DevLan/vispubs huggingface dataset:
ExPlates: Spatializing Interactive Analysis to Scaffold Visual Exploration
Authors: Waqas Javed, Niklas Elmqvist
Published at EuroVis, 2013

Visual exploration involves using visual representations to investigate data where the goals of the process are unclear and poorly defined. However, this often places unduly high cognitive load on the user, particularly in terms of keeping track of multiple investigative branches, remembering earlier results, and correlating between different views. We propose a new methodology for automatically spatializing the individual steps in visual exploration onto a large visual canvas, allowing users to easily recall, reflect, and assess their progress. We also present a web‐based implementation of our methodology called ExPlatesJS where users can manipulate multidimensional data in their browsers, automatically building visual queries as they explore the data.
Fingerprint Matrices: Uncovering the dynamics of social networks in prose literature
Authors: Daniela Oelke, Dimitrios Kokkinakis, Daniel A. Keim
Published at EuroVis, 2013

In prose literature often complex dynamics of interpersonal relationships can be observed between the different characters. Traditionally, node‐link diagrams are used to depict the social network of a novel. However, static graphs can only visualize the overall social network structure but not the development of the networks over the course of the story, while dynamic graphs have the serious problem that there are many sudden changes between different portions of the overall social network. In this paper we explore means to show the relationships between the characters of a plot and at the same time their development over the course of a novel. Based on a careful exploration of the design space, we suggest a new visualization technique called Fingerprint Matrices. A case study exemplifies the usage of Fingerprint Matrices and shows that they are an effective means to analyze prose literature with respect to the development of relationships between the different characters.
Gestaltlines
Authors: Ulrik Brandes, Bobo Nick, Brigitte Rockstroh, Astrid Steffen
Published at EuroVis, 2013

We propose a general technique to visualize multivariate data sequences. It is based on a symbiotic combination of three powerful concepts from information visualization: sparklines, glyphs and gestalt theory. By visualizing several well‐known data sets in new ways we first demonstrate how explicit consideration of gestalt principles can be used to leverage visual perception capabilities for the identification of patterns such as trends, periodicities, change points, or outliers. A more detailed case study with complex and noisy data from a psychological experiment then demonstrates how basic design ideas for gestaltlines can be applied in less controlled, and thus more realistic, situations. The case study is complemented with reports on feedback from domain experts and a user study, both indicating that gestaltlines can be a convenient and valid means to explore and communicate patterns in micro‐visualizations.
HiFiVE: A Hilbert Space Embedding of Fiber Variability Estimates for Uncertainty Modeling and Visualization
Authors: Thomas Schultz 0001, Lara Schlaffke, Bernhard Schölkopf, Tobias Schmidt-Wilcke
Published at EuroVis, 2013

Obtaining reproducible fiber direction estimates from diffusion MRI is crucial for successful fiber tracking. Modeling and visualizing the probability distribution of the inferred fiber directions is an important step in evaluating and comparing different acquisition schemes and fiber models. However, this distribution is usually strongly dominated by its main direction, which makes it difficult to examine when plotted naively.In this work, we propose a new visualization of the fiber probability distribution. It is based on embedding the probability measure into a particular reproducing kernel Hilbert space. This permits a decomposition into an embedded delta peak, representing the main direction, and a non‐negative residual. They are then combined into a new glyph representation which visually enhances the residual, in order to highlight even subtle differences. Moreover, the magnitude of the delta peak component quantifies precision of the main fiber direction. We demonstrate that our new glyph provides a more detailed impression of the uncertainty than the current standard method, cones that contain 95% of the estimated directions. We use our new method to contribute to the validation of different ways of resampling the data (bootstrapping), and to visualize the differences between alternative acquisition schemes and models for high angular resolution diffusion imaging (HARDI).
imMens: Real-time Visual Querying of Big Data
Authors: Zhicheng Liu, Biye Jiang, Jeffrey Heer
Published at EuroVis, 2013

Data analysts must make sense of increasingly large data sets, sometimes with billions or more records. We present methods for interactive visualization of big data, following the principle that perceptual and interactive scalability should be limited by the chosen resolution of the visualized data, not the number of records. We first describe a design space of scalable visual summaries that use data reduction methods (such as binned aggregation or sampling) to visualize a variety of data types. We then contribute methods for interactive querying (e.g., brushing &amp; linking) among binned plots through a combination of multivariate data tiles and parallel query processing. We implement our techniques in imMens, a browser‐based visual analysis system that uses WebGL for data processing and rendering on the GPU. In benchmarks imMens sustains 50 frames‐per‐second brushing &amp; linking among dozens of visualizations, with invariant performance on data sizes ranging from thousands to billions of records.
Interactive Extraction and Tracking of Biomolecular Surface Features
Authors: Michael Krone, Guido Reina, Christoph Schulz 0001, Tobias Kulschewski, Jürgen Pleiss, Thomas Ertl
Published at EuroVis, 2013

