DevLan/vispubs huggingface dataset:
The Readability of Path-Preserving Clusterings of Graphs
Authors: Daniel Archambault, Helen C. Purchase, Bruno Pinaud
Published at EuroVis, 2010

Graph visualization systems often exploit opaque metanodes to reduce visual clutter and improve the readability of large graphs. This filtering can be done in a path‐preserving way based on attribute values associated with the nodes of the graph. Despite extensive use of these representations, as far as we know, no formal experimentation exists to evaluate if they improve the readability of graphs.In this paper, we present the results of a user study that formally evaluates how such representations affect the readability of graphs. We also explore the effect of graph size and connectivity in terms of this primary research question. Overall, for our tasks, we did not find a significant difference when this clustering is used. However, if the graph is highly connected, these clusterings can improve performance. Also, if the graph is large enough and can be simplified into a few metanodes, benefits in performance on global tasks are realized. Under these same conditions, however, performance of local attribute tasks may be reduced.
Topographic Map Visualization from Adaptively Compressed Textures
Authors: Carlos Andújar
Published at EuroVis, 2010

Raster‐based topographic maps are commonly used in geoinformation systems to overlay geographic entities on top of digital terrain models. Using compressed texture formats for encoding topographic maps allows reducing latency times while visualizing large geographic datasets. Topographic maps encompass high‐frequency content with large uniform regions, making current compressed texture formats inappropriate for encoding them. In this paper we present a method for locally‐adaptive compression of topographic maps. Key elements include a Hilbert scan to maximize spatial coherence, efficient encoding of homogeneous image regions through arbitrarily‐sized texel runs, a cumulative run‐length encoding supporting fast random‐access, and a compression algorithm supporting lossless and lossy compression. Our scheme can be easily implemented on current programmable graphics hardware allowing real‐time GPU decompression and rendering of bilinear‐filtered topographic maps.
Topological Landscape Ensembles for Visualization of Scalar-Valued Functions
Authors: William Harvey 0001, Yusu Wang 0001
Published at EuroVis, 2010

Visual representation techniques enable perception and exploration of scientific data. Following the topological landscapes metaphor of Weber et al., we provide a new algorithm for visualizing scalar functions defined on simply connected domains of arbitrary dimension. For a potentially high dimensional scalar field, our algorithm produces a collection of, in some sense complete, two‐dimensional terrain models whose contour trees and corresponding topological persistences are identical to those of the input scalar field. The algorithm exactly preserves the volume of each region corresponding to an arc in the contour tree. We also introduce an efficiently computable metric on terrain models we generate. Based on this metric, we develop a tool that can help the users to explore the space of possible terrain models.
Topology Aware Stream Surfaces
Authors: Dominic Schneider, Wieland Reich, Alexander Wiebel, Gerik Scheuermann
Published at EuroVis, 2010

We present an algorithm that allows stream surfaces to recognize and adapt to vector field topology. Standard stream surface algorithms either refine the surface uncontrolled near critical points which slows down the computation considerably and may lead to a poor surface approximation. Alternatively, the concerned region is omitted from the stream surface by severing it into two parts thus generating an incomplete stream surface. Our algorithm utilizes topological information to provide a fast, accurate, and complete triangulation of the stream surface near critical points. The required topological information is calculated in a preprocessing step. We compare our algorithm against the standard approach both visually and in performance.
Topology-based Smoothing of 2D Scalar Fields with C1-Continuity
Authors: Tino Weinkauf, Yotam I. Gingold, Olga Sorkine
Published at EuroVis, 2010

Data sets coming from simulations or sampling of real‐world phenomena often contain noise that hinders their processing and analysis. Automatic filtering and denoising can be challenging: when the nature of the noise is unknown, it is difficult to distinguish between noise and actual data features; in addition, the filtering process itself may introduce “artificial” features into the data set that were not originally present. In this paper, we propose a smoothing method for 2D scalar fields that gives the user explicit control over the data features. We define features as critical points of the given scalar function, and the topological structure they induce (i.e., the Morse‐Smale complex). Feature significance is rated according to topological persistence. Our method allows filtering out spurious features that arise due to noise by means of topological simplification, providing the user with a simple interface that defines the significance threshold, coupled with immediate visual feedback of the remaining data features. In contrast to previous work, our smoothing method guarantees a C1‐continuous output scalar field with the exact specified features and topological structures.
Toward a Lagrangian Vector Field Topology
Authors: Raphael Fuchs, Jan Kemmler, Benjamin Schindler, Jürgen Waser, Filip Sadlo, Helwig Hauser, Ronald Peikert
Published at EuroVis, 2010

