DevLan/vispubs huggingface dataset:
Quality Metrics for Information Visualization
Authors: Michael Behrisch 0001, Michael Blumenschein, Nam Wook Kim, Lin Shao 0001, Mennatallah El-Assady, Johannes Fuchs 0001, Daniel Seebacher, Alexandra Diehl, Ulrik Brandes, Hanspeter Pfister, Tobias Schreck, Daniel Weiskopf, Daniel A. Keim
Published at EuroVis, 2018

The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi‐ and high‐dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.
Rendering and Extracting Extremal Features in 3D Fields
Authors: Gordon L. Kindlmann, Charisee Chiw, T. Huynh, Attila Gyulassy, John H. Reppy, Peer-Timo Bremer
Published at EuroVis, 2018

Visualizing and extracting three‐dimensional features is important for many computational science applications, each with their own feature definitions and data types. While some are simple to state and implement (e.g. isosurfaces), others require more complicated mathematics (e.g. multiple derivatives, curvature, eigenvectors, etc.). Correctly implementing mathematical definitions is difficult, so experimenting with new features requires substantial investments. Furthermore, traditional interpolants rarely support the necessary derivatives, and approximations can reduce numerical stability. Our new approach directly translates mathematical notation into practical visualization and feature extraction, with minimal mental and implementation overhead. Using a mathematically expressive domain‐specific language, Diderot, we compute direct volume renderings and particle‐based feature samplings for a range of mathematical features. Non‐expert users can experiment with feature definitions without any exposure to meshes, interpolants, derivative computation, etc. We demonstrate high‐quality results on notoriously difficult features, such as ridges and vortex cores, using working code simple enough to be presented in its entirety.
Representative Consensus from Limited-Size Ensembles
Authors: Mahsa Mirzargar, Ross T. Whitaker
Published at EuroVis, 2018

Characterizing the uncertainty and extracting reliable visual information from ensemble data have been persistent challenges in various disciplines, specifically in simulation sciences. Many ensemble analysis and visualization techniques take a probabilistic approach to this problem with the assumption that the ensemble size is large enough to extract reliable statistical or probabilistic summaries. However, many real‐life ensembles are rather limited in size, with only a handful of members, due to various restrictions such as storage, computational power, or sampling limitations. As a result, probabilistic inference is subject to imprecision and can potentially result in untrustworthy information in the presence of a limited sample‐size ensemble. In this case, a more reliable approach is to fuse the information present in an ensemble with a limited number of members with minimal assumptions. In this paper, we propose a technique to construct a representative consensus that is particularly suited for ensembles of a relatively small size. The proposed technique casts the problem as an ordering problem in which at each point in the domain, the ensemble members are ranked based on the local neighborhood. This local approach allows us to provide shape and irregularity sensitivity. The local order statistics will then be fused to construct a global consensus using a Bayesian approach to ensure spatial coherency of the local information. We demonstrate the utility of the proposed technique using a synthetic and two real‐life examples.
SetCoLa: High-Level Constraints for Graph Layout
Authors: Jane Hoffswell, Alan Borning, Jeffrey Heer
Published at EuroVis, 2018

Constraints enable flexible graph layout by combining the ease of automatic layout with customizations for a particular domain. However, constraint‐based layout often requires many individual constraints defined over specific nodes and node pairs. In addition to the effort of writing and maintaining a large number of similar constraints, such constraints are specific to the particular graph and thus cannot generalize to other graphs in the same domain. To facilitate the specification of customized and generalizable constraint layouts, we contribute SetCoLa: a domain‐specific language for specifying high‐level constraints relative to properties of the backing data. Users identify node sets based on data or graph properties and apply high‐level constraints within each set. Applying constraints to node sets rather than individual nodes reduces specification effort and facilitates reapplication of customized layouts across distinct graphs. We demonstrate the conciseness, generalizability, and expressiveness of SetCoLa on a series of real‐world examples from ecological networks, biological systems, and social networks.
Spatio-Temporal Contours from Deep Volume Raycasting
Authors: Steffen Frey
Published at EuroVis, 2018

