DevLan/vispubs huggingface dataset:
Minimum-Displacement Overlap Removal for Geo-referenced Data Visualization
Authors: Mereke van Garderen, Barbara Pampel, Arlind Nocaj, Ulrik Brandes
Published at EuroVis, 2017

Given a set of rectangles embedded in the plane, we consider the problem of adjusting the layout to remove all overlap while preserving the orthogonal order of the rectangles. The objective is to minimize the displacement of the rectangles. We call this problem Minimum-Displacement Overlap Removal (mdor). Our interest in this problem is motivated by the application of displaying metadata of archaeological sites. Because most existing overlap removal algorithms are not designed to minimize displacement while preserving orthogonal order, we present and compare several approaches which are tailored to our particular usecase. We introduce a new overlap removal heuristic which we call reArrange. Although conceptually simple, it is very effective in removing the overlap while keeping the displacement small. Furthermore, we propose an additional procedure to repair the orthogonal order after every iteration, with which we extend both our new heuristic and PRISM, a widely used overlap removal algorithm. We compare the performance of both approaches with and without this order repair method. The experimental results indicate that reArrange is very effective for heterogeneous input data where the overlap is concentrated in few dense regions.
NEREx: Named-Entity Relationship Exploration in Multi-Party Conversations
Authors: Mennatallah El-Assady, Rita Sevastjanova, Bela Gipp, Daniel A. Keim, Christopher Collins 0001
Published at EuroVis, 2017

We present NEREx, an interactive visual analytics approach for the exploratory analysis of verbatim conversational transcripts. By revealing different perspectives on multi‐party conversations, NEREx gives an entry point for the analysis through high‐level overviews and provides mechanisms to form and verify hypotheses through linked detail‐views. Using a tailored named‐entity extraction, we abstract important entities into ten categories and extract their relations with a distance‐restricted entity‐relationship model. This model complies with the often ungrammatical structure of verbatim transcripts, relating two entities if they are present in the same sentence within a small distance window. Our tool enables the exploratory analysis of multi‐party conversations using several linked views that reveal thematic and temporal structures in the text. In addition to distant‐reading, we integrated close‐reading views for a text‐level investigation process. Beyond the exploratory and temporal analysis of conversations, NEREx helps users generate and validate hypotheses and perform comparative analyses of multiple conversations. We demonstrate the applicability of our approach on real‐world data from the 2016 U.S. Presidential Debates through a qualitative study with three domain experts from political science.
Nested Tracking Graphs
Authors: Jonas Lukasczyk, Gunther H. Weber, Ross Maciejewski, Christoph Garth, Heike Leitte
Published at EuroVis, 2017

Tracking graphs are a well established tool in topological analysis to visualize the evolution of components and their properties over time, i.e., when components appear, disappear, merge, and split. However, tracking graphs are limited to a single level threshold and the graphs may vary substantially even under small changes to the threshold. To examine the evolution of features for varying levels, users have to compare multiple tracking graphs without a direct visual link between them. We propose a novel, interactive, nested graph visualization based on the fact that the tracked superlevel set components for different levels are related to each other through their nesting hierarchy. This approach allows us to set multiple tracking graphs in context to each other and enables users to effectively follow the evolution of components for different levels simultaneously. We demonstrate the effectiveness of our approach on datasets from finite pointset methods, computational fluid dynamics, and cosmology simulations.
Overview + Detail Visualization for Ensembles of Diffusion Tensors
Authors: Changgong Zhang, Matthan W. A. Caan, Thomas Höllt, Elmar Eisemann, Anna Vilanova
Published at EuroVis, 2017

A Diffusion Tensor Imaging (DTI) group study consists of a collection of volumetric diffusion tensor datasets (i.e., an ensemble) acquired from a group of subjects. The multivariate nature of the diffusion tensor imposes challenges on the analysis and the visualization. These challenges are commonly tackled by reducing the diffusion tensors to scalar‐valued quantities that can be analyzed with common statistical tools. However, reducing tensors to scalars poses the risk of losing intrinsic information about the tensor. Visualization of tensor ensemble data without loss of information is still a largely unsolved problem. In this work, we propose an overview + detail visualization to facilitate the tensor ensemble exploration. We define an ensemble representative tensor and variations in terms of the three intrinsic tensor properties (i.e., scale, shape, and orientation) separately. The ensemble summary information is visually encoded into the newly designed aggregate tensor glyph which, in a spatial layout, functions as the overview. The aggregate tensor glyph guides the analyst to interesting areas that would need further detailed inspection. The detail views reveal the original information that is lost during aggregation. It helps the analyst to further understand the sources of variation and formulate hypotheses. To illustrate the applicability of our prototype, we compare with most relevant previous work through a user study and we present a case study on the analysis of a brain diffusion tensor dataset ensemble from healthy volunteers.
Reverse-Engineering Visualizations: Recovering Visual Encodings from Chart Images
Authors: Jorge Poco, Jeffrey Heer
Published at EuroVis, 2017

