Statistical visualization and analysis of large data using a value-based spatial distribution

Wang, Ko-Chih; Lu, Kai; Wei, Tzu-Hsuan; Shareef, Naeem; Shen, Han‐Wei

doi:10.1109/pacificvis.2017.8031590

Cited by 37 publications

(22 citation statements)

References 26 publications

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“…Neuroth et al [26] used spatially organized histograms in order to distinguish spatial variations and exploit isosurfaces to visualize time-varying trends found within histogram distribution. Wang et al [27] used warm colors for a high probability of the investigated subjects appearing on the isosurface and cold colors for a low probability. We aim to help users effectively make sense of the spatial distribution of geospatial datasets.…”

Section: Spatial Distribution Visualizationsmentioning

confidence: 99%

Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions

Zhao

Liu

Guo

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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The multidimensional nature of spatial data poses a challenge for visualization. In this paper, we introduce Phoenixmap, a simple abstract visualization method to address the issue of visualizing multiple spatial distributions at once. The Phoenixmap approach starts by identifying the enclosed outline of the point collection, then assigns different widths to outline segments according to the segments' corresponding inside regions. Thus, one 2D distribution is represented as an outline with varied thicknesses. Phoenixmap is capable of overlaying multiple outlines and comparing them across categories of objects in a 2D space. We chose heatmap as a benchmark spatial visualization method and conducted user studies to compare performances among Phoenixmap, heatmap, and dot distribution map. Based on the analysis and participant feedback, we demonstrate that Phoenixmap 1) allows users to perceive and compare spatial distribution data efficiently; 2) frees up graphics space with a concise form that can provide visualization design possibilities like overlapping; and 3) provides a good quantitative perceptual estimating capability given the proper legends. Finally, we discuss several possible applications of Phoenixmap and present one visualization of multiple species of birds' active regions in a nature preserve.

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Section: Spatial Distribution Visualizationsmentioning

confidence: 99%

Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions

Zhao

Liu

Guo

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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show abstract

“…Distribution of chunk with respect to size. To represent the chunk of dataset with a distribution-based representation that summarizes scalar information into muchreduced groups, one of statistical distribution methods should be used [13]. In this paper, CB-TTTD with Multi hashing Technique used the histogram.…”

Section: Matchingmentioning

confidence: 99%

New Techniques to Enhance Data Deduplication using Content based-TTTD Chunking Algorithm

Jasim¹,

Fahad²

2018

ijacsa

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Due to the fast indiscriminate increase of digital data, data reduction has acquired increasing concentration and became a popular approach in large-scale storage systems. One of the most effective approaches for data reduction is Data Deduplication technique in which the redundant data at the file or sub-file level is detected and identifies by using a hash algorithm. Data Deduplication showed that it was much more efficient than the conventional compression technique in largescale storage systems in terms of space reduction. Two Threshold Two Divisor (TTTD) chunking algorithm is one of the popular chunking algorithm used in deduplication. This algorithm needs time and many system resources to compute its chunk boundary. This paper presents new techniques to enhance TTTD chunking algorithm using a new fingerprint function, a multi-level hashing and matching technique, new indexing technique to store the Metadata. These new techniques consist of four hashing algorithm to solve the collision problem and adding a new chunk condition to the TTTD chunking conditions in order to increase the number of the small chunks which leads to increasing the Deduplication Ratio. This enhancement improves the Deduplication Ratio produced by TTTD algorithm and reduces the system resources needed by this algorithm. The proposed algorithm is tested in terms of Deduplication Ratio, execution time, and Metadata size.

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“…Each component is characterized by the mean vector µ j and the covariance matrix Σ j , and is associated with a weight w j where all weights sum up to 1. As a frequently used model for block-wise data reduction [Du a et al 2017; Du a and Shen 2016; Liu et al 2012;Wang et al 2017], GMM can capture data statistical properties when the distribution follows a multiple-peak shape, for which a single Gaussian distribution does not work e ectively. Figure 1 gives an overview of our partition and reduction procedure.…”

Section: Data Reduction Model 31 Model Vector Data Distribution By Gmmmentioning

confidence: 99%

“…In this case, what has been lost is spatial information. Directly modeling the spatial information of distributions has been studied [Wang et al 2017], for visual analytical tasks that require deterministic spatial locations such as isosurface or volume rendering. However, when performing ow line tracing, the location of the particle at each step is highly impacted by previous tracing status, thus is not derterministic but with large uncertainty, especially for distribution-based data.…”

Section: Model the Vector Transitionmentioning

confidence: 99%

Winding angle assisted particle tracing in distribution-based vector field

Shen

2017

SIGGRAPH Asia 2017 Symposium on Visualization

Self Cite

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Distribution models are widely used for data reduction applications. e Gaussian mixture model (GMM) is a powerful tool to capture multiple-peak distributions. For distribution-based vector eld datasets represented by GMM, there are still loss of information which sometimes causes too much error when performing ow line tracing tasks. As a compensation, we analyze the vector transition pa ern between consecutive vector directions. e vector transition is depicted by distributions of winding angles. When performing streamline and pathline tracing, we utilize the winding angle to estimate a conditional distribution of local vectors, using the Bayes eorem. e conditional distribution can be used for both Monte Carlo ow line tracing, and single ow line tracing. We applied our distribution model on data reduction applications, and demonstrated that improved ow line tracing quality was achieved. CCS CONCEPTS •Computing methodologies →Parametric curve and surface models;

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Statistical visualization and analysis of large data using a value-based spatial distribution

Cited by 37 publications

References 26 publications

Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions

Phoenixmap: An Abstract Approach to Visualize 2D Spatial Distributions

New Techniques to Enhance Data Deduplication using Content based-TTTD Chunking Algorithm

Winding angle assisted particle tracing in distribution-based vector field

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