EuroVisSTAR2018

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https://diglib.eg.org/handle/10.2312/2632401

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    EuroVis 2018 STARs: Frontmatter
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Laramee, Robert S.; Marai, G. Elisabeta; Sedlmair, Michael; Robert S. Laramee and G. Elisabeta Marai and Michael Sedlmair
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    State of the Art of Sports Data Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Perin, Charles; Vuillemot, Romain; Stolper, Charles D.; Stasko, John T.; Wood, Jo; Carpendale, Sheelagh; Robert S. Laramee and G. Elisabeta Marai and Michael Sedlmair
    In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially-growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real-world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in-game actions and trajectories), and meta-data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high-level discussion of sports visualization research informed by our analysis—-identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/.
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    Quality Metrics for Information Visualization
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Behrisch, Michael; Blumenschein, Michael; Kim, Nam Wook; Shao, Lin; El-Assady, Mennatallah; Fuchs, Johannes; Seebacher, Daniel; Diehl, Alexandra; Brandes, Ulrik; Pfister, Hanspeter; Schreck, Tobias; Weiskopf, Daniel; Keim, Daniel A.; Robert S. Laramee and G. Elisabeta Marai and Michael Sedlmair
    The visualization community has developed to date many intuitions and understandings of how to judge the quality of views in visualizing data. The computation of a visualization's quality and usefulness ranges from measuring clutter and overlap, up to the existence and perception of specific (visual) patterns. This survey attempts to report, categorize and unify the diverse understandings and aims to establish a common vocabulary that will enable a wide audience to understand their differences and subtleties. For this purpose, we present a commonly applicable quality metric formalization that should detail and relate all constituting parts of a quality metric. We organize our corpus of reviewed research papers along the data types established in the information visualization community: multi- and high-dimensional, relational, sequential, geospatial and text data. For each data type, we select the visualization subdomains in which quality metrics are an active research field and report their findings, reason on the underlying concepts, describe goals and outline the constraints and requirements. One central goal of this survey is to provide guidance on future research opportunities for the field and outline how different visualization communities could benefit from each other by applying or transferring knowledge to their respective subdomain. Additionally, we aim to motivate the visualization community to compare computed measures to the perception of humans.
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    A Survey of Flattening-Based Medical Visualization Techniques
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Kreiser, Julian; Meuschke, Monique; Mistelbauer, Gabriel; Preim, Bernhard; Ropinski, Timo; Robert S. Laramee and G. Elisabeta Marai and Michael Sedlmair
    In many areas of medicine, visualization research can help with task simplification, abstraction or complexity reduction. A common visualization approach is to facilitate parameterization techniques which flatten a usually 3D object into a 2D plane. Within this state of the art report (STAR), we review such techniques used in medical visualization and investigate how they can be classified with respect to the handled data and the underlying tasks. Many of these techniques are inspired by mesh parameterization algorithms which help to project a triangulation in R3 to a simpler domain in R2. It is often claimed that this makes complex structures easier to understand and compare by humans and machines. Within this STAR we review such flattening techniques which have been developed for the analysis of the following medical entities: the circulation system, the colon, the brain, tumors, and bones. For each of these five application scenarios, we have analyzed the tasks and requirements, and classified the reviewed techniques with respect to a developed coding system. Furthermore, we present guidelines for the future development of flattening techniques in these areas.
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    Information Visualization Evaluation Using Crowdsourcing
    (The Eurographics Association and John Wiley & Sons Ltd., 2018) Borgo, Rita; Micallef, Luana; Bach, Benjamin; McGee, Fintan; Lee, Bongshin; Robert S. Laramee and G. Elisabeta Marai and Michael Sedlmair
    Visualization researchers have been increasingly leveraging crowdsourcing approaches to overcome a number of limitations of controlled laboratory experiments, including small participant sample sizes and narrow demographic backgrounds of study participants. However, as a community, we have little understanding on when, where, and how researchers use crowdsourcing approaches for visualization research. In this paper, we review the use of crowdsourcing for evaluation in visualization research. We analyzed 190 crowdsourcing experiments, reported in 82 papers that were published in major visualization conferences and journals between 2006 and 2017. We tagged each experiment along 36 dimensions that we identified for crowdsourcing experiments.We grouped our dimensions into six important aspects: study design & procedure, task type, participants, measures & metrics, quality assurance, and reproducibility. We report on the main findings of our review and discuss challenges and opportunities for improvements in conducting crowdsourcing studies for visualization research.