We investigate how to automatically recover visual encodings from a chart image, primarily using inferred text elements. We contribute an end‐to‐end pipeline which takes a bitmap image as input and returns a visual encoding specification as output. We present a text analysis pipeline which detects text elements in a chart, classifies their role (e.g., chart title, x‐axis label, y‐axis title, etc.), and recovers the text content using optical character recognition. We also train a Convolutional Neural Network for mark type classification. Using the identified text elements and graphical mark type, we can then infer the encoding specification of an input chart image. We evaluate our techniques on three chart corpora: a set of automatically labeled charts generated using Vega, charts from the Quartz news website, and charts extracted from academic papers. We demonstrate accurate automatic inference of text elements, mark types, and chart specifications across a variety of input chart types.
Sclow Plots: Visualizing Empty Space
Authors: Joachim Giesen, Lars Kuehne, P. Lucas
Published at EuroVis, 2017

Scatter plots are mostly used for correlation analysis, but are also a useful tool for understanding the distribution of high‐dimensional 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.
Sliceplorer: 1D slices for multi-dimensional continuous functions
Authors: Thomas Torsney-Weir, Michael Sedlmair, Torsten Möller
Published at EuroVis, 2017

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.
Social Media Visual Analytics
Authors: Siming Chen 0001, Lijing Lin, Xiaoru Yuan
Published at EuroVis, 2017

With the development of social media (e.g. Twitter, Flickr, Foursquare, Sina Weibo, etc.), a large number of people are now using them and post microblogs, messages and multi‐media information. The everyday usage of social media results in big open social media data. The data offer fruitful information and reflect social behaviors of people. There is much visualization and visual analytics research on such data. We collect state‐of‐the‐art research and put it into three main categories: social network, spatial temporal information and text analysis. We further summarize the visual analytics pipeline for the social media, combining the above categories and supporting complex tasks. With these techniques, social media analytics can apply to multiple disciplines. We summarize the applications and public tools to further investigate the challenges and trends.
STAR: Visual Computing in Materials Science
Authors: Christoph Heinzl, S. Stappen
Published at EuroVis, 2017

Visual computing has become highly attractive for boosting research endeavors in the materials science domain. Using visual computing, a multitude of different phenomena may now be studied, at various scales, dimensions, or using different modalities. This was simply impossible before. Visual computing techniques provide novel insights in order to understand complex material systems of interest, which is demonstrated by strongly rising number of new approaches, publishing new techniques for materials analysis and simulation.Outlining the proximity of materials science and visual computing, this state of the art report focuses on the intersection of both domains in order to guide research endeavors in this field. We provide a systematic survey on the close interrelations of both fields as well as how they profit from each other. Analyzing the existing body of literature, we review the domain of visual computing supported materials science, starting with the definition of materials science as well as material systems for which visual computing is frequently used. Major tasks for visual computing, visual analysis and visualization in materials sciences are identified, as well as simulation and testing techniques, which are providing the data for the respective analyses. We reviewed the input data characteristics and the direct and derived outputs, the visualization techniques and visual metaphors used, as well as the interactions and analysis workflows employed. All our findings are finally integrated in a cumulative matrix, giving insights about the different interrelations of both domains. We conclude our report with the identification of open high level and low level challenges for future research.
Stardust: Accessible and Transparent GPU Support for Information Visualization Rendering
Authors: Donghao Ren, Bongshin Lee, Tobias Höllerer
Published at EuroVis, 2017

Web‐based visualization libraries are in wide use, but performance bottlenecks occur when rendering, and especially animating, a large number of graphical marks. While GPU‐based rendering can drastically improve performance, that paradigm has a steep learning curve, usually requiring expertise in the computer graphics pipeline and shader programming. In addition, the recent growth of virtual and augmented reality poses a challenge for supporting multiple display environments beyond regular canvases, such as a Head Mounted Display (HMD) and Cave Automatic Virtual Environment (CAVE). In this paper, we introduce a new web‐based visualization library called Stardust, which provides a familiar API while leveraging GPU's processing power. Stardust also enables developers to create both 2D and 3D visualizations for diverse display environments using a uniform API. To demonstrate Stardust's expressiveness and portability, we present five example visualizations and a coding playground for four display environments. We also evaluate its performance by comparing it against the standard HTML5 Canvas, D3, and Vega.
State of the Art in Edge and Trail Bundling Techniques
Authors: Antoine Lhuillier, Christophe Hurter, Alexandru C. Telea
Published at EuroVis, 2017