We visualize contours for spatio‐temporal processes to indicate where and when non‐continuous changes occur or spatial bounds are encountered. All time steps are comprised densely in one visualization, with contours allowing to efficiently analyze processes in the data even in case of spatial or temporal overlap. Contours are determined on the basis of deep raycasting that collects samples across time and depth along each ray. For each sample along a ray, its closest neighbors from adjacent rays are identified, considering time, depth, and value in the process. Large distances are represented as contours in image space, using color to indicate temporal occurrence. This contour representation can easily be combined with volume rendering‐based techniques, providing both full spatial detail for individual time steps and an outline of the whole time series in one view. Our view‐dependent technique supports efficient progressive computation, and requires no prior assumptions regarding the shape or nature of processes in the data. We discuss and demonstrate the performance and utility of our approach via a variety of data sets, comparison and combination with an alternative technique, and feedback by a domain scientist.
State of the Art of Sports Data Visualization
Authors: Charles Perin, Romain Vuillemot, Charles D. Stolper, John T. Stasko, Jo Wood, Sheelagh Carpendale
Published at EuroVis, 2018

In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially‐growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real‐world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in‐game actions and trajectories), and meta‐data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high‐level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.
The Perception of Graph Properties in Graph Layouts
Authors: Utkarsh Soni, Yafeng Lu, Brett Hansen, Helen C. Purchase, Stephen G. Kobourov, Ross Maciejewski
Published at EuroVis, 2018

When looking at drawings of graphs, questions about graph density, community structures, local clustering and other graph properties may be of critical importance for analysis. While graph layout algorithms have focused on minimizing edge crossing, symmetry, and other such layout properties, there is not much known about how these algorithms relate to a user's ability to perceive graph properties for a given graph layout. In this study, we apply previously established methodologies for perceptual analysis to identify which graph drawing layout will help the user best perceive a particular graph property. We conduct a large scale (n = 588) crowdsourced experiment to investigate whether the perception of two graph properties (graph density and average local clustering coefficient) can be modeled using Weber's law. We study three graph layout algorithms from three representative classes (Force Directed ‐ FD, Circular, and Multi‐Dimensional Scaling ‐ MDS), and the results of this experiment establish the precision of judgment for these graph layouts and properties. Our findings demonstrate that the perception of graph density can be modeled with Weber's law. Furthermore, the perception of the average clustering coefficient can be modeled as an inverse of Weber's law, and the MDS layout showed a significantly different precision of judgment than the FD layout.
ThreadReconstructor: Modeling Reply-Chains to Untangle Conversational Text through Visual Analytics
Authors: Mennatallah El-Assady, Rita Sevastjanova, Daniel A. Keim, Christopher Collins 0001
Published at EuroVis, 2018

We present ThreadReconstructor, a visual analytics approach for detecting and analyzing the implicit conversational structure of discussions, e.g., in political debates and forums. Our work is motivated by the need to reveal and understand single threads in massive online conversations and verbatim text transcripts. We combine supervised and unsupervised machine learning models to generate a basic structure that is enriched by user‐defined queries and rule‐based heuristics. Depending on the data and tasks, users can modify and create various reconstruction models that are presented and compared in the visualization interface. Our tool enables the exploration of the generated threaded structures and the analysis of the untangled reply‐chains, comparing different models and their agreement. To understand the inner‐workings of the models, we visualize their decision spaces, including all considered candidate relations. In addition to a quantitative evaluation, we report qualitative feedback from an expert user study with four forum moderators and one machine learning expert, showing the effectiveness of our approach.
Time Lattice: A Data Structure for the Interactive Visual Analysis of Large Time Series
Authors: Fabio Miranda 0001, Marcos Lage, Harish Doraiswamy, Charlie Mydlarz, Justin Salamon, Yitzchak Lockerman, Juliana Freire, Cláudio T. Silva
Published at EuroVis, 2018

Advances in technology coupled with the availability of low‐cost sensors have resulted in the continuous generation of large time series from several sources. In order to visually explore and compare these time series at different scales, analysts need to execute online analytical processing (OLAP) queries that include constraints and group‐by's at multiple temporal hierarchies. Effective visual analysis requires these queries to be interactive. However, while existing OLAP cube‐based structures can support interactive query rates, the exponential memory requirement to materialize the data cube is often unsuitable for large data sets. Moreover, none of the recent space‐efficient cube data structures allow for updates. Thus, the cube must be re‐computed whenever there is new data, making them impractical in a streaming scenario. We propose Time Lattice, a memory‐efficient data structure that makes use of the implicit temporal hierarchy to enable interactive OLAP queries over large time series. Time Lattice is a subset of a fully materialized cube and is designed to handle fast updates and streaming data. We perform an experimental evaluation which shows that the space efficiency of the data structure does not hamper its performance when compared to the state of the art. In collaboration with signal processing and acoustics research scientists, we use the Time Lattice data structure to design the Noise Profiler, a web‐based visualization framework that supports the analysis of noise from cities. We demonstrate the utility of Noise Profiler through a set of case studies.
Towards Easy Comparison of Local Businesses Using Online Reviews
Authors: Yong Wang 0021, Hammad Haleem, Conglei Shi, Yanhong Wu, Xun Zhao, Siwei Fu, Huamin Qu
Published at EuroVis, 2018

