation. Our approach is based on the interactive analysis of the correlation of topological and dynamical features using coordinated views. We designed suitable visual encodings for both the topological and the dynamical features. A degree map and an adjacency matrix visualization allow for the interaction with hubs and modules, respectively. A barycentric-coordinates layout and a multi-dimensional scaling approach allow for the analysis of excitation ratio and co-activation, respectively. We demonstrate how the interplay of the visual encodings allows us to quickly reconstruct recent findings in the field within an interactive analysis and even discovered new patterns. We apply our approach to network models of commonly investigated topologies as well as to the structural networks representing the connectomes of different species. We evaluate our approach with domain experts in terms of its intuitiveness, expressiveness, and usefulness.
Visual Analysis of Tumor Control Models for Prediction of Radiotherapy Response
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Raidou, Renata Georgia; Casares-Magaz, Oscar; Muren, Ludvig Paul; Heide, Uulke A. van der; Rørvik, Jarle; Breeuwer, Marcel; Vilanova, Anna; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
In radiotherapy, tumors are irradiated with a high dose, while surrounding healthy tissues are spared. To quantify the probability that a tumor is effectively treated with a given dose, statistical models were built and employed in clinical research. These are called tumor control probability (TCP) models. Recently, TCP models started incorporating additional information from imaging modalities. In this way, patient-specific properties of tumor tissues are included, improving the radiobiological accuracy of models. Yet, the employed imaging modalities are subject to uncertainties with significant impact on the modeling outcome, while the models are sensitive to a number of parameter assumptions. Currently, uncertainty and parameter sensitivity are not incorporated in the analysis, due to time and resource constraints. To this end, we propose a visual tool that enables clinical researchers working on TCP modeling, to explore the information provided by their models, to discover new knowledge and to confirm or generate hypotheses within their data. Our approach incorporates the following four main components: (1) It supports the exploration of uncertainty and its effect on TCP models; (2) It facilitates parameter sensitivity analysis to common assumptions; (3) It enables the identification of inter-patient response variability; (4) It allows starting the analysis from the desired treatment outcome, to identify treatment strategies that achieve it. We conducted an evaluation with nine clinical researchers. All participants agreed that the proposed visual tool provides better understanding and new opportunities for the exploration and analysis of TCP modeling.
Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Höllt, Thomas; Pezzotti, Nicola; Unen, Vincent van; Koning, Frits; Eisemann, Elmar; Lelieveldt, Boudewijn P. F.; Vilanova, Anna; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
To understand how the immune system works, one needs to have a clear picture of its cellular compositon and the cells' corresponding properties and functionality. Mass cytometry is a novel technique to determine the properties of single-cells with unprecedented detail. This amount of detail allows for much finer differentiation but also comes at the cost of more complex analysis. In this work, we present Cytosplore, implementing an interactive workflow to analyze mass cytometry data in an integrated system, providing multiple linked views, showing different levels of detail and enabling the rapid definition of known and unknown cell types. Cytosplore handles millions of cells, each represented as a high-dimensional data point, facilitates hypothesis generation and confirmation, and provides a significant speed up of the current workflow. We show the effectiveness of Cytosplore in a case study evaluation.
MCFTLE: Monte Carlo Rendering of Finite-Time Lyapunov Exponent Fields
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Günther, Tobias; Kuhn, Alexander; Theisel, Holger; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Traditionally, Lagrangian fields such as finite-time Lyapunov exponents (FTLE) are precomputed on a discrete grid and are ray casted afterwards. This, however, introduces both grid discretization errors and sampling errors during ray marching. In this work, we apply a progressive, view-dependent Monte Carlo-based approach for the visualization of such Lagrangian fields in time-dependent flows. Our approach avoids grid discretization and ray marching errors completely, is consistent, and has a low memory consumption. The system provides noisy previews that converge over time to an accurate high-quality visualization. Compared to traditional approaches, the proposed system avoids explicitly predefined fieldline seeding structures, and uses a Monte Carlo sampling strategy named Woodcock tracking to distribute samples along the view ray. An acceleration of this sampling strategy requires local upper bounds for the FTLE values, which we progressively acquire during the rendering. Our approach is tailored for high-quality visualizations of complex FTLE fields and is guaranteed to faithfully represent detailed ridge surface structures as indicators for Lagrangian coherent structures (LCS). We demonstrate the effectiveness of our approach by using a set of analytic test cases and real-world numerical simulations.
