DevLan/vispubs huggingface dataset:
A Design Study of Direct-Touch Interaction for Exploratory 3D Scientific Visualization
Authors: Tijmen R. Klein, Florimond Guéniat, Luc Pastur, Frédéric Vernier, Tobias Isenberg 0001
Published at EuroVis, 2012

We present an interaction design study of several non‐overlapping direct‐touch interaction widgets, postures, and bi‐manual techniques to support the needs of scientists who are exploring a dataset. The final interaction design supports navigation/zoom, cutting plane interaction, a drilling exploration, the placement of seed particles in 3D space, and the exploration of temporal data evolution. To ground our design, we conducted a requirements analysis and used a participatory design approach throughout development. We chose simulations in the field of fluid mechanics as our example domain and, in the paper, discuss our choice of techniques, their adaptation to our target domain, and discuss how they facilitate the necessary combination of visualization control and data exploration. We evaluated our resulting interactive data exploration system with seven fluid mechanics experts and report on their qualitative feedback. While we use flow visualization as our application domain, the developed techniques were designed with generalizability in mind and we discuss several implications of our work on further development of direct‐touch data exploration techniques for scientific visualization in general.
A Qualitative Study on the Exploration of Temporal Changes in Flow Maps with Animation and Small-Multiples
Authors: Ilya Boyandin, Enrico Bertini, Denis Lalanne
Published at EuroVis, 2012

We present a qualitative user study analyzing findings made while exploring changes over time in spatial interactions. We analyzed findings made by the study participants with flow maps, one of the most popular representations of spatial interactions, using animation and small‐multiples as two alternative ways of representing temporal changes. Our goal was not to measure the subjects’ performance with the two views, but to find out whether there are qualitative differences between the types of findings users make with these two representations. To achieve this goal we performed a deep analysis of the collected findings, the interaction logs, and the subjective feedback from the users. We observed that with animation the subjects tended to make more findings concerning geographically local events and changes between subsequent years. With small‐multiples more findings concerning longer time periods were made. Besides, our results suggest that switching from one view to the other might lead to an increase in the numbers of findings of specific types made by the subjects which can be beneficial for certain tasks.
A Quantized Boundary Representation of 2D Flows
Authors: Joshua A. Levine, Shreeraj Jadhav, Harsh Bhatia, Valerio Pascucci, Peer-Timo Bremer
Published at EuroVis, 2012

Analysis and visualization of complex vector fields remain major challenges when studying large scale simulation of physical phenomena. The primary reason is the gap between the concepts of smooth vector field theory and their computational realization. In practice, researchers must choose between either numerical techniques, with limited or no guarantees on how they preserve fundamental invariants, or discrete techniques which limit the precision at which the vector field can be represented. We propose a new representation of vector fields that combines the advantages of both approaches. In particular, we represent a subset of possible streamlines by storing their paths as they traverse the edges of a triangulation. Using only a finite set of streamlines creates a fully discrete version of a vector field that nevertheless approximates the smooth flow up to a user controlled error bound. The discrete nature of our representation enables us to directly compute and classify analogues of critical points, closed orbits, and other common topological structures. Further, by varying the number of divisions (quantizations) used per edge, we vary the resolution used to represent the field, allowing for controlled precision. This representation is compact in memory and supports standard vector field operations.
A Taxonomy of Visual Cluster Separation Factors
Authors: Michael Sedlmair, A. Tatu, Tamara Munzner, Melanie Tory
Published at EuroVis, 2012

We provide two contributions, a taxonomy of visual cluster separation factors in scatterplots, and an in‐depth qualitative evaluation of two recently proposed and validated separation measures. We initially intended to use these measures to provide guidance for the use of dimension reduction (DR) techniques and visual encoding (VE) choices, but found that they failed to produce reliable results. To understand why, we conducted a systematic qualitative data study covering a broad collection of 75 real and synthetic high‐dimensional datasets, four DR techniques, and three scatterplot‐based visual encodings. Two authors visually inspected over 800 plots to determine whether or not the measures created plausible results. We found that they failed in over half the cases overall, and in over two‐thirds of the cases involving real datasets. Using open and axial coding of failure reasons and separability characteristics, we generated a taxonomy of visual cluster separability factors. We iteratively refined its explanatory clarity and power by mapping the studied datasets and success and failure ranges of the measures onto the factor axes. Our taxonomy has four categories, ordered by their ability to influence successors: Scale, Point Distance, Shape, and Position. Each category is split into Within‐Cluster factors such as density, curvature, isotropy, and clumpiness, and Between‐Cluster factors that arise from the variance of these properties, culminating in the overarching factor of class separation. The resulting taxonomy can be used to guide the design and the evaluation of cluster separation measures.
Automatic Stream Surface Seeding: A Feature Centered Approach
Authors: Matt Edmunds, Robert S. Laramee, Rami Malki, Ian Masters, Nick Croft, Guoning Chen, Eugene Zhang
Published at EuroVis, 2012

