The Many Faces of Similarity - A Visual Analytics Approach for Design Space Simplification

Abuzuraiq, Ahmed M.; Erhan, Halil

doi:10.52842/conf.caadria.2020.1.485

Cited by 5 publications

(7 citation statements)

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“…Juxtaposition is a popular strategy in our survey. It allows specialized visualizations of the respective data type, for example, a parallel coordinates plot for the parameter and a gallery of the resulting 3D models [ AE20 ], or 2D embeddings of parameters and time series (Figure 11a , [ OKB*19 ]). Involved views are often conceptually linked through the Gestalt principles of common fate (when the analyst manipulates one view, the other changes immediately as well), or similarity (selected data cases highlighted in the same fashion everywhere).…”

Section: Input/output Visualizationmentioning

confidence: 99%

“…Such systems allow analysts to filter in either parameter or output space and see the effect on the other. Parallel coordinates (Figure 19b ) and related visualizations are especially common for this task [ SRS*13 , CLEK13 , BHGK14 , DFL*15 , OKB*19 , RPI19 , AE20 ], possibly after feature derivation (Section 7.2 ), but so are histograms and scatterplots. In a multiple‐linked view system, InfoVis can be combined with spatial/temporal data.…”

Section: Data Case Organizationmentioning

confidence: 99%

“…From the visual appearance of 3D models ( output) [ MGB*18 ] to how far sandbags ( parameter) were swept by a flood ( output) from their initial position [ RWF*13 ]. Energy use can be derived from a building design ( output) [ AE20 ], and the amount or length of labels from a time series segmentation (Figure 20b , [ EST20 ]). Well‐known summary statistics are also used, like minimum/maximum value of a time series [ MGJ*10 , MGS*14 ].…”

Section: Data Case Organizationmentioning

confidence: 99%

“…Abuzuraiq and Erhan [ AE20 ] use hierarchical clustering both on the parameter space and on the resulting 3D shape ( ) of the building in the context of generative architecture. Similarly, Beham et al.…”

Section: Data Case Organizationmentioning

confidence: 99%

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Visual Parameter Space Exploration in Time and Space

Piccolotto

Bögl

Miksch

2023

Computer Graphics Forum

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Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.

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Section: Input/output Visualizationmentioning

confidence: 99%

Section: Data Case Organizationmentioning

confidence: 99%

Section: Data Case Organizationmentioning

confidence: 99%

Section: Data Case Organizationmentioning

confidence: 99%

See 2 more Smart Citations

Visual Parameter Space Exploration in Time and Space

Piccolotto

Bögl

Miksch

2023

Computer Graphics Forum

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“…A consequence of juxtaposition is that general visualization-independent approaches may be used together. E.g., first grouping data cases by similarity, then inspecting properties of individual groups [1,8,25] is popular. Orban et al [47] devised two linked dimensionally-reduced (DR) scatterplots, an approach that can generally be extended to complex data and SBSS parameters/outputs.…”

Section: Visual Parameter Analysismentioning

confidence: 99%

Data Type Agnostic Visual Sensitivity Analysis

Piccolotto,

Bögl,

Muehlmann

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Illustration of the visualization pipeline used for the Discrepancy Dendrogram. The key idea of it is to use cluster diameters as a measure for variation in complex parameter settings and outputs. Given dissimilarity measures for each, we perform hierarchical clustering separately in each space. Distances are normalized by ranking or min-max normalization for comparison. For every cluster obtained through hierarchical clustering, we evaluate the difference in its diameter and visualize that by color.

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VR-DesignSpace: A Creativity Support Environment Integrating Virtual Reality Technology into Collaborative Data-Informed Evaluation of Design Alternatives

Zarei

DiPaola

2021

HCI International 2021 - Posters

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The Many Faces of Similarity - A Visual Analytics Approach for Design Space Simplification

Cited by 5 publications

References 0 publications

Visual Parameter Space Exploration in Time and Space

Visual Parameter Space Exploration in Time and Space

Data Type Agnostic Visual Sensitivity Analysis

VR-DesignSpace: A Creativity Support Environment Integrating Virtual Reality Technology into Collaborative Data-Informed Evaluation of Design Alternatives

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