In this paper we present an extended critical point concept which allows us to apply vector field topology in the case of unsteady flow. We propose a measure for unsteadiness which describes the rate of change of the velocities in a fluid element over time. This measure allows us to select particles for which topological properties remain intact inside a finite spatio‐temporal neighborhood. One benefit of this approach is that the classification of critical points based on the eigenvalues of the Jacobian remains meaningful. In the steady case the proposed criterion reduces to the classical definition of critical points. As a first step we show that finding an optimal Galilean frame of reference can be obtained implicitly by analyzing the acceleration field. In a second step we show that this can be extended by switching to the Lagrangian frame of reference. This way the criterion can detect critical points moving along intricate trajectories. We analyze the behavior of the proposed criterion based on two analytical vector fields for which a correct solution is defined by their inherent symmetries and present results for numerical vector fields.
Understanding Interactive Legends: a Comparative Evaluation with Standard Widgets
Authors: Nathalie Henry Riche, Bongshin Lee, Catherine Plaisant
Published at EuroVis, 2010

Interactive information visualization systems rely on widgets to allow users to interact with the data and modify the representation. We define interactive legends as a class of controls combining the visual representation of static legends and interaction mechanisms of widgets. As interactive legends start to appear in popular websites, we categorize their designs for common data types and evaluate their effectiveness compare to standard widgets. Results suggest that 1) interactive legends can lead to faster perception of the mapping between data values and visual encodings and 2) interaction time is affected differently depending on the data type. Additionally, our study indicates superiority both in terms of perception and interaction of ordinal controls over numerical ones. Numerical techniques are mostly used in today's systems. By providing solutions to allowing users to modify ranges interactively, we believe that interactive legends make it possible to increase the use of ordinal techniques for visual exploration.
Video Visualization for Snooker Skill Training
Authors: Markus Höferlin, Edward Grundy, Rita Borgo, Daniel Weiskopf, Min Chen 0001, Iwan W. Griffiths, W. Griffiths
Published at EuroVis, 2010

We present a feasibility study on using video visualization to aid snooker skill training. By involving the coaches and players in the loop of intelligent reasoning, our approach addresses the difficulties of automated semantic reasoning, while benefiting from mature video processing techniques. This work was conducted in conjunction with a snooker club and a sports scientist. In particular, we utilized the principal design of the VideoPerpetuoGram (VPG) to convey spatiotemporal information to the viewers through static visualization, removing the burden of repeated video viewing. We extended the VPG design to accommodate the need for depicting multiple video streams and respective temporal attribute fields, including silhouette extrusion, spatial attributes, and non‐spatial attributes. Our results and evaluation have shown that video visualization can provide snooker coaching with visually quantifiable and comparable summary records, and is thus a cost‐effective means for assessing skill levels and monitoring progress objectively and consistently.
Visual Analysis of Multi-Joint Kinematic Data
Authors: Peter R. Krekel, Edward R. Valstar, Jurriaan H. De Groot, Frits H. Post, Rob G. H. H. Nelissen, Charl P. Botha
Published at EuroVis, 2010

Kinematics is the analysis of motions without regarding forces or inertial effects, with the purpose of understanding joint behaviour. Kinematic data of linked joints, for example the upper extremity, i.e. the shoulder and arm joints, contains many related degrees of freedom that complicate numerical analysis. Visualisation techniques enhance the analysis process, thus improving the effectiveness of kinematic experiments.This paper describes a new visualisation system specifically designed for the analysis of multi‐joint kinematic data of the upper extremity. The challenge inherent in the data is that the upper extremity is comprised of five cooperating joints with a total of fifteen degrees of freedom. The range of motion may be affected by subtle deficiencies of individual joints that are difficult to pinpoint. To highlight these subtleties our approach combines interactive filtering and multiple visualisation techniques.Our system is further differentiated by the fact that it integrates simultaneous acquisition and visual analysis of biokinematic data. Also, to facilitate complex queries, we have designed a visual query interface with visualisation and interaction elements that are based on the domain‐specific anatomical representation of the data. The combination of these techniques form an effective approach specifically tailored for the investigation and comparison of large collections of kinematic data. This claim is supported by an evaluation experiment where the technique was used to inspect the kinematics of the left and right arm of a patient with a healed proximal humerus fracture, i.e. a healed shoulder fracture.
Visual Support for Interactive Post-Interventional Assessment of Radiofrequency Ablation Therapy
Authors: Christian Rieder, Andreas Weihusen, Christian Schumann, Stephan Zidowitz, Heinz-Otto Peitgen
Published at EuroVis, 2010