The ability to capture and visualize information within the flow poses challenges for visualizing 3D flow fields. Stream surfaces are one of many useful integration based techniques for visualizing 3D flow. However seeding integral surfaces can be challenging. Previous research generally focuses on manual placement of stream surfaces. Little attention has been given to the problem of automatic stream surface seeding. This paper introduces a novel automatic stream surface seeding strategy based on vector field clustering. It is important that the user can define and target particular characteristics of the flow. Our framework provides this ability. The user is able to specify different vector clustering parameters enabling a range of abstraction for the density and placement of seeding curves and their associated stream surfaces. We demonstrate the effectiveness of this automatic stream surface approach on a range of flow simulations and incorporate illustrative visualization techniques. Domain expert evaluation of the results provides valuable insight into the users requirements and effectiveness of our approach.
Automating Transfer Function Design with Valley Cell-Based Clustering of 2D Density Plots
Authors: Yunhai Wang, Jian Zhang 0070, Dirk J. Lehmann, Holger Theisel, Xuebin Chi
Published at EuroVis, 2012

Two‐dimensional transfer functions are an effective and well‐accepted tool in volume classification. The design of them mostly depends on the user's experience and thus remains a challenge. Therefore, we present an approach in this paper to automate the transfer function design based on 2D density plots. By exploiting their smoothness, we adopted the Morse theory to automatically decompose the feature space into a set of valley cells. We design a simplification process based on cell separability to eliminate cells which are mainly caused by noise in the original volume data. Boundary persistence is first introduced to measure the separability between adjacent cells and to suitably merge them. Afterward, a reasonable classification result is achieved where each cell represents a potential feature in the volume data. This classification procedure is automatic and facilitates an arbitrary number and shape of features in the feature space. The opacity of each feature is determined by its persistence and size. To further incorporate the user's prior knowledge, a hierarchical feature representation is created by successive merging of the cells. With this representation, the user is allowed to merge or split features of interest and set opacity and color freely. Experiments on various volumetric data sets demonstrate the effectiveness and usefulness of our approach in transfer function generation.
Biopsy Planner - Visual Analysis for Needle Pathway Planning in Deep Seated Brain Tumor Biopsy
Authors: Paul-Corneliu Herghelegiu, Vasile-Ion Manta, Radu Perin, Stefan Bruckner, M. Eduard Gröller
Published at EuroVis, 2012

Biopsies involve taking samples from living tissue using a biopsy needle. In current clinical practice they are a first mandatory step before any further medical actions are planned. Performing a biopsy on a deep seated brain tumor requires considerable time for establishing and validating the desired biopsy needle pathway to avoid damage. In this paper, we present a system for the visualization, analysis, and validation of biopsy needle pathways. Our system uses a multi‐level approach for identifying stable needle placements which minimize the risk of hitting blood vessels. This is one of the major dangers in this type of intervention. Our approach helps in identifying and visualizing the point on the pathway that is closest to a surrounding blood vessel, requiring a closer inspection by the neurosurgeon. An evaluation by medical experts is performed to demonstrate the utility of our system.
Comparative Evaluation of an Interactive Time-Series Visualization that Combines Quantitative Data with Qualitative Abstractions
Authors: Wolfgang Aigner, Alexander Rind, Stephan Hoffmann
Published at EuroVis, 2012

In many application areas, analysts have to make sense of large volumes of multivariate time‐series data. Explorative analysis of this kind of data is often difficult and overwhelming at the level of raw data. Temporal data abstraction reduces data complexity by deriving qualitative statements that reflect domain‐specific key characteristics. Visual representations of abstractions and raw data together with appropriate interaction methods can support analysts in making their data easier to understand. Such a visualization technique that applies smooth semantic zooming has been developed in the context of patient data analysis. However, no empirical evidence on its effectiveness and efficiency is available. In this paper, we aim to fill this gap by reporting on a controlled experiment that compares this technique with another visualization method used in the well‐known KNAVE‐II framework. Both methods integrate quantitative data with qualitative abstractions whereas the first one uses a composite representation with color‐coding to display the qualitative data and spatial position coding for the quantitative data. The second technique uses juxtaposed representations for quantitative and qualitative data with spatial position coding for both. Results show that the test persons using the composite representation were generally faster, particularly for more complex tasks that involve quantitative values as well as qualitative abstractions.
Comparative Visual Analysis of 2D Function Ensembles
Authors: Harald Piringer, Stephan Pajer, Wolfgang Berger, H. Teichmann
Published at EuroVis, 2012