Bundling techniques provide a visual simplification of a graph drawing or trail set, by spatially grouping similar graph edges or trails. This way, the structure of the visualization becomes simpler and thereby easier to comprehend in terms of assessing relations that are encoded by such paths, such as finding groups of strongly interrelated nodes in a graph, finding connections between spatial regions on a map linked by a number of vehicle trails, or discerning the motion structure of a set of objects by analyzing their paths. In this state of the art report, we aim to improve the understanding of graph and trail bundling via the following main contributions. First, we propose a data‐based taxonomy that organizes bundling methods on the type of data they work on (graphs vs trails, which we refer to as paths). Based on a formal definition of path bundling, we propose a generic framework that describes the typical steps of all bundling algorithms in terms of high‐level operations and show how existing method classes implement these steps. Next, we propose a description of tasks that bundling aims to address. Finally, we provide a wide set of example applications of bundling techniques and relate these to the above‐mentioned taxonomies. Through these contributions, we aim to help both researchers and users to understand the bundling landscape as well as its technicalities.
Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics
Authors: Sriram Karthik Badam, Niklas Elmqvist, Jean-Daniel Fekete
Published at EuroVis, 2017

Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called InsightsFeed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.
Survey of Surveys (SoS) - Mapping The Landscape of Survey Papers in Information Visualization
Authors: Liam McNabb, Robert S. Laramee
Published at EuroVis, 2017

Information visualization as a field is growing rapidly in popularity since the first information visualization conference in 1995. However, as a consequence of its growth, it is increasingly difficult to follow the growing body of literature within the field. Survey papers and literature reviews are valuable tools for managing the great volume of previously published research papers, and the quantity of survey papers in visualization has reached a critical mass. To this end, this survey paper takes a quantum step forward by surveying and classifying literature survey papers in order to help researchers understand the current landscape of Information Visualization. It is, to our knowledge, the first survey of survey papers (SoS) in Information Visualization. This paper classifies survey papers into natural topic clusters which enables readers to find relevant literature and develops the first classification of classifications. The paper also enables researchers to identify both mature and less developed research directions as well as identify future directions. It is a valuable resource for both newcomers and experienced researchers in and outside the field of Information Visualization and Visual Analytics.
The State-of-the-Art in Predictive Visual Analytics
Authors: Yafeng Lu, Rolando Garcia, Brett Hansen, Michael Gleicher, Ross Maciejewski
Published at EuroVis, 2017

Predictive analytics embraces an extensive range of techniques including statistical modeling, machine learning, and data mining and is applied in business intelligence, public health, disaster management and response, and many other fields. To date, visualization has been broadly used to support tasks in the predictive analytics pipeline. Primary uses have been in data cleaning, exploratory analysis, and diagnostics. For example, scatterplots and bar charts are used to illustrate class distributions and responses. More recently, extensive visual analytics systems for feature selection, incremental learning, and various prediction tasks have been proposed to support the growing use of complex models, agent‐specific optimization, and comprehensive model comparison and result exploration. Such work is being driven by advances in interactive machine learning and the desire of end‐users to understand and engage with the modeling process. In this state‐of‐the‐art report, we catalogue recent advances in the visualization community for supporting predictive analytics. First, we define the scope of predictive analytics discussed in this article and describe how visual analytics can support predictive analytics tasks in a predictive visual analytics (PVA) pipeline. We then survey the literature and categorize the research with respect to the proposed PVA pipeline. Systems and techniques are evaluated in terms of their supported interactions, and interactions specific to predictive analytics are discussed. We end this report with a discussion of challenges and opportunities for future research in predictive visual analytics.
Uncertainty Footprint: Visualization of Nonuniform Behavior of Iterative Algorithms Applied to 4D Cell Tracking
Authors: Y. Wan, C. Hansen
Published at EuroVis, 2017

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.
Understanding Indirect Causal Relationships in Node-Link Graphs
Authors: Juhee Bae, Tove Helldin, Maria Riveiro 0001
Published at EuroVis, 2017

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.
Visual Analysis of Confocal Raman Spectroscopy Data using Cascaded Transfer Function Design
Authors: Christoph M. Schikora, Markus Plack, Rainer Bornemann, Peter Haring Bolívar, Andreas Kolb 0001
Published at EuroVis, 2017

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.