We present a coordinated‐view application for the analysis of molecular surface features like cavities, channels and pockets. Our tool employs object‐space ambient occlusion for the detection of such features and tracks them over time. It offers time‐dependent graphs of metrics concerning those features and allows analyzing the temporal relationship of the features, i.e. when they (dis)appear, split or merge and which features participate in each of these events. The automated analysis process is performed in real time while the user interactively explores a dynamic data set. The system supports linking and brushing to allow for a user‐guided visual analysis based on different aspects of the data. We demonstrate the effectiveness of our approach by applying it to data sets from biochemistry and report the insights that can be gained. We also evaluate the benefits of our method with respect to recent advancements in the field. The algorithmic pipeline leverages the computing power of modern GPUs, thus achieving interactive frame rates without any precomputation for fully dynamic data sets.
Interactive Ray Casting of Geodesic Grids
Authors: Jinrong Xie, Hongfeng Yu 0001, Kwan-Liu Ma
Published at EuroVis, 2013

Geodesic grids are commonly used to model the surface of a sphere and are widely applied in numerical simulations of geoscience applications. These applications range from biodiversity, to climate change and to ocean circulation. Direct volume rendering of scalar fields defined on a geodesic grid facilitates scientists in visually understanding their large scale data. Previous solutions requiring to first transform the geodesic grid into another grid structure (e.g., hexahedral or tetrahedral grid) for using graphics hardware are not acceptable for large data, because such approaches incur significant computing and storage overhead. In this paper, we present a new method for efficient ray casting of geodesic girds by leveraging the power of Graphics Processing Units (GPUs). A geodesic grid can be directly fetched from storage or streamed from simulations to the rendering stage without the need of any intermediate grid transformation. We have designed and implemented a new analytic scheme to efficiently perform value interpolation for ray integration and gradient calculations for lighting. This scheme offers a more cost‐effective rendering solution over the existing direct rendering approach. We demonstrate the effectiveness of our rendering solution using real‐world geoscience data.
Mass-Dependent Integral Curves in Unsteady Vector Fields
Authors: Tobias Günther, Alexander Kuhn, Benjamin Kutz, Holger Theisel
Published at EuroVis, 2013

Recent research in flow visualization is focusing on the analysis of time‐dependent, but mass‐less particles. However, in many application scenarios, the mass of particles – and their resulting inertia – is essential in understanding fluid mechanics. This includes critical processes, such as dust particles interacting with aircraft (e.g., brown‐or white‐out effects) and particle separation based on density variation. In this paper, we contribute a generalized description of mass‐dependent particle trajectories and apply existing unsteady flow visualization methods to the mass‐dependent case. This comprises the extension of common concepts, i.e., path lines, streak lines, and time lines. Furthermore, we introduce a new class of integral curves, called mass lines that effectively visualizes mass separation and captures mass‐related features in unsteady flow fields that are inaccessible using traditional methods. We demonstrate the applicability of our method, using a number of real‐world and artificial data sets, in which mass is a crucial parameter. In particular, we focus on the analysis of brown‐out conditions, introduced by a helicopter in forward flight close to the ground.
Maximum Entropy Summary Trees
Authors: Howard J. Karloff, Kenneth E. Shirley
Published at EuroVis, 2013

Given a very large, node‐weighted, rooted tree on, say, n nodes, if one has only enough space to display a k‐node summary of the tree, what is the most informative way to draw the tree? We define a type of weighted tree that we call a summary tree of the original tree that results from aggregating nodes of the original tree subject to certain constraints. We suggest that the best choice of which summary tree to use (among those with a fixed number of nodes) is the one that maximizes the information‐theoretic entropy of a natural probability distribution associated with the summary tree, and we provide a (pseudopolynomial‐time) dynamic‐programming algorithm to compute this maximum entropy summary tree, when the weights are integral. The result is an automated way to summarize large trees and retain as much information about them as possible, while using (and displaying) only a fraction of the original node set. We illustrate the computation and use of maximum entropy summary trees on five real data sets whose weighted tree representations vary widely in structure. We also provide an additive approximation algorithm and a greedy heuristic that are faster than the optimal algorithm, and generalize to trees with real‐valued weights.
Nonparametric Models for Uncertainty Visualization
Authors: Kai Pöthkow, Hans-Christian Hege
Published at EuroVis, 2013