In the development process of powertrain systems, 2D function ensembles frequently occur in the context of multi‐run simulations. An analysis has many facets, including distributions of extracted features, comparisons between ensemble members and target functions, and details‐on‐demand. The primary contribution of this paper is a design study of an interactive approach for a comparative visual analysis of 2D function ensembles. The design focuses on a tight integration of domain‐oriented and member‐oriented visualization techniques, and it seeks to preserve the mental model of 2D functions on multiple levels of detail. In this context, we propose a novel focus+context approach for visualizations relying on data‐driven placement which is based on labeling. We also extend work on feature‐preserving downsampling of 2D functions. Our design supports a comparison of 2D functions based on juxtaposition, overlay, and explicit differences. It also enables an analysis in terms of extracted scalar features and 1D aggregations. An evaluation illustrates a workflow in our application context. User feedback indicates a time saving of 70% for common tasks and a qualitative gain for the entire development process.
Computing Voronoi Treemaps: Faster, Simpler, and Resolution-independent
Authors: Arlind Nocaj, Ulrik Brandes
Published at EuroVis, 2012

Voronoi treemaps represent hierarchies as nested polygons. We here show that, contrary to the apparent popular belief, utilization of an algorithm for weighted Voronoi diagrams is not only feasible, but also more efficient than previous low‐resolution approximations, even when the latter are implemented on graphics hardware. More precisely, we propose an instantiation of Lloyd's method for centroidal Voronoi diagrams with Aurenhammer's algorithm for power diagrams that yields an algorithm running in 𝒪(n log n) rather than Ω(n2) time per iteration, with n the number of sites. We describe its implementation and present evidence that it is faster also in practice.
Conceptualizing Visual Uncertainty in Parallel Coordinates
Authors: Aritra Dasgupta, Min Chen 0001, Robert Kosara
Published at EuroVis, 2012

Uncertainty is an intrinsic part of any visual representation in visualization, no matter how precise the input data. Existing research on uncertainty in visualization mainly focuses on depicting data‐space uncertainty in a visual form. Uncertainty is thus often seen as a problem to deal with, in the data, and something to be avoided if possible. In this paper, we highlight the need for analyzing visual uncertainty in order to design more effective visual representations. We study various forms of uncertainty in the visual representation of parallel coordinates and propose a taxonomy for categorizing them. By building a taxonomy, we aim to identify different sources of uncertainty in the screen space and relate them to different effects of uncertainty upon the user. We examine the literature on parallel coordinates and apply our taxonomy to categorize various techniques for reducing uncertainty. In addition, we consider uncertainty from a different perspective by identifying cases where increasing certain forms of uncertainty may even be useful, with respect to task, data type and analysis scenario. This work suggests that uncertainty is a feature that can be both useful and problematic in visualization, and it is beneficial to augment an information visualization pipeline with a facility for visual uncertainty analysis.
ConnectedCharts: Explicit Visualization of Relationships between Data Graphics
Authors: Christophe Viau, Michael J. McGuffin
Published at EuroVis, 2012

Multidimensional multivariate data can be visualized using many different well‐known charts, such as bar charts, stacked bar charts, grouped bar charts, scatterplots, or pivot tables, or also using more advanced high‐dimensional techniques such as scatterplot matrices (SPLOMs) or parallel coordinate plots (PCPs). These many techniques have different advantages, and users may wish to use several charts or data graphics to understand a dataset from different perspectives. We present ConnectedCharts, a technique for displaying relationships between multiple charts. ConnectedCharts allow for hybrid combinations of bar charts, scatterplots, and parallel coordinates, with curves drawn to show the conceptual links between charts. The charts can be thought of as coordinated views, where linking is achieved not only through interactive brushing, but also with explicitly drawn curves that connect corresponding data tuples or axes. We present a formal description of a design space of many simple charts, and also identify different kinds of connections that can be displayed between related charts. Our prototype implementation demonstrates how the connections between multiple charts can make relationships clearer and can serve to document the history of a user's analytical process, leading to potential applications in visual analytics and dashboard design.
COVRA: A compression-domain output-sensitive volume rendering architecture based on a sparse representation of voxel blocks
Authors: Enrico Gobbetti, José Antonio Iglesias Guitián, Fabio Marton
Published at EuroVis, 2012

