anchor.diff:  ReviewVenue,PublicationVenue,Title,delete,Authors,AuthorPDF,Abstract,ExplanationPage,SourceMaterials,Data,Preregistered,Video,DOI,PublicationYear,ConferenceYear,ConferenceTrack,ConferenceRoom,ConferenceDay,ConferenceSession,date,ConferenceTimeStart,ConferenceTimeEnd
  -VAST,TVCG,TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis,J),"Dongyu Liu, Panpan Xu, Liu Ren",https://lliquid.github.io/homepage/files/draft_vis18_st.pdf,"Consider a multi-dimensional spatio-temporal (ST) dataset where each entry is a numerical measure defined by the corresponding temporal, spatial and other domain-specific dimensions. A typical approach to explore such data utilizes interactive visualizations with multiple coordinated views. Each view displays the aggregated measures along one or two dimensions. By brushing on the views, analysts can obtain detailed information. However, this approach often cannot provide sufficient guidance for analysts to identify patterns hidden within subsets of data. Without a priori hypotheses, analysts need to manually select and iterate through different slices to search for patterns, which can be a tedious and lengthy process. In this work, we model multidimensional ST data as tensors and propose a novel piecewise rank-one tensor decomposition algorithm which supports automatically slicing the data into homogeneous partitions and extracting the latent patterns in each partition for comparison and visual summarization. The algorithm optimizes a quantitative measure about how faithfully the extracted patterns visually represent the original data. Based on the algorithm we further propose a visual analytics framework that supports a top-down, progressive partitioning workflow for level-of-detail multidimensional ST data exploration. We demonstrate the general applicability and effectiveness of our technique on three datasets from different application domains: regional sales trend analysis, customer traffic analysis in department stores, and taxi trip analysis with origin-destination (OD) data. We further interview domain experts to verify the usability of the prototype.",,,,,video https://lliquid.github.io/homepage/files/video_vast18_tpflow_1920x1080_v7.mp4,10.1109/tvcg.2018.2865018,2018,2018,Other,"Convention Hall 1, Section C+D",Tuesday,VIS Awards & Best Papers,23-Oct,10:00 AM,10:20 AM
  -InfoVis,TVCG,Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco,J),"Dominik Moritz, Chenglong Wang, Greg L. Nelson, Halden Lin, Adam M. Smith, Bill Howe, Jeffrey Heer",https://osf.io/3eg9c/,"There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.",https://medium.com/@uwdata/draco-representing-applying-learning-visualization-design-guidelines-64ce20287e9d,https://uwdata.github.io/draco/,,,vimeo 289784521,10.1109/tvcg.2018.2865240,2018,2018,Other,"Convention Hall 1, Section C+D",Tuesday,VIS Awards & Best Papers,23-Oct,10:20 AM,10:40 AM
  -SciVis,TVCG,Deadeye: A Novel Preattentive Visualization Technique Based on Dichoptic Presentation,J),"Andrey Krekhov, Jens Krueger",,,,,,,vimeo 290325240,10.1109/tvcg.2018.2864498,2018,2018,Other,"Convention Hall 1, Section C+D",Tuesday,VIS Awards & Best Papers,23-Oct,10:40 AM,11:00 AM
  -VAST,TVCG,Evaluating Multi-Dimensional Visualizations for Understanding Fuzzy Clusters,J),"Ying Zhao, Feng Luo, Minghui Chen, Yingchao Wang, Jiazhi Xia, Fangfang Zhou, Yunhai Wang, Yi Chen, Wei Chen",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/Fuzzy%20Clusters.pdf,"Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.",,,,,vimeo 289787891,10.1109/tvcg.2018.2865020,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:20 PM,2:40 PM
  -VAST,VAST,The Effect of Proximity in Social Data Charts on Perceived Unity,C),"Marlen Promann, Sabine Brunswicker",,,,,,,vimeo 289787716,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:40 PM,3:00 PM
  -VAST,TVCG,Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices,J),"Sara Alspaugh, Nava Zokaei, Andrea Liu, Cindy Jin, Marti Hearst",http://people.ischool.berkeley.edu/~hearst/papers/vast2018.pdf,"We report the results of interviewing thirty professional data analysts working in a range of industrial, academic, and regulatory environments. This study focuses on participants' descriptions of exploratory activities and tool usage in these activities. Highlights of the ndings include: distinctions between exploration as a precursor to more directed analysis versus truly open-ended exploration; conrmation that some analysts see ""finding something interesting"" as a valid goal of data exploration while others explicitly disavow this goal; conicting views about the role of intelligent tools in data exploration; and pervasive use of visualization for exploration, but with only a subset using direct manipulation interfaces. These findings provide guidelines for future tool development, as well as a better understanding of the meaning of the term ""data exploration"" based on the words of practitioners ""in the wild.""",,,,,vimeo 289787961,10.1109/TVCG.2018.2865040,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:00 PM,3:20 PM
  -VAST,VAST,The Effect of Semantic Interaction on Foraging in Text Analysis,C),"John Wenskovitch, Lauren Bradel, Michelle Dowling, Leanna House, Chris North",https://infovis.cs.vt.edu/sites/default/files/effect-semantic-interaction.pdf,"Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users’ semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), “semantic interaction foraging” (SIF) occurs as a result of the user’s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.",,,,,vimeo 289787752,,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:20 PM,3:40 PM
  -VAST,TVCG,An Information-Theoretic Approach to the Cost-benefit Analysis of Visualization in Virtual Environments,J),"Min Chen, Kelly Gaither, Nigel John, Brian McCann",,,,,,,vimeo 289787910,10.1109/TVCG.2018.2865025,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:40 PM,4:00 PM
  -InfoVis,TVCG,Shape-preserving Star Coordinates,J),"Vladimir Molchanov, Lars Linsen",,,,,,,vimeo 289785145,10.1109/tvcg.2018.2865118,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:20 PM,2:40 PM
  -TVCG,TVCG,Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer,T),"Jürgen Bernard, David Sessler, Jörn Kohlhammer, Roy A. Ruddle",http://eprints.whiterose.ac.uk/128739/1/bernard-ieee-tvcg-dashboard-networks.pdf,"In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.",,,,,vimeo 289789053,10.1109/tvcg.2018.2803829,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:40 PM,3:00 PM
  -InfoVis,TVCG,SRVis: Towards Better Spatial Integration in Ranking Visualization,J),"Di Weng, Ran Chen, Zikun Deng, Feiran Wu, Jingmin Chen, Yingcai Wu",http://zjuvis.org/files/srvis.pdf,"Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.",https://srvis.zjuvis.org/,,,,vimeo 289784783,10.1109/TVCG.2018.2865126,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:00 PM,3:20 PM
  -InfoVis,TVCG,A Declarative Rendering Model for Multiclass Density Maps,J),"Jaemin Jo, Frédéric Vernier, Pierre Dragicevic, Jean-Daniel Fekete",https://hal.inria.fr/hal-01848427/document,"Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model—a simple yet expressive JSON grammar associated with visual semantics—that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.",https://jaeminjo.github.io/Multiclass-Density-Maps/,https://github.com/e-/Multiclass-Density-Maps,,,vimeo 289785026,10.1109/TVCG.2018.2865141,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:20 PM,3:40 PM
  -InfoVis,TVCG,DimReader: Axis lines that explain non-linear projections,J),"Rebecca Faust, David Glickenstein, Carlos Scheidegger",https://arxiv.org/pdf/1710.00992.pdf,"Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular with visualization researchers and experienced data analysts, but present serious problems of interpretation. In this paper, we present DimReader, a technique that recovers readable axes from such techniques. DimReader is based on analyzing infinitesimal perturbations of the dataset with respect to variables of interest. The perturbations define exactly how we want to change each point in the original dataset and we measure the effect that these changes have on the projection. The recovered axes are in direct analogy with the axis lines (grid lines) of traditional scatterplots. We also present methods for discovering perturbations on the input data that change the projection the most. The calculation of the perturbations is efficient and easily integrated into programs written in modern programming languages. We present results of DimReader on a variety of NDR methods and datasets both synthetic and real-life, and show how it can be used to compare different NDR methods. Finally, we discuss limitations of our proposal and situations where further research is needed.",,,,,vimeo 289784574,10.1109/tvcg.2018.2865194,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:40 PM,4:00 PM
  +VAST,TVCG,TPFlow: Progressive Partition and Multidimensional Pattern Extraction for Large-Scale Spatio-Temporal Data Analysis,J),"Dongyu Liu, Panpan Xu, Liu Ren",https://lliquid.github.io/homepage/files/draft_vis18_st.pdf,"Consider a multi-dimensional spatio-temporal (ST) dataset where each entry is a numerical measure defined by the corresponding temporal, spatial and other domain-specific dimensions. A typical approach to explore such data utilizes interactive visualizations with multiple coordinated views. Each view displays the aggregated measures along one or two dimensions. By brushing on the views, analysts can obtain detailed information. However, this approach often cannot provide sufficient guidance for analysts to identify patterns hidden within subsets of data. Without a priori hypotheses, analysts need to manually select and iterate through different slices to search for patterns, which can be a tedious and lengthy process. In this work, we model multidimensional ST data as tensors and propose a novel piecewise rank-one tensor decomposition algorithm which supports automatically slicing the data into homogeneous partitions and extracting the latent patterns in each partition for comparison and visual summarization. The algorithm optimizes a quantitative measure about how faithfully the extracted patterns visually represent the original data. Based on the algorithm we further propose a visual analytics framework that supports a top-down, progressive partitioning workflow for level-of-detail multidimensional ST data exploration. We demonstrate the general applicability and effectiveness of our technique on three datasets from different application domains: regional sales trend analysis, customer traffic analysis in department stores, and taxi trip analysis with origin-destination (OD) data. We further interview domain experts to verify the usability of the prototype.",,,,,video https://lliquid.github.io/homepage/files/video_vast18_tpflow_1920x1080_v7.mp4,10.1109/tvcg.2018.2865018,2018,2018,Other,"Hall 1, Section C+D",Tuesday,VIS Awards & Best Papers,23-Oct,10:00 AM,10:20 AM
  +InfoVis,TVCG,Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco,J),"Dominik Moritz, Chenglong Wang, Greg L. Nelson, Halden Lin, Adam M. Smith, Bill Howe, Jeffrey Heer",https://osf.io/3eg9c/,"There exists a gap between visualization design guidelines and their application in visualization tools. While empirical studies can provide design guidance, we lack a formal framework for representing design knowledge, integrating results across studies, and applying this knowledge in automated design tools that promote effective encodings and facilitate visual exploration. We propose modeling visualization design knowledge as a collection of constraints, in conjunction with a method to learn weights for soft constraints from experimental data. Using constraints, we can take theoretical design knowledge and express it in a concrete, extensible, and testable form: the resulting models can recommend visualization designs and can easily be augmented with additional constraints or updated weights. We implement our approach in Draco, a constraint-based system based on Answer Set Programming (ASP). We demonstrate how to construct increasingly sophisticated automated visualization design systems, including systems based on weights learned directly from the results of graphical perception experiments.",https://medium.com/@uwdata/draco-representing-applying-learning-visualization-design-guidelines-64ce20287e9d,https://uwdata.github.io/draco/,,,vimeo 289784521,10.1109/tvcg.2018.2865240,2018,2018,Other,"Hall 1, Section C+D",Tuesday,VIS Awards & Best Papers,23-Oct,10:20 AM,10:40 AM
  +SciVis,TVCG,Deadeye: A Novel Preattentive Visualization Technique Based on Dichoptic Presentation,J),"Andrey Krekhov, Jens Krueger",,,,,,,vimeo 290325240,10.1109/tvcg.2018.2864498,2018,2018,Other,"Hall 1, Section C+D",Tuesday,VIS Awards & Best Papers,23-Oct,10:40 AM,11:00 AM
  +VAST,TVCG,Evaluating Multi-Dimensional Visualizations for Understanding Fuzzy Clusters,J),"Ying Zhao, Feng Luo, Minghui Chen, Yingchao Wang, Jiazhi Xia, Fangfang Zhou, Yunhai Wang, Yi Chen, Wei Chen",http://www.cad.zju.edu.cn/home/vagblog/VAG_Work/Fuzzy%20Clusters.pdf,"Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.",,,,,vimeo 289787891,10.1109/tvcg.2018.2865020,2018,2018,VAST,"Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:20 PM,2:40 PM
  +VAST,VAST,The Effect of Proximity in Social Data Charts on Perceived Unity,C),"Marlen Promann, Sabine Brunswicker",,,,,,,vimeo 289787716,,2018,2018,VAST,"Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,2:40 PM,3:00 PM
  +VAST,TVCG,Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices,J),"Sara Alspaugh, Nava Zokaei, Andrea Liu, Cindy Jin, Marti Hearst",http://people.ischool.berkeley.edu/~hearst/papers/vast2018.pdf,"We report the results of interviewing thirty professional data analysts working in a range of industrial, academic, and regulatory environments. This study focuses on participants' descriptions of exploratory activities and tool usage in these activities. Highlights of the ndings include: distinctions between exploration as a precursor to more directed analysis versus truly open-ended exploration; conrmation that some analysts see ""finding something interesting"" as a valid goal of data exploration while others explicitly disavow this goal; conicting views about the role of intelligent tools in data exploration; and pervasive use of visualization for exploration, but with only a subset using direct manipulation interfaces. These findings provide guidelines for future tool development, as well as a better understanding of the meaning of the term ""data exploration"" based on the words of practitioners ""in the wild.""",,,,,vimeo 289787961,10.1109/TVCG.2018.2865040,2018,2018,VAST,"Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:00 PM,3:20 PM
  +VAST,VAST,The Effect of Semantic Interaction on Foraging in Text Analysis,C),"John Wenskovitch, Lauren Bradel, Michelle Dowling, Leanna House, Chris North",https://infovis.cs.vt.edu/sites/default/files/effect-semantic-interaction.pdf,"Completing text analysis tasks is a continuous sensemaking loop of foraging for information and incrementally synthesizing it into hypotheses. Past research has shown the advantages of using spatial workspaces as a means for synthesizing information through externalizing hypotheses and creating spatial schemas. However, spatializing the entirety of datasets becomes prohibitive as the number of documents available to the analysts grows, particularly when only a small subset are relevant to the task at hand. StarSPIRE is a visual analytics tool designed to explore collections of documents, leveraging users’ semantic interactions to steer (1) a synthesis model that aids in document layout, and (2) a foraging model to automatically retrieve new relevant information. In contrast to traditional keyword search foraging (KSF), “semantic interaction foraging” (SIF) occurs as a result of the user’s synthesis actions. To quantify the value of semantic interaction foraging, we use StarSPIRE to evaluate its utility for an intelligence analysis sensemaking task. Semantic interaction foraging accounted for 26% of useful documents found, and it also resulted in increased synthesis interactions and improved sensemaking task performance by users in comparison to only using keyword search.",,,,,vimeo 289787752,,2018,2018,VAST,"Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:20 PM,3:40 PM
  +VAST,TVCG,An Information-Theoretic Approach to the Cost-benefit Analysis of Visualization in Virtual Environments,J),"Min Chen, Kelly Gaither, Nigel John, Brian McCann",,,,,,,vimeo 289787910,10.1109/TVCG.2018.2865025,2018,2018,VAST,"Hall 1, Section C",Tuesday,Evaluation and Theory,23-Oct,3:40 PM,4:00 PM
  +InfoVis,TVCG,Shape-preserving Star Coordinates,J),"Vladimir Molchanov, Lars Linsen",,,,,,,vimeo 289785145,10.1109/tvcg.2018.2865118,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:20 PM,2:40 PM
  +TVCG,TVCG,Using Dashboard Networks to Visualize Multiple Patient Histories: A Design Study on Post-operative Prostate Cancer,T),"Jürgen Bernard, David Sessler, Jörn Kohlhammer, Roy A. Ruddle",http://eprints.whiterose.ac.uk/128739/1/bernard-ieee-tvcg-dashboard-networks.pdf,"In this design study, we present a visualization technique that segments patients' histories instead of treating them as raw event sequences, aggregates the segments using criteria such as the whole history or treatment combinations, and then visualizes the aggregated segments as static dashboards that are arranged in a dashboard network to show longitudinal changes. The static dashboards were developed in nine iterations, to show 15 important attributes from the patients' histories. The final design was evaluated with five non-experts, five visualization experts and four medical experts, who successfully used it to gain an overview of a 2,000 patient dataset, and to make observations about longitudinal changes and differences between two cohorts. The research represents a step-change in the detail of large-scale data that may be successfully visualized using dashboards, and provides guidance about how the approach may be generalized.",,,,,vimeo 289789053,10.1109/tvcg.2018.2803829,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,2:40 PM,3:00 PM
  +InfoVis,TVCG,SRVis: Towards Better Spatial Integration in Ranking Visualization,J),"Di Weng, Ran Chen, Zikun Deng, Feiran Wu, Jingmin Chen, Yingcai Wu",http://zjuvis.org/files/srvis.pdf,"Interactive ranking techniques have substantially promoted analysts' ability in making judicious and informed decisions effectively based on multiple criteria. However, the existing techniques cannot satisfactorily support the analysis tasks involved in ranking large-scale spatial alternatives, such as selecting optimal locations for chain stores, where the complex spatial contexts involved are essential to the decision-making process. Limitations observed in the prior attempts of integrating rankings with spatial contexts motivate us to develop a context-integrated visual ranking technique. Based on a set of generic design requirements we summarized by collaborating with domain experts, we propose SRVis, a novel spatial ranking visualization technique that supports efficient spatial multi-criteria decision-making processes by addressing three major challenges in the aforementioned context integration, namely, a) the presentation of spatial rankings and contexts, b) the scalability of rankings' visual representations, and c) the analysis of context-integrated spatial rankings. Specifically, we encode massive rankings and their cause with scalable matrix-based visualizations and stacked bar charts based on a novel two-phase optimization framework that minimizes the information loss, and the flexible spatial filtering and intuitive comparative analysis are adopted to enable the in-depth evaluation of the rankings and assist users in selecting the best spatial alternative. The effectiveness of the proposed technique has been evaluated and demonstrated with an empirical study of optimization methods, two case studies, and expert interviews.",https://srvis.zjuvis.org/,,,,vimeo 289784783,10.1109/TVCG.2018.2865126,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:00 PM,3:20 PM
  +InfoVis,TVCG,A Declarative Rendering Model for Multiclass Density Maps,J),"Jaemin Jo, Frédéric Vernier, Pierre Dragicevic, Jean-Daniel Fekete",https://hal.inria.fr/hal-01848427/document,"Multiclass maps are scatterplots, multidimensional projections, or thematic geographic maps where data points have a categorical attribute in addition to two quantitative attributes. This categorical attribute is often rendered using shape or color, which does not scale when overplotting occurs. When the number of data points increases, multiclass maps must resort to data aggregation to remain readable. We present multiclass density maps: multiple 2D histograms computed for each of the category values. Multiclass density maps are meant as a building block to improve the expressiveness and scalability of multiclass map visualization. In this article, we first present a short survey of aggregated multiclass maps, mainly from cartography. We then introduce a declarative model—a simple yet expressive JSON grammar associated with visual semantics—that specifies a wide design space of visualizations for multiclass density maps. Our declarative model is expressive and can be efficiently implemented in visualization front-ends such as modern web browsers. Furthermore, it can be reconfigured dynamically to support data exploration tasks without recomputing the raw data. Finally, we demonstrate how our model can be used to reproduce examples from the past and support exploring data at scale.",https://jaeminjo.github.io/Multiclass-Density-Maps/,https://github.com/e-/Multiclass-Density-Maps,,,vimeo 289785026,10.1109/TVCG.2018.2865141,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:20 PM,3:40 PM
  +InfoVis,TVCG,DimReader: Axis lines that explain non-linear projections,J),"Rebecca Faust, David Glickenstein, Carlos Scheidegger",https://arxiv.org/pdf/1710.00992.pdf,"Non-linear dimensionality reduction (NDR) methods such as LLE and t-SNE are popular with visualization researchers and experienced data analysts, but present serious problems of interpretation. In this paper, we present DimReader, a technique that recovers readable axes from such techniques. DimReader is based on analyzing infinitesimal perturbations of the dataset with respect to variables of interest. The perturbations define exactly how we want to change each point in the original dataset and we measure the effect that these changes have on the projection. The recovered axes are in direct analogy with the axis lines (grid lines) of traditional scatterplots. We also present methods for discovering perturbations on the input data that change the projection the most. The calculation of the perturbations is efficient and easily integrated into programs written in modern programming languages. We present results of DimReader on a variety of NDR methods and datasets both synthetic and real-life, and show how it can be used to compare different NDR methods. Finally, we discuss limitations of our proposal and situations where further research is needed.",,,,,vimeo 289784574,10.1109/tvcg.2018.2865194,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Multiple Dimensions,23-Oct,3:40 PM,4:00 PM
   SciVis,TVCG,Recirculation Surfaces for Flow Visualization,J),"Thomas Wilde, Christian Rössl, Holger Theisel",http://vc.cs.ovgu.de/files/publications/2018/Wilde_2018_VISa.pdf,"We present a formal approach to the visual analysis of recirculation in flows by introducing recirculation surfaces for 3D unsteady flow fields. Recirculation surfaces are the loci where massless particle integration returns to its starting point after some variable, finite integration. We give a rigorous definition of recirculation surfaces as 2-manifolds embedded in 5D space and study their properties. Based on this we construct an algorithm for their extraction, which searches for intersections of a recirculation surface with lines defined in 3D. This reduces the problem to a repeated search for critical points in 3D vector fields. We provide a uniform sampling of the search space paired with a surface reconstruction and visualize results. This way, we present the first algorithm for a comprehensive feature extraction in the 5D flow map of a 3D flow. The problem of finding isolated closed orbits in steady vector fields occurs as a special case of recirculation surfaces. This includes isolated closed orbits with saddle behavior. We show recirculation surfaces for a number of artificial and real flow data sets.",,,,,video http://isgwww.cs.uni-magdeburg.de/visual/files/publications/2018/Wilde_2018_VISa.mp4,10.1109/tvcg.2018.2864813,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,2:20 PM,2:40 PM
   SciVis,TVCG,Objective Vortex Corelines of Finite-sized Objects in Fluid Flows,J),"Tobias Günther, Holger Theisel",https://isgwww.cs.uni-magdeburg.de/visual/files/publications/2018/Guenther_2018_VIS.pdf,"Vortices are one of the most-frequently studied phenomena in fluid flows. The center of the rotating motion is called the vortex coreline and its successful detection strongly depends on the choice of the reference frame. The optimal frame moves with the center of the vortex, which incidentally makes the observed fluid flow steady and thus standard vortex coreline extractors such as Sujudi-Haimes become applicable. Recently, an objective optimization framework was proposed that determines a near-steady reference frame for tracer particles. In this paper, we extend this technique to the detection of vortex corelines of inertial particles. An inertial particle is a finite-sized object that is carried by a fluid flow. In contrast to the usual tracer particles, they do not move tangentially with the flow, since they are subject to gravity and exhibit mass-dependent inertia. Their particle state is determined by their position and own velocity, which makes the search for the optimal frame a high-dimensional problem. We demonstrate in this paper that the objective detection of an inertial vortex coreline can be reduced in 2D to a critical point search in 2D. For 3D flows, however, the vortex coreline criterion remains a parallel vectors condition in 6D. To detect the vortex corelines we propose a recursive subdivision approach that is tailored to the underlying structure of the 6D vectors. The resulting algorithm is objective, and we demonstrate the vortex coreline extraction in a number of 2D and 3D vector fields.",,,,,video http://isgwww.cs.uni-magdeburg.de/visual/files/publications/2018/Guenther_2018_VIS.mp4,10.1109/TVCG.2018.2864828,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,2:40 PM,3:00 PM
   TVCG,TVCG,Towards High-quality Visualization of Superfluid Vortices,T),"Yulong Guo, Xiaopei Liu, Chi Xiong, Xuemiao Xu, Chi-Wing Fu",https://arxiv.org/pdf/1710.02630.pdf,"Superfluidity is a special state of matter exhibiting macroscopic quantum phenomena and acting like a fluid with zero viscosity. In such a state, superfluid vortices exist as phase singularities of the model equation with unique distributions. This paper presents novel techniques to aid the visual understanding of superfluid vortices based on the state-of-the-art non-linear Klein-Gordon equation, which evolves a complex scalar field, giving rise to special vortex lattice/ring structures with dynamic vortex formation, reconnection, and Kelvin waves, etc. By formulating a numerical model with theoretical physicists in superfluid research, we obtain high-quality superfluid flow data sets without noise-like waves, suitable for vortex visualization. By further exploring superfluid vortex properties, we develop a new vortex identification and visualization method: a novel mechanism with velocity circulation to overcome phase singularity and an orthogonal-plane strategy to avoid ambiguity. Hence, our visualizations can help reveal various superfluid vortex structures and enable domain experts for related visual analysis, such as the steady vortex lattice/ring structures, dynamic vortex string interactions with reconnections and energy radiations, where the famous Kelvin waves and decaying vortex tangle were clearly observed. These visualizations have assisted physicists to verify the superfluid model, and further explore its dynamic behavior more intuitively.",,,,,youtube TlEQbPSbYTQ ,10.1109/TVCG.2017.2719684,2017,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,3:00 PM,3:20 PM
   TVCG,TVCG,Semantic Flow Graph: A Framework for Discovering Object Relationships in Flow Fields,T),"Jun Tao, Chaoli Wang, Nitesh Chawla, Lei Shi, Seung Hyun Kim",https://www3.nd.edu/~cwang11/research/tvcg18-sfg.pdf,"Visual exploration of flow fields is important for studying dynamic systems. We introduce semantic flow graph (SFG), a novel graph representation and interaction framework that enables users to explore the relationships among key objects (i.e., field lines, features, and spatiotemporal regions) of both steady and unsteady flow fields. The objects and their relationships are organized as a heterogeneous graph. We assign each object a set of attributes, based on which a semantic abstraction of the heterogeneous graph is generated. This semantic abstraction is SFG. We design a suite of operations to explore the underlying flow fields based on this graph representation and abstraction mechanism. Users can flexibly reconfigure SFG to examine the relationships among groups of objects at different abstraction levels. Three linked views are developed to display SFG, its node split criteria and history, and the objects in the spatial volume. For simplicity, we introduce SFG construction and exploration for steady flow fields with critical points being the only features. Then we demonstrate that SFG can be naturally extended to deal with unsteady flow fields and multiple types of features. We experiment with multiple data sets and conduct an expert evaluation to demonstrate the effectiveness of our approach. ",,,,,video http://www.nd.edu/~cwang11/research/tvcg18-sfg.wmv,10.1109/tvcg.2017.2773071,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Flow Features,23-Oct,3:20 PM,3:40 PM
  -VAST,TVCG,Visual Abstraction of the Large Scale Geospatial Origin-Destination Movement Data,J),"Zhiguang Zhou, Linhao Meng, Cheng Tang, Ying Zhao, Zhiyong Guo, Miaoxin Hu, Wei Chen",,,,,,,vimeo 289787224,10.1109/TVCG.2018.2864503,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,4:20 PM,4:40 PM
  -VAST,TVCG,Analysis of Flight Variability: a Systematic Approach,J),"Natalia Andrienko, Gennady Andrienko, Jose Manuel Cordero Garcia, David Scarlatti",http://openaccess.city.ac.uk/20334/1/analysis-flight-variability.pdf,"In movement data analysis, there exists a problem of comparing multiple trajectories of moving objects to common or distinct reference trajectories. We introduce a general conceptual framework for comparative analysis of trajectories and an analytical procedure, which consists of (1) finding corresponding points in pairs of trajectories, (2) computation of pairwise difference measures, and (3) interactive visual analysis of the distributions of the differences with respect to space, time, set of moving objects, trajectory structures, and spatio-temporal context. We propose a combination of visualisation, interaction, and data transformation techniques supporting the analysis and demonstrate the use of our approach for solving a challenging problem from the aviation domain.",,,,,vimeo 289787283,10.1109/TVCG.2018.2864811,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,4:40 PM,5:00 PM
  -VAST,TVCG,ForVizor: Visualizing Spatio-Temporal Team Formations in Soccer,J),"Yingcai Wu, Xiao Xie, Jiachen Wang, Dazhen Deng, Hongye Liang, Hui Zhang, Shoubin Cheng, Wei Chen",http://zjuvis.org/files/forvizor.pdf,"Regarded as a high-level tactic in soccer, a team formation assigns players different tasks and indicates their active regions on the pitch, thereby influencing the team performance significantly. Analysis of formations in soccer has become particularly indispensable for soccer analysts. However, formations of a team are intrinsically time-varying and contain inherent spatial information. The spatio-temporal nature of formations and other characteristics of soccer data, such as multivariate features, make analysis of formations in soccer a challenging problem. In this study, we closely worked with domain experts to characterize domain problems of formation analysis in soccer and formulated several design goals. We design a novel spatio-temporal visual representation of changes in team formation, allowing analysts to visually analyze the evolution of formations and track the spatial flow of players within formations over time. Based on the new design, we further design and develop ForVizor, a visual analytics system, which empowers users to track the spatio-temporal changes in formation and understand how and why such changes occur. With ForVizor, domain experts conduct formation analysis of two games. Analysis results with insights and useful feedback are summarized in two case studies.",,,,,vimeo 289787566,10.1109/tvcg.2018.2865041,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:00 PM,5:20 PM
  -VAST,TVCG,MotionRugs: Visualizing Collective Trends in Space and Time,J),"Juri Buchmuller, Dominik Jackle, Eren Cakmak, Ulrik Brandes, Daniel Keim",https://bib.dbvis.de/uploadedFiles/MotionRugsPreprint.pdf,"Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.",,https://github.com/jbuchmueller/motionrugs,,,youtube xSewCmdgN40,10.1109/tvcg.2018.2865049,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:20 PM,5:40 PM
  -VAST,TVCG,Identification of Temporally Varying Areas of Interest in Long Duration Eye Tracking Data Sets,J),"Prithiviraj Kaliappa Gounder Muthumanickam, Katerina Vrotsou, Aida Nordman, Jimmy Johansson, Matthew Cooper",,,,,,,vimeo 289787582,10.1109/TVCG.2018.2865042,2018,2018,VAST,"Convention Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:40 PM,6:00 PM
  -InfoVis,TVCG,A Heuristic Approach to Value-Driven Evaluation of Visualizations,J),"Emily Wall, Meeshu Agnihotri, Laura Matzen, Kristin Divis, Michael Haass, Alex Endert, John Stasko",https://www.cc.gatech.edu/~ewall9/media/papers/ValueVIS2018.pdf,"Recently, an approach for determining the value of a visualization was proposed, one moving beyond simple measurements of task accuracy and speed. The value equation contains components for the time savings a visualization provides, the insights and insightful questions it spurs, the overall essence of the data it conveys, and the confidence about the data and its domain it inspires. This articulation of value is purely descriptive, however, providing no actionable method of assessing a visualization's value. In this work, we create a heuristic-based evaluation methodology to accompany the value equation for assessing interactive visualizations. We refer to the methodology colloquially as ICE-T, based on an anagram of the four value components. Our approach breaks the four components down into guidelines, each of which is made up of a small set of low-level heuristics. Evaluators who have knowledge of visualization design principles then assess the visualization with respect to the heuristics. We conducted an initial trial of the methodology on three interactive visualizations of the same data set, each evaluated by 15 visualization experts. We found that the methodology showed promise, obtaining consistent ratings across the three visualizations and mirroring judgments of the utility of the visualizations by instructors of the course in which they were developed.",,,,,vimeo 289784928,10.1109/tvcg.2018.2865146,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,4:20 PM,4:40 PM
  -InfoVis,TVCG,Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web,J),"Mi Feng, Evan Peck, Lane Harrison",https://osf.io/9wqgk/,"The diverse and vibrant ecosystem of interactive visualizations on the web presents an opportunity for researchers and practitioners to observe and analyze how everyday people interact with data visualizations. However, existing metrics of visualization interaction behavior used in research do not fully reveal the breadth of peoples' open-ended explorations with visualizations. One possible way to address this challenge is to determine high-level goals for visualization interaction metrics, and infer corresponding features from user interaction data that characterize different aspects of peoples' explorations of visualizations. In this paper, we address this challenge by identifying needs for visualization behavior analysis, and by developing corresponding candidate features that can be inferred from users' interaction data with visualization. We then propose metrics that capture novel aspects of peoples' open-ended explorations, including exploration uniqueness and exploration pacing. We evaluate these metrics along with four other metrics recently proposed in visualization literature by applying them to interaction data from prior visualization studies. The results of these evaluations suggest that these new metrics 1) reveal new characteristics of peoples' use of visualizations, 2) can be used to evaluate statistical differences between visualization designs, and 3) are statistically independent of prior metrics used in visualization research. We discuss implications of these results for future studies, including the potential for applying these metrics in visualization interaction analysis, as well as emerging challenges in developing a design space of metrics for visualization engagement.",,,https://osf.io/dx43q/wiki/home/,,vimeo 289785167,10.1109/tvcg.2018.2865117,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,4:40 PM,5:00 PM
  -TVCG,TVCG,Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs,T),"Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex",https://www.biorxiv.org/content/early/2018/02/27/128579,"The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.",,https://github.com/caleydo/lineage,,,vimeo 289789223,10.1109/tvcg.2018.2811488,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,5:00 PM,5:20 PM
  -InfoVis,TVCG,IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support,J),"Yixuan Zhang, Kartik Chanana, Cody Dunne",https://github.com/zjanice/PHI/blob/master/IDMVis_IEEEVIS18_preprint.pdf,"Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician—making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.",,https://github.com/VisDunneRight/IDMVis,,,vimeo 289785100,10.1109/tvcg.2018.2865076,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,5:20 PM,5:40 PM
  -TVCG,TVCG,Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges,T),"Florian Windhager, Paolo Federico, Günther Schreder, Katrin Glinka, Marian Dörk, Silvia Miksch, Eva Mayr",https://uclab.fh-potsdam.de/wp/wp-content/uploads/tvcg2018.pdf,"After decades of digitization, large cultural heritage collections have emerged on the web, which contain massive stocks of content from galleries, libraries, archives, and museums. This increase in digital cultural heritage data promises new modes of analysis and increased levels of access for academic scholars and casual users alike. Going beyond the standard representations of search-centric and grid-based interfaces, a multitude of approaches has recently started to enable visual access to cultural collections, and to explore them as complex and comprehensive information spaces by the means of interactive visualizations. In contrast to conventional web interfaces, we witness a widening spectrum of innovative visualization types specially designed for rich collections from the cultural heritage sector. This new class of information visualizations gives rise to a notable diversity of interaction and representation techniques while lending currency and urgency to a discussion about principles such as serendipity, generosity, and criticality in connection with visualization design. With this survey, we review information visualization approaches to digital cultural heritage collections and reflect on the state of the art in techniques and design choices. We contextualize our survey with humanist perspectives on the field and point out opportunities for future research.",,,,,vimeo 289789286,10.1109/tvcg.2018.2830759,2018,2018,InfoVis,"Convention Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,5:40 PM,6:00 PM
  +VAST,TVCG,Visual Abstraction of the Large Scale Geospatial Origin-Destination Movement Data,J),"Zhiguang Zhou, Linhao Meng, Cheng Tang, Ying Zhao, Zhiyong Guo, Miaoxin Hu, Wei Chen",,,,,,,vimeo 289787224,10.1109/TVCG.2018.2864503,2018,2018,VAST,"Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,4:20 PM,4:40 PM
  +VAST,TVCG,Analysis of Flight Variability: a Systematic Approach,J),"Natalia Andrienko, Gennady Andrienko, Jose Manuel Cordero Garcia, David Scarlatti",http://openaccess.city.ac.uk/20334/1/analysis-flight-variability.pdf,"In movement data analysis, there exists a problem of comparing multiple trajectories of moving objects to common or distinct reference trajectories. We introduce a general conceptual framework for comparative analysis of trajectories and an analytical procedure, which consists of (1) finding corresponding points in pairs of trajectories, (2) computation of pairwise difference measures, and (3) interactive visual analysis of the distributions of the differences with respect to space, time, set of moving objects, trajectory structures, and spatio-temporal context. We propose a combination of visualisation, interaction, and data transformation techniques supporting the analysis and demonstrate the use of our approach for solving a challenging problem from the aviation domain.",,,,,vimeo 289787283,10.1109/TVCG.2018.2864811,2018,2018,VAST,"Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,4:40 PM,5:00 PM
  +VAST,TVCG,ForVizor: Visualizing Spatio-Temporal Team Formations in Soccer,J),"Yingcai Wu, Xiao Xie, Jiachen Wang, Dazhen Deng, Hongye Liang, Hui Zhang, Shoubin Cheng, Wei Chen",http://zjuvis.org/files/forvizor.pdf,"Regarded as a high-level tactic in soccer, a team formation assigns players different tasks and indicates their active regions on the pitch, thereby influencing the team performance significantly. Analysis of formations in soccer has become particularly indispensable for soccer analysts. However, formations of a team are intrinsically time-varying and contain inherent spatial information. The spatio-temporal nature of formations and other characteristics of soccer data, such as multivariate features, make analysis of formations in soccer a challenging problem. In this study, we closely worked with domain experts to characterize domain problems of formation analysis in soccer and formulated several design goals. We design a novel spatio-temporal visual representation of changes in team formation, allowing analysts to visually analyze the evolution of formations and track the spatial flow of players within formations over time. Based on the new design, we further design and develop ForVizor, a visual analytics system, which empowers users to track the spatio-temporal changes in formation and understand how and why such changes occur. With ForVizor, domain experts conduct formation analysis of two games. Analysis results with insights and useful feedback are summarized in two case studies.",,,,,vimeo 289787566,10.1109/tvcg.2018.2865041,2018,2018,VAST,"Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:00 PM,5:20 PM
  +VAST,TVCG,MotionRugs: Visualizing Collective Trends in Space and Time,J),"Juri Buchmuller, Dominik Jackle, Eren Cakmak, Ulrik Brandes, Daniel Keim",https://bib.dbvis.de/uploadedFiles/MotionRugsPreprint.pdf,"Understanding the movement patterns of collectives, such as flocks of birds or fish swarms, is an interesting open research question. The collectives are driven by mutual objectives or react to individual direction changes and external influence factors and stimuli. The challenge in visualizing collective movement data is to show space and time of hundreds of movements at the same time to enable the detection of spatiotemporal patterns. In this paper, we propose MotionRugs, a novel space efficient technique for visualizing moving groups of entities. Building upon established space-partitioning strategies, our approach reduces the spatial dimensions in each time step to a one-dimensional ordered representation of the individual entities. By design, MotionRugs provides an overlap-free, compact overview of the development of group movements over time and thus, enables analysts to visually identify and explore group-specific temporal patterns. We demonstrate the usefulness of our approach in the field of fish swarm analysis and report on initial feedback of domain experts from the field of collective behavior.",,https://github.com/jbuchmueller/motionrugs,,,youtube xSewCmdgN40,10.1109/tvcg.2018.2865049,2018,2018,VAST,"Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:20 PM,5:40 PM
  +VAST,TVCG,Identification of Temporally Varying Areas of Interest in Long Duration Eye Tracking Data Sets,J),"Prithiviraj Kaliappa Gounder Muthumanickam, Katerina Vrotsou, Aida Nordman, Jimmy Johansson, Matthew Cooper",,,,,,,vimeo 289787582,10.1109/TVCG.2018.2865042,2018,2018,VAST,"Hall 1, Section C",Tuesday,Spatio-Temporal Data,23-Oct,5:40 PM,6:00 PM
  +InfoVis,TVCG,A Heuristic Approach to Value-Driven Evaluation of Visualizations,J),"Emily Wall, Meeshu Agnihotri, Laura Matzen, Kristin Divis, Michael Haass, Alex Endert, John Stasko",https://www.cc.gatech.edu/~ewall9/media/papers/ValueVIS2018.pdf,"Recently, an approach for determining the value of a visualization was proposed, one moving beyond simple measurements of task accuracy and speed. The value equation contains components for the time savings a visualization provides, the insights and insightful questions it spurs, the overall essence of the data it conveys, and the confidence about the data and its domain it inspires. This articulation of value is purely descriptive, however, providing no actionable method of assessing a visualization's value. In this work, we create a heuristic-based evaluation methodology to accompany the value equation for assessing interactive visualizations. We refer to the methodology colloquially as ICE-T, based on an anagram of the four value components. Our approach breaks the four components down into guidelines, each of which is made up of a small set of low-level heuristics. Evaluators who have knowledge of visualization design principles then assess the visualization with respect to the heuristics. We conducted an initial trial of the methodology on three interactive visualizations of the same data set, each evaluated by 15 visualization experts. We found that the methodology showed promise, obtaining consistent ratings across the three visualizations and mirroring judgments of the utility of the visualizations by instructors of the course in which they were developed.",,,,,vimeo 289784928,10.1109/tvcg.2018.2865146,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,4:20 PM,4:40 PM
  +InfoVis,TVCG,Patterns and Pace: Quantifying Diverse Exploration Behavior with Visualizations on the Web,J),"Mi Feng, Evan Peck, Lane Harrison",https://osf.io/9wqgk/,"The diverse and vibrant ecosystem of interactive visualizations on the web presents an opportunity for researchers and practitioners to observe and analyze how everyday people interact with data visualizations. However, existing metrics of visualization interaction behavior used in research do not fully reveal the breadth of peoples' open-ended explorations with visualizations. One possible way to address this challenge is to determine high-level goals for visualization interaction metrics, and infer corresponding features from user interaction data that characterize different aspects of peoples' explorations of visualizations. In this paper, we address this challenge by identifying needs for visualization behavior analysis, and by developing corresponding candidate features that can be inferred from users' interaction data with visualization. We then propose metrics that capture novel aspects of peoples' open-ended explorations, including exploration uniqueness and exploration pacing. We evaluate these metrics along with four other metrics recently proposed in visualization literature by applying them to interaction data from prior visualization studies. The results of these evaluations suggest that these new metrics 1) reveal new characteristics of peoples' use of visualizations, 2) can be used to evaluate statistical differences between visualization designs, and 3) are statistically independent of prior metrics used in visualization research. We discuss implications of these results for future studies, including the potential for applying these metrics in visualization interaction analysis, as well as emerging challenges in developing a design space of metrics for visualization engagement.",,,https://osf.io/dx43q/wiki/home/,,vimeo 289785167,10.1109/tvcg.2018.2865117,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,4:40 PM,5:00 PM
  +TVCG,TVCG,Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs,T),"Carolina Nobre, Nils Gehlenborg, Hilary Coon, Alexander Lex",https://www.biorxiv.org/content/early/2018/02/27/128579,"The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.",,https://github.com/caleydo/lineage,,,vimeo 289789223,10.1109/tvcg.2018.2811488,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,5:00 PM,5:20 PM
  +InfoVis,TVCG,IDMVis: Temporal Event Sequence Visualization for Type 1 Diabetes Treatment Decision Support,J),"Yixuan Zhang, Kartik Chanana, Cody Dunne",https://github.com/zjanice/PHI/blob/master/IDMVis_IEEEVIS18_preprint.pdf,"Type 1 diabetes is a chronic, incurable autoimmune disease affecting millions of Americans in which the body stops producing insulin and blood glucose levels rise. The goal of intensive diabetes management is to lower average blood glucose through frequent adjustments to insulin protocol, diet, and behavior. Manual logs and medical device data are collected by patients, but these multiple sources are presented in disparate visualization designs to the clinician—making temporal inference difficult. We conducted a design study over 18 months with clinicians performing intensive diabetes management. We present a data abstraction and novel hierarchical task abstraction for this domain. We also contribute IDMVis: a visualization tool for temporal event sequences with multidimensional, interrelated data. IDMVis includes a novel technique for folding and aligning records by dual sentinel events and scaling the intermediate timeline. We validate our design decisions based on our domain abstractions, best practices, and through a qualitative evaluation with six clinicians. The results of this study indicate that IDMVis accurately reflects the workflow of clinicians. Using IDMVis, clinicians are able to identify issues of data quality such as missing or conflicting data, reconstruct patient records when data is missing, differentiate between days with different patterns, and promote educational interventions after identifying discrepancies.",,https://github.com/VisDunneRight/IDMVis,,,vimeo 289785100,10.1109/tvcg.2018.2865076,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,5:20 PM,5:40 PM
  +TVCG,TVCG,Visualization of Cultural Heritage Collection Data: State of the Art and Future Challenges,T),"Florian Windhager, Paolo Federico, Günther Schreder, Katrin Glinka, Marian Dörk, Silvia Miksch, Eva Mayr",https://uclab.fh-potsdam.de/wp/wp-content/uploads/tvcg2018.pdf,"After decades of digitization, large cultural heritage collections have emerged on the web, which contain massive stocks of content from galleries, libraries, archives, and museums. This increase in digital cultural heritage data promises new modes of analysis and increased levels of access for academic scholars and casual users alike. Going beyond the standard representations of search-centric and grid-based interfaces, a multitude of approaches has recently started to enable visual access to cultural collections, and to explore them as complex and comprehensive information spaces by the means of interactive visualizations. In contrast to conventional web interfaces, we witness a widening spectrum of innovative visualization types specially designed for rich collections from the cultural heritage sector. This new class of information visualizations gives rise to a notable diversity of interaction and representation techniques while lending currency and urgency to a discussion about principles such as serendipity, generosity, and criticality in connection with visualization design. With this survey, we review information visualization approaches to digital cultural heritage collections and reflect on the state of the art in techniques and design choices. We contextualize our survey with humanist perspectives on the field and point out opportunities for future research.",,,,,vimeo 289789286,10.1109/tvcg.2018.2830759,2018,2018,InfoVis,"Hall 1, Section D",Tuesday,Evaluation & Applications,23-Oct,5:40 PM,6:00 PM
   SciVis,TVCG,Interactive Visualization of RNA and DNA Structures,J),"Norbert Lindow, Daniel Baum, Morgan Leborgne, Hans-Christian Hege",,,,,,,vimeo 290325417,10.1109/tvcg.2018.2864507,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Biological Applications,23-Oct,4:20 PM,4:40 PM
   SciVis,TVCG,Labels on Levels: Labeling of Multi-Scale Multi-Instance and Crowded 3D Biological Environments,J),"David Kouřil, Ladislav Čmolík, Barbora Kozlíková, Hsiang-Yun Wu, Graham Johnson, David S. Goodsell, Arthur Olson, Eduard Gröller, Ivan Viola",https://www.cg.tuwien.ac.at/research/publications/2019/kouril-2018-LoL/kouril-2018-LoL-paper.pdf,"Labeling is intrinsically important for exploring and understanding complex environments and models in a variety of domains. We present a method for interactive labeling of crowded 3D scenes containing very many instances of objects spanning multiple scales in size. In contrast to previous labeling methods, we target cases where many instances of dozens of types are present and where the hierarchical structure of the objects in the scene presents an opportunity to choose the most suitable level for each placed label. Our solution builds on and goes beyond labeling techniques in medical 3D visualization, cartography, and biological illustrations from books and prints. In contrast to these techniques, the main characteristics of our new technique are: 1) a novel way of labeling objects as part of a bigger structure when appropriate, 2) visual clutter reduction by labeling only representative instances for each type of an object, and a strategy of selecting those. The appropriate level of label is chosen by analyzing the scene’s depth buffer and the scene objects’ hierarchy tree. We address the topic of communicating the parent-children relationship between labels by employing visual hierarchy concepts adapted from graphic design. Selecting representative instances considers several criteria tailored to the character of the data and is combined with a greedy optimization approach. We demonstrate the usage of our method with models from mesoscale biology where these two characteristics—multi-scale and multi-instance—are abundant, along with the fact that these scenes are extraordinarily dense.",,,,,vimeo 290325281,10.1109/tvcg.2018.2864491,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Biological Applications,23-Oct,4:40 PM,5:00 PM
   SciVis,TVCG,Visualization of Large Molecular Trajectories,J),"David Duran Rosich, Pedro Hermosilla Casajus, Timo Ropinski, Barbora Kozlíková, Àlvar Vinacua, Pere-Pau Vázquez",https://www.uni-ulm.de/fileadmin/website_uni_ulm/iui.inst.100/institut/Papers/viscom/2018/vazquez2018-largemol.pdf,"The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.",,,,,vimeo 290328133,10.1109/tvcg.2018.2864851,2018,2018,SciVis,Estrel Hall A+B,Tuesday,Biological Applications,23-Oct,5:00 PM,5:20 PM
   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",http://www.in.tum.de/fileadmin/w00bws/cg/Research/Publications/2018/ESAWorkflow/Preprint_Correlation_wDOI.pdf,"Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applications include determining the origin of forecast errors and placing targeted observations to improve future forecasts. We—a team of visualization scientists and meteorologists—present a visual analysis framework to improve upon current practice of ESA. We support the user in selecting regions to compute a meaningful target forecast quantity by embedding correlation-based grid-point clustering to obtain statistically coherent regions. The evolution of sensitivity features computed via ESA are then traced through time, by integrating a quantitative measure of feature matching into optical-flow-based feature assignment, and displayed by means of a swipe-path showing the geo-spatial evolution of the sensitivities. Visualization of the internal correlation structure of computed features guides the user towards those features robustly predicting a certain weather event. We demonstrate the use of our method by application to real-world 2D and 3D cases that occurred during the 2016 NAWDEX field campaign, showing the interactive generation of hypothesis chains to explore how atmospheric processes sensitive to each other are interrelated.",,,,,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.2018.2810919,2018,2018,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 289789365,10.1109/TVCG.2018.2796591,2018,2018,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 289784452,10.1109/TVCG.2018.2865265,2018,2018,InfoVis,"Convention Hall 1, Section D",Wednesday,Time,24-Oct,10:20 AM,10:40 AM
  +VAST,TVCG,Visual Analysis of the Temporal Evolution of Ensemble Forecast Sensitivities,J),"Alexander Kumpf, Marc Rautenhaus, Michael Riemer, Rüdiger Westermann",http://www.in.tum.de/fileadmin/w00bws/cg/Research/Publications/2018/ESAWorkflow/Preprint_Correlation_wDOI.pdf,"Ensemble sensitivity analysis (ESA) has been established in the atmospheric sciences as a correlation-based approach to determine the sensitivity of a scalar forecast quantity computed by a numerical weather prediction model to changes in another model variable at a different model state. Its applications include determining the origin of forecast errors and placing targeted observations to improve future forecasts. We—a team of visualization scientists and meteorologists—present a visual analysis framework to improve upon current practice of ESA. We support the user in selecting regions to compute a meaningful target forecast quantity by embedding correlation-based grid-point clustering to obtain statistically coherent regions. The evolution of sensitivity features computed via ESA are then traced through time, by integrating a quantitative measure of feature matching into optical-flow-based feature assignment, and displayed by means of a swipe-path showing the geo-spatial evolution of the sensitivities. Visualization of the internal correlation structure of computed features guides the user towards those features robustly predicting a certain weather event. We demonstrate the use of our method by application to real-world 2D and 3D cases that occurred during the 2016 NAWDEX field campaign, showing the interactive generation of hypothesis chains to explore how atmospheric processes sensitive to each other are interrelated.",,,,,vimeo 289787833,10.1109/tvcg.2018.2864901,2018,2018,VAST,"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,"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.2018.2810919,2018,2018,VAST,"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,"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,"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,"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 289789365,10.1109/TVCG.2018.2796591,2018,2018,InfoVis,"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,"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,"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 289784452,10.1109/TVCG.2018.2865265,2018,2018,InfoVis,"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",https://cgl.ethz.ch/Downloads/Publications/Papers/2018/Meu18b/Meu18b.pdf,"This paper presents a framework to explore multi-field data of aneurysms occurring at intracranial and cardiac arteries by using statistical graphics. The rupture of an aneurysm is often a fatal scenario, whereas during treatment serious complications for the patient can occur. Whether an aneurysm ruptures or whether a treatment is successful depends on the interaction of different morphological such as wall deformation and thickness, and hemodynamic attributes like wall shear stress and pressure. Therefore, medical researchers are very interested in better understanding these relationships. However, the required analysis is a time-consuming process, where suspicious wall regions are difficult to detect due to the time-dependent behavior of the data. Our proposed visualization framework enables medical researchers to efficiently assess aneurysm risk and treatment options. This comprises a powerful set of views including 2D and 3D depictions of the aneurysm morphology as well as statistical plots of different scalar fields. Brushing and linking aids the user to identify interesting wall regions and to understand the influence of different attributes on the aneurysm’s state. Moreover, a visual comparison of pre- and post-treatment as well as different treatment options is provided. Our analysis techniques are designed in collaboration with domain experts, e.g., physicians, and we provide details about the evaluation.",,,,,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",http://scivis.itn.liu.se/publications/2019/FYTL19//falk-3dhistology-preprint.pdf," We present a visualization application that enables effective interactive visual analysis of large-scale 3D histopathology, that is, high-resolution 3D microscopy data of human tissue. Clinical work flows and research based on pathology have, until now, largely been dominated by 2D imaging. As we will show in the paper, studying volumetric histology data will open up novel and useful opportunities for both research and clinical practice. Our starting point is the current lack of appropriate visualization tools in histopathology, which has been a limiting factor in the uptake of digital pathology. Visualization of 3D histology data does pose difficult challenges in several aspects. The full-color datasets are dense and large in scale, on the order of 100,000 × 100,000× 100 voxels. This entails serious demands on both rendering performance and user experience design. Despite this, our developed application supports interactive study of 3D histology datasets at native resolution. Our application is based on tailoring and tuning of existing methods, system integration work, as well as a careful study of domain specific demands emanating from a close participatory design process with domain experts as team members. Results from a user evaluation employing the tool demonstrate a strong agreement among the 14 participating pathologists that 3D histopathology will be a valuable and enabling tool for their work",,,,,vimeo 290325921,10.1109/TVCG.2018.2864816,2018,2018,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 290328215,10.1109/TVCG.2018.2864852,2018,2018,SciVis,Estrel Hall A+B,Wednesday,Biomedical Visualization,24-Oct,9:40 AM,10:00 AM
  
positive.code: 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",http://www.dca.fee.unicamp.br/projects/prosim/publications/journals/wu-loos-voltoline-rubianes-2017-3dmediv-draft.pdf,"Focal cortical dysplasia (FCD) is a malformation of cortical development and a common cause of pharmacoresistant epilepsy. Resective surgery of clear-cut lesions may be curative. However, the localization of the seizure focus and the evaluation of its spatial extent can be challenging in many situations. For concordance assessment, medical studies show the relevance of accurate correlation of multi-source imaging sequences. to improve the sensitivity and specificity of the evaluation. In this paper, we share the process we went through to reach our simple, but effective, solution for integrating multi-volume rendering into an exploratory visualization environment for the diagnosis of FCD. We focus on fetching of multiple data assigned to a sample when they are rendered. Knowing that the major diagnostic role of multiple volumes is to complement information, we demonstrate that appropriate geometric transformations in the texture space are sufficient for accomplishing this task. This allows us to fully implement our proposal in the OpenGL rendering pipeline and to easily integrate it into the existing visual diagnostic application. Both time performance and the visual quality of our proposal were evaluated with a set of clinical data volumes for assessing the potential practical impact of our solution in routine diagnostic use. ",,,,,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://osf.io/ah5y6/,"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.",,https://osf.io/u4jxh/,,,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",http://zjuvis.org/files/imgvis.pdf,"Interactive visualization of large image collections is important and useful in many applications, such as personal album management and user profiling on images. However, most prior studies focus on using low-level visual features of images, such as texture and color histogram, to create visualizations without considering the more important semantic information embedded in images. This paper proposes a novel visual analytic system to analyze images in a semantic-aware manner. The system mainly comprises two components: a semantic information extractor and a visual layout generator. The semantic information extractor employs an image captioning technique based on convolutional neural network (CNN) to produce descriptive captions for images, which can be transformed into semantic keywords. The layout generator employs a novel co-embedding model to project images and the associated semantic keywords to the same 2D space. Inspired by the galaxy metaphor, we further turn the projected 2D space to a galaxy visualization of images, in which semantic keywords and images are visually encoded as stars and planets. Our system naturally supports multi-scale visualization and navigation, in which users can immediately see a semantic overview of an image collection and drill down for detailed inspection of a certain group of images. Users can iteratively refine the visual layout by integrating their domain knowledge into the co-embedding process. Two task-based evaluations are conducted to demonstrate the effectiveness of our system.",,,,,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",http://shixialiu.com/publications/photorecomposer/paper.pdf,"We present a visual analysis method for interactively recomposing a large number of photos based on example photos with high-quality composition. The recomposition method is formulated as a matching problem between photos. The key to this formulation is a new metric for accurately measuring the composition distance between photos. We have also developed an earth-mover-distancebased online metric learning algorithm to support the interactive adjustment of the composition distance based on user preferences. To better convey the compositions of a large number of example photos, we have developed a multi-level, example photo layout method to balance multiple factors such as compactness, aspect ratio, composition distance, stability, and overlaps. By introducing an EulerSmooth-based straightening method, the composition of each photos is clearly displayed. The effectiveness and usefulness of the method has been demonstrated by the experimental results, user study, and case studies.",,,,,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 289784435,10.1109/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",https://arxiv.org/pdf/1807.11212.pdf,"This paper presents a new approach for the visualization and analysis of the spatial variability of features of interest represented by critical points in ensemble data. Our framework, called Persistence Atlas, enables the visualization of the dominant spatial patterns of critical points, along with statistics regarding their occurrence in the ensemble. The persistence atlas represents in the geometrical domain each dominant pattern in the form of a confidence map for the appearance of critical points. As a by-product, our method also provides 2-dimensional layouts of the entire ensemble, highlighting the main trends at a global level. Our approach is based on the new notion of Persistence Map, a measure of the geometrical density in critical points which leverages the robustness to noise of topological persistence to better emphasize salient features. We show how to leverage spectral embedding to represent the ensemble members as points in a low-dimensional Euclidean space, where distances between points measure the dissimilarities between critical point layouts and where statistical tasks, such as clustering, can be easily carried out. Further, we show how the notion of mandatory critical point can be leveraged to evaluate for each cluster confidence regions for the appearance of critical points. Most of the steps of this framework can be trivially parallelized and we show how to efficiently implement them. Extensive experiments demonstrate the relevance of our approach. The accuracy of the confidence regions provided by the persistence atlas is quantitatively evaluated and compared to a baseline strategy using an off-the-shelf clustering approach. We illustrate the importance of the persistence atlas in a variety of real-life datasets, where clear trends in feature layouts are identified and analyzed. We provide a lightweight VTK-based C++ implementation of our approach that can be used for reproduction purposes.",,,,,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",http://tusharathawale.info/wp-content/uploads/2018/09/vis18ProbabilisticAsympDecider.pdf,"We present a framework for the analysis of uncertainty in isocontour extraction. The marching squares (MS) algorithm for isocontour reconstruction generates a linear topology that is consistent with hyperbolic curves of a piecewise bilinear interpolation. The saddle points of the bilinear interpolant cause topological ambiguity in isocontour extraction. The midpoint decider and the asymptotic decider are well-known mathematical techniques for resolving topological ambiguities. The latter technique investigates the data values at the cell saddle points for ambiguity resolution. The uncertainty in data, however, leads to uncertainty in underlying bilinear interpolation functions for the MS algorithm, and hence, their saddle points. In our work, we study the behavior of the asymptotic decider when data at grid vertices is uncertain. First, we derive closed-form distributions characterizing variations in the saddle point values for uncertain bilinear interpolants. The derivation assumes uniform and nonparametric noise models, and it exploits the concept of ratio distribution for analytic formulations. Next, the probabilistic asymptotic decider is devised for ambiguity resolution in uncertain data using distributions of the saddle point values derived in the first step. Finally, the confidence in probabilistic topological decisions is visualized using a colormapping technique. We demonstrate the higher accuracy and stability of the probabilistic asymptotic decider in uncertain data with regard to existing decision frameworks, such as deciders in the mean field and the probabilistic midpoint decider, through the isocontour visualization of synthetic and real datasets.",,,,,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",http://www2.cs.uh.edu/~chengu/Publications/HexMesh/Vis2018_BaseComplexVis.pdf,"Understanding hexahedral (hex-) mesh structures is important for a number of hex-mesh generation and optimization tasks. However, due to various configurations of the singularities in a valid pure hex-mesh, the structure (or base complex) of the mesh can be arbitrarily complex. In this work, we present a first and effective method to help meshing practitioners understand the possible configurations in a valid 3D base complex for the characterization of their complexity. In particular, we propose a strategy to decompose the complex hex-mesh structure into multi-level sub-structures so that they can be studied separately, from which we identify a small set of the sub-structures that can most efficiently represent the whole mesh structure. Furthermore, from this set of sub-structures, we attempt to define the first metric for the quantification of the complexity of hex-mesh structure. To aid the exploration of the extracted multi-level structure information, we devise a visual exploration system coupled with a matrix view to help alleviate the common challenge of 3D data exploration (e.g., clutter and occlusion). We have applied our tool and metric to a large number of hex-meshes generated with different approaches to reveal different characteristics of these methods in terms of the mesh structures they can produce. We also use our metric to assess the existing structure simplification techniques in terms of their effectiveness.",,,,,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
  
negative.code: SciVis,SciVis,"TimeTubes: Automatic Extraction of Observable Blazar Features from Long-Term, Multi-Dimensional Datasets",,"Naoko Sawada, Masanori Nakayama, Makoto Uemura, Issei Fujishiro",,,,,,,vimeo 290328934,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:22 PM,5:34 PM
  SciVis,SciVis,aflak: Pluggable Visual Programming Environment with Quick Feedback Loop Tuned for Astrophysical Observations,,"Malik Olivier Boussejra, Kazuya Matsubayashi, Yuriko Takeshima, Shunya Takekawa, Rikuo Uchiki, Makoto Uemura, Issei Fujishiro",,,,,,,vimeo 290328343,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:35 PM,5:47 PM
  SciVis,SciVis,Biclusters based Visual Exploration of Multivariate Scientific Data,,"Xiangyang He, Yubo Tao, Qirui Wang, Hai Lin",http://www.cad.zju.edu.cn/home/ybtao/papers/SciVis18_Biclusters.pdf,"This paper proposes a co-analysis framework based on biclusters, i.e., two subsets of variables and voxels with close scalar-value relationships, to guide the visual exploration process of multivariate data. We first automatically extract all meaningful biclusters, each of which only contains voxels with a similar scalar-value pattern over a subset of variables. These biclusters are organized according to their variable sets, and further grouped by a similarity metric to reduce redundancy and encourage diversity during visual exploration. Biclusters are visually represented in coordinated views to facilitate interactive exploration of multivariate data from the similarity between biclusters and the correlation of scalar values with different variables. Experiments demonstrate the effectiveness of our framework in exploring local relationships among variables, biclusters and scalar values in the data.",,,,,vimeo 290328582,,2018,2018,SciVis Short Papers,Estrel Hall C,Thursday,"Flow, Astrophysics, and Computationally Intensive Data Visualization",25-Oct,5:47 PM,6:00 PM
  VAST,TVCG,MAQUI: Interweaving Queries and Pattern Mining for Recursive Event Sequence Exploration,J),"Po-Ming Law, Zhicheng Liu, Sana Malik, Rahul Basole",https://osf.io/tg7w5/,"Exploring event sequences by defining queries alone or by using mining algorithms alone is often not sufficient to support analysis. Analysts often interweave querying and mining in a recursive manner during event sequence analysis: sequences extracted as query results are used for mining patterns, patterns generated are incorporated into a new query for segmenting the sequences, and the resulting segments are mined or queried again. To support flexible analysis, we propose a framework that describes the process of interwoven querying and mining. Based on this framework, we developed MAQUI, a Mining And Querying User Interface that enables recursive event sequence exploration. To understand the efficacy of MAQUI, we conducted two case studies with domain experts. The findings suggest that the capability of interweaving querying and mining helps the participants articulate their questions and gain novel insights from their data.",,https://osf.io/w5e62/,,,vimeo 289787863,10.1109/TVCG.2018.2864886,2018,2018,VAST,"Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:00 AM,9:20 AM
  VAST,TVCG,iForest: Interpreting Random Forests via Visual Analytics,J),"Xun Zhao, Yanhong Wu, Dik Lun Lee, Weiwei Cui",http://zhaoxun.me/publication/iForest.pdf,"As an ensemble model that consists of many independent decision trees, random forests generate predictions by feeding the input to internal trees and summarizing their outputs. The ensemble nature of the model helps random forests outperform any individual decision tree. However, it also leads to a poor model interpretability, which significantly hinders the model from being used in fields that require transparent and explainable predictions, such as medical diagnosis and financial fraud detection. The interpretation challenges stem from the variety and complexity of the contained decision trees. Each decision tree has its unique structure and properties, such as the features used in the tree and the feature threshold in each tree node. Thus, a data input may lead to a variety of decision paths. To understand how a final prediction is achieved, it is desired to understand and compare all decision paths in the context of all tree structures, which is a huge challenge for any users. In this paper, we propose a visual analytic system aiming at interpreting random forest models and predictions. In addition to providing users with all the tree information, we summarize the decision paths in random forests, which eventually reflects the working mechanism of the model and reduces users’ mental burden of interpretation. To demonstrate the effectiveness of our system, two usage scenarios and a qualitative user study are conducted.",,,,,vimeo 289787165,10.1109/tvcg.2018.2864475,2018,2018,VAST,"Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:20 AM,9:40 AM
  VAST,TVCG,Visual Progression Analysis of Event Sequence Data,J),"Shunan Guo, Zhuochen Jin, David Gotz, Fan Du, Hongyuan Zha, Nan Cao",http://gotz.web.unc.edu/files/2018/08/2018_VAST_EventThread_V2_Preprint.pdf,"Event sequence data is common to a broad range of application domains, from security to health care to scholarly communication. This form of data captures information about the progression of events for an individual entity (e.g., a computer network device; a patient; an author) in the form of a series of time-stamped observations. Moreover, each event is associated with an event type (e.g., a computer login attempt, or a hospital discharge). Analyses of event sequence data have been shown to help reveal important temporal patterns, such as clinical paths resulting in improved outcomes, or an understanding of common career trajectories for scholars. Moreover, recent research has demonstrated a variety of techniques designed to overcome methodological challenges such as large volumes of data and high dimensionality. However, the effective identication and analysis of latent stages of progression, which can allow for variation within different but similarly evolving event sequences, remain a signicant challenge with important real-world motivations. In this paper, we propose an unsupervised stage analysis algorithm to identify semantically meaningful progression stages as well as the critical events which help dene those stages. The algorithm follows three key steps: (1) event representation estimation, (2) event sequence warping and alignment, and (3) sequence segmentation. We also present a novel visualization system, ET2 , which interactively illustrates the results of the stage analysis algorithm to help reveal evolution patterns across stages. Finally, we report three forms of evaluation for ET2 : (1) case studies with two real-world datasets, (2) interviews with domain expert users, and (3) a performance evaluation on the progression analysis algorithm and the visualization design",,,,,vimeo 289787414,10.1109/TVCG.2018.2864885,2018,2018,VAST,"Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,9:40 AM,10:00 AM
  TVCG,TVCG,StreamExplorer: A Multi-Stage System for Visually Exploring Events in Social Streams,T),"Yingcai Wu, Zhutian Chen, Guodao Sun, Xiao Xie, Nan Cao, Shixia Liu, Weiwei Cui",http://zjuvis.org/files/streamexplorer.pdf,"Analyzing social streams is important for many applications, such as crisis management. However, the considerable diversity, increasing volume, and high dynamics of social streams of large events continue to be significant challenges that must be overcome to ensure effective exploration. We propose a novel framework by which to handle complex social streams on a budget PC. This framework features two components: 1) an online method to detect important time periods (i.e., subevents), and 2) a tailored GPU-assisted Self-Organizing Map (SOM) method, which clusters the tweets of subevents stably and efficiently. Based on the framework, we present StreamExplorer to facilitate the visual analysis, tracking, and comparison of a social stream at three levels. At a macroscopic level, StreamExplorer uses a new glyph-based timeline visualization, which presents a quick multi-faceted overview of the ebb and flow of a social stream. At a mesoscopic level, a map visualization is employed to visually summarize the social stream from either a topical or geographical aspect. At a microscopic level, users can employ interactive lenses to visually examine and explore the social stream from different perspectives. Two case studies and a task-based evaluation are used to demonstrate the effectiveness and usefulness of StreamExplorer.",,,,,vimeo 289788908,10.1109/tvcg.2017.2764459,2017,2018,VAST,"Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:00 AM,10:20 AM
  VAST,TVCG,Duet: Helping Data Analysis Novices Conduct Pairwise Comparisons by Minimal Specification,J),"Po-Ming Law, Rahul Basole, Yanhong Wu",https://osf.io/rzk49/,"Data analysis novices often encounter barriers in executing low-level operations for pairwise comparisons. They may also run into barriers in interpreting the artifacts (e.g., visualizations) created as a result of the operations. We developed Duet, a visual analysis system designed to help data analysis novices conduct pairwise comparisons by addressing execution and interpretation barriers. To reduce the barriers in executing low-level operations during pairwise comparison, Duet employs minimal specication: when one object group (i.e. a group of records in a data table) is specied, Duet recommends object groups that are similar to or different from the specied one; when two object groups are specied, Duet recommends similar and different attributes between them. To lower the barriers in interpreting its recommendations, Duet explains the recommended groups and attributes using both visualizations and textual descriptions. We conducted a qualitative evaluation with eight participants to understand the effectiveness of Duet. The results suggest that minimal specication is easy to use and Duet's explanations are helpful for interpreting the recommendations despite some usability issues.",,,,,vimeo 289787850,10.1109/TVCG.2018.2864526,2018,2018,VAST,"Hall 1, Section C",Friday,"Event, Sequence, and ML",26-Oct,10:20 AM,10:40 AM
  InfoVis,TVCG,Visualizing Uncertain Tropical Cyclone Predictions using Representative Samples from Ensembles of Forecast Tracks,J),"Le Liu, Lace M. K. Padilla, Sarah Creem-Regehr, Donald House",http://lacepadilla.com/Downloads/publications/Liuetal_2018.pdf,"A common approach to sampling the space of a prediction is the generation of an ensemble of potential outcomes, where the ensemble's distribution reveals the statistical structure of the prediction space. For example, the US National Hurricane Center generates multiple day predictions for a storm's path, size, and wind speed, and then uses a Monte Carlo approach to sample this prediction into a large ensemble of potential storm outcomes. Various forms of summary visualizations are generated from such an ensemble, often using spatial spread to indicate its statistical characteristics. However, studies have shown that changes in the size of such summary glyphs, representing changes in the uncertainty of the prediction, are frequently confounded with other attributes of the phenomenon, such as its size or strength. In addition, simulation ensembles typically encode multivariate information, which can be difficult or confusing to include in a summary display. This problem can be overcome by directly displaying the ensemble as a set of annotated trajectories, however this solution will not be effective if ensembles are densely overdrawn or structurally disorganized. We propose to overcome these difficulties by selectively sampling the original ensemble, constructing a smaller representative and spatially well organized ensemble. This can be drawn directly as a set of paths that implicitly reveals the underlying spatial uncertainty distribution of the prediction. Since this approach does not use a visual channel to encode uncertainty, additional information can more easily be encoded in the display without leading to visual confusion. To demonstrate our argument, we describe the development of a visualization for ensembles of tropical cyclone forecast tracks, explaining how their spatial and temporal predictions, as well as other crucial storm characteristics such as size and intensity, can be clearly revealed. We verify the effectiveness of this visualization approach through a cognitive study exploring how storm damage estimates are affected by the density of tracks drawn, and by the presence or absence of annotating information on storm size and intensity.",,https://osf.io/hy3ba/,https://osf.io/hy3ba/,,vimeo 289784601,10.1109/TVCG.2018.2865193,2018,2018,InfoVis,"Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:00 AM,9:20 AM
  InfoVis,TVCG,Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data,J),"Alex Kale, Francis Nguyen, Matthew Kay, Jessica Hullman",http://users.eecs.northwestern.edu/~jhullman/hops_jobs_pfs.pdf,"Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.",https://medium.com/@uwdata/hypothetical-outcome-plots-hops-help-users-separate-signal-from-noise-870d4e2b75d7,,https://github.com/kalealex/jobs-report-hops,https://osf.io/8cte3/registrations/,vimeo 289785258,10.1109/tvcg.2018.2864909,2018,2018,InfoVis,"Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:20 AM,9:40 AM
  InfoVis,TVCG,In Pursuit of Error: A Survey of Uncertainty Visualization Evaluation,J),"Jessica Hullman, Xiaoli Qiao, Michael Correll, Alex Kale, Matthew Kay",https://github.com/jhullman/uncertaintyVisEval/blob/master/uncertainty_vis_eval.pdf,"Understanding and accounting for uncertainty is critical to effectively reasoning about visualized data. However, evaluating the impact of an uncertainty visualization is complex due to the difficulties that people have interpreting uncertainty and the challenge of defining correct behavior with uncertainty information. Currently, evaluators of uncertainty visualization must rely on general purpose visualization evaluation frameworks which can be ill-equipped to provide guidance with the unique difficulties of assessing judgments under uncertainty. To help evaluators navigate these complexities, we present a taxonomy for characterizing decisions made in designing an evaluation of an uncertainty visualization. Our taxonomy differentiates six levels of decisions that comprise an uncertainty visualization evaluation: the behavioral targets of the study, expected effects from an uncertainty visualization, evaluation goals, measures, elicitation techniques, and analysis approaches. Applying our taxonomy to 86 user studies of uncertainty visualizations, we find that existing evaluation practice, particularly in visualization research, focuses on Performance and Satisfaction-based measures that assume more predictable and statistically-driven judgment behavior than is suggested by research on human judgment and decision making. We reflect on common themes in evaluation practice concerning the interpretation and semantics of uncertainty, the use of confidence reporting, and a bias toward evaluating performance as accuracy rather than decision quality. We conclude with a concrete set of recommendations for evaluators designed to reduce the mismatch between the conceptualization of uncertainty in visualization versus other fields.",,https://github.com/jhullman/uncertaintyVisEval,,,vimeo 289785271,10.1109/tvcg.2018.2864889,2018,2018,InfoVis,"Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,9:40 AM,10:00 AM
  InfoVis,TVCG,Where's my data? Evaluating Visualizations with Missing Data,J),"Hayeong Song, Danielle Albers Szafir",http://cmci.colorado.edu/visualab/papers/song_VIS_2018.pdf,Many real-world datasets are incomplete due to factors such as data collection failures or misalignments between fused datasets. Visualizations of incomplete datasets should allow analysts to draw conclusions from their data while effectively reasoning about the quality of the data and resulting conclusions. We conducted a pair of crowdsourced studies to measure how the methods used to impute and visualize missing data may influence analysts' perceptions of data quality and their confidence in their conclusions. Our experiments used different design choices for line graphs and bar charts to estimate averages and trends in incomplete time series datasets. Our results provide preliminary guidance for visualization designers to consider when working with incomplete data in different domains and scenarios.,,,http://cmci.colorado.edu/visualab/MissingData/,,vimeo 289785184,10.1109/TVCG.2018.2864914,2018,2018,InfoVis,"Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:00 AM,10:20 AM
  InfoVis,TVCG,A Framework for Externalizing Implicit Error Using Visualization,J),"Nina McCurdy, Julie Gerdes, Miriah Meyer",http://sci.utah.edu/~vdl/papers/2018_infovis_IE-Framework.pdf,"This paper presents a framework for externalizing and analyzing expert knowledge about discrepancies in data through the use of visualization. Grounded in an 18-month design study with global health experts, the framework formalizes the notion of data discrepancies as implicit error, both in global health data and more broadly. We use the term implicit error to describe measurement error that is inherent to and pervasive throughout a dataset, but that isn't explicitly accounted for or defined. Instead, implicit error exists in the minds of experts, is mainly qualitative, and is accounted for subjectively during expert interpretation of the data. Externalizing knowledge surrounding implicit error can assist in synchronizing, validating, and enhancing interpretation, and can inform error analysis and mitigation. The framework consists of a description of implicit error components that are important for downstream analysis, along with a process model for externalizing and analyzing implicit error using visualization. As a second contribution, we provide a rich, reflective, and verifiable description of our research process as an exemplar summary toward the ongoing inquiry into ways of increasing the validity and transferability of design study research.",,https://github.com/visdesignlab/IEFramework,,,vimeo 289785202,10.1109/tvcg.2018.2864913,2018,2018,InfoVis,"Hall 1, Section D",Friday,Uncertainty & Error,26-Oct,10:20 AM,10:40 AM
  SciVis,TVCG,Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps,J),"Jun Tao, Martin Imre, Chaoli Wang, Nitesh V Chawla, Hanqi Guo, Gökhan Sever, Seung Hyun Kim",https://www3.nd.edu/~cwang11/research/vis18-mism.pdf,"We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.",,,,,vimeo 290325769,10.1109/tvcg.2018.2864808,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:00 AM,9:20 AM
  SciVis,TVCG,Visual Analysis of Spatio-temporal Relations of Pairwise Attributes in Unsteady Flow,J),"Marzieh Berenjkoub, Rodolfo Ostilla Monico, Robert S. Laramee, Guoning Chen",,,,,,,vimeo 290325964,10.1109/tvcg.2018.2864817,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:20 AM,9:40 AM
  SciVis,TVCG,Time-Dependent Flow seen through Approximate Observer Killing Fields,J),"Markus Hadwiger, Matej Mlejnek, Thomas Theussl, Peter Rautek",http://vccvisualization.org/publications/2018_hadwiger_killingobservers.pdf,"Flow fields are usually visualized relative to a global observer, i.e., a single frame of reference. However, often no global frame can depict all flow features equally well. Likewise, objective criteria for detecting features such as vortices often use either a global reference frame, or compute a separate frame for each point in space and time. We propose the first general framework that enables choosing a smooth trade-off between these two extremes. Using global optimization to minimize specific differential geometric properties, we compute a time-dependent observer velocity field that describes the motion of a continuous field of observers adapted to the input flow. This requires developing the novel notion of an observed time derivative. While individual observers are restricted to rigid motions, overall we compute an approximate Killing field, corresponding to almost-rigid motion. This enables continuous transitions between different observers. Instead of focusing only on flow features, we furthermore develop a novel general notion of visualizing how all observers jointly perceive the input field. This in fact requires introducing the concept of an observation time, with respect to which a visualization is computed. We develop the corresponding notions of observed stream, path, streak, and time lines. For efficiency, these characteristic curves can be computed using standard approaches, by first transforming the input field accordingly. Finally, we prove that the input flow perceived by the observer field is objective. This makes derived flow features, such as vortices, objective as well.",,https://github.com/vccvisualization/killingobservers,,,vimeo 290327432,10.1109/tvcg.2018.2864839,2018,2018,SciVis,Estrel Hall C,Friday,Time-varying Data,26-Oct,9:40 AM,10:00 AM
  