An uncertain (scalar, vector, tensor) field is usually perceived as a discrete random field with a priori unknown probability distributions. To compute derived probabilities, e.g. for the occurrence of certain features, an appropriate probabilistic model has to be selected. The majority of previous approaches in uncertainty visualization were restricted to Gaussian fields. In this paper we extend these approaches to nonparametric models, which are much more flexible, as they can represent various types of distributions, including multimodal and skewed ones. We present three examples of nonparametric representations: (a) empirical distributions, (b) histograms and (c) kernel density estimates (KDE). While the first is a direct representation of the ensemble data, the latter two use reconstructed probability density functions of continuous random variables. For KDE we propose an approach to compute valid consistent marginal distributions and to efficiently capture correlations using a principal component transformation. Furthermore, we use automatic bandwidth selection, obtaining a model for probabilistic local feature extraction. The methods are demonstrated by computing probabilities of level crossings, critical points and vortex cores in simulated biofluid dynamics and climate data.
Progressive High-Quality Response Surfaces for Visually Guided Sensitivity Analysis
Authors: Ismail Demir, Rüdiger Westermann
Published at EuroVis, 2013

In this paper we present a technique which allows us to perform high quality and progressive response surface prediction from multidimensional input samples in an efficient manner. We utilize kriging interpolation to estimate a response surface which minimizes the expectation value and variance of the prediction error. High computational efficiency is achieved by employing parallel matrix and vector operations on the GPU. Our approach differs from previous kriging approaches in that it uses a novel progressive updating scheme for new samples based on blockwise matrix inversion. In this way we can handle very large sample sets to which new samples are continually added. Furthermore, we can monitor the incremental evolution of the surface, providing a means to early terminate the computation when no significant changes have occurred. When the generation of input samples is fast enough, our technique enables steering this generation process interactively to find relevant dependency relations.
Rule-based Visual Mappings - with a Case Study on Poetry Visualization
Authors: Alfie Abdul-Rahman, Julie Lein, Katherine Coles, Eamonn Maguire, Miriah D. Meyer, Martin Wynne, Chris R. Johnson 0001, Anne E. Trefethen, Min Chen 0001
Published at EuroVis, 2013

In this paper, we present a user‐centered design study on poetry visualization. We develop a rule‐based solution to address the conflicting needs for maintaining the flexibility of visualizing a large set of poetic variables and for reducing the tedium and cognitive load in interacting with the visual mapping control panel. We adopt Munzner's nested design model to maintain high‐level interactions with the end users in a closed loop. In addition, we examine three design options for alleviating the difficulty in visualizing poems latitudinally. We present several example uses of poetry visualization in scholarly research on poetry.
Scale-Stack Bar Charts
Authors: Marcel Hlawatsch, Filip Sadlo, Michael Burch, Daniel Weiskopf
Published at EuroVis, 2013

It is difficult to create appropriate bar charts for data that cover large value ranges. The usual approach for these cases employs a logarithmic scale, which, however, suffers from issues inherent to its non‐linear mapping: for example, a quantitative comparison of different values is difficult. We present a new approach for bar charts that combines the advantages of linear and logarithmic scales, while avoiding their drawbacks. Our scale‐stack bar charts use multiple scales to cover a large value range, while the linear mapping within each scale preserves the ability to visually compare quantitative ratios. Scale‐stack bar charts can be used for the same applications as classic bar charts; in particular, they can readily handle stacked bar representations and negative values. Our visualization technique is demonstrated with results for three different application areas and is assessed by an expert review and a quantitative user study confirming advantages of our technique for quantitative comparisons.
Selecting Semantically-Resonant Colors for Data Visualization
Authors: Sharon Lin, Julie Fortuna, Chinmay Kulkarni 0001, Maureen C. Stone, Jeffrey Heer
Published at EuroVis, 2013

We introduce an algorithm for automatic selection of semantically‐resonant colors to represent data (e.g., using blue for data about “oceans”, or pink for “love”). Given a set of categorical values and a target color palette, our algorithm matches each data value with a unique color. Values are mapped to colors by collecting representative images, analyzing image color distributions to determine value‐color affinity scores, and choosing an optimal assignment. Our affinity score balances the probability of a color with how well it discriminates among data values. A controlled study shows that expert‐chosen semantically‐resonant colors improve speed on chart reading tasks compared to a standard palette, and that our algorithm selects colors that lead to similar gains. A second study verifies that our algorithm effectively selects colors across a variety of data categories.
Small Multiples, Large Singles: A New Approach for Visual Data Exploration
Authors: Stef van den Elzen, Jarke J. van Wijk
Published at EuroVis, 2013