Percutaneous radiofrequency (RF) ablation is a minimally invasive, image‐guided therapy for the treatment of liver tumors. The assessment of the ablation area (coagulation) is performed to verify the treatment success as an essential part of the therapy. Traditionally, pre‐ and post‐interventional CT images are used to visually compare the shape, size, and position of tumor and coagulation.In this work, we present a novel visualization as well as a navigation tool, the so‐called tumor map. The tumor map is a pseudo‐cylindrical mapping of the tumor surface onto a 2D image. It is used for a combined visualization of all ablation zones of the tumor to allow a reliable therapy assessment. Additionally, the tumor map serves as an interactive tool for intuitive navigation within the 3D volume rendering of the tumor vicinity as well as with familiar 2D viewers.
Visualization and Analysis-Oriented Reconstruction of Material Interfaces
Authors: Jeremy S. Meredith, Hank Childs
Published at EuroVis, 2010

Reconstructing boundaries along material interfaces from volume fractions is a difficult problem, especially because the under‐resolved nature of the input data allows for many correct interpretations. Worse, algorithms widely accepted as appropriate for simulation are inappropriate for visualization. In this paper, we describe a new algorithm that is specifically intended for reconstructing material interfaces for visualization and analysis requirements. The algorithm performs well with respect to memory footprint and execution time, has desirable properties in various accuracy metrics, and also produces smooth surfaces with few artifacts, even when faced with more than two materials per cell.
Visualizing Summary Statistics and Uncertainty
Authors: Kristin Potter, Joe Kniss, Richard F. Riesenfeld, Chris R. Johnson 0001
Published at EuroVis, 2010

The graphical depiction of uncertainty information is emerging as a problem of great importance. Scientific data sets are not considered complete without indications of error, accuracy, or levels of confidence. The visual portrayal of this information is a challenging task. This work takes inspiration from graphical data analysis to create visual representations that show not only the data value, but also important characteristics of the data including uncertainty. The canonical box plot is reexamined and a new hybrid summary plot is presented that incorporates a collection of descriptive statistics to highlight salient features of the data. Additionally, we present an extension of the summary plot to two dimensional distributions. Finally, a use‐case of these new plots is presented, demonstrating their ability to present high‐level overviews as well as detailed insight into the salient features of the underlying data distribution.
Winding Roads: Routing edges into bundles
Authors: Antoine Lambert, Romain Bourqui, David Auber
Published at EuroVis, 2010

Visualizing graphs containing many nodes and edges efficiently is quite challenging. Drawings of such graphs generally suffer from visual clutter induced by the large amount of edges and their crossings. Consequently it is difficult to read the relationships between nodes and the high‐level edge patterns that may exist in standard node‐link diagram representations. Edge bundling techniques have been proposed to help solve this issue, which rely on high quality edge rerouting. In this paper, we introduce an intuitive edge bundling technique which efficiently reduces edge clutter in graphs drawings. Our method is based on the use of a grid built using the original graph to compute the edge rerouting. In comparison with previously proposed edge bundling methods, our technique improves both the level of clutter reduction and the computation performance. The second contribution of this paper is a GPU‐based rendering method which helps users perceive bundles densities while preserving edge color.
A comparative evaluation on tree visualization methods for hierarchical structures with large fan-outs
Authors: Hyunjoo Song, Bo Hyoung Kim, Bongshin Lee, Jinwook Seo
Published at CHI, 2010

Hierarchical structures with large fan-outs are hard to browse and understand. In the conventional node-link tree visualization, the screen quickly becomes overcrowded as users open nodes that have too many child nodes to fit in one screen. To address this problem, we propose two extensions to the conventional node-link tree visualization: a list view with a scrollbar and a multi-column interface. We compared them against the conventional tree visualization interface in a user study. Results show that users are able to browse and understand the tree structure faster with the multi-column interface than the other two interfaces. Overall, they also liked the multi-column better than others.
Apatite: a new interface for exploring APIs
Authors: Daniel S. Eisenberg, Jeffrey Stylos, Brad A. Myers
Published at CHI, 2010

We present Apatite, a new tool that aids users in learning and understanding a complex API by visualizing the common associations between its various components. Current object-oriented API documentation is usually navigated in a fixed tree structure, starting with a package and then filtering by a specific class. For large APIs, this scheme is overly restrictive, because it prevents users from locating a particular action without first knowing which class it belongs to. Apatite's design instead enables users to search across any level of an API's hierarchy. This is made possible by the introduction of a novel interaction technique that presents popular items from multiple categories simultaneously, determining their relevance by approximating the strength of their association using search engine data. The design of Apatite was refined through iterative usability testing, and it has been released publicly as a web application.
Biketastic: sensing and mapping for better biking
Authors: Sasank Reddy, Katie Shilton, Gleb Denisov, Christian Cenizal, Deborah Estrin, Mani B. Srivastava
Published at CHI, 2010

