Pixnostics: Towards Measuring the Value of Visualization

Schneidewind, Jörn; Sips, Mike; Keim, Daniel A.

doi:10.1109/vast.2006.261423

Cited by 43 publications

(31 citation statements)

References 17 publications

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“…Similarly, in Pixnostics [21] the authors use image-and dataanalysis techniques in conjunction to rank the different lowerdimensional views of the dataset and present only the best to the user. The method creates lower-dimensional projections that provide maximum insight into the data and optimizes the parameter space for pixeloriented visualizations.…”

Section: General Screen-space Metricsmentioning

confidence: 99%

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Pargnostics: Screen-Space Metrics for Parallel Coordinates

Dasgupta

Kosara

2010

IEEE Trans. Visual. Comput. Graphics

118

106

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Abstract-Interactive visualization requires the translation of data into a screen space of limited resolution. While currently ignored by most visualization models, this translation entails a loss of information and the introduction of a number of artifacts that can be useful, (e.g., aggregation, structures) or distracting (e.g., over-plotting, clutter) for the analysis. This phenomenon is observed in parallel coordinates, where overlapping lines between adjacent axes form distinct patterns, representing the relation between variables they connect. However, even for a small number of dimensions, the challenge is to effectively convey the relationships for all combinations of dimensions. The size of the dataset and a large number of dimensions only add to the complexity of this problem. To address these issues, we propose Pargnostics, parallel coordinates diagnostics, a model based on screen-space metrics that quantify the different visual structures. Pargnostics metrics are calculated for pairs of axes and take into account the resolution of the display as well as potential axis inversions. Metrics include the number of line crossings, crossing angles, convergence, overplotting, etc. To construct a visualization view, the user can pick from a ranked display showing pairs of coordinate axes and the structures between them, or examine all possible combinations of axes at once in a matrix display. Picking the best axes layout is an NP-complete problem in general, but we provide a way of automatically optimizing the display according to the user's preferences based on our metrics and model.

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Section: General Screen-space Metricsmentioning

confidence: 99%

“…In case of parallel coordinates, we compute the entropy for the region between a pair of axes: regions of high entropy imply high information density [21,27]. Entropy is computed from a rendered parallel coordinates image, using the gray levels as the alphabet that is being transmitted.…”

Section: Pixel-based Entropymentioning

confidence: 99%

Pargnostics: Screen-Space Metrics for Parallel Coordinates

Dasgupta

Kosara

2010

IEEE Trans. Visual. Comput. Graphics

118

106

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“…Their main objective is to rank a group of views according to their potential relevance. Quality metrics that can be used for evaluating performance views are metrics that are designed for pixel-based visualization techniques, for example, the NoiseDissimilarity measure [25], or the entropy and standard deviation that are used in the Pixnostics approach [26]. Whereas the Noise-Dissimilarity evaluates the mappings based on their dissimilarity to a noise function, Pixnostics evaluates them based on entropy or standard deviation.…”

Section: Methods For Automatic Analysismentioning

confidence: 99%

Separating the wheat from the chaff

Rüden

Hermanns

Behrisch

et al. 2015

Proceedings of the 2nd Workshop on Visual Performance Analysis

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Performance-analysis tools are indispensable for understanding and optimizing the behavior of parallel programs running on increasingly powerful supercomputers. However, with size and complexity of hardware and software on the rise, performance data sets are becoming so voluminous that their analysis poses serious challenges. In particular, the search space that must be traversed and the number of individual performance views that must be explored to identify phenomena of interest becomes too large. To mitigate this problem, we use visual analytics. Specifically, we accelerate the analysis of performance profiles by automatically identifying (1) relevant and (2) similar data subsets and their performance views. We focus on views of the virtual-process topology, showing that their relevance can be well captured with visual-quality metrics and that they can be further assigned to topical groups according to their visual features. A case study demonstrates that our approach helps reduce the search space by up to 80%.

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“…Scheidewind et al [22] proposed automatic analysis of pixel images produced by different parameter mappings to rank them according to their potential value to the user. This allows reducing the parameter space to obtain insights more quickly.…”

Section: Visual Quality Metricsmentioning

confidence: 99%

Towards Perceptual Optimization of the Visual Design of Scatterplots

Micallef

Palmas

Oulasvirta

et al. 2017

IEEE Trans. Visual. Comput. Graphics

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Abstract-Designing a good scatterplot can be difficult for non-experts in visualization, because they need to decide on many parameters, such as marker size and opacity, aspect ratio, color, and rendering order. This paper contributes to research exploring the use of perceptual models and quality metrics to set such parameters automatically for enhanced visual quality of a scatterplot. A key consideration in this paper is the construction of a cost function to capture several relevant aspects of the human visual system, examining a scatterplot design for some data analysis task. We show how the cost function can be used in an optimizer to search for the optimal visual design for a user's dataset and task objectives (e.g., "reliable linear correlation estimation is more important than class separation"). The approach is extensible to different analysis tasks. To test its performance in a realistic setting, we pre-calibrated it for correlation estimation, class separation, and outlier detection. The optimizer was able to produce designs that achieved a level of speed and success comparable to that of those using human-designed presets (e.g., in R or MATLAB). Case studies demonstrate that the approach can adapt a design to the data, to reveal patterns without user intervention.

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Pixnostics: Towards Measuring the Value of Visualization

Cited by 43 publications

References 17 publications

Pargnostics: Screen-Space Metrics for Parallel Coordinates

Pargnostics: Screen-Space Metrics for Parallel Coordinates

Separating the wheat from the chaff

Towards Perceptual Optimization of the Visual Design of Scatterplots

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