anchor.diff:  InfoVis,TVCG,Narvis: Authoring Narrative Slideshows for Introducing Data Visualization Designs,J),"Qianwen Wang, Zhen Li, Siwei Fu, Weiwei Cui, Huamin Qu",https://wangqianwen0418.github.io/assets/pdf/Narvis.pdf,"Visual designs can be complex in modern data visualization systems, which poses special challenges for explaining them to the non-experts. However, few if any presentation tools are tailored for this purpose. In this study, we present Narvis, a slideshow authoring tool designed for introducing data visualizations to non-experts. Narvis targets two types of end users: teachers, experts in data visualization who produce tutorials for explaining a data visualization, and students, non-experts who try to understand visualization designs through tutorials. We present an analysis of requirements through close discussions with the two types of end users. The resulting considerations guide the design and implementation of Narvis. Additionally, to help teachers better organize their introduction slideshows, we specify a data visualization as a hierarchical combination of components, which are automatically detected and extracted by Narvis. The teachers craft an introduction slideshow through first organizing these components, and then explaining them sequentially. A series of templates are provided for adding annotations and animations to improve efficiency during the authoring process. We evaluate Narvis through a qualitative analysis of the authoring experience, and a preliminary evaluation of the generated slideshows.",,,,,vimeo 289784711,10.1109/tvcg.2018.2865232,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Design & Storytelling,25-Oct,12:20 PM,12:40 PM
   SciVis,TVCG,Culling for Extreme-Scale Segmentation Volumes: A Hybrid Deterministic and Probabilistic Approach,J),"Johanna Beyer, Haneen Mohammed, Marco Agus, Ali K. Al-Awami, Hanspeter Pfister, Markus Hadwiger",,,,,,,vimeo 290327965,10.1109/tvcg.2018.2864847,2018,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,11:00 AM,11:20 AM
   SciVis,TVCG,CPU Iso-surface Ray Tracing of Adaptive Mesh Refinement Data,J),"Feng Wang, Ingo Wald, Qi Wu, Will Usher, Chris R. Johnson",,,,,,,vimeo 290328099,,2018,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,11:20 AM,11:40 AM
  -TVCG,TVCG,Efficient Local Statistical Analysis via Point-Wise Histograms in Tetrahedral Meshes and Curvilinear Grids,T),"Bo Zhou, Yi-Jen Chiang, Cong Wang",,,,,,,vimeo 289789310,10.1109/tvcg.2018.2796555,,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,11:40 AM,12:00 PM
  -TVCG,TVCG,Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows over Time,T),"Fabio Miranda, Harish Doraiswamy, Marcos Lage, Luc Wilson, Mondrian Hsieh, Claudio T. Silva",,,,,,,vimeo 289789412,10.1109/tvcg.2018.2802945,,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,12:00 PM,12:20 PM
  -TVCG,TVCG,A Scalable Hybrid Scheme for Ray-Casting of Unstructured Volume Data,T),"Roba Binyahib, Tom Peterka, Matthew Larsen, Kwan-Liu Ma, Hank Childs",,,,,,,vimeo 289789339,10.1109/tvcg.2018.2833113,,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,12:20 PM,12:40 PM
  +TVCG,TVCG,Efficient Local Statistical Analysis via Point-Wise Histograms in Tetrahedral Meshes and Curvilinear Grids,T),"Bo Zhou, Yi-Jen Chiang, Cong Wang",,,,,,,vimeo 289789310,10.1109/tvcg.2018.2796555,2018,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,11:40 AM,12:00 PM
  +TVCG,TVCG,Shadow Accrual Maps: Efficient Accumulation of City-Scale Shadows over Time,T),"Fabio Miranda, Harish Doraiswamy, Marcos Lage, Luc Wilson, Mondrian Hsieh, Claudio T. Silva",,,,,,,vimeo 289789412,10.1109/tvcg.2018.2802945,2018,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,12:00 PM,12:20 PM
  +TVCG,TVCG,A Scalable Hybrid Scheme for Ray-Casting of Unstructured Volume Data,T),"Roba Binyahib, Tom Peterka, Matthew Larsen, Kwan-Liu Ma, Hank Childs",,,,,,,vimeo 289789339,10.1109/tvcg.2018.2833113,2018,2018,SciVis,Estrel Hall A+B,Thursday,Scalable Techniques,25-Oct,12:20 PM,12:40 PM
   InfoVis,TVCG,Charticulator: Interactive Construction of Bespoke Chart Layouts,J),"Donghao Ren, Bongshin Lee, Matthew Brehmer",https://donghaoren.org/publications/infovis18-charticulator.pdf,"We present Charticulator, an interactive authoring tool that enables the creation of bespoke and reusable chart layouts. Charticulator is our response to most existing chart construction interfaces that require authors to choose from predefined chart layouts, thereby precluding the construction of novel charts. In contrast, Charticulator transforms a chart specification into mathematical layout constraints and automatically computes a set of layout attributes using a constraint-solving algorithm to realize the chart. It allows for the articulation of compound marks or glyphs as well as links between these glyphs, all without requiring any coding or knowledge of constraint satisfaction. Furthermore, thanks to the constraint-based layout approach, Charticulator can export chart designs into reusable templates that can be imported into other visualization tools. In addition to describing Charticulator's conceptual framework and design, we present three forms of evaluation: a gallery to illustrate its expressiveness, a user study to verify its usability, and a click-count comparison between Charticulator and three existing tools. Finally, we discuss the limitations and potentials of Charticulator as well as directions for future research. Charticulator is available with its source code at https://charticulator.com.",https://charticulator.com/app/index.html,https://github.com/Microsoft/charticulator,,,video https://charticulator.azureedge.net/videos/charticulator-supplemental.mp4,10.1109/tvcg.2018.2865158,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:00 AM,11:20 AM
   InfoVis,TVCG,Embedded Merge & Split: Visual Adjustment of Data Grouping,J),"Ali Sarvghad, Bahador Saket, Alex Endert, Nadir Weibel",http://bahadorsaket.com/publication/EMS.pdf,"Data grouping is among the most frequently used operations in data visualization. It is the process through which relevant information is gathered, simplified, and expressed in summary form. Many popular visualization tools support automatic grouping of data (e.g., dividing up a numerical variable into bins). Although grouping plays a pivotal role in supporting data exploration, further adjustment and customization of auto-generated grouping criteria is non-trivial. Such adjustments are currently performed either programmatically or through menus and dialogues which require specific parameter adjustments over several steps. In response, we introduce Embedded Merge & Split (EMS), a new interaction technique for direct adjustment of data grouping criteria. We demonstrate how the EMS technique can be designed to directly manipulate width and position in bar charts and histograms, as a means for adjustment of data grouping criteria. We also offer a set of design guidelines for supporting EMS. Finally, we present the results of two user studies, providing initial evidence that EMS can significantly reduce interaction time compared to WIMP-based technique and was subjectively preferred by participants.",,,,,youtube Z2rL6WF6TLY,10.1109/tvcg.2018.2865075,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:20 AM,11:40 AM
   TVCG,TVCG,Smart Brushing for Parallel Coordinates,T),"Richard Roberts, Robert S Laramee, Gary A Smith, Paul Brookes, Tony D'Cruze",http://cs.swan.ac.uk/~csbob/research/callCenter/brushing/roberts18smart.pdf,"The Parallel Coordinates plot is a popular tool for the visualization of high-dimensional data. One of the main challenges when using parallel coordinates is occlusion and overplotting resulting from large data sets. Brushing is a popular approach to address these challenges. Since its conception, limited improvements have been made to brushing both in the form of visual design and functional interaction. We present a set of novel, smart brushing techniques that enhance the standard interactive brushing of a parallel coordinates plot. We introduce two new interaction concepts: Higher-order, sketch-based brushing, and smart, data-driven brushing. Higher-order brushes support interactive, flexible, n-dimensional pattern searches involving an arbitrary number of dimensions. Smart, data-driven brushing provides interactive, real-time guidance to the user during the brushing process based on derived meta-data. In addition, we implement a selection of novel enhancements and user options that complement the two techniques as well as enhance the exploration and analytical ability of the user. We demonstrate the utility and evaluate the results using a case study with a large, high-dimensional, real-world telecommunication data set and we report domain expert feedback from the data suppliers.",,,,,vimeo 289789480,10.1109/tvcg.2018.2808969,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,11:40 AM,12:00 PM
   TVCG,TVCG,"Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment ",T),Luis Gustavo Nonato and Michael Aupetit,http://www.lcad.icmc.usp.br/~nonato/pubs/mdp-survey.pdf,"Visual analysis of multidimensional data requires expressive and effective ways to reduce data dimensionality to encode them visually. Multidimensional projections (MDP) figure among the most important visualization techniques in this context, transforming multidimensional data into scatter plots whose visual patterns reflect some notion of similarity in the original data. However, MDP come with distortions that make these visual patterns not trustworthy, hindering users to infer actual data characteristics. Moreover, the patterns present in the scatter plots might not be enough to allow a clear understanding of multidimensional data, motivating the development of layout enrichment methodologies to operate together with MDP. This survey attempts to cover the main aspects of MDP as a visualization and visual analytic tool. It provides detailed analysis and taxonomies as to the organization of MDP techniques according to their main properties and traits, discussing the impact of such properties for visual perception and other human factors. The survey also approaches the different types of distortions that can result from MDP mappings and it overviews existing mechanisms to quantitatively evaluate such distortions. A qualitative analysis of the impact of distortions on the different analytic tasks performed by users when exploring multidimensional data through MDP is also presented. Guidelines for choosing the best MDP for an intended task are also provided as a result of this analysis. Finally, layout enrichment schemes to debunk MDP distortions and/or reveal relevant information not directly inferable from the scatter plot are reviewed and discussed in the light of new taxonomies. We conclude the survey providing future research axes to fill discovered gaps in this domain.",,,,,vimeo 289789153,10.1109/tvcg.2018.2846735,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,12:00 PM,12:20 PM
