Adaptive, multiresolution visualization of large data sets using a distributed memory octree

Freitag, Lori A.; Loy, R. M.

doi:10.1145/331532.331592

Cited by 24 publications

(19 citation statements)

References 6 publications

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“…Multiresolution compression techniques have been employed for several years in computer graphics, signal processing, and efficient mesh generation [3], [4], [6], [17], [6]. A complete list of references is too lengthly to include here.…”

Section: Discussionmentioning

confidence: 99%

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An Adaptive Sub-sampling Method for In-memory Compression of Scientific Data

Unat

Hromadka

Baden

2009

2009 Data Compression Conference

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A current challenge in scientific computing is how to curb the growth of simulation datasets without losing valuable information. While wavelet based methods are popular, they require that data be decompressed before it can analyzed, for example, when identifying time-dependent structures in turbulent flows. We present Adaptive Coarsening, an adaptive subsampling compression strategy that enables the compressed data product to be directly manipulated in memory without requiring costly decompression. We demonstrate compression factors of up to 8 in turbulent flow simulations in three dimensions. Our compression strategy produces a non-progressive multiresolution representation, subdividing the dataset into fixed sized regions and compressing each region independently.

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Section: Discussionmentioning

confidence: 99%

“…The majority of the published compression techniques involve data visualization [3], [4] and include multiresolution techniques such as wavelets [5], [6].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Sub-sampling Method for In-memory Compression of Scientific Data

Unat

Hromadka

Baden

2009

2009 Data Compression Conference

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“…Several techniques for reducing data sets by providing coarse presentations have been proposed in the past. Typically these techniques construct a series of coarse data representations by employing tree structures or wavelets [5,15].…”

Section: Related Workmentioning

confidence: 99%

Scalable Pixel Based Visual Data Exploration

Keim

Schneidewind

Sips

2007

Pixelization Paradigm

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Abstract. Pixel based visualization techniques have proven to be of high value in visual data exploration. Mapping data points to pixels not only allows the analysis and visualization of large data sets, but also provides an intuitive way to convert raw data into a graphical form. The graphical representation fosters new insights and encourages the formation and validation of new hypotheses to the end of better problem solving and gaining deeper domain knowledge. However, the ever increasing amount of information leads to new challenges for pixel-based techniques and concepts, especially if the number of data points significantly exceeds the available screen resolution. The paper focuses on ways to improve the scalability of pixel based techniques by proposing a multiresolution pixel-oriented visual exploration approach for large datasets. This approach combines clustering techniques with pixel-oriented mappings to preserve local clusters while providing space filling relevancedriven representations of the whole data set or portions of the data. The paper presents different application scenarios from the fields of financial analysis, geo-visualization, and network data analysis that clearly show the practical benefit of the multi-resolution approach for tackling the problem of scalability

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“…Depending on the server configuration, parallel data filters (22) , efficient file formats (23) , or format independent data filters (24) may be used. A number of representative client-server or remote data access architectures are available including the Storage Resource Broker (25) , DataCutter (26) , Active Data Repository (27) , or OGSA (28) .…”

Section: Remote Data Filteringmentioning

confidence: 99%

Towards Distributed Visualization and Analysis of Large Flow Data

Hege

Weinkauf

Prohaska

et al. 2005

JSME international journal. Ser. B, Fluids and thermal engineer

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Fluid dynamics applications require a good understanding of the underlying physical phenomena. Therefore, effective procedures are necessary for analyzing and visualizing the various physical fields. Beside interactive and perceptually efficient techniques for visualizing flow fields directly, there is strong demand for methods that uncover hidden flow structures. Some recently developed feature based visual analysis methods are exemplarily presented. Fluid flow data typically are large and often are stored remotely or distributedly. The interactive visual analysis of such large data sets requires new software architectures -ideally utilizing emerging Grid standards. We discuss such architectures and report on specific software realizations.

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Adaptive, multiresolution visualization of large data sets using a distributed memory octree

Cited by 24 publications

References 6 publications

An Adaptive Sub-sampling Method for In-memory Compression of Scientific Data

An Adaptive Sub-sampling Method for In-memory Compression of Scientific Data

Scalable Pixel Based Visual Data Exploration

Towards Distributed Visualization and Analysis of Large Flow Data

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