Centrality clustering-based sampling for big data visualization

Nguyen, Tam T.; Song, Insu

doi:10.1109/ijcnn.2016.7727433

Cited by 11 publications

(4 citation statements)

References 27 publications

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“…In another work, Nguyen and Song [15] used a centrality-driven clustering approach to improve random sampling. Some other works which utilize information quantification techniques such as entropy have been proposed for sampling scientific datasets.…”

Section: Related Workmentioning

confidence: 99%

Filling the Void: Deep Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

Mishra

Hazarika²,

Biswas³

et al. 2022

Preprint

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As high-performance computing systems continue to advance, thegap between computing performance and I/O capabilities is widen-ing. This bottleneck limits the storage capabilities of increasinglylarge-scale simulations, which generate data at never-before-seengranularities while only being able to store a small subset of the rawdata. Recently, strategies for data-driven sampling have been pro-posed to intelligently sample the data in a way that achieves high datareduction rates while preserving important regions or features withhigh fidelity. However, a thorough analysis of how such intelligentsamples can be used for data reconstruction is lacking. We proposean AI-driven approach based on training neural networks to recon-struct full-scale datasets based on a simulation’s sampled output.Compared to current state-of-the-art reconstruction approaches suchas Delaunay triangulation, we demonstrate that deep learning-basedreconstruction has several advantages, including reconstruction qual-ity and time-to-reconstruct. We propose and evaluate strategies thatbalance the sampling rates with model training and data reconstruc-tion time to demonstrate how such AI approaches can be tailored forboth speed and quality, and develop a visual analytics interface forcomparing the reconstruction quality of grid-based datasets

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Section: Related Workmentioning

confidence: 99%

Filling the Void: Deep Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

Mishra

Hazarika²,

Biswas³

et al. 2022

Preprint

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show abstract

“…To enable interactive visualization of large-scale cosmology simulation data, Woodring et al [26] proposed a stratified random sampling approach. Nguyen and Song [17] incorporated centrality-driven clustering information during random sampling. Using the ideas of entropy maximization, Biswas et al [4,5] recently proposed in situ data-driven sampling schemes that preserve important data features along with their gradient properties.…”

Section: Related Workmentioning

confidence: 99%

Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

Hazarika

Biswas

Wolfram

et al. 2020

2020 IEEE Visualization Conference (VIS)

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With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses of single variables. In this work, we propose a multivariate sampling strategy which preserves the original variable relationships and enables different multivariate analyses directly on the sampled data. Our proposed strategy utilizes principal component analysis to capture the variance of multivariate data and can be built on top of any existing state-of-the-art sampling algorithms for single variables. In addition, we also propose variants of different data partitioning schemes (regular and irregular) to efficiently model the local multivariate relationships. Using two real-world multivariate data sets, we demonstrate the efficacy of our proposed multivariate sampling strategy with respect to its data reduction capabilities as well as the ease of performing efficient post-hoc multivariate analyses.

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“…In this work, Park et al [ 54 ] generated samples for scatter plot and map plot to still retain important visualization properties. Nguyen and Song [ 55 ] proposed a sampling approach that used the centrality-driven clustering for getting higher performance and quality over existing simple random sampling methods. Use of information theory has also been an important direction for researchers [ 56 , 57 , 58 ] while finding the optimal subset of the original data.…”

Section: Related Workmentioning

confidence: 99%

Multivariate Pointwise Information-Driven Data Sampling and Visualization

Dutta

Biswas

Ahrens

2019

Entropy

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With increasing computing capabilities of modern supercomputers, the size of the data generated from the scientific simulations is growing rapidly. As a result, application scientists need effective data summarization techniques that can reduce large-scale multivariate spatiotemporal data sets while preserving the important data properties so that the reduced data can answer domain-specific queries involving multiple variables with sufficient accuracy. While analyzing complex scientific events, domain experts often analyze and visualize two or more variables together to obtain a better understanding of the characteristics of the data features. Therefore, data summarization techniques are required to analyze multi-variable relationships in detail and then perform data reduction such that the important features involving multiple variables are preserved in the reduced data. To achieve this, in this work, we propose a data sub-sampling algorithm for performing statistical data summarization that leverages pointwise information theoretic measures to quantify the statistical association of data points considering multiple variables and generates a sub-sampled data that preserves the statistical association among multi-variables. Using such reduced sampled data, we show that multivariate feature query and analysis can be done effectively. The efficacy of the proposed multivariate association driven sampling algorithm is presented by applying it on several scientific data sets.

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Centrality clustering-based sampling for big data visualization

Cited by 11 publications

References 27 publications

Filling the Void: Deep Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

Filling the Void: Deep Learning-based Reconstruction of Sampled Spatiotemporal Scientific Simulation Data

Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

Multivariate Pointwise Information-Driven Data Sampling and Visualization

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