Interactive Visual Exploration and Analysis

Weber, Gunther H.; Hauser, Helwig

doi:10.1007/978-1-4471-6497-5_15

Cited by 9 publications

(13 citation statements)

References 24 publications

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“…: In the context of information theory and CFD, Chen et al [15] directly relate details to spatial features. Obermaier and Peikert [68] further note that the concept of feature in scientific visualization is derived from its definition in computer vision [12], where it describes a salient feature of an image, such as an edge or a ridge. For example, features in flow visualization include vortices, shock waves, isosurfaces, separation lines, and statistical features.…”

Section: Relevant Paradigms and Terminologymentioning

confidence: 99%

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Luciani

Burks

Sugiyama³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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Visualization research often seeks designs that first establish an overview of the data, in accordance to the information seeking mantra: "Overview first, zoom and filter, then details on demand". However, in computational fluid dynamics (CFD), as well as in other domains, there are many situations where such a spatial overview is not relevant or practical for users, for example when the experts already have a good mental overview of the data, or when an analysis of a large overall structure may not be related to the specific, information-driven tasks of users. Using scientific workflow theory and, as a vehicle, the problem of viscous finger evolution, we advocate an alternative model that allows domain experts to explore features of interest first, then explore the context around those features, and finally move to a potentially unfamiliar summarization overview. In a model instantiation, we show how a computational back-end can identify and track over time low-level, small features, then be used to filter the context of those features while controlling the complexity of the visualization, and finally to summarize and compare simulations. We demonstrate the effectiveness of this approach with an online web-based exploration of a total volume of data approaching half a billion seven-dimensional data points, and report supportive feedback provided by domain experts with respect to both the instantiation and the theoretical model.

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Section: Relevant Paradigms and Terminologymentioning

confidence: 99%

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Luciani

Burks

Sugiyama³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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“…The concept of linking&brushing is key to interactive visual analysis (IVA) [WH14,KH13]. It is modeled as an interactive and iterative method to reveal insight into large and multi-faceted datasets.…”

Section: Related Workmentioning

confidence: 99%

Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics

Rados

Splechtna

Matković

et al. 2016

Computer Graphics Forum

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Until now a lot of visual analytics predominantly delivers qualitative results-based, for example, on a continuous color map or a detailed spatial encoding. Important target applications, however, such as medical diagnosis and decision making, clearly benefit from quantitative analysis results. In this paper we propose several specific extensions to the well-established concept of linking&brushing in order to make the analysis results more quantitative. We structure the brushing space in order to improve the reproducibility of the brushing operation, e.g., by introducing the percentile grid. We also enhance the linked visualization with overlaid descriptive statistics to enable a more quantitative reading of the resulting focus+context visualization. Additionally, we introduce two novel brushing techniques: the percentile brush and the Mahalanobis brush. Both use the underlying data to support statistically meaningful interactions with the data. We illustrate the use of the new techniques in the context of two case studies, one based on meteorological data and the other one focused on data from the automotive industry where we evaluate a shaft design in the context of mechanical power transmission in cars.

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“…The use of a graphical interface for navigation and exploration of data sets allows the analyst to search different, correlated views and iteratively select and examine features that appear interesting and illuminating (Shamir & Stolpnik, 2012; Tominski, Abello, & Schumann, 2009; Weber & Hauser, 2014). …”

Section: Cognitive-analytic Processes In Scientific Visualizationmentioning

confidence: 99%

“…For example, interactive visualizations are suitable for analyzing high-dimensional data that have a large number of time points, and for researchers searching for correlations among attributes, or generating and testing hypotheses (Weber & Hauser, 2014). Interesting applications of interactive visualizations abound in the literature.…”

Section: Cognitive-analytic Processes In Scientific Visualizationmentioning

confidence: 99%

Visualization of Multidimensional Data in Nursing Science

et al. 2016

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Nursing scientists have long been interested in complex, context-dependent questions addressing individual- and population-level challenges in health and illness. These critical questions require multilevel data (e.g., genetic, physiologic, biologic, behavioral, affective, and social). Advances in data-gathering methods have resulted in the collection of large sets of complex, multifaceted, and often non-comparable data. Scientific visualization is a powerful methodological tool for facilitating understanding of these multidimensional data sets. Our purpose is to demonstrate the utility of scientific visualization as a method for identifying associations, patterns, and trends in multidimensional data as exemplified in two studies. We describe a brief history of visual analysis, processes involved in scientific visualization, and opportunities and challenges in the use of visualization methods. Scientific visualization can play a crucial role in helping nurse scientists make sense of the structure and underlying patterns in their data to answer vital questions in the field.

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Interactive Visual Exploration and Analysis

Cited by 9 publications

References 24 publications

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Details-First, Show Context, Overview Last: Supporting Exploration of Viscous Fingers in Large-Scale Ensemble Simulations

Towards Quantitative Visual Analytics with Structured Brushing and Linked Statistics

Visualization of Multidimensional Data in Nursing Science

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