Visualizing Statistical Properties of Smoothly Brushed Data Subsets

Unger, Andrea; Muigg, P.; Doleisch, Helmut; Schumann, Heidrun

doi:10.1109/iv.2008.29

Cited by 9 publications

(5 citation statements)

References 7 publications

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“…To support the comparison of deviations among multiple runs, functionality will be added to analyze subsets of runs, which are derived by similarities in the data. In this regard, methods to interactively derive and compare subsets of interest have already been addressed in previous work [13]. Also, we have carried out first investigations to perform an automated derivation of subsets.…”

Section: Discussionmentioning

confidence: 99%

Visual support for the understanding of simulation processes

Unger

Schumann

2009

2009 IEEE Pacific Visualization Symposium

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Current visualization systems are typically based on the concept of interactive post-processing. This decoupling of data visualization from the process of data generation offers a flexible application of visualization tools. It can also lead, however, to information loss in the visualization. Therefore, a combination of the visualization of the data generating process with the visualization of the produced data offers significant support for the understanding of the abstract data sets as well as the underlying process. Due to the application-specific characteristics of data generating processes, the task requires tailored visualization concepts.In this work, we focus on the application field of simulating biochemical reaction networks as discrete-event systems. These stochastic processes generate multi-run and multivariate timeseries, which are analyzed and compared on three different process levels: model, experiment, and the level of multi-run simulation data, each associated with a broad range of analysis goals. To meet these challenging characteristics, we present visualization concepts specifically tailored to all three process levels. The fundament of all three visualization concepts is a compact view that relates the multirun simulation data to the characteristics of the model structure and the experiment. The view provides the visualization at the experiment level. The visualization at the model level coordinates multiple instances of this view for the comparison of experiments. At the level of multi-run simulation data, the views gives an overview on the data, which can be analyzed in detail in time-series views suited for the analysis goals. Although we derive our visualization concepts for one concrete simulation process, our general concept of tailoring the visualization concepts to process levels is generally applicable for the visualization of simulation processes.

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Section: Discussionmentioning

confidence: 99%

Visual support for the understanding of simulation processes

Unger

Schumann

2009

2009 IEEE Pacific Visualization Symposium

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“…Unger et al . [UMDS08] use smooth brushing in the visualization of statistical characteristics for subsets of large data sets. Various methods for increasing the usefulness of 3D scatterplots incorporating brushing have been developed [KSH04], [PKH04].…”

Section: Related Workmentioning

confidence: 99%

Managing Spatial Selections With Contextual Snapshots

Mindek

Gröller

Brückner

2014

Computer Graphics Forum

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Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analysed and compared in different views. However, the semantics of such selections often depend on specific parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means for managing spatial selections in visualized data. The selections are automatically associated with the context in which they have been created. Contextual snapshots can also be used as the basis for interactive integrated and linked views, which enable in-place investigation and comparison of multiple visual representations of data. Our approach is implemented as a flexible toolkit with well-defined interfaces for integration into existing systems. We demonstrate the power and generality of our techniques by applying them to several distinct scenarios such as the visualization of simulation data, the analysis of historical documents and the display of anatomical data.

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“…Ulinski et al [Ulinski et al 2007] proposed two-handed methods for creating selections in volume rendering. Unger et al [Unger et al 2008] used smooth brushing in the visualization of statistical characteristics in subsets of large datasets. Various methods for increasing the usefulness of 3D scatterplots incorporating brushing have been developed ] [Piringer et al 2004].…”

Section: Related Workmentioning

confidence: 99%

Contextual Snapshots

Mindek

Brückner

Gröller

2013

Proceedings of the 29th Spring Conference on Computer Graphics

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Spatial selections are a ubiquitous concept in visualization. By localizing particular features, they can be analyzed and compared in different views. However, the semantics of such selections are often dependent on other parameter settings and it can be difficult to reconstruct them without additional information. In this paper, we present the concept of contextual snapshots as an effective means for managing spatial selections in visualized data. The selections are automatically associated with the context in which they have been created. Contextual snapshots can be also used as the basis for interactive integrated and linked views, which enable in-place investigation and comparison of multiple visual representations of data. Our approach is implemented as a flexible toolkit with welldefined interfaces for integration into existing systems. We demonstrate the power and generality of our techniques by applying them to several distinct scenarios such as the visualization of simulation data and the analysis of historical documents.

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Visualizing Statistical Properties of Smoothly Brushed Data Subsets

Cited by 9 publications

References 7 publications

Visual support for the understanding of simulation processes

Visual support for the understanding of simulation processes

Managing Spatial Selections With Contextual Snapshots

Contextual Snapshots

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