With the rapid development of e‐commerce, there is an increasing number of online review websites, such as Yelp, to help customers make better purchase decisions. Viewing online reviews, including the rating score and text comments by other customers, and conducting a comparison between different businesses are the key to making an optimal decision. However, due to the massive amount of online reviews, the potential difference of user rating standards, and the significant variance of review time, length, details and quality, it is difficult for customers to achieve a quick and comprehensive comparison. In this paper, we present E‐Comp, a carefully‐designed visual analytics system based on online reviews, to help customers compare local businesses at different levels of details. More specifically, intuitive glyphs overlaid on maps are designed for quick candidate selection. Grouped Sankey diagram visualizing the rating difference by common customers is chosen for more reliable comparison of two businesses. Augmented word cloud showing adjective‐noun word pairs, combined with a temporal view, is proposed to facilitate in‐depth comparison of businesses in terms of different time periods, rating scores and features. The effectiveness and usability of E‐Comp are demonstrated through a case study and in‐depth user interviews.
Towards User-Centered Active Learning Algorithms
Authors: Jürgen Bernard, Matthias Zeppelzauer, Markus Lehmann, Martin Müller, Michael Sedlmair
Published at EuroVis, 2018

The labeling of data sets is a time‐consuming task, which is, however, an important prerequisite for machine learning and visual analytics. Visual‐interactive labeling (VIAL) provides users an active role in the process of labeling, with the goal to combine the potentials of humans and machines to make labeling more efficient. Recent experiments showed that users apply different strategies when selecting instances for labeling with visual‐interactive interfaces. In this paper, we contribute a systematic quantitative analysis of such user strategies. We identify computational building blocks of user strategies, formalize them, and investigate their potentials for different machine learning tasks in systematic experiments. The core insights of our experiments are as follows. First, we identified that particular user strategies can be used to considerably mitigate the bootstrap (cold start) problem in early labeling phases. Second, we observed that they have the potential to outperform existing active learning strategies in later phases. Third, we analyzed the identified core building blocks, which can serve as the basis for novel selection strategies. Overall, we observed that data‐based user strategies (clusters, dense areas) work considerably well in early phases, while model‐based user strategies (e.g., class separation) perform better during later phases. The insights gained from this work can be applied to develop novel active learning approaches as well as to better guide users in visual interactive labeling.
Track Xplorer: A System for Visual Analysis of Sensor-based Motor Activity Predictions
Authors: Marco Cavallo, Çagatay Demiralp
Published at EuroVis, 2018

With the rapid commoditization of wearable sensors, detecting human movements from sensor datasets has become increasingly common over a wide range of applications. To detect activities, data scientists iteratively experiment with different classifiers before deciding which model to deploy. Effective reasoning about and comparison of alternative classifiers are crucial in successful model development. This is, however, inherently difficult in developing classifiers for sensor data, where the intricacy of long temporal sequences, high prediction frequency, and imprecise labeling make standard evaluation methods relatively ineffective and even misleading.We introduce Track Xplorer, an interactive visualization system to query, analyze, and compare the predictions of sensor‐data classifiers. Track Xplorer enables users to interactively explore and compare the results of different classifiers, and assess their accuracy with respect to the ground‐truth labels and video. Through integration with a version control system, Track Xplorer supports tracking of models and their parameters without additional workload on model developers. Track Xplorer also contributes an extensible algebra over track representations to filter, compose, and compare classification outputs, enabling users to reason effectively about classifier performance. We apply Track Xplorer in a collaborative project to develop classifiers to detect movements from multisensor data gathered from Parkinson's disease patients. We demonstrate how Track Xplorer helps identify early on possible systemic data errors, effectively track and compare the results of different classifiers, and reason about and pinpoint the causes of misclassifications.
VirtualDesk: A Comfortable and Efficient Immersive Information Visualization Approach
Authors: Jorge A. Wagner Filho, Carla Maria Dal Sasso Freitas, Luciana P. Nedel
Published at EuroVis, 2018