Similarity Voting based Viewpoint Selection for Volumes
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Tao, Yubo; Wang, Qirui; Chen, Wei; Wu, Yingcai; Lin, Hai; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Previous viewpoint selection methods in volume visualization are generally based on some deterministic measures of viewpoint quality. However, they may not express the familiarity and aesthetic sense of users for features of interest. In this paper, we propose an image-based viewpoint selection model to learn how visualization experts choose representative viewpoints for volumes with similar features. For a given volume, we first collect images with similar features, and these images reflect the viewpoint preferences of the experts when visualizing these features. Each collected image tallies votes to the viewpoints with the best matching based on an image similarity measure, which evaluates the spatial shape and appearance similarity between the collected image and the rendered image from the viewpoint. The optimal viewpoint is the one with the most votes from the collected images, that is, the viewpoint chosen by most visualization experts for similar features. We performed experiments on various volumes available in volume visualization, and made comparisons with traditional viewpoint selection methods. The results demonstrate that our model can select more canonical viewpoints, which are consistent with human perception.
Visual Debugging Techniques for Reactive Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Hoffswell, Jane; Satyanarayan, Arvind; Heer, Jeffrey; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Interaction is critical to effective visualization, but can be difficult to author and debug due to dependencies among input events, program state, and visual output. Recent advances leverage reactive semantics to support declarative design and avoid the ''spaghetti code'' of imperative event handlers. While reactive programming improves many aspects of development, textual specifications still fail to convey the complex runtime dynamics. In response, we contribute a set of visual debugging techniques to reveal the runtime behavior of reactive visualizations. A timeline view records input events and dynamic variable updates, allowing designers to replay and inspect the propagation of values step-by-step. On-demand annotations overlay the output visualization to expose relevant state and scale mappings in-situ. Dynamic tables visualize how backing datasets change over time. To evaluate the effectiveness of these techniques, we study how first-time Vega users debug interactions in faulty, unfamiliar specifications; with no prior knowledge, participants were able to accurately trace errors through the specification.
Exploring and Comparing Clusterings of Multivariate Data Sets Using Persistent Homology
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Rieck, Bastian; Leitte, Heike; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Clustering algorithms support exploratory data analysis by grouping inputs that share similar features. Especially the clustering of unlabelled data is said to be a fiendishly difficult problem, because users not only have to choose a suitable clustering algorithm but also a suitable number of clusters. The known issues of existing clustering validity measures comprise instabilities in the presence of noise and restrictive assumptions about cluster shapes. In addition, they cannot evaluate individual clusters locally. We present a new measure for assessing and comparing different clusterings both on a global and on a local level. Our measure is based on the topological method of persistent homology, which is stable and unbiased towards cluster shapes. Based on our measure, we also describe a new visualization that displays similarities between different clusterings (using a global graph view) and supports their comparison on the individual cluster level (using a local glyph view). We demonstrate how our visualization helps detect different—-but equally valid-clusterings of data sets from multiple application domains.
Critical Points of Gaussian-Distributed Scalar Fields on Simplicial Grids
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Liebmann, Tom; Scheuermann, Gerik; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Simulations and measurements often result in scalar fields with uncertainty due to errors or output sensitivity estimates. Methods for analyzing topological features of such fields usually are not capable of handling all aspects of the data. They either are not deterministic due to using Monte Carlo approaches, approximate the data with confidence intervals, or miss out on incorporating important properties, such as correlation. In this paper, we focus on the analysis of critical points of Gaussiandistributed scalar fields. We introduce methods to deterministically extract critical points, approximate their probability with high precision, and even capture relations between them resulting in an abstract graph representation. Unlike many other methods, we incorporate all information contained in the data including global correlation. Our work therefore is a first step towards a reliable and complete description of topological features of Gaussian-distributed scalar fields.
Pathfinder: Visual Analysis of Paths in Graphs
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Partl, Christian; Gratzl, Samuel; Streit, Marc; Wassermann, Anne-Mai; Pfister, Hanspeter; Schmalstieg, Dieter; Lex, Alexander; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The analysis of paths in graphs is highly relevant in many domains. Typically, path-related tasks are performed in node-link layouts. Unfortunately, graph layouts often do not scale to the size of many real world networks. Also, many networks are multivariate, i.e., contain rich attribute sets associated with the nodes and edges. These attributes are often critical in judging paths, but directly visualizing attributes in a graph layout exacerbates the scalability problem. In this paper, we present visual analysis solutions dedicated to path-related tasks in large and highly multivariate graphs. We show that by focusing on paths, we can address the scalability problem of multivariate graph visualization, equipping analysts with a powerful tool to explore large graphs. We introduce Pathfinder, a technique that provides visual methods to query paths, while considering various constraints. The resulting set of paths is visualized in both a ranked list and as a node-link diagram. For the paths in the list, we display rich attribute data associated with nodes and edges, and the node-link diagram provides topological context. The paths can be ranked based on topological properties, such as path length or average node degree, and scores derived from attribute data. Pathfinder is designed to scale to graphs with tens of thousands of nodes and edges by employing strategies such as incremental query results. We demonstrate Pathfinder's fitness for use in scenarios with data from a coauthor network and biological pathways.