We present a novel visual exploration method based on small multiples and large singles for effective and efficient data analysis. Users are enabled to explore the state space by offering multiple alternatives from the current state. Users can then select the alternative of choice and continue the analysis. Furthermore, the intermediate steps in the exploration process are preserved and can be revisited and adapted using an intuitive navigation mechanism based on the well‐known undo‐redo stack and filmstrip metaphor. As proof of concept the exploration method is implemented in a prototype. The effectiveness of the exploration method is tested using a formal user study comparing four different interaction methods. By using Small Multiples as data exploration method users need fewer steps in answering questions and also explore a significantly larger part of the state space in the same amount of time, providing them with a broader perspective on the data, hence lowering the chance of missing important features. Also, users prefer visual exploration with small multiples over non‐small multiple variants.
Spatially Efficient Design of Annotated Metro Maps
Authors: Hsiang-Yun Wu, Shigeo Takahashi, Daichi Hirono, Masatoshi Arikawa, Chun-Cheng Lin, Hsu-Chun Yen
Published at EuroVis, 2013

Annotating metro maps with thumbnail photographs is a commonly used technique for guiding travelers. However, conventional methods usually suffer from small labeling space around the metro stations especially when they are interchange stations served by two or more metro lines. This paper presents an approach for aesthetically designing schematic metro maps while ensuring effective placement of large annotation labels that are sufficiently close to their corresponding stations. Our idea is to distribute such labels in a well‐balanced manner to labeling regions around the metro network first and then adjust the lengths of metro line and leader line segments, which allows us to fully maximize the space coverage of the entire annotated map. This is accomplished by incorporating additional constraints into the conventional mixed‐integer programming formulation, while we devised a three‐step algorithm for accelerating the overall optimization process. We include several design examples to demonstrate the spatial efficiency of the map layout generated using the proposed approach through minimal user intervention.
Streamlines for Illustrative Real-Time Rendering
Authors: Kai Lawonn, Tobias Mönch, Bernhard Preim
Published at EuroVis, 2013

Line drawing techniques are important methods to illustrate shapes. Existing feature line methods, e.g., suggestive contours, apparent ridges, or photic extremum lines, solely determine salient regions and illustrate them with separate lines. Hatching methods convey the shape by drawing a wealth of lines on the whole surface. Both approaches are often not sufficient for a faithful visualization of organic surface models, e.g., in biology or medicine. In this paper, we present a novel object‐space line drawing algorithm that conveys the shape of such surface models in real‐time. Our approach employs contour‐ and feature‐based illustrative streamlines to convey surface shape (ConFIS). For every triangle, precise streamlines are calculated on the surface with a given curvature vector field. Salient regions are detected by determining maxima and minima of a scalar field. Compared with existing feature lines and hatching methods, ConFIS uses the advantages of both categories in an effective and flexible manner. We demonstrate this with different anatomical and artificial surface models. In addition, we conducted a qualitative evaluation of our technique to compare our results with exemplary feature line and hatching methods.
Synthetic Brainbows
Authors: Yong Wan, Hideo Otsuna, Charles D. Hansen
Published at EuroVis, 2013

Brainbow is a genetic engineering technique that randomly colorizes cells. Biological samples processed with this technique and imaged with confocal microscopy have distinctive colors for individual cells. Complex cellular structures can then be easily visualized. However, the complexity of the Brainbow technique limits its applications. In practice, most confocal microscopy scans use different florescence staining with typically at most three distinct cellular structures. These structures are often packed and obscure each other in rendered images making analysis difficult. In this paper, we leverage a process known as GPU framebuffer feedback loops to synthesize Brainbow‐like images. In addition, we incorporate ID shuffling and Monte‐Carlo sampling into our technique, so that it can be applied to single‐channel confocal microscopy data. The synthesized Brainbow images are presented to domain experts with positive feedback. A user survey demonstrates that our synthetic Brainbow technique improves visualizations of volume data with complex structures for biologists.
TAMRESH - Tensor Approximation Multiresolution Hierarchy for Interactive Volume Visualization
Authors: Susanne K. Suter, Maxim Makhynia, Renato Pajarola
Published at EuroVis, 2013

Interactive visual analysis of large and complex volume datasets is an ongoing and challenging problem. We tackle this challenge in the context of state‐of‐the‐art out‐of‐core multiresolution volume rendering by introducing a novel hierarchical tensor approximation (TA) volume visualization approach. The TA framework allows us (a) to use a rank‐truncated basis for compact volume representation, (b) to visualize features at multiple scales, and (c) to visualize the data at multiple resolutions. In this paper, we exploit the special properties of the TA factor matrix bases and define a novel multiscale and multiresolution volume rendering hierarchy. Different from previous approaches, to represent one volume dataset we use but one set of global bases (TA factor matrices) to reconstruct at all resolution levels and feature scales. In particular, we propose a coupling of multiscalable feature visualization and multiresolution DVR through the properties of global TA bases. We demonstrate our novel TA multiresolution hierarchy based volume representation and visualization on a number of μCT volume datasets.