3D representations are potentially useful under many circumstances, but suffer from long known perception and interaction challenges. Current immersive technologies, which combine stereoscopic displays and natural interaction, are being progressively seen as an opportunity to tackle this issue, but new guidelines and studies are still needed, especially regarding information visualization. Many proposed approaches are impractical for actual usage, resulting in user discomfort or requiring too much time or space. In this work, we implement and evaluate an alternative data exploration metaphor where the user remains seated and viewpoint change is only realisable through physical movements. All manipulation is done directly by natural mid‐air gestures, with the data being rendered at arm's reach. The virtual reproduction of the analyst's desk aims to increase immersion and enable tangible interaction with controls and two dimensional associated information. A comparative user study was carried out against a desktop‐based equivalent, exploring a set of 9 perception and interaction tasks based on previous literature and a multidimensional projection use case. We demonstrate that our prototype setup, named VirtualDesk, presents excellent results regarding user comfort and immersion, and performs equally or better in all analytical tasks, while adding minimal or no time overhead and amplifying user subjective perceptions of efficiency and engagement. Results are also contrasted to a previous experiment employing artificial flying navigation, with significant observed improvements.
Visual Analysis of protein-ligand interactions
Authors: Pere-Pau Vázquez, Pedro Hermosilla, Victor Guallar, Jorge Estrada, Àlvar Vinacua
Published at EuroVis, 2018

The analysis of protein‐ligand interactions is complex because of the many factors at play. Most current methods for visual analysis provide this information in the form of simple 2D plots, which, besides being quite space hungry, often encode a low number of different properties. In this paper we present a system for compact 2D visualization of molecular simulations. It purposely omits most spatial information and presents physical information associated to single molecular components and their pairwise interactions through a set of 2D InfoVis tools with coordinated views, suitable interaction, and focus+context techniques to analyze large amounts of data. The system provides a wide range of motifs for elements such as protein secondary structures or hydrogen bond networks, and a set of tools for their interactive inspection, both for a single simulation and for comparing two different simulations. As a result, the analysis of protein‐ligand interactions of Molecular Simulation trajectories is greatly facilitated.
Visual and quantitative analysis of great arteries' blood flow jets in cardiac 4D PC-MRI data
Authors: Benjamin Köhler 0001, Matthias Grothoff, Matthias Gutberlet, Bernhard Preim
Published at EuroVis, 2018

Flow in the great arteries (aorta, pulmonary artery) is normally laminar with a parabolic velocity profile. Eccentric flow jets are linked to various diseases like aneurysms. Cardiac 4D PC‐MRI data provide spatio‐temporally resolved blood flow information for the whole cardiac cycle. In this work, we establish a time‐dependent visualization and quantification of flow jets. For this purpose, equidistant measuring planes are automatically placed along the vessel's centerline. The flow jet position and region with highest velocities are extracted for every plane in each time step. This is done during pre‐processing and without user‐defined parameters. We visualize the main flow jet as geometric tube. High‐velocity areas are depicted as a net around this tube. Both geometries are time‐dependent and can be animated. Quantitative values are provided during cross‐sectional measuring plane‐based evaluation. Moreover, we offer a plot visualization as summary of flow jet characteristics for the selected plane. Our physiologically plausible results are in accordance with medical findings. Our clinical collaborators appreciate the possibility to view the flow jet in the whole vessel at once, which normally requires repeated pathline filtering due to varying velocities along the vessel course. The overview plots are considered as valuable for documentation purposes.
Visualization of 4D Vector Field Topology
Authors: Lutz Hofmann, Bastian Rieck, Filip Sadlo
Published at EuroVis, 2018

In this paper, we present an approach to the topological analysis of four‐dimensional vector fields. In analogy to traditional 2D and 3D vector field topology, we provide a classification and visual representation of critical points, together with a technique for extracting their invariant manifolds. For effective exploration of the resulting four‐dimensional structures, we present a 4D camera that provides concise representation by exploiting projection degeneracies, and a 4D clipping approach that avoids self‐intersection in the 3D projection. We exemplify the properties and the utility of our approach using specific synthetic cases.