EuroVisSTAR2019

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https://diglib.eg.org/handle/10.2312/2632786

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A Review of Guidance Approaches in Visual Data Analysis: A Multifocal Perspective
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Ceneda, Davide; Gschwandtner, Theresia; Miksch, Silvia; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Visual data analysis can be envisioned as a collaboration of the user and the computational system with the aim of completing a given task. Pursuing an effective system-user integration, in which the system actively helps the user to reach his/her analysis goal has been focus of visualization research for quite some time. However, this problem is still largely unsolved. As a result, users might be overwhelmed by powerful but complex visual analysis systems which also limits their ability to produce insightful results. In this context, guidance is a promising step towards enabling an effective mixed-initiative collaboration to promote the visual analysis. However, the way how guidance should be put into practice is still to be unravelled. Thus, we conducted a comprehensive literature research and provide an overview of how guidance is tackled by different approaches in visual analysis systems. We distinguish between guidance that is provided by the system to support the user, and guidance that is provided by the user to support the system. By identifying open problems, we highlight promising research directions and point to missing factors that are needed to enable the envisioned human-computer collaboration, and thus, promote a more effective visual data analysis.
The State of the Art in Visual Analysis Approaches for Ocean and Atmospheric Datasets
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Afzal, Shehzad; Hittawe, Mohamad Mazen; Ghani, Sohaib; Jamil, Tahira; Knio, Omar; Hadwiger, Markus; Hoteit, Ibrahim; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
The analysis of ocean and atmospheric datasets offers a unique set of challenges to scientists working in different application areas. These challenges include dealing with extremely large volumes of multidimensional data, supporting interactive visual analysis, ensembles exploration and visualization, exploring model sensitivities to inputs, mesoscale ocean features analysis, predictive analytics, heterogeneity and complexity of observational data, representing uncertainty, and many more. Researchers across disciplines collaborate to address such challenges, which led to significant research and development advances in ocean and atmospheric sciences, and also in several relevant areas such as visualization and visual analytics, big data analytics, machine learning and statistics. In this report, we perform an extensive survey of research advances in the visual analysis of ocean and atmospheric datasets. First, we survey the task requirements by conducting interviews with researchers, domain experts, and end users working with these datasets on a spectrum of analytics problems in the domain of ocean and atmospheric sciences. We then discuss existing models and frameworks related to data analysis, sense-making, and knowledge discovery for visual analytics applications. We categorize the techniques, systems, and tools presented in the literature based on the taxonomies of task requirements, interaction methods, visualization techniques, machine learning and statistical methods, evaluation methods, data types, data dimensions and size, spatial scale and application areas. We then evaluate the task requirements identified based on our interviews with domain experts in the context of categorized research based on our taxonomies, and existing models and frameworks of visual analytics to determine the extent to which they fulfill these task requirements, and identify the gaps in current research. In the last part of this report, we summarize the trends, challenges, and opportunities for future research in this area.
External Labeling Techniques: A Taxonomy and Survey
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Bekos, Michael A.; Niedermann, Benjamin; Nöllenburg, Martin; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
External labeling is frequently used for annotating features in graphical displays and visualizations, such as technical illustrations, anatomical drawings, or maps, with textual information. Such a labeling connects features within an illustration by thin leader lines with their labels, which are placed in the empty space surrounding the image. Over the last twenty years, a large body of literature in diverse areas of computer science has been published that investigates many different aspects, models, and algorithms for automatically placing external labels for a given set of features. This state-of-the-art report introduces a first unified taxonomy for categorizing the different results in the literature and then presents a comprehensive survey of the state of the art, a sketch of the most relevant algorithmic techniques for external labeling algorithms, as well as a list of open research challenges in this multidisciplinary research field.
State-of-the-Art Report: Visual Computing in Radiation Therapy Planning
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Schlachter, Matthias; Raidou, Renata Georgia; Muren, Ludvig P.; Preim, Bernhard; Putora, Paul Martin; Bühler, Katja; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Radiation therapy (RT) is one of the major curative approaches for cancer. It is a complex and risky treatment approach, which requires precise planning, prior to the administration of the treatment. Visual Computing (VC) is a fundamental component of RT planning, providing solutions in all parts of the process-from imaging to delivery. Despite the significant technological advancements of RT over the last decades, there are still many challenges to address. This survey provides an overview of the compound planning process of RT, and of the ways that VC has supported RT in all its facets. The RT planning process is described to enable a basic understanding in the involved data, users and workflow steps. A systematic categorization and an extensive analysis of existing literature in the joint VC/RT research is presented, covering the entire planning process. The survey concludes with a discussion on lessons learnt, current status, open challenges, and future directions in VC/RT research.
EuroVis 2019 CGF 38-3 STARs: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Laramee, Robert S.; Oeltze, Steffen; Sedlmair, Michael; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Tasks, Techniques, and Tools for Genomic Data Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Nusrat, Sabrina; Harbig, Theresa; Gehlenborg, Nils; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data-driven and many studies lack welldefined hypotheses. A key challenge in data-driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.
State-of-the-art in Multi-Light Image Collections for Surface Visualization and Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Pintus, Ruggero; Dulecha, Tinsae Gebrechristos; Ciortan, Irina Mihaela; Gobbetti, Enrico; Giachetti, Andrea; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Multi-Light Image Collections (MLICs), i.e., stacks of photos of a scene acquired with a fixed viewpoint and a varying surface illumination, provide large amounts of visual and geometric information. In this survey, we provide an up-to-date integrative view of MLICs as a mean to gain insight on objects through the analysis and visualization of the acquired data. After a general overview of MLICs capturing and storage, we focus on the main approaches to produce representations usable for visualization and analysis. In this context, we first discuss methods for direct exploration of the raw data. We then summarize approaches that strive to emphasize shape and material details by fusing all acquisitions in a single enhanced image. Subsequently, we focus on approaches that produce relightable images through intermediate representations. This can be done both by fitting various analytic forms of the light transform function, or by locally estimating the parameters of physically plausible models of shape and reflectance and using them for visualization and analysis. We finally review techniques that improve object understanding by using illustrative approaches to enhance relightable models, or by extracting features and derived maps. We also review how these methods are applied in several, main application domains, and what are the available tools to perform MLIC visualization and analysis. We finally point out relevant research issues, analyze research trends, and offer guidelines for practical applications.
The State of the Art in Visualizing Multivariate Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Nobre, Carolina; Meyer, Miriah; Streit, Marc; Lex, Alexander; Laramee, Robert S. and Oeltze, Steffen and Sedlmair, Michael
Multivariate networks are made up of nodes and their relationships (links), but also data about those nodes and links as attributes. Most real-world networks are associated with several attributes, and many analysis tasks depend on analyzing both, relationships and attributes. Visualization of multivariate networks, however, is challenging, especially when both the topology of the network and the attributes need to be considered concurrently. In this state-of-the-art report, we analyze current practices and classify techniques along four axes: layouts, view operations, layout operations, and data operations. We also provide an analysis of tasks specific to multivariate networks and give recommendations for which technique to use in which scenario. Finally, we survey application areas and evaluation methodologies.

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