AVOCADO: Visualization of Workflow-Derived Data Provenance for Reproducible Biomedical Research
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Stitz, Holger; Luger, Stefan; Streit, Marc; Gehlenborg, Nils; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
A major challenge in data-driven biomedical research lies in the collection and representation of data provenance information to ensure that findings are reproducibile. In order to communicate and reproduce multi-step analysis workflows executed on datasets that contain data for dozens or hundreds of samples, it is crucial to be able to visualize the provenance graph at different levels of aggregation. Most existing approaches are based on node-link diagrams, which do not scale to the complexity of typical data provenance graphs. In our proposed approach, we reduce the complexity of the graph using hierarchical and motif-based aggregation. Based on user action and graph attributes, a modular degree-of-interest (DoI) function is applied to expand parts of the graph that are relevant to the user. This interest-driven adaptive approach to provenance visualization allows users to review and communicate complex multi-step analyses, which can be based on hundreds of files that are processed by numerous workflows. We have integrated our approach into an analysis platform that captures extensive data provenance information, and demonstrate its effectiveness by means of a biomedical usage scenario.
Time-Series Plots Integrated in Parallel-Coordinates Displays
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Gruendl, Henning; Riehmann, Patrick; Pausch, Yves; Froehlich, Bernd; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present a natural extension of two-dimensional parallel-coordinates plots for revealing relationships in time-dependent multi-attribute data by building on the idea that time can be considered as the third dimension. A time slice through the visualization represents a certain point in time and can be viewed as a regular parallel-coordinates display. A vertical slice through one of the axes of the parallel-coordinates display would show a time-series plot. For a focus-and-context integration of both views, we embed time-series plots between two adjacent axes of the parallel-coordinates plot. Both time-series plots are drawn using a pseudo three-dimensional perspective with a single vanishing point. An independent parallel-coordinates panel that connects the two perspectively displayed time-series plots can move forward and backward in time to reveal changes in the relationship between the time-dependent attributes. The visualization of time-series plots in the context of the parallelcoordinates plot facilitates the exploration of time-related aspects of the data without the need to switch to a separate display. We provide a consistent set of tools for selecting and contrasting subsets of the data, which are important for various application domains.
Visual Analysis of Defects in Glass Fiber Reinforced Polymers for 4DCT Interrupted In situ Tests
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Amirkhanov, Alexander; Amirkhanov, Artem; Salaberger, Dietmar; Kastner, Johann; Gröller, Eduard; Heinzl, Christoph; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Material engineers use interrupted in situ tensile testing to investigate the damage mechanisms in composite materials. For each subsequent scan, the load is incrementally increased until the specimen is completely fractured. During the interrupted in situ testing of glass fiber reinforced polymers (GFRPs) defects of four types are expected to appear: matrix fracture, fiber/matrix debonding, fiber pull-out, and fiber fracture. There is a growing demand for the detection and analysis of these defects among the material engineers. In this paper, we present a novel workflow for the detection, classification, and visual analysis of defects in GFRPs using interrupted in situ tensile tests in combination with X-ray Computed Tomography. The workflow is based on the automatic extraction of defects and fibers. We introduce the automatic Defect Classifier assigning the most suitable type to each defect based on its geometrical features. We present a visual analysis system that integrates four visualization methods: 1) the Defect Viewer highlights defects with visually encoded type in the context of the original CT image, 2) the Defect Density Maps provide an overview of the defect distributions according to type in 2D and 3D, 3) the Final Fracture Surface estimates the material fracture's location and displays it as a 3D surface, 4) the 3D Magic Lens enables interactive exploration by combining detailed visualizations in the region of interest with overview visualizations as context. In collaboration with material engineers, we evaluate our solution and demonstrate its practical applicability.
Comparing Bar Chart Authoring with Microsoft Excel and Tangible Tiles
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Wun, Tiffany; Payne, Jennifer; Huron, Samuel; Carpendale, Sheelagh; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Providing tools that make visualization authoring accessible to visualization non-experts is a major research challenge. Currently the most common approach to generating a visualization is to use software that quickly and automatically produces visualizations based on templates. However, it has recently been suggested that constructing a visualization with tangible tiles may be a more accessible method, especially for people without visualization expertise. There is still much to be learned about the differences between these two visualization authoring practices. To better understand how people author visualizations in these two conditions, we ran a qualitative study comparing the use of software to the use of tangible tiles, for the creation of bar charts. Close observation of authoring activities showed how each of the following varied according to the tool used: 1) sequences of action; 2) distribution of time spent on different aspects of the InfoVis pipeline; 3) pipeline task separation; and 4) freedom to manipulate visual variables. From these observations, we discuss the implications of the variations in activity sequences, noting tool design considerations and pointing to future research questions.
TextDNA: Visualizing Word Usage with Configurable Colorfields
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Szafir, Danielle Albers; Stuffer, Deidre; Sohail, Yusef; Gleicher, Michael; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Patterns of words used in different text collections can characterize interesting properties of a corpus. However, these patterns are challenging to explore as they often involve complex relationships across many words and collections in a large space of words. In this paper, we propose a configurable colorfield design to aid this exploration. Our approach uses a dense colorfield overview to present large amounts of data in ways that make patterns perceptible. It allows flexible configuration of both data mappings and aggregations to expose different kinds of patterns, and provides interactions to help connect detailed patterns to the corpus overview. TextDNA, our prototype implementation, leverages the GPU to provide interactivity in the web browser even on large corpora. We present five case studies showing how the tool supports inquiry in corpora ranging in size from single document to millions of books. Our work shows how to make a configurable colorfield approach practical for a range of analytic tasks.
Exploring Items and Features with IF,FI-Tables
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Corput, Paul van der; Wijk, Jarke J. van; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
The exploration of high-dimensional data is challenging because humans have difficulty to understand more than three dimensions. We present a new visualization concept that enables users to explore such data and, specifically, to learn about important items and features that are unknown or overlooked, based on the items and features that are already known. The visualization consists of two juxtaposed tables: an IF-Table, showing all items with a selection of features; and an FI-Table, showing all features with a selection of items. This enables the user to limit the number of visible items and features to those needed for the exploration. The interaction is kept simple: each selection of items and features results in a complete overview of similar and relevant items and features.
Arcs, Angles, or Areas: Individual Data Encodings in Pie and Donut Charts
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Skau, Drew; Kosara, Robert; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Pie and donut charts have been a hotly debated topic in the visualization community for some time now. Even though pie charts have been around for over 200 years, our understanding of the perceptual factors used to read data in them is still limited. Data is encoded in pie and donut charts in three ways: arc length, center angle, and segment area. For our first study, we designed variations of pie charts to test the importance of individual encodings for reading accuracy. In our second study, we varied the inner radius of a donut chart from a filled pie to a thin outline to test the impact of removing the central angle. Both studies point to angle being the least important visual cue for both charts, and the donut chart being as accurate as the traditional pie chart.
Using Visualization to Explore Original and Anonymized LBSN Data
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Tarameshloo, Ebrahim; Loorak, Mona Hosseinkhani; Fong, Philip W. L.; Carpendale, Sheelagh; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
We present GSUVis, a visualization tool designed to provide better understanding of location-based social network (LBSN) data. LBSN data is one of the most important sources of information for transportation, marketing, health, and public safety. LBSN data consumers are interested in accessing and analysing data that is as complete and as accurate as possible. However, LBSN data contains sensitive information about individuals. Consequently, data anonymization is of critical importance if this data is to be made available to consumers. However, anonymization commonly reduces the utility of information available. Working with privacy experts, we designed GSUVis a visual analytic tool to help experts better understand the effects of anonymization techniques on LBSN data utility. One of GSUVis's primary goals is to make it possible for people to use LBSN data, without requiring them to gain deep knowledge about data anonymization. To inform the design of GSUVis, we interviewed privacy experts, and collected their tasks and system requirements. Based on this understanding, we designed and implemented GSUVis. It applies two anonymization algorithms for social and location trajectory data to a real-world LBSN dataset and visualizes the data both before and after anonymization. Through feedback from domain experts, we reflect on the effectiveness of GSUVis and the impact of anonymization using visualization.
Exploratory Visual Analysis for Animal Movement Ecology
(The Eurographics Association and John Wiley & Sons Ltd., 2016) Slingsby, Aidan; Loon, Emiel van; Kwan-Liu Ma and Giuseppe Santucci and Jarke van Wijk
Movement ecologists study animals' movement to help understand their behaviours and interactions with each other and the environment. Data from GPS loggers are increasingly important for this. These data need to be processed, segmented and summarised for further visual and statistical analysis, often using predefined parameters. Usually, this process is separate from the subsequent visual and statistical analysis, making it difficult for these results to inform the data processing and to help set appropriate scale and thresholds parameters. This paper explores the use of highly interactive visual analytics techniques to close the gap between processing raw data and exploratory visual analysis. Working closely with animal movement ecologists, we produced requirements to enable data characteristics to be determined, initial research questions to be investigated, and the suitability of data for further analysis to be assessed. We design visual encodings and interactions to meet these requirements and provide software that implements them. We demonstrate these techniques with indicative research questions for a number of bird species, provide software, and discuss wider implications for animal movement ecology.

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