Bicycling is an affordable, environmentally friendly alternative transportation mode to motorized travel. A common task performed by bikers is to find good routes in an area, where the quality of a route is based on safety, efficiency, and enjoyment. Finding routes involves trial and error as well as exchanging information between members of a bike community. Biketastic is a platform that enriches this experimentation and route sharing process making it both easier and more effective. Using a mobile phone application and online map visualization, bikers are able to document and share routes, ride statistics, sensed information to infer route roughness and noisiness, and media that documents ride experience. Biketastic was designed to ensure the link between information gathering, visualization, and bicycling practices. In this paper, we present architecture and algorithms for route data inferences and visualization. We evaluate the system based on feedback from bicyclists provided during a two-week pilot.
Crowdsourcing graphical perception: using mechanical turk to assess visualization design
Authors: Jeffrey Heer, Michael Bostock
Published at CHI, 2010

Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In this paper, we assess the viability of Amazon's Mechanical Turk as a platform for graphical perception experiments. We replicate previous studies of spatial encoding and luminance contrast and compare our results. We also conduct new experiments on rectangular area perception (as in treemaps or cartograms) and on chart size and gridline spacing. Our results demonstrate that crowdsourced perception experiments are viable and contribute new insights for visualization design. Lastly, we report cost and performance data from our experiments and distill recommendations for the design of crowdsourced studies.
InAir: sharing indoor air quality measurements and visualizations
Authors: Sunyoung Kim, Eric Paulos
Published at CHI, 2010

This paper describes inAir, a tool for sharing measurements and visualizations of indoor air quality within one's social network. Poor indoor air quality is difficult for humans to detect through sight and smell alone and can contribute to the development of chronic diseases. Through a four-week long study of fourteen households as six groups, we found that inAir (1) increased awareness of, and reflection on air quality, (2) promoted behavioral changes that resulted in improved indoor air quality, and (3) demonstrated the persuasive power of sharing for furthering improvements to indoor air quality in terms of fostering new social awareness and behavior changes as well as strengthening social bonds and prompting collaborative efforts across social networks to improve human health and well being.
Individual models of color differentiation to improve interpretability of information visualization
Authors: David R. Flatla, Carl Gutwin
Published at CHI, 2010

Color is commonly used to represent categories and values in many computer applications, but differentiating these colors can be difficult in many situations (e.g., for users with color vision deficiency (CVD), or in bright light). Current solutions to this problem can adapt colors based on standard simulations of CVD, but these models cover only a fraction of the ways in which color perception can vary. To improve the specificity and accuracy of these approaches, we have developed the first ever individualized model of color differentiation (ICD). The model is based on a short calibration performed by a particular user for a particular display, and so automatically covers all aspects of the user's ability to see and differentiate colors in an environment. In this paper we introduce the new model and the manner in which differentiability limits are predicted. We gathered empirical data from 16 users to assess the model's accuracy and robustness. We found that the model is highly effective at capturing individual differentiation abilities, works for users with and without CVD, can be tuned to balance accuracy and color availability, and can serve as the basis for improved color adaptation schemes.
Interactive optimization for steering machine classification
Authors: Ashish Kapoor, Bongshin Lee, Desney S. Tan, Eric Horvitz
Published at CHI, 2010

Interest has been growing within HCI on the use of machine learning and reasoning in applications to classify such hidden states as user intentions, based on observations. HCI researchers with these interests typically have little expertise in machine learning and often employ toolkits as relatively fixed "black boxes" for generating statistical classifiers. However, attempts to tailor the performance of classifiers to specific application requirements may require a more sophisticated understanding and custom-tailoring of methods. We present ManiMatrix, a system that provides controls and visualizations that enable system builders to refine the behavior of classification systems in an intuitive manner. With ManiMatrix, users directly refine parameters of a confusion matrix via an interactive cycle of re-classification and visualization. We present the core methods and evaluate the effectiveness of the approach in a user study. Results show that users are able to quickly and effectively modify decision boundaries of classifiers to tai-lor the behavior of classifiers to problems at hand.
ManyNets: an interface for multiple network analysis and visualization
Authors: Manuel Freire 0001, Catherine Plaisant, Ben Shneiderman, Jennifer Golbeck
Published at CHI, 2010

Traditional network analysis tools support analysts in studying a single network. ManyNets offers these analysts a powerful new approach that enables them to work on multiple networks simultaneously. Several thousand networks can be presented as rows in a tabular visualization, and then inspected, sorted and filtered according to their attributes. The networks to be displayed can be obtained by subdivision of larger networks. Examples of meaningful subdivisions used by analysts include ego networks, community extraction, and time-based slices. Cell visualizations and interactive column overviews allow analysts to assess the distribution of attributes within particular sets of networks. Details, such as traditional node-link diagrams, are available on demand. We describe a case study analyzing a social network geared towards film recommendations by means of decomposition. A small usability study provides feedback on the use of the interface on a set of tasks issued from the case study.