We present a novel multiresolution compression‐domain GPU volume rendering architecture designed for interactive local and networked exploration of rectilinear scalar volumes on commodity platforms. In our approach, the volume is decomposed into a multiresolution hierarchy of bricks. Each brick is further subdivided into smaller blocks, which are compactly described by sparse linear combinations of prototype blocks stored in an overcomplete dictionary. The dictionary is learned, using limited computational and memory resources, by applying the K‐SVD algorithm to a re‐weighted non‐uniformly sampled subset of the input volume, harnessing the recently introduced method of coresets. The result is a scalable high quality coding scheme, which allows very large volumes to be compressed off‐line and then decompressed on‐demand during real‐time GPU‐accelerated rendering. Volumetric information can be maintained in compressed format through all the rendering pipeline. In order to efficiently support high quality filtering and shading, a specialized real‐time renderer closely coordinates decompression with rendering, combining at each frame images produced by raycasting selectively decompressed portions of the current view‐ and transfer‐function‐dependent working set. The quality and performance of our approach is demonstrated on massive static and time‐varying datasets.
Document Thumbnails with Variable Text Scaling
Authors: Andreas Stoffel, Hendrik Strobelt, Oliver Deussen, Daniel A. Keim
Published at EuroVis, 2012

Document reader applications usually offer an overview of the layout for each page as thumbnail views. Reading the text in these becomes impossible when the font size becomes very small. We improve the readability of these thumbnails using a distortion method, which retains a readable font size of interesting text while shrinking less interesting text further. In contrast to existing approaches, our method preserves the global layout of a page and is able to show context around important terms. We evaluate our technique and show application examples.
Drawing Large Graphs by Low-Rank Stress Majorization
Authors: Marc Khoury, Yifan Hu 0001, Shankar Krishnan, Carlos Eduardo Scheidegger
Published at EuroVis, 2012

Optimizing a stress model is a natural technique for drawing graphs: one seeks an embedding into Rd which best preserves the induced graph metric. Current approaches to solving the stress model for a graph with |𝒱| nodes and |ɛ| edges require the full all‐pairs shortest paths (APSP) matrix, which takes O(|𝒱|2 log |ɛ|+|𝒱‖ɛ|) time and O(|𝒱|2) space. We propose a novel algorithm based on a low‐rank approximation to the required matrices. The crux of our technique is an observation that it is possible to approximate the full APSP matrix, even when only a small subset of its entries are known. Our algorithm takes time O(k|𝒱|+|𝒱|log|𝒱|+|ɛ|) per iteration with a preprocessing time of O(k3+ k(|ɛ|+|𝒱| log |𝒱|) + k2|𝒱|) and memory usage of O(k|𝒱|), where a user‐defined parameter k trades off quality of approximation with running time and space. We give experimental results which show, to the best of our knowledge, the largest (albeit approximate) full stress model based layouts to date.
Employing 2D Projections for Fast Visual Exploration of Large Fiber Tracking Data
Authors: Jorge Poco, Danilo Medeiros Eler, Fernando Vieira Paulovich, Rosane Minghim
Published at EuroVis, 2012

Fiber tracts detection is an increasingly common technology for diagnosis and also understanding of brain function. Although tools for tracing and presenting brain fibers are advanced, it is still difficult for physicians or students to explore the dataset in 3D due to their intricate topology. In this work we present a visual exploration approach for fiber tracts data aimed at supporting exploration of such data. The work employs a local, precise and fast 2D multidimensional projection technique that allows a large number of fibers to be handled simultaneously and to select groups of bundled fibers for further exploration. In this approach, a DTI feature dataset, including curvature as well as spatial features, is projected on a 2D or 3D view. By handling groups formed in this view, exploration is linked to corresponding brain fibers in object space. The link exists in both directions and fibers selected in object space are also mapped to feature space. Our approach also allows users to modify the projection, controlling and improving, if necessary, the definition of groups of fibers for small and large datasets, due to the local nature of the projection. Compared to other related work, the method presented here is faster for creating visual representations, making it possible to explore complete sets of fibers tracts up to 250K fibers, which was not possible previously. Additionally, the ability to change configuration of the feature space representation adds a high degree of flexibility to the process.
Graph Bundling by Kernel Density Estimation
Authors: Christophe Hurter, Ozan Ersoy, Alexandru C. Telea
Published at EuroVis, 2012

We present a fast and simple method to compute bundled layouts of general graphs. For this, we first transform a given graph drawing into a density map using kernel density estimation. Next, we apply an image sharpening technique which progressively merges local height maxima by moving the convolved graph edges into the height gradient flow. Our technique can be easily and efficiently implemented using standard graphics acceleration techniques and produces graph bundlings of similar appearance and quality to state‐of‐the‐art methods at a fraction of the cost. Additionally, we show how to create bundled layouts constrained by obstacles and use shading to convey information on the bundling quality. We demonstrate our method on several large graphs.
I-SI: Scalable Architecture for Analyzing Latent Topical-Level Information From Social Media Data
Authors: Xiaoyu Wang 0001, Wenwen Dou, Zhiqiang Ma 0004, J. Villalobos, Yang Chen, Thomas Kraft, William Ribarsky
Published at EuroVis, 2012

We present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high‐performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I‐SI, closely integrates data‐driven analytical methods and user‐centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I‐SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I‐SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I‐SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics.