anchor.diff:  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,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
   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
  
positive.code: VAST,TVCG,Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,J),"Jiawei Zhang, Yang Wang, Piero Molino, Lezhi Li, David Ebert",https://arxiv.org/pdf/1808.00196.pdf,"Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models’ outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.",,,,,vimeo 289787203,10.1109/tvcg.2018.2864499,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:00 PM,5:20 PM
  VAST,TVCG,Visual Analytics for Topic Model Optimization based on User-Steerable Speculative Execution,J),"Mennatallah El-Assady, Fabian Sperrle, Oliver Deussen, Daniel Keim, Christopher Collins",http://graphics.uni-konstanz.de/publikationen/ElAssady2018VisualAnalytic/visualanalyticstopic.pdf,"To effectively assess the potential consequences of human interventions in model-driven analytics systems, we establish the concept of speculative execution as a visual analytics paradigm for creating user-steerable preview mechanisms. This paper presents an explainable, mixed-initiative topic modeling framework that integrates speculative execution into the algorithmic decisionmaking process. Our approach visualizes the model-space of our novel incremental hierarchical topic modeling algorithm, unveiling its inner-workings. We support the active incorporation of the user’s domain knowledge in every step through explicit model manipulation interactions. In addition, users can initialize the model with expected topic seeds, the backbone priors. For a more targeted optimization, the modeling process automatically triggers a speculative execution of various optimization strategies, and requests feedback whenever the measured model quality deteriorates. Users compare the proposed optimizations to the current model state and preview their effect on the next model iterations, before applying one of them. This supervised human-in-the-loop process targets maximum improvement for minimum feedback and has proven to be effective in three independent studies that confirm topic model quality improvements.",,,,,vimeo 289787264,10.1109/tvcg.2018.2864769,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:20 PM,5:40 PM
  VAST,TVCG,VIS4ML: An Ontology for Visual Analytics Assisted Machine Learning,J),"Dominik Sacha, Matthias Kraus, Daniel Keim, Min Chen",https://bib.dbvis.de/uploadedFiles/TVCG2864838.pdf,"While many VA workflows make use of machine-learned models to support analytical tasks, VA workflows have become increasingly important in understanding and improving Machine Learning (ML) processes. In this paper, we propose an ontology (VIS4ML) for a subarea of VA, namely “VA-assisted ML”. The purpose of VIS4ML is to describe and understand existing VA workflows used in ML as well as to detect gaps in ML processes and the potential of introducing advanced VA techniques to such processes. Ontologies have been widely used to map out the scope of a topic in biology, medicine, and many other disciplines. We adopt the scholarly methodologies for constructing VIS4ML, including the specification, conceptualization, formalization, implementation, and validation of ontologies. In particular, we reinterpret the traditional VA pipeline to encompass model-development workflows. We introduce necessary definitions, rules, syntaxes, and visual notations for formulating VIS4ML and make use of semantic web technologies for implementing it in the Web Ontology Language (OWL). VIS4ML captures the high-level knowledge about previous workflows where VA is used to assist in ML. It is consistent with the established VA concepts and will continue to evolve along with the future developments in VA and ML. While this ontology is an effort for building the theoretical foundation of VA, it can be used by practitioners in real-world applications to optimize model-development workflows by systematically examining the potential benefits that can be brought about by either machine or human capabilities. Meanwhile, VIS4ML is intended to be extensible and will continue to be updated to reflect future advancements in using VA for building high-quality data-analytical models or for building such models rapidly",,,,,vimeo 289787814,10.1109/TVCG.2018.2864838,2018,2018,VAST,"Convention Hall 1, Section C",Thursday,Explainable ML,25-Oct,5:40 PM,6:00 PM
  InfoVis,TVCG,Mitigating the Attraction Effect with Visualizations,J),"Evanthia Dimara, Gilles Bailly, Anastasia Bezerianos, Steven Franconeri",https://hal.inria.fr/hal-01845004v2/document,"Human decisions are prone to biases, and this is no less true for decisions made within data visualizations. Bias mitigation strategies often focus on the person, by educating people about their biases, typically with little success. We focus instead on the system, presenting the first evidence that altering the design of an interactive visualization tool can mitigate a strong bias � the attraction effect. Participants viewed 2D scatterplots where choices between superior alternatives were affected by the placement of other suboptimal points. We found that highlighting the superior alternatives weakened the bias, but did not eliminate it. We then tested an interactive approach where participants completely removed locally dominated points from the view, inspired by the elimination by aspects strategy in the decision-making literature. This approach strongly decreased the bias, leading to a counterintuitive suggestion: tools that allow removing inappropriately salient or distracting data from a view may help lead users to make more rational decisions.",,https://aviz.fr/deletion,https://aviz.fr/deletion,,vimeo 289784657,10.1109/TVCG.2018.2865233,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:20 PM,4:40 PM