   TVCG,TVCG,Exploration Strategies for Discovery of Interactivity in Visualizations,T),"Tanja Blascheck, Lindsay MacDonald Vermeulen, Jo Vermeulen, Charles Perin, Wesley Willett, Thomas Ertl, Sheelagh Carpendale",https://hal.archives-ouvertes.fr/hal-01705792/document,"We investigate how people discover the functionality of an interactive visualization that was designed for the general public. While interactive visualizations are increasingly available for public use, we still know little about how the general public discovers what they can do with these visualizations and what interactions are available. Developing a better understanding of this discovery process can help inform the design of visualizations for the general public, which in turn can help make data more accessible. To unpack this problem, we conducted a lab study in which participants were free to use their own methods to discover the functionality of a connected set of interactive visualizations of public energy data. We collected eye movement data and interaction logs as well as video and audio recordings. By analyzing this combined data, we extract exploration strategies that the participants employed to discover the functionality in these interactive visualizations. These exploration strategies illuminate possible design directions for improving the discoverability of a visualization's functionality.",,,http://innovis.cpsc.ucalgary.ca/supplemental/Exploration-Strategies/#data,,vimeo 289789025,10.1109/tvcg.2018.2802520,2018,2018,InfoVis,Estrel Hall C,Thursday,Interaction,25-Oct,12:20 PM,12:40 PM
  -CG&A,CGA,OpenSpace: Changing the Narrative of Public Dissemination in Astronomical Visualization from What to How,,"Alexander Bock, Emil Axelsson, Carter Emmart, Masha Kuznetsova, Charles Hansen, Anders Ynnerman",,,,,,,vimeo 289785793,10.1109/mcg.2018.032421653,,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:00 AM,11:20 AM
  -CG&A,CGA,Belle2VR: A Virtual-Reality Visualization of Subatomic Particle Physics in the Belle II Experiment,,"Zach Duer, Leo Piilonen, and George Glasson",,,,,,,vimeo 289785809,10.1109/mcg.2018.032421652,,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:20 AM,11:40 AM
  -CG&A,CGA,Application-Driven Design: Help Students Understand Employment and See the “Big Picture”,,"Li Liu, Deborah Silver, Karen Bemis",,,,,,,vimeo 289785826,10.1109/mcg.2018.032421656,,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:40 AM,12:00 PM
  +CG&A,CGA,OpenSpace: Changing the Narrative of Public Dissemination in Astronomical Visualization from What to How,,"Alexander Bock, Emil Axelsson, Carter Emmart, Masha Kuznetsova, Charles Hansen, Anders Ynnerman",,,,,,,vimeo 289785793,10.1109/mcg.2018.032421653,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:00 AM,11:20 AM
  +CG&A,CGA,Belle2VR: A Virtual-Reality Visualization of Subatomic Particle Physics in the Belle II Experiment,,"Zach Duer, Leo Piilonen, and George Glasson",,,,,,,vimeo 289785809,10.1109/mcg.2018.032421652,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:20 AM,11:40 AM
  +CG&A,CGA,Application-Driven Design: Help Students Understand Employment and See the “Big Picture”,,"Li Liu, Deborah Silver, Karen Bemis",,,,,,,vimeo 289785826,10.1109/mcg.2018.032421656,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,11:40 AM,12:00 PM
   CG&A,CGA,Management of Cerebral Aneurysm Descriptors based on an Automatic Ostium Extraction,,"Monique Meuschke, Tobias Günther, Ralph Wickenhöfer, Markus Gross, Bernhard Preim, Kai Lawonn",https://cgl.ethz.ch/Downloads/Publications/Papers/2018/Meu18a/Meu18a.pdf,"We present a framework to manage cerebral aneurysms. Rupture risk evaluation is based on manually extracted descriptors, which is timeconsuming. Thus, we provide an automatic solution by considering several questions: How can expert knowledge be integrated? How should metadata be defined? Which interaction techniques are needed for data exploration?",,,,,vimeo 289785840,10.1109/mcg.2018.032421654,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,12:00 PM,12:20 PM
  -CG&A,CGA,Toward a Multimodal Diagnostic Exploratory Visualization of Focal Cortical Dysplasia,,"Shin-Ting Wu, Raphael Voltoline, Wallace S. Loos, J.A. Iván Rubianes, Lionis S. Watanabe, Bárbara J. Amorim, A. Carolina Coan, Fernando Cendes, Clarissa L. Yasuda",,,,,,,vimeo 289785856,10.1109/mcg.2018.032421655,,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,12:20 PM,12:40 PM
  +CG&A,CGA,Toward a Multimodal Diagnostic Exploratory Visualization of Focal Cortical Dysplasia,,"Shin-Ting Wu, Raphael Voltoline, Wallace S. Loos, J.A. Iván Rubianes, Lionis S. Watanabe, Bárbara J. Amorim, A. Carolina Coan, Fernando Cendes, Clarissa L. Yasuda",,,,,,,vimeo 289785856,10.1109/mcg.2018.032421655,2018,2018,Other,Room III,Thursday,CG&A Session 2,25-Oct,12:20 PM,12:40 PM
   VAST,TVCG,ViBr: Visualizing Bipartite Relations at Scale with the Minimum Description Length Principle,J),"Gromit Yeuk-Yin Chan, Panpan Xu, Zeng Dai, Liu Ren",https://vgc.poly.edu/~yychan/papers/chan-vast2018.pdf,"Bipartite graphs model the key relations in many large scale real-world data: customers purchasing items, legislators voting for bills, people’s affiliation with different social groups, faults occurring in vehicles, etc. However, it is challenging to visualize large scale bipartite graphs with tens of thousands or even more nodes or edges. In this paper, we propose a novel visual summarization technique for bipartite graphs based on the minimum description length (MDL) principle. The method simultaneously groups the two different set of nodes and constructs aggregated bipartite relations with balanced granularity and precision. It addresses the key trade-off that often occurs for visualizing large scale and noisy data: acquiring a clear and uncluttered overview while maximizing the information content in it. We formulate the visual summarization task as a co-clustering problem and propose an efficient algorithm based on locality sensitive hashing (LSH) that can easily scale to large graphs under reasonable interactive time constraints that previous related methods cannot satisfy. The method leads to the opportunity of introducing a visual analytics framework with multiple levels-of-detail to facilitate interactive data exploration. In the framework, we also introduce a compact visual design inspired by adjacency list representation of graphs as the building block for a small multiples display to compare the bipartite relations for different subsets of data. We showcase the applicability and effectiveness of our approach by applying it on synthetic data with ground truth and performing case studies on real-world datasets from two application domains including roll-call vote record analysis and vehicle fault pattern analysis. Interviews with experts in the political science community and the automotive industry further highlight the benefits of our approach.",,,,,vimeo 289787354,10.1109/TVCG.2018.2864826,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,2:20 PM,2:40 PM
   VAST,VAST,Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts,C),"Po-Ming Law, Yanhong Wu, Rahul Basole",https://terrancelaw.github.io/publications/segue_vast18.pdf,"Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we develop Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.",,,,,vimeo 289788023,,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,2:40 PM,3:00 PM
   VAST,TVCG,A Visual Analytics Framework for Spatiotemporal Trade Network Analysis,J),"Hong Wang, Yafeng Lu, Shade Shutters, Michael Steptoe, Feng Wang, Steven Landis, Ross Maciejewski",http://www.public.asu.edu/~hxwang/trade.pdf,"Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts. This is especially true of countries that may not be able to compete for scarce commodities during supply shocks. Thus, network-induced vulnerability to supply disruption is typically exported from wealthier populations to disadvantaged populations. As such, researchers and stakeholders concerned with supply chains, political science, environmental studies, etc. need tools to explore the complex dynamics within global trade networks and how the structure of these networks relates to regional instability. However, the multivariate, spatiotemporal nature of the network structure creates a bottleneck in the extraction and analysis of correlations and anomalies for exploratory data analysis and hypothesis generation. Working closely with experts in political science and sustainability, we have developed a highly coordinated, multi-view framework that utilizes anomaly detection, network analytics, and spatiotemporal visualization methods for exploring the relationship between global trade networks and regional instability. Requirements for analysis and initial research questions to be investigated are elicited from domain experts, and a variety of visual encoding techniques for rapid assessment of analysis and correlations between trade goods, network patterns, and time series signatures are explored. We demonstrate the application of our framework through case studies focusing on armed conflicts in Africa, regional instability measures, and their relationship to international global trade.",,,,,vimeo 289787400,10.1109/TVCG.2018.2864844,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:00 PM,3:20 PM
  -TVCG,TVCG,A Semantic-based Method for Visualizing Large Image Collections,T),"Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai Wu",,,,,,,vimeo 289789441,10.1109/tvcg.2018.2835485,,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:20 PM,3:40 PM
  -TVCG,TVCG,PhotoRecomposer: Interactive Photo Recomposition by Cropping,T),"Yuan Liang, Xiting Wang, Song-Hai Zhang, Shi-Min Hu, Shixia Liu",,,,,,,vimeo 289788924,10.1109/tvcg.2017.2764895,,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:40 PM,4:00 PM
  +TVCG,TVCG,A Semantic-based Method for Visualizing Large Image Collections,T),"Xiao Xie, Xiwen Cai, Junpei Zhou, Nan Cao, Yingcai Wu",,,,,,,vimeo 289789441,10.1109/tvcg.2018.2835485,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:20 PM,3:40 PM
  +TVCG,TVCG,PhotoRecomposer: Interactive Photo Recomposition by Cropping,T),"Yuan Liang, Xiting Wang, Song-Hai Zhang, Shi-Min Hu, Shixia Liu",,,,,,,vimeo 289788924,10.1109/tvcg.2017.2764895,2017,2018,VAST,"Convention Hall 1, Section C",Thursday,Graph and Image,25-Oct,3:40 PM,4:00 PM
   InfoVis,TVCG,Mapping Color to Meaning in Colormap Data Visualizations,J),"Karen B. Schloss, Connor C. Gramazio, Allison T. Silverman, Madeline L. Parker, Audrey S. Wang",https://schlosslab.discovery.wisc.edu/wp-content/uploads/2018/09/SchlossGramazioSilvermanParkerWanginPress.pdf,"To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's <i>inferred mappings</i>. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (<i>dark-is-more bias</i>). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (<i>opaque-is-more bias</i>). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.",,,https://schlosslab.discovery.wisc.edu/resources/,,vimeo 289784916,10.1109/tvcg.2018.2865147,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,2:20 PM,2:40 PM
   InfoVis,TVCG,Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots,J),"Yunhai Wang, Xin Chen, Tong Ge, Chen Bao, Michael Sedlmair, Chi-Wing Fu, Oliver Deussen, Baoquan Chen",http://www.yunhaiwang.org/infovis18/color/vis-color.pdf,"Appropriate choice of colors significantly aids viewers in understanding the structures in multiclass scatterplots and becomes more important with a growing number of data points and groups. An appropriate color mapping is also an important parameter for the creation of an aesthetically pleasing scatterplot. Currently, users of visualization software routinely rely on color mappings that have been pre-defined by the software. A default color mapping, however, cannot ensure an optimal perceptual separability between groups, and sometimes may even lead to a misinterpretation of the data. In this paper, we present an effective approach for color assignment based on a set of given colors that is designed to optimize the perception of scatterplots. Our approach takes into account the spatial relationships, density, degree of overlap between point clusters, and also the background color. For this purpose, we use a genetic algorithm that is able to efficiently find good color assignments. We implemented an interactive color assignment system with three extensions of the basic method that incorporates top K suggestions, user-defined color subsets, and classes of interest for the optimization. To demonstrate the effectiveness of our assignment technique, we conducted a numerical study and a controlled user study to compare our approach with default color assignments; our findings were verified by two expert studies. The results show that our approach is able to support users in distinguishing cluster numbers faster and more precisely than default assignment methods.",http://www.color-assignment.net/,,,,vimeo 289785222,10.1109/tvcg.2018.2864912,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,2:40 PM,3:00 PM
   InfoVis,TVCG,Looks Good To Me: Visualizations As Sanity Checks,J),"Michael Correll, Mingwei Li, Gordon L Kindlmann, Carlos Scheidegger",https://github.com/AlgebraicVis/SanityCheck/blob/master/InfoVis/preprint.pdf,"Famous examples such as Anscombe's Quartet highlight that one of the core benefits of visualizations is allowing people to discover visual patterns that might otherwise be hidden by summary statistics. This visual inspection is particularly important in exploratory data analysis, where analysts can use visualizations such as histograms and dot plots to identify data quality issues. Yet, these visualizations are driven by parameters such as histogram bin size or mark opacity that have a great deal of impact on the final visual appearance of the chart, but are rarely optimized to make important features visible. In this paper, we show that data flaws have varying impact on the visual features of visualizations, and that the adversarial or merely uncritical setting of design parameters of visualizations can obscure the visual signatures of these flaws. Drawing on the framework of Algebraic Visualization Design, we present the results of a crowdsourced study showing that common visualization types can appear to reasonably summarize distributional data while hiding large and important flaws such as missing data and extraneous modes. We make use of these results to propose additional best practices for visualizations of distributions for data quality tasks.",https://medium.com/@mcorrell/looks-good-to-me-visualizations-as-sanity-checks-6fd1ffa37ab9,https://github.com/AlgebraicVis/SanityCheck/tree/master/study,https://github.com/AlgebraicVis/SanityCheck/tree/master/study/data,,vimeo 289785290,10.1109/tvcg.2018.2864907,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:00 PM,3:20 PM
   TVCG,TVCG,Is There a Robust Technique for Selecting Aspect Ratios in Line Charts?,T),"Yunhai Wang, Zeyu Wang, Lifeng Zhu, Jian Zhang, Chi-Wing Fu, Changhe Tu, Baoquan Chen, Zhanglin Cheng",http://www.yunhaiwang.org/bankto45/as-tvcg.pdf,"The aspect ratio of a line chart heavily influences the perception of the underlying data. Different methods explore different criteria in choosing aspect ratios, but so far, it was still unclear how to select aspect ratios appropriately for any given data. This paper provides a guideline for the user to choose aspect ratios for any input 1D curves by conducting an in-depth analysis of aspect ratio selection methods both theoretically and experimentally. By formulating several existing methods as line integrals, we explain their parameterization invariance. Moreover, we derive a new and improved aspect ratio selection method, namely the�L1-LOR (local orientation resolution), with a certain degree of parameterization invariance. Furthermore, we connect different methods, including AL (arc length based method), the banking to 45� principle, RV (resultant vector) and AS (average absolute slope), as well as�L1-LOR and AO (average absolute orientation). We verify these connections by a comparative evaluation involving various data sets, and show that the selections by RV and�L1-LOR are complementary to each other for most data. Accordingly, we propose the dual-scale banking technique that combines the strengths of RV and�L1-LOR, and demonstrate its practicability using multiple real-world data sets.",,,,,vimeo 289789006,10.1109/tvcg.2017.2787113,2017,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:20 PM,3:40 PM
  -InfoVis,TVCG,Image-based Aspect Ratio Selection,J),"Yunhai Wang, Zeyu Wang, Chi-Wing Fu, Hansjörg Schmauder, Oliver Deussen, Daniel Weiskopf",http://www.yunhaiwang.org/infovis18/dbar/paper.pdf,"Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.",,,,,vimeo 289784435,,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:40 PM,4:00 PM
  +InfoVis,TVCG,Image-based Aspect Ratio Selection,J),"Yunhai Wang, Zeyu Wang, Chi-Wing Fu, Hansjörg Schmauder, Oliver Deussen, Daniel Weiskopf",http://www.yunhaiwang.org/infovis18/dbar/paper.pdf,"Selecting a good aspect ratio is crucial for effective 2D diagrams. There are several aspect ratio selection methods for function plots and line charts, but only few can handle general, discrete diagrams such as 2D scatter plots. However, these methods either lack a perceptual foundation or heavily rely on intermediate isoline representations, which depend on choosing the right isovalues and are time-consuming to compute. This paper introduces a general image-based approach for selecting aspect ratios for a wide variety of 2D diagrams, ranging from scatter plots and density function plots to line charts. Our approach is derived from Federer's co-area formula and a line integral representation that enable us to directly construct image-based versions of existing selection methods using density fields. In contrast to previous methods, our approach bypasses isoline computation, so it is faster to compute, while following the perceptual foundation to select aspect ratios. Furthermore, this approach is complemented by an anisotropic kernel density estimation to construct density fields, allowing us to more faithfully characterize data patterns, such as the subgroups in scatterplots or dense regions in time series. We demonstrate the effectiveness of our approach by quantitatively comparing to previous methods and revisiting a prior user study. Finally, we present extensions for ROI banking, multi-scale banking, and the application to image data.",,,,,vimeo 289784435,10.1109/TVCG.2018.2865266,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 1,25-Oct,3:40 PM,4:00 PM
   SciVis,TVCG,Persistence Atlas for Critical Point Variability in Ensembles,J),"Guillaume Favelier, Noura Faraj, Brian Summa, Julien Tierny",,,,,,,vimeo 290325207,10.1109/tvcg.2018.2864432,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,2:20 PM,2:40 PM
  -SciVis,TVCG,Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data,J),"Tushar Athawale, Chris R. Johnson",,,,,,,vimeo 290325318,,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,2:40 PM,3:00 PM
  +SciVis,TVCG,Probabilistic Asymptotic Decider for Topological Ambiguity Resolution in Level-Set Extraction for Uncertain 2D Data,J),"Tushar Athawale, Chris R. Johnson",,,,,,,vimeo 290325318,10.1109/TVCG.2018.2864505,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,2:40 PM,3:00 PM
   SciVis,TVCG,Hexahedral Mesh Structure Visualization and Evaluation,J),"Kaoji Cotrik Xu, Guoning Chen",,,,,,,vimeo 290327525,10.1109/tvcg.2018.2864827,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:00 PM,3:20 PM
  -SciVis,TVCG,Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy,J),"Attila Gyulassy, Peer-Timo Bremer, Valerio Pascucci",,,,,,,vimeo 290328033,,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:20 PM,3:40 PM
  -SciVis,TVCG,A Study of the Trade-off between Reduced Precision and Resolution for Scientific Data Analysis and Visualization,J),"Duong Hoang, Pavol Klacansky, Harsh Bhatia, Peer-Timo Bremer, Peter Lindstrom, Valerio Pascucci",,,,,,,vimeo 290328254,,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:40 PM,4:00 PM
  +SciVis,TVCG,Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy,J),"Attila Gyulassy, Peer-Timo Bremer, Valerio Pascucci",,,,,,,vimeo 290328033,10.1109/TVCG.2018.2864848,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:20 PM,3:40 PM
  +SciVis,TVCG,A Study of the Trade-off between Reduced Precision and Resolution for Scientific Data Analysis and Visualization,J),"Duong Hoang, Pavol Klacansky, Harsh Bhatia, Peer-Timo Bremer, Peter Lindstrom, Valerio Pascucci",,,,,,,vimeo 290328254,10.1109/TVCG.2018.2864853,2018,2018,SciVis,Estrel A+B,Thursday,"Topology, Geometry, and Precision",25-Oct,3:40 PM,4:00 PM
   SciVis,SciVis,An Organic Visual Metaphor for Public Understanding of Conditional Co-occurrences,,"Keshav Dasu, Takanori Fujiwara, Kwan-Liu Ma",,,,,,,vimeo 290328908,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:20 PM,2:32 PM
   SciVis,SciVis,VAPLI: Novel Visual Abstraction for Protein-Lipid Interactions,,"Naif Alharbi, Matthieu Chavent, Michael Krone, Robert S. Laramee",,,,,,,vimeo 290328550,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:32 PM,2:44 PM
   SciVis,SciVis,Color Interpolation for non-Euclidean Color Spaces,,"Max Zeyen, Tobias Post, Hans Hagen, James Ahrens, David Rogers, Roxana Bujack",,,,,,,vimeo 290328621,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,2:45 PM,2:57 PM
  
positive.code: TVCG,TVCG,Robust Tracing and Visualization of Heterogeneous Microvascular Networks,T),"Pavel Govyadinov, Tasha Womack, Jason L. Eriksen, Guoning Chen, David Mayerich",,,,,,,vimeo 289789196,10.1109/tvcg.2018.2818701,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Biological Applications,23-Oct,5:20 PM,5:40 PM
  VAST,TVCG,Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities,J),"Alexander Kumpf, Marc Rautenhaus, Michael Riemer, Rüdiger Westermann",,,,,,,vimeo 289787833,10.1109/tvcg.2018.2864901,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:00 AM,9:20 AM
  VAST,TVCG,EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection Algorithms with Multidimensional Data,J),"Ke Xu, Meng Xia, Xing Mu, Yun Wang, Nan Cao",https://lukeluker.github.io/papers/EnsembleLens_VAST2018_KeXu.pdf,"The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.",,,,,vimeo 289787339,10.1109/TVCG.2018.2864825,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:20 AM,9:40 AM
  TVCG,TVCG,Exploring Variability within Ensembles of Decadal Climate Predictions,T),"Christopher P. Kappe, Michael Böttinger, Heike Leitte",,,,,,,vimeo 289789327,10.1109/tvcg.2018.2810919,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,9:40 AM,10:00 AM
  VAST,TVCG,KnowledgePearls: Provenance-Based Visualization Retrieval,J),"Holger Stitz, Samuel Gratzl, Harald Piringer, Thomas Zichner, Marc Streit",https://www.vrvis.at/publications/pdfs/PB-VRVis-2018-031.pdf,"Storing analytical provenance generates a knowledge base with a large potential for recalling previous results and guiding users in future analyses. However, without extensive manual creation of meta information and annotations by the users, search and retrieval of analysis states can become tedious. We present KnowledgePearls, a solution for efficient retrieval of analysis states that are structured as provenance graphs containing automatically recorded user interactions and visualizations. As a core component, we describe a visual interface for querying and exploring analysis states based on their similarity to a partial definition of a requested analysis state. Depending on the use case, this definition may be provided explicitly by the user by formulating a search query or inferred from given reference states. We explain our approach using the example of efficient retrieval of demographic analyses by Hans Rosling and discuss our implementation for a fast look-up of previous states. Our approach is independent of the underlying visualization framework. We discuss the applicability for visualizations which are based on the declarative grammar Vega and we use a Vega-based implementation of Gapminder as guiding example. We additionally present a biomedical case study to illustrate how KnowledgePearls facilitates the exploration process by recalling states from earlier analyses.",,https://github.com/Caleydo/knowledge-pearls,,,vimeo 289787528,10.1109/tvcg.2018.2865024,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,10:00 AM,10:20 AM
  VAST,TVCG,Enhancing Web-based Analytics Applications through Provenance,J),"Akhilesh Camisetty, Chaitanya Chandurkar, Maoyuan Sun, David Koop",http://www.cis.umassd.edu/~dkoop/pubs/simprov.pdf,"Visual analytics systems continue to integrate new technologies and leverage modern environments for exploration and collaboration, making tools and techniques available to a wide audience through web browsers. Many of these systems have been developed with rich interactions, offering users the opportunity to examine details and explore hypotheses that have not been directly encoded by a designer. Understanding is enhanced when users can replay and revisit the steps in the sensemaking process, and in collaborative settings, it is especially important to be able to review not only the current state but also what decisions were made along the way. Unfortunately, many web-based systems lack the ability to capture such reasoning, and the path to a result is transient, forgotten when a user moves to a new view. This paper explores the requirements to augment existing client-side web applications with support for capturing, reviewing, sharing, and reusing steps in the reasoning process. Furthermore, it considers situations where decisions are made with streaming data, and the insights gained from revisiting those choices when more data is available. It presents a proof of concept, the Shareable Interactive Manipulation Provenance framework (SIMProv.js), that addresses these requirements in a modern, client-side JavaScript library, and describes how it can be integrated with existing frameworks.",,,,,vimeo 289787554,10.1109/tvcg.2018.2865039,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Ensemble and Provenance,24-Oct,10:20 AM,10:40 AM
  InfoVis,TVCG,Comparing Similarity Perception in Time Series Visualizations,J),"Anna Gogolou, Theophanis Tsandilas, Themis Palpanas, Anastasia Bezerianos",https://hal.inria.fr/hal-01845008/document,"A common challenge faced by many domain experts working with time series data is how to identify and compare similar patterns. This operation is fundamental in high-level tasks, such as detecting recurring phenomena or creating clusters of similar temporal sequences. While automatic measures exist to compute time series similarity, human intervention is often required to visually inspect these automatically generated results. The visualization literature has examined similarity perception and its relation to automatic similarity measures for line charts, but has not yet considered if alternative visual representations, such as horizon graphs and colorfields, alter this perception. Motivated by how neuroscientists evaluate epileptiform patterns, we conducted two experiments that study how these three visualization techniques affect similarity perception in EEG signals. We seek to understand if the time series results returned from automatic similarity measures are perceived in a similar manner, irrespective of the visualization technique; and if what people perceive as similar with each visualization aligns with different automatic measures and their similarity constraints. Our findings indicate that horizon graphs align with similarity measures that allow local variations in temporal position or speed (i.e., dynamic time warping) more than the two other techniques. On the other hand, horizon graphs do not align with measures that are insensitive to amplitude and y-offset scaling (i.e., measures based on z-normalization), but the inverse seems to be the case for line charts and colorfields. Overall, our work indicates that the choice of visualization affects what temporal patterns we consider as similar, i.e., the notion of similarity in time series is not visualization independent.",,,,,vimeo 289785077,10.1109/tvcg.2018.2865077,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:00 AM,9:20 AM
  TVCG,TVCG,A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series,T),"Erick Cuenca, Arnaud Sallaberry, Florence Ying Wang, Pascal Poncelet",http://www.lirmm.fr/~poncelet/publications/papers/Multistream2018.pdf,"Multiple time series are a set of multiple quantitative variables occurring at the same interval. They are present in many domains such as medicine, finance, and manufacturing for analytical purposes. In recent years, streamgraph visualization (evolved from ThemeRiver) has been widely used for representing temporal evolution patterns in multiple time series. However, streamgraph as well as ThemeRiver suffer from scalability problems when dealing with several time series. To solve this problem, multiple time series can be organized into a hierarchical structure where individual time series are grouped hierarchically according to their proximity. In this paper, we present a new streamgraph-based approach to convey the hierarchical structure of multiple time series to facilitate the exploration and comparisons of temporal evolution. Based on a focus+context technique, our method allows time series exploration at different granularities (e. g., from overview to details). To illustrate our approach, two usage examples are presented.",http://advanse.lirmm.fr/multistream/,https://github.com/erickedu85/multistream,,,vimeo 289789365,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:20 AM,9:40 AM
  TVCG,TVCG,Line Graph or Scatter Plot? Automatic Selection of Methods for Visualizing Trends in Time Series,T),"Yunhai Wang, Fubo Han, Lifeng Zhu, Oliver Deussen, Baoquan Chen",http://www.yunhaiwang.org/vis-selection/timeseries.pdf,"Line graphs are usually considered to be the best choice for visualizing time series data, whereas sometimes also scatter plots are used for showing main trends. So far there are no guidelines that indicate which of these visualization methods better display trends in time series for a given canvas. Assuming that the main information in a time series is its overall trend, we propose an algorithm that automatically picks the visualization method that reveals this trend best. This is achieved by measuring the visual consistency between the trend curve represented by a LOESS fit and the trend described by a scatter plot or a line graph. To measure the consistency between our algorithm and user choices, we performed an empirical study with a series of controlled experiments that show a large correspondence. In a factor analysis we furthermore demonstrate that various visual and data factors have effects on the preference for a certain type of visualization.",,,,,vimeo 289788836,10.1109/tvcg.2017.2653106,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,A Vector Field Design Approach to Animated Transitions,T),"Yong Wang, Daniel Archambault, Carlos E. Scheidegger, Huamin Qu",http://www.cs.swansea.ac.uk/~csdarchambault/publications/animatedTransitionFinalSubVersion.pdf,"Animated transitions can be effective in explaining and exploring a small number of visualizations where there are drastic changes in the scene over a short interval of time. This is especially true if data elements cannot be visually distinguished by other means. Current research in animated transitions has mainly focused on linear transitions (all elements follow straight line paths) or enhancing coordinated motion through bundling of linear trajectories. In this paper, we introduce animated transition design, a technique to build smooth, non-linear transitions for clustered data with either minimal or no user involvement. The technique is flexible and simple to implement, and has the additional advantage that it explicitly enhances coordinated motion and can avoid crowding, which are both important factors to support object tracking in a scene. We investigate its usability, provide preliminary evidence for the effectiveness of this technique through metric evaluations and user study and discuss limitations and future directions.",,,,,vimeo 289789555,10.1109/tvcg.2017.2750689,2017,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:00 AM,10:20 AM
  InfoVis,TVCG,Temporal Treemaps: Static Visualization of Evolving Trees,J),"Wiebke Köpp, Tino Weinkauf",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a.pdf,"We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.",http://www.csc.kth.se/~weinkauf/publications/documents/koepp19a_algorithm.mp4,,,,vimeo 289784452,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:20 AM,10:40 AM
  SciVis,TVCG,Visual Analysis of Aneurysm Data using Statistical Graphics,J),"Monique Meuschke, Tobias Günther, Philipp Berg, Ralph Wickenhoefer, Markus Gross, Bernhard Preim, Kai Lawonn",,,,,,,vimeo 290325604,10.1109/tvcg.2018.2864509,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:00 AM,9:20 AM
  SciVis,TVCG,Interactive Visualization of 3D Histopathology in Native Resolution,J),"Martin Falk, Anders Ynnerman, Darren Treanor, Claes Lundström",,,,,,,vimeo 290325921,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:20 AM,9:40 AM
  SciVis,TVCG,Visualization of Neuronal Structures in Wide-field Microscopy Brain Images,J),"Saeed Boorboor, Shreeraj Jadhav, Mala Ananth, David Talmage, Lorna W Role, Arie Kaufman",,,,,,,vimeo 290328215,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,Classification of Blood Flow Patterns in Cerebral Aneurysms,T),"Monique Meuschke, Steffen Oeltze-Jafra, Oliver Beuing, Bernhard Preim, Kai Lawonn",,,,,,,vimeo 289789890,10.1109/tvcg.2018.2834923,,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,10:00 AM,10:20 AM
  TVCG,TVCG,Bridging Text Visualization and Mining: A Task-Driven Survey,T),"Shixia Liu, Xiting Wang, Christopher Collins, Wenwen Dou, Fangxin Ouyang, Mennatallah El-Assady, Liu Jiang, Daniel Keim",,,,,,,vimeo 289789579,10.1109/tvcg.2018.2834341,,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:00 AM,9:20 AM
  VAST,TVCG,Doccurate: A Curation-Based Approach for Clinical Text Visualization,J),Nicole Sultanum,,,,,,,vimeo 289787431,10.1109/TVCG.2018.2864905,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:20 AM,9:40 AM
  VAST,TVCG,VIS Author Profiles: Interactive Descriptions of Publication Records Combining Text and Visualization,J),"Shahid Latif, Fabian Beck, Devin Singh, Michael Brudno, Fanny Chevalier",https://www.vis.wiwi.uni-due.de/uploads/tx_itochairt3/publications/vap-preprint.pdf,"Publication records and collaboration networks are important for assessing the expertise and experience of researchers. Existing digital libraries show the raw publication lists in author profiles, whereas visualization techniques focus on specific subproblems. Instead, we look at publication records from various perspectives mixing low-level publication data with high-level abstractions and background information. This work presents VIS Author Profiles, a novel approach to generate integrated textual and visual descriptions to highlight patterns in publication records. We leverage template-based natural language generation to summarize notable publication statistics, evolution of research topics, and collaboration relationships. Seamlessly integrated visualizations augment the textual description and are interactively connected with each other and the text. The underlying publication data and detailed explanations of the analysis are available on demand. We compare our approach to existing systems by taking into account information needs of users and demonstrate its usefulness in two realistic application examples.",https://vis-tools.paluno.uni-due.de/vap/,,,,vimeo 289787504,10.1109/TVCG.2018.2865022,2018,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,A Visual Analytics Framework for Identifying Topic Drivers in Media,T),"Yafeng Lu, Hong Wang, Steven Landis, Ross Maciejewski",,,,,,,vimeo 289788885,,,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:00 AM,10:20 AM
  TVCG,TVCG,Visualizing a Thinker’s Life,T),"Patrick Riehmann, Dora Kiesel, Martin Kohlhaas, Bernd Fröhlich",,,,,,,vimeo 289789544,,,2018,VAST,Estrel Hall C,Wednesday,Text,24-Oct,10:20 AM,10:40 AM
  TVCG,TVCG,Commercial Visual Analytics Systems – Advances in the Big Data Analytics Field,T),"Michael Behrisch, Dirk Streeb, Florian Stoffel, Daniel Seebacher, Stefan Hagen Weber, Sebastian Mittelstaedt, Hanspeter Pfister, Daniel Keim",,,,,,,vimeo 289789498,,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:00 AM,11:20 AM
  VAST,TVCG,BitExTract: Interactive Visualization for Extracting Bitcoin Exchange Intelligence,J),"Xuanwu Yue, Xinhuan Shu, Xinyu ZHU, Xinnan Du, Zheqing Yu, Dimitrios Papadopoulos, Siyuan Liu",,,,,,,vimeo 289787312,10.1109/TVCG.2018.2864814,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:20 AM,11:40 AM
  TVCG,TVCG,KAVAGait: Knowledge-Assisted Visual Analytics for Clinical Gait Analysis,T),"Markus Wagner, Djordje Slijepcevic, Brian Horsak, Alexander Rind, Matthias Zeppelzauer, Wolfgang Aigner",,,,,,,vimeo 289788975,10.1109/tvcg.2017.2785271,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,11:40 AM,12:00 PM
  TVCG,TVCG,Precision Risk Analysis of Cancer Therapy with Interactive Nomograms and Survival Plots,T),"G.Elisabeta Marai, Chihua Ma, Andrew T. Burks, Filippo Pellolio, Guadalupe Canahuate, David M. Vock, Abdallah S.R. Mohamed, C. David Fuller",,,,,,,vimeo 289789079,10.1109/tvcg.2018.2817557,,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:00 PM,12:20 PM
  VAST,VAST,VUSphere: Visual Analysis of Video Utilization in Online Distance Education,C),"Huan He, Qinghua Zheng, Bo Dong",,,,,,,vimeo 289788052,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,Applications,24-Oct,12:20 PM,12:40 PM
  InfoVis,TVCG,Juniper: A Tree+Table Approach to Multivariate Graph Visualization,J),"Carolina Nobre, Marc Streit, Alexander Lex",http://sci.utah.edu/~vdl/papers/2018_infovis_juniper.pdf,"Analyzing large, multivariate graphs is an important problem in many domains, yet such graphs are challenging to visualize. In this paper, we introduce a novel, scalable, tree+table multivariate graph visualization technique, which makes many tasks related to multivariate graph analysis easier to achieve. The core principle we follow is to selectively query for nodes or subgraphs of interest and visualize these subgraphs as a spanning tree of the graph. The tree is laid out linearly, which enables us to juxtapose the nodes with a table visualization where diverse attributes can be shown. We also use this table as an adjacency matrix, so that the resulting technique is a hybrid node-link/adjacency matrix technique. We implement this concept in Juniper and complement it with a set of interaction techniques that enable analysts to dynamically grow, restructure, and aggregate the tree, as well as change the layout or show paths between nodes. We demonstrate the utility of our tool in usage scenarios for different multivariate networks: a bipartite network of scholars, papers, and citation metrics and a multitype network of story characters, places, books, etc.",,https://github.com/caleydo/lineage/tree/juniper,,,vimeo 289784894,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:00 AM,11:20 AM
  TVCG,TVCG,Graph Thumbnails: Identifying and Comparing Multiple Graphs at a Glance,T),"Vahan Yoghourdjian, Tim Dwyer, Karsten Klein, Kim Marriott, Michael Wybrow",http://vahany.com/docs/Graph_Thumbnails_preprint.pdf,"We propose <i>Graph Thumbnails</i>, small icon-like visualisations of the high-level structure of network data. Graph Thumbnails are designed to be legible in small multiples to support rapid browsing within large graph corpora. Compared to existing graph-visualisation techniques our representation has several advantages: (1) the visualisation can be computed in linear time; (2) it is canonical in the sense that isomorphic graphs will always have identical thumbnails; and (3) it provides precise information about the graph structure. We report the results of two user studies. The first study compares Graph Thumbnails to node-link and matrix views for identifying similar graphs. The second study investigates the comprehensibility of the different representations. We demonstrate the usefulness of this representation for summarising the evolution of protein-protein interaction networks across a range of species.",,,,,vimeo 289789353,10.1109/tvcg.2018.2790961,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:20 AM,11:40 AM
  InfoVis,TVCG,Structure-Based Suggestive Exploration: A New Approach for Effective Exploration of Large Networks,J),"Fangzhou Guo, Wei Chen, Dongming Han, Jiacheng Pan, Xiaotao Nie, Jiazhi Xia, Xiaolong (Luke) Zhang",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/Structure-Based%20Suggestive%20Exploration.pdf,"When analyzing a visualized network, users need to explore different sections of the network to gain insight. However, effective exploration of large networks is often a challenge. While various tools are available for users to explore the global and local features of a network, these tools usually require significant interaction activities, such as repetitive navigation actions to follow network nodes and edges. In this paper, we propose a structure-based suggestive exploration approach to support effective exploration of large networks by suggesting appropriate structures upon user request. Encoding nodes with vectorized representations by transforming information of surrounding structures of nodes into a high dimensional space, our approach can identify similar structures within a large network, enable user interaction with multiple similar structures simultaneously, and guide the exploration of unexplored structures. We develop a web-based visual exploration system to incorporate this suggestive exploration approach and compare performances of our approach under different vectorizing methods and networks. We also present the usability and effectiveness of our approach through a controlled user study with two datasets.",,,,,vimeo 289785045,10.1109/tvcg.2018.2865139,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,11:40 AM,12:00 PM
  InfoVis,TVCG,Structure-aware Fisheye Views for Efficient Large Graph Exploration,J),"Yunhai Wang, Yanyan Wang, Yinqi Sun, Haifeng Zhang, Chi-Wing Fu, Michael Sedlmair, Baoquan Chen, Oliver Deussen",https://homepage.univie.ac.at/michael.sedlmair/papers/wang2019fisheye.pdf,"Traditional fisheye views for exploring large graphs introduce substantial distortions that often lead to a decreased readability of paths and other interesting structures. To overcome these problems, we propose a framework for <i>structure-aware</i> fisheye views. Using edge orientations as constraints for graph layout optimization allows us not only to reduce spatial and temporal distortions during fisheye zooms, but also to improve the readability of the graph structure. Furthermore, the framework enables us to optimize fisheye lenses towards specific tasks and design a family of new lenses: polyfocal, cluster, and path lenses. A GPU implementation lets us process large graphs with up to 15,000 nodes at interactive rates. A comprehensive evaluation, a user study, and two case studies demonstrate that our structure-aware fisheye views improve layout readability and user performance.",,,,,vimeo 289785238,10.1109/tvcg.2018.2864911,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,12:00 PM,12:20 PM
  InfoVis,TVCG,"Graphicle: Exploring Units, Networks, and Context in a Blended Visualization Approach ",J),"Timothy Major, Rahul C. Basole",http://entsci.gatech.edu/resources/basole-2019-tvcg-graphicle.pdf,"Many real-world datasets are large, multivariate, and relational in nature and relevant associated decisions frequently require a simultaneous consideration of both attributes and connections. Existing visualization systems and approaches, however, often make an explicit trade-off between either affording rich exploration of individual data units and their attributes or exploration of the underlying network structure. In doing so, important analysis opportunities and insights are potentially missed. In this study, we aim to address this gap by (1) considering visualizations and interaction techniques that blend the spectrum between unit and network visualizations, (2) discussing the nature of different forms of contexts and the challenges in implementing them, and (3) demonstrating the value of our approach for visual exploration of multivariate, relational data for a real-world use case. Specifically, we demonstrate through a system called Graphicle how network structure can be layered on top of unit visualization techniques to create new opportunities for visual exploration of physician characteristics and referral data. We report on the design, implementation, and evaluation of the system and effectiveness of our blended approach.",,,,,vimeo 262664447,10.1109/tvcg.2018.2865151,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Graphs & Trees,24-Oct,12:20 PM,12:40 PM
  SciVis,TVCG,Interactive Obstruction-free Lensing for Volumetric Data Visualization,J),"Michael Traoré, Christophe Hurter, Alexandru Telea",,,,,,,vimeo 290325542,,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:00 AM,11:20 AM
  SciVis,TVCG,Dynamic Volume Lines: Visual Comparison of 3D Volumes through Space-filling Curves,J),"Johannes Weissenböck, Bernhard Fröhler, Eduard Gröller, Johann Kastner, Christoph Heinzl",,,,,,,vimeo 290325634,10.1109/tvcg.2018.2864510,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:20 AM,11:40 AM
  SciVis,TVCG,A Declarative Grammar of Flexible Volume Visualization Pipelines,J),"Min Shih, Charles Rozhon, Kwan-Liu Ma",,,,,,,vimeo 290327611,10.1109/tvcg.2018.2864841,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,11:40 AM,12:00 PM
  TVCG,TVCG,Multi-Material Volume Rendering with a Physically-Based Surface Reflection Model,T),"Oleg Igouchkine, Yubo Zhang, Kwan-Liu Ma",,,,,,,vimeo 289789120,10.1109/tvcg.2017.2784830,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:00 PM,12:20 PM
  TVCG,TVCG,A Generative Model for Volume Rendering,T),"Matthew Berger, Jixian Li, Joshua A. Levine",,,,,,,vimeo 289789468,10.1109/tvcg.2018.2816059,,2018,SciVis,Estrel Hall A+B,Wednesday,Volume Visualization,24-Oct,12:20 PM,12:40 PM
  InfoVis,TVCG,Vistrates: A Component Model for Ubiquitous Analytics,J),"Sriram Karthik Badam, Andreas Mathisen, Roman Rädle, Clemens Nylandsted Klokmose, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/vistrates/vistrates.pdf,"Visualization tools are often specialized for specific tasks, which turns the user's analytical workflow into a fragmented process performed across many tools. In this paper, we present a component model design for data visualization to promote modular designs of visualization tools that enhance their analytical scope. Rather than fragmenting tasks across tools, the component model supports unification, where components�the building blocks of this model�can be assembled to support a wide range of tasks. Furthermore, the model also provides additional key properties, such as support for collaboration, sharing across multiple devices, and adaptive usage depending on expertise, from creating visualizations using dropdown menus, through instantiating components, to actually modifying components or creating entirely new ones from scratch using JavaScript or Python source code. To realize our model, we introduce VISTRATES, a literate computing platform for developing, assembling, and sharing visualization components. From a visualization perspective, Vistrates features cross-cutting components for visual representations, interaction, collaboration, and device responsiveness maintained in a component repository. From a development perspective, Vistrates offers a collaborative programming environment where novices and experts alike can compose component pipelines for specific analytical activities. Finally, we present several Vistrates use cases that span the full range of the classic �anytime� and �anywhere� motto for ubiquitous analysis: from mobile and on-the-go usage, through office settings, to collaborative smart environments covering a variety of tasks and devices.",,https://github.com/karthikbadam/Vistrates,,,vimeo 289784994,10.1109/tvcg.2018.2865144,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:00 AM,11:20 AM
  InfoVis,TVCG,SmartCues: A Multitouch Query Approach for Details-on-Demand through Dynamically Computed Overlays,J),"Hariharan Subramonyam, Eytan Adar",,,,,,,vimeo 289784720,10.1109/tvcg.2018.2865231,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:20 AM,11:40 AM
  InfoVis,TVCG,"Multiple Coordinated Views at Large Displays for Multiple Users: Empirical Findings on User Behavior, Movements, and Distances ",J),"Ricardo Langner, Ulrike Kister, Raimund Dachselt",,,,,,,vimeo 289784543,10.1109/tvcg.2018.2865235,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,11:40 AM,12:00 PM
  InfoVis,TVCG,Visualizing Ranges over Time on Mobile Phones: A Task-Based Crowdsourced Evaluation,J),"Matthew Brehmer, Bongshin Lee, Petra Isenberg, Eun Kyoung Choe",https://hal.inria.fr/hal-01857469/document,"In the first crowdsourced visualization experiment conducted exclusively on mobile phones, we compare approaches to visualizing ranges over time on small displays. People routinely consume such data via a mobile phone, from temperatures in weather forecasting apps to sleep and blood pressure readings in personal health apps. However, we lack guidance on how to effectively visualize ranges on small displays in the context of different value retrieval and comparison tasks, or with respect to different data characteristics such as periodicity, seasonality, or the cardinality of ranges. Central to our experiment is a comparison between two ways to lay out ranges: a more conventional linear layout strikes a balance between quantitative and chronological scale resolution, while a less conventional radial layout emphasizes the cyclicality of time and may prioritize discrimination between values at its periphery. With results from 87 crowd workers, we found that while participants completed tasks more quickly with linear layouts than with radial ones, there were few differences in terms of error rate between layout conditions. We also found that participants performed similarly with both layouts in tasks that involved comparing superimposed observed and average ranges.",,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyApp,https://github.com/Microsoft/RangesOnMobile/blob/master/StudyDataAnalysis,,vimeo 289784625,10.1109/tvcg.2018.2865234,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:00 PM,12:20 PM
  InfoVis,TVCG,Glanceable Visualization: Studies of Data Comparison Performance on Smartwatches,J),"Tanja Blascheck, Lonni Besançon, Anastasia Bezerianos, Bongshin Lee, Petra Isenberg",https://hal.inria.fr/hal-01851306/file/Blascheck_2018_Glanceable_Visualization.pdf,"We present the results of two perception studies to assess how quickly people can perform a simple data comparison task for small-scale visualizations on a smartwatch. The main goal of these studies is to extend our understanding of design constraints for smartwatch visualizations. Previous work has shown that a vast majority of smartwatch interactions last under 5 s. It is still unknown what people can actually perceive from visualizations during such short glances, in particular with such a limited display space of smartwatches. To shed light on this question, we conducted two perception studies that assessed the lower bounds of task time for a simple data comparison task. We tested three chart types common on smartwatches: bar charts, donut charts, and radial bar charts with three different data sizes: 7, 12, and 24 data values. In our first study, we controlled the differences of the two target bars to be compared, while the second study varied the difference randomly. For both studies, we found that participants performed the task on average in <300 ms for the bar chart, <220 ms for the donut chart, and in <1780 ms for the radial bar chart. Thresholds in the second study per chart type were on average 1.14–1.35× higher than in the first study. Our results show that bar and donut charts should be preferred on smartwatch displays when quick data comparisons are necessary.",,,,,vimeo 289785013,10.1109/tvcg.2018.2865142,2018,2018,InfoVis,Estrel Hall C,Wednesday,Devices,24-Oct,12:20 PM,12:40 PM
  CG&A,CGA,Physical Visualization of Geospatial Datasets,,"Hessam Djavaherpour, Ali Mahdavi-Amiri, Faramarz F. Samavati",,,,,,,vimeo 289785692,10.1109/mcg.2017.38,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:00 AM,11:20 AM
  CG&A,CGA,Typology of Uncertainty in Static Geolocated Graphs for Visualization,,"Tatiana von Landesberger, Sebastian Bremm, Marcel Wunderlich",,,,,,,vimeo 289785707,10.1109/mcg.2017.3621220,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:20 AM,11:40 AM
  CG&A,CGA,Impact of Spatial Scales on the Intercomparison of Climate Scenarios,,"Wei Luo, Michael Steptoe, Zheng Chang, Robert Link, Leon Clarke, Ross Maciejewski",,,,,,,vimeo 289785725,10.1109/mcg.2017.3621222,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,11:40 AM,12:00 PM
  CG&A,CGA,Urban Space Explorer: A Visual Analytics System for Urban Planning,,"Alireza Karduni, Isaac Cho, Ginette Wessel, William Ribarsky, Eric Sauda, Wenwen Dou",,,,,,,vimeo 289785751,10.1109/mcg.2017.3621223,,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:00 PM,12:20 PM
  CG&A,CGA,Name Profiler Toolkit,,"Feng Wang, Brett Hansen, Ryan Simmons, Ross Maciejewski",http://rmaciejewski.faculty.asu.edu/papers/2017/Names-CGA.pdf,"The Name Profiler Toolkit is a visual analytics system designed to enable the interactive exploration and analysis of forename and surname geographical distributions across the United States. Using demographic data from the US Census Bureau and Zillow, the toolkit lets users interactively compare distributions of names and name attributes.",,,,,vimeo 289785766,10.1109/mcg.2017.3621224,2017,2018,Other,Room III,Wednesday,CG&A Session 1,24-Oct,12:20 PM,12:40 PM
  VAST,TVCG,"SIRIUS: Dual, Symmetric, Interactive Dimension Reductions ",J),"Michelle Dowling, John Wenskovitch, J.T. Fry, Leanna House, Scotland Leman, Chris North",,,,,,,vimeo 289787667,10.1109/tvcg.2018.2865047,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:20 PM,2:40 PM
  TVCG,TVCG,A Perception-Driven Approach to Supervised Dimensionality Reduction for Visualization,T),"Yunhai Wang, Kang Feng, Xiaowei Chu, Jian Zhang, Chi-Wing Fu, Michael Sedlmair, Xiaohui Yu, Baoquan Chen",,,,,,,vimeo 289788938,10.1109/tvcg.2017.2701829,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,2:40 PM,3:00 PM
  VAST,VAST,SMARTexplore: Simplifying High-Dimensional Data Analysis through a Table-Based Visual Analytics Approach,C),"Michael Blumenschein, Michael Behrisch, Stefanie Schmid, Simon Butscher, Deborah R. Wahl, Karoline Villinger, Britta Renner, Harald Reiterer, Daniel Keim",https://bib.dbvis.de/uploadedFiles/BlumenscheinetalVAST2018SMARTexplore.pdf,"We present SMARTEXPLORE, a novel visual analytics technique that simplifies the identification and understanding of clusters, correlations, and complex patterns in high-dimensional data. The analysis is integrated into an interactive table-based visualization that maintains a consistent and familiar representation throughout the analysis. The visualization is tightly coupled with pattern matching, subspace analysis, reordering, and layout algorithms. To increase the analyst’s trust in the revealed patterns, SMARTEXPLORE automatically selects and computes statistical measures based on dimension and data properties. While existing approaches to analyzing highdimensional data (e.g., planar projections and Parallel coordinates) have proven effective, they typically have steep learning curves for non-visualization experts. Our evaluation, based on three expert case studies, confirms that non-visualization experts successfully reveal patterns in high-dimensional data when using SMARTEXPLORE.",,,,,vimeo 289787735,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:00 PM,3:20 PM
  TVCG,TVCG,ColorMapND: A Data-Driven Approach and Tool for Mapping Multivariate Data to Color,T),"Shenghui Cheng, Wei Xu, Klaus Mueller",,,,,,,vimeo 289788955,10.1109/tvcg.2018.2808489,,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:20 PM,3:40 PM
  VAST,VAST,EmbeddingVis: A Visual Analytics Approach to Comparative Network Embedding Inspection,C),"Quan Li, Kristanto Sean Njotoprawiro, Hammad Haleem, Qiaoan Chen, Chris YI, Xiaojuan Ma",https://arxiv.org/pdf/1808.09074.pdf,"Constructing latent vector representation for nodes in a network through embedding models has shown its practicality in many graph analysis applications, such as node classification, clustering, and link prediction. However, despite the high efficiency and accuracy of learning an embedding model, people have little clue of what information about the original network is preserved in the embedding vectors. The abstractness of low-dimensional vector representation, stochastic nature of the construction process, and non-transparent hyper-parameters all obscure understanding of network embedding results. Visualization techniques have been introduced to facilitate embedding vector inspection, usually by projecting the embedding space to a two-dimensional display. Although the existing visualization methods allow simple examination of the structure of embedding space, they cannot support in-depth exploration of the embedding vectors. In this paper, we design an exploratory visual analytics system that supports the comparative visual interpretation of embedding vectors at the cluster, instance, and structural levels. To be more specific, it facilitates comparison of what and how node metrics are preserved across different embedding models and investigation of relationships between node metrics and selected embedding vectors. Several case studies confirm the efficacy of our system. Experts’ feedback suggests that our approach indeed helps them better embrace the understanding of network embedding models.",,,,,vimeo 289788000,,2018,2018,VAST,"Convention Hall 1, Section C",Wednesday,High Dimensional Data,24-Oct,3:40 PM,4:00 PM
  InfoVis,TVCG,Evaluating 'Graphical Perception' with CNNs,,"Daniel Haehn, James Tompkin, Hanspeter Pfister",https://danielhaehn.com/papers/haehn2018evaluating.pdf,"Convolutional neural networks can successfully perform many computer vision tasks on images. For visualization, how do CNNs perform when applied to graphical perception tasks? We investigate this question by reproducing Cleveland and McGill's seminal 1984 experiments, which measured human perception efficiency of different visual encodings and defined elementary perceptual tasks for visualization. We measure the graphical perceptual capabilities of four network architectures on five different visualization tasks and compare to existing and new human performance baselines. While under limited circumstances CNNs are able to meet or outperform human task performance, we find that CNNs are not currently a good model for human graphical perception. We present the results of these experiments to foster the understanding of how CNNs succeed and fail when applied to data visualizations.",,https://github.com/rhoana/perception,,,vimeo 280506639,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:20 PM,2:40 PM
  InfoVis,TVCG,NLIZE: A Perturbation-Driven Visual Interrogation Tool for Analyzing and Interpreting Natural Language Inference Models,J),"Shusen Liu, Zhimin Li, Tao Li, Vivek Srikumar, Valerio Pascucci, Peer-Timo Bremer",http://www.sci.utah.edu/~shusenl/publications/paper_entailVis.pdf,"With the recent advances in deep learning, neural network models have obtained state-of-the-art performances for many linguistic tasks in natural language processing. However, this rapid progress also brings enormous challenges. The opaque nature of a neural network model leads to hard-to-debug-systems and difficult-to-interpret mechanisms. Here, we introduce a visualization system that, through a tight yet flexible integration between visualization elements and the underlying model, allows a user to interrogate the model by perturbing the input, internal state, and prediction while observing changes in other parts of the pipeline. We use the natural language inference problem as an example to illustrate how a perturbation-driven paradigm can help domain experts assess the potential limitation of a model, probe its inner states, and interpret and form hypotheses about fundamental model mechanisms such as attention.",,,,,vimeo 289784737,10.1109/tvcg.2018.2865230,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,2:40 PM,3:00 PM
  InfoVis,TVCG,Elastic Documents: Coupling Text and Tables through Contextual Visualizations for Enhanced Document Reading,J),"Sriram Karthik Badam, Zhicheng Liu, Niklas Elmqvist",http://users.umiacs.umd.edu/~elm/projects/elastic-documents/elastic-documents.pdf,"Today's data-rich documents are often complex datasets in themselves, consisting of information in different formats such as text, gures, and data tables. These additional media augment the textual narrative in the document. However, the static layout of a traditional for-print document often impedes deep understanding of its content because of the need to navigate to access content scattered throughout the text. In this paper, we seek to facilitate enhanced comprehension of such documents through a contextual visualization technique that couples text content with data tables contained in the document. We parse the text content and data tables, cross-link the components using a keyword-based matching algorithm, and generate on-demand visualizations based on the reader's current focus within a document. We evaluate this technique in a user study comparing our approach to a traditional reading experience. Results from our study show that (1) participants comprehend the content better with tighter coupling of text and data, (2) the contextual visualizations enable participants to develop better summaries that capture the main data-rich insights within the document, and (3) overall, our method enables participants to develop a more detailed understanding of the document content.",,,,,vimeo 289784966,,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:00 PM,3:20 PM
  InfoVis,TVCG,Augmenting Visualizations with Interactive Data Facts to Facilitate Interpretation and Communication,J),"Arjun Srinivasan, Steven Drucker, Alex Endert, John Stasko",https://arjun010.github.io/static/papers/voder-infovis18.pdf,"Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.",,,,,vimeo 289784943,10.1109/tvcg.2018.2865145,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:20 PM,3:40 PM
  InfoVis,TVCG,What Do We Talk About When We Talk About Dashboards?,J),"Alper Sarikaya, Michael Correll, Lyn Bartram, Melanie Tory, Danyel A Fisher",https://alper.datav.is/assets/publications/dashboards/dashboards-preprint.pdf,"Dashboards are one of the most common use cases for data visualization, and their design and contexts of use are considerably different from exploratory visualization tools. In this paper, we look at the broad scope of how dashboards are used in practice through an analysis of dashboard examples and documentation about their use. We systematically review the literature surrounding dashboard use, construct a design space for dashboards, and identify major dashboard types. We characterize dashboards by their design goals, levels of interaction, and the practices around them. Our framework and literature review suggest a number of fruitful research directions to better support dashboard design, implementation, and use.",,,,,vimeo 289785315,10.1109/tvcg.2018.2864903,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Text & Communication,24-Oct,3:40 PM,4:00 PM
  SciVis,TVCG,Visualization of Bubble Formation in Porous Media,J),"Hui Zhang, Steffen Frey, Holger Steeb, David Uribe, Thomas Ertl, Wenping Wang",,,,,,,vimeo 290325359,10.1109/tvcg.2018.2864506,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:20 PM,2:40 PM
  SciVis,TVCG,Gaia Sky: Navigating the Gaia Catalog,J),"Toni Sagristà Sellés, Stefan Jordan, Thomas Mueller, Filip Sadlo",,,,,,,vimeo 290325474,10.1109/tvcg.2018.2864508,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,2:40 PM,3:00 PM
  SciVis,TVCG,Interactive 3D Visual Analysis of Atmospheric Fronts,J),"Michael Alexander Kern, Timothy David Hewson, Andreas Schäfler, Rüdiger Westermann, Marc Rautenhaus",,,,,,,vimeo 290325737,10.1109/tvcg.2018.2864806,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:00 PM,3:20 PM
  SciVis,TVCG,An Interactive Framework for Visualization of Weather Forecast Ensembles,J),"Bo Ma, Alireza Entezari",,,,,,,vimeo 290325855,10.1109/tvcg.2018.2864815,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:20 PM,3:40 PM
  TVCG,TVCG,Animation Plans for Before-And-After Satellite images,T),"María-Jesús Lobo, Caroline Appert, Emmanuel Pietriga",,,,,,,vimeo 289789265,,,2018,SciVis,Estrel Hall A+B,Wednesday,Space and Physics,24-Oct,3:40 PM,4:00 PM
  VAST,TVCG,Vulnus: Visual Vulnerability Analysis for Network Security,J),"Marco Angelini, Graziano Blasilli, Tiziana Catarci, Simone Lenti, Giuseppe Santucci",,,,,,,vimeo 289787623,10.1109/tvcg.2018.2865028,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:00 AM,9:20 AM