  InfoVis,TVCG,Face to Face: Evaluating Visual Comparison,J),"Brian David Ondov, Nicole Jardine, Niklas Elmqvist, Steven Franconeri",http://users.umiacs.umd.edu/~elm/projects/face2face/face2face.pdf,"Data are often viewed as a single set of values, but those values frequently must be compared with another set. The existing evaluations of designs that facilitate these comparisons tend to be based on intuitive reasoning, rather than quantifiable measures. We build on this work with a series of crowdsourced experiments that use low-level perceptual comparison tasks that arise frequently in comparisons within data visualizations (e.g., which value changes the most between the two sets of data?). Participants completed these tasks across a variety of layouts: overlaid, two arrangements of juxtaposed small multiples, mirror-symmetric small multiples, and animated transitions. A staircase procedure sought the difficulty level (e.g., value change delta) that led to equivalent accuracy for each layout. Confirming prior intuition, we observe high levels of performance for overlaid versus standard small multiples. However, we also find performance improvements for both mirror symmetric small multiples and animated transitions. While some results are incongruent with common wisdom in data visualization, they align with previous work in perceptual psychology, and thus have potentially strong implications for visual comparison designs.",,,,,vimeo 289785372,10.1109/tvcg.2018.2864884,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,4:40 PM,5:00 PM
  TVCG,TVCG,Task-Based Effectiveness of Basic Visualizations,T),"Bahador Saket, Alex Endert, Çagatay Demiralp",https://arxiv.org/pdf/1709.08546.pdf,"Visualizations of tabular data are widely used; understanding their effectiveness in different task and data contexts is fundamental to scaling their impact. However, little is known about how basic tabular data visualizations perform across varying data analysis tasks. In this paper, we report results from a crowdsourced experiment to evaluate the effectiveness of five small scale (5-34 data points) two-dimensional visualization types�Table, Line Chart, Bar Chart, Scatterplot, and Pie Chart�across ten common data analysis tasks using two datasets. We find the effectiveness of these visualization types significantly varies across task, suggesting that visualization design would benefit from considering context-dependent effectiveness. Based on our findings, we derive recommendations on which visualizations to choose based on different tasks. We finally train a decision tree on the data we collected to drive a recommender, showcasing how to effectively engineer experimental user data into practical visualization systems.",,,https://github.com/gtvalab/ChartsEffectiveness/tree/master/Raw-Data,,vimeo 289789506,10.1109/tvcg.2018.2829750,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:00 PM,5:20 PM
  InfoVis,TVCG,At a Glance: Approximate Entropy as a Measure of Line Chart Visualization Complexity,J),"Eugene Wu, Remco Chang, Abigail Mosca, Gabriel Ryan",,,,,,,vimeo 289784475,10.1109/TVCG.2018.2865264,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:20 PM,5:40 PM
  TVCG,TVCG,Correlation Judgment and Visualization Features: A Comparative Study,T),"Fumeng Yang, Lane Harrison, Ronald A. Rensink, Steven Franconeri, Remco Chang",https://github.com/Fumeng-Yang/VisualFeature_TVCG/blob/master/paper/VF_preprint.pdf,"Recent visualization research efforts have incorporated experimental techniques and perceptual models from the vision science community. Perceptual laws such as Weber's law, for example, have been used to model the perception of correlation in scatterplots. While this thread of research has progressively refined the modeling of the perception of correlation in scatterplots, it remains unclear as to why such perception can be modeled using relatively simple functions, e.g., linear and log-linear. In this paper, we investigate a longstanding hypothesis that people use visual features in a chart as a proxy for statistical measures like correlation. For a given scatterplot, we extract 49 candidate visual features and evaluate which best align with existing models and participant judgments. The results support the hypothesis that people attend to a small number of visual features when discriminating correlation in scatterplots. We discuss how this result may account for prior conflicting findings, and how visual features provide a baseline for future model-based approaches in visualization evaluation and design.",,,,,vimeo 289789613,10.1109/tvcg.2018.2810918,2018,2018,InfoVis,"Convention Hall 1, Section D",Thursday,Perception & Cognition 2,25-Oct,5:40 PM,6:00 PM
  SciVis,TVCG,Firefly: Illumination Drones for Interactive Visualization,J),"Sergej Stoppel, Magnus Paulson Erga, Stefan Bruckner",https://vis.uib.no/wp-content/papercite-data/pdfs/VIS2018-Firefly.pdf,"Light specification in three dimensional scenes is a complex problem and several approaches have been presented that aim to automate this process. However, there are many scenarios where a static light setup is insufficient, as the scene content and camera position may change. Simultaneous manual control over the camera and light position imposes a high cognitive load on the user. To address this challenge, we introduce a novel approach for automatic scene illumination with Fireflies. Fireflies are intelligent virtual light drones that illuminate the scene by traveling on a closed path. The Firefly path automatically adapts to changes in the scene based on an outcome-oriented energy function. To achieve interactive performance, we employ a parallel rendering pipeline for the light path evaluations. We provide a catalog of energy functions for various application scenarios and discuss the applicability of our method on several examples.",,,,,video https://vis.uib.no/wp-content/papercite-data/vids/FinalVideo.mp4,10.1109/TVCG.2018.2864656,2018,2018,SciVis,Estrel Hall A+B,Thursday,Interaction and Multivariate Data,25-Oct,4:20 PM,4:40 PM
  
negative.code: 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",http://sci.utah.edu/~vdl/papers/2018_tvcg_lineage.pdf,"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
  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
  