  VAST,TVCG,GraphProtector: a Visual Interface for Employing and Assessing Multiple Privacy Preserving Graph Algorithms,J),"Xumeng Wang, Wei Chen, Huihua Guan, Wenlong Chen, Rusheng Pan, Jia-Kai Chou, Chris Bryan, Kwan-Liu Ma",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/GraphProtector.pdf,"Analyzing social networks reveals the relationships between individuals and groups in the data. However, such analysis can also lead to privacy exposure (whether intentionally or inadvertently): leaking the real-world identity of ostensibly anonymous individuals. Most sanitization strategies modify the graph's structure based on hypothesized tactics that an adversary would employ. While combining multiple anonymization schemes provides a more comprehensive privacy protection, deciding the appropriate set of techniques—along with evaluating how applying the strategies will affect the utility of the anonymized results—remains a signicant challenge. To address this problem, we introduce GraphProtector, a visual interface that guides a user through a privacy preservation pipeline. GraphProtector enables multiple privacy protection schemes which can be simultaneously combined together as a hybrid approach. To demonstrate the effectiveness of GraphProtector, we report several case studies and feedback collected from interviews with expert users in various scenarios.",,,,,vimeo 289787471,10.1109/TVCG.2018.2865021,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:20 AM,9:40 AM
  VAST,TVCG,Situ: Identifying and explaining suspicious behavior in networks,J),"John Goodall, Eric Ragan, Chad Steed, Joel Reed, Gregory Richardson, Kelly Huffer, Robert Bridges, Jason Laska",https://www.cise.ufl.edu/~eragan/papers/Goodall_Situ_2018.pdf,"Despite the best efforts of cyber security analysts, networked computing assets are routinely compromised, resulting in the loss of intellectual property, the disclosure of state secrets, and major nancial damages. Anomaly detection methods are benecial for detecting new types of attacks and abnormal network activity, but such algorithms can be difcult to understand and trust. Network operators and cyber analysts need fast and scalable tools to help identify suspicious behavior that bypasses automated security systems, but operators do not want another automated tool with algorithms they do not trust. Experts need tools to augment their own domain expertise and to provide a contextual understanding of suspicious behavior to help them make decisions. In this paper we present Situ, a visual analytics system for discovering suspicious behavior in streaming network data. Situ provides a scalable solution that combines anomaly detection with information visualization. The system's visualizations enable operators to identify and investigate the most anomalous events and IP addresses, and the tool provides context to help operators understand why they are anomalous. Finally, operators need tools that can be integrated into their workow and with their existing tools. This paper describes the Situ platform and its deployment in an operational network setting. We discuss how operators are currently using the tool in a large organization's security operations center and present the results of expert reviews with professionals.",,,,,vimeo 289787635,10.1109/TVCG.2018.2865029,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,"Security, Privacy, and Anomaly",25-Oct,9:40 AM,10:00 AM
  
negative.code: SciVis,SciVis,Ordering Perceptions about Perceptual Order,,"Roxana Bujack, Terece L Turton, David Rogers, James Ahrens",,,,,,,vimeo 290328692,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:35 PM,3:47 PM
  SciVis,SciVis,QuFlow: Visualizing Parameter Flow in Quantum Circuits for Understanding Quantum Computation,,"Siyuan Lin, Hao Jiang, Lingyun Sun",,,,,,,vimeo 290328716,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Visual Abstractions, Perceptual Study and Immersive Visualization",25-Oct,3:47 PM,3:59 PM
  VAST,TVCG,RuleMatrix: Visualizing and Understanding Classifiers with Rules,J),"Yao Ming, Huamin Qu, Enrico Bertini",https://arxiv.org/pdf/1807.06228.pdf,"With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.",http://myaooo.com/projects/rule-matrix/,,,,vimeo 289787299,10.1109/TVCG.2018.2864812,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:20 PM,4:40 PM
  VAST,TVCG,Seq2Seq-Vis: A Visual Debugging Tool for Sequence to Sequence Models,J),"Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M. Rush",https://arxiv.org/pdf/1804.09299.pdf,"Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and ”what if”-style exploration of trained sequence-to-sequence models through each stage of the translation process. The aim is to identify which patterns have been learned, to detect model errors, and to probe the model with counterfactual scenario. We demonstrate the utility of our tool through several real-world sequence-to-sequence use cases on large-scale models.",,https://github.com/HendrikStrobelt/Seq2Seq-Vis,,,vimeo 289787650,10.1109/TVCG.2018.2865044,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,4:40 PM,5:00 PM
  VAST,TVCG,Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,J),"Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David Ebert",https://arxiv.org/pdf/1808.00196.pdf,"Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models’ outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.",,,,,vimeo 289787203,10.1109/tvcg.2018.2864499,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:00 PM,5:20 PM
  VAST,TVCG,Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution,J),"Mennatallah El-Assady, Fabian Sperrle, Oliver Deussen, Daniel Keim, Christopher Collins",http://graphics.uni-konstanz.de/publikationen/ElAssady2018VisualAnalytic/visualanalyticstopic.pdf,"To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decisionmaking process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user’s domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-loop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.",,,,,vimeo 289787264,10.1109/tvcg.2018.2864769,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:20 PM,5:40 PM
  VAST,TVCG,VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning,J),"Dominik Sacha, Matthias Kraus, Daniel Keim, Min Chen",https://bib.dbvis.de/uploadedFiles/TVCG2864838.pdf,"While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely “VA-assisted ML”. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly",,,,,vimeo 289787814,10.1109/TVCG.2018.2864838,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:40 PM,6:00 PM
  InfoVis,TVCG,Mitigating the Attraction Effect with Visualizations,J),"Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri",https://hal.inria.fr/hal-01845004v2/document,"Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias � the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.",,https://aviz.fr/deletion,https://aviz.fr/deletion,,vimeo 289784657,10.1109/TVCG.2018.2865233,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:20 PM,4:40 PM
  InfoVis,TVCG,Face to Face: Evaluating Visual Comparison,J),"Brian David Ondov, Nicole Jardine, Niklas Elmqvist, Steven Franconeri",http://users.umiacs.umd.edu/~elm/projects/face2face/face2face.pdf,"Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.",,,,,vimeo 289785372,10.1109/tvcg.2018.2864884,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:40 PM,5:00 PM
  TVCG,TVCG,Task-Based Effectiveness of Basic Visualizations,T),"Bahador Saket, Alex Endert, Çagatay Demiralp",https://arxiv.org/pdf/1709.08546.pdf,"Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types�Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart�across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.",,,https://github.com/gtvalab/ChartsEffectiveness/tree/master/Raw-Data,,vimeo 289789506,10.1109/tvcg.2018.2829750,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:00 PM,5:20 PM
  InfoVis,TVCG,At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity,J),"Eugene Wu, Remco Chang, Abigail Mosca, Gabriel Ryan",,,,,,,vimeo 289784475,10.1109/TVCG.2018.2865264,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:20 PM,5:40 PM
  TVCG,TVCG,Correlation Judgment and Visualization Features: A Comparative Study,T),"Fumeng Yang, Lane Harrison, Ronald A. Rensink, Steven Franconeri, Remco Chang",https://github.com/Fumeng-Yang/VisualFeature_TVCG/blob/master/paper/VF_preprint.pdf,"Recent visualization research efforts have incorporated experimental techniques and perceptual models from the vision science community. Perceptual laws such as Weber's law, for example, have been used to model the perception of correlation in scatterplots. While this thread of research has progressively refined the modeling of the perception of correlation in scatterplots, it remains unclear as to why such perception can be modeled using relatively simple functions, e.g., linear and log-linear. In this paper, we investigate a longstanding hypothesis that people use visual features in a chart as a proxy for statistical measures like correlation. For a given scatterplot, we extract 49 candidate visual features and evaluate which best align with existing models and participant judgments. The results support the hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots. We discuss how this result may account for prior conflicting findings, and how visual features provide a baseline for future model-based approaches in visualization evaluation and design.",,,,,vimeo 289789613,10.1109/tvcg.2018.2810918,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:40 PM,6:00 PM
  SciVis,TVCG,Firefly: Illumination Drones for Interactive Visualization,J),"Sergej Stoppel, Magnus Paulson Erga, Stefan Bruckner",,,,,,,vimeo 290325505,10.1109/TVCG.2018.2864656,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:20 PM,4:40 PM
  SciVis,TVCG,CoDDA: A Flexible Copula-based Distribution Driven Analysis Framework for Large-Scale Multivariate Data,J),"Subhashis Hazarika, Soumya Dutta, Han-Wei Shen, Jen-Ping Chen",,,,,,,vimeo 290325658,10.1109/tvcg.2018.2864801,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:40 PM,5:00 PM
  SciVis,TVCG,"Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations ",J),"Timothy Basil Luciani, Andrew T Burks, Cassiano Sugiyama, Jonathan Komperda, Liz G.Elisabeta Marai",,,,,,,vimeo 290328058,10.1109/TVCG.2018.2864849,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:00 PM,5:20 PM
  TVCG,TVCG,A Model of Spatial Directness in Interactive Visualization,T),"Stefan Bruckner, Tobias Isenberg, Timo Ropinski, Alexander Wiebel",,,,,,,vimeo 289789140,10.1109/tvcg.2018.2848906,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:20 PM,5:40 PM
  TVCG,TVCG,Decal-Lenses: Interactive Lenses on Surfaces for Multivariate Visualization,T),"Allan Rocha, Julio Daniel Silva, Usman R. Alim, Sheelagh Carpendale, Mario Costa Sousa",,,,,,,vimeo 289789528,10.1109/tvcg.2018.2850781,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,5:40 PM,6:00 PM
  SciVis,SciVis,Cluster-Based Visualization for Merger Tree Data: The Challenge of Missing Expectations,,"Annie Preston, Kwan-Liu Ma",,,,,,,vimeo 290328886,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:20 PM,4:32 PM
  SciVis,SciVis,Visualization of Uncertainty for Computationally Intensive Simulations Using High Fidelity Emulators,,"Ayan Biswas, Earl Lawrence, James Ahrens",,,,,,,vimeo 290328642,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:32 PM,4:44 PM
  SciVis,SciVis,A Lagrangian Method for Extracting Eddy Boundaries in the Red Sea and the Gulf of Aden,,"Anke Friederici, Habib Toye, Ibrahim Hoteit, Tino Weinkauf, Holger Theisel, Markus Hadwiger",,,,,,,vimeo 290328836,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,4:45 PM,4:57 PM
  