scholarly journals Parallel Coordinates-based Visual Analytics for Materials Property

2019 ◽  
Author(s):  
Diwas Bhattarai ◽  
Bijaya Karki ◽  
Jian Zhang
Keyword(s):  
Visual Analytics ◽  
Parallel Coordinates

scholarly journals Visual analytics for BigData variety and its behaviours

2015 ◽  
Vol 12 (4) ◽  
pp. 1171-1191 ◽  
Author(s):  
Jinson Zhang ◽  
Mao Huang ◽  
Zhao-Peng Meng
Keyword(s):  
Visual Analytics ◽  
Data Flow ◽  
Flow Patterns ◽  
Unstructured Data ◽  
Parallel Coordinates ◽  
Traditional Methods ◽  
Multiple Datasets ◽  
New Framework

BigData, defined as structured and unstructured data containing images, videos, texts, audio and other forms of data collected from multiple datasets, is too big, too complex and moves too fast to analyze using traditional methods. This has given rise to a few issues that must be addressed; 1) how to analyze BigData across multiple datasets, 2) how to classify the different data forms, 3) how to identify BigData patterns based on its behaviours, 4) how to visualize BigData attributes in order to gain a better understanding of data. It is therefore necessary to establish a new framework for BigData analysis and visualization. In this paper, we have extended our previous works for classifying the BigData attributes into the "5Ws" dimensions based on different data behaviours. Our approach not only classifies BigData attributes for different data forms across multiple datasets, but also establishes the "5Ws" densities to represent the characteristics of data flow patterns. We use additional non-dimensional parallel axes in parallel coordinates to display the ?5Ws? sending and receiving densities, which provide more analytic features for BigData analysis. The experiment shows that our approach with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.

Adjustable general line coordinates for visual knowledge discovery in n-D data

2017 ◽  
Vol 18 (1) ◽  
pp. 3-32 ◽  
Author(s):  
Boris Kovalerchuk ◽  
Vladimir Grishin
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Real Data ◽  
Two Dimensions ◽  
Visual Pattern ◽  
Multidimensional Data ◽  
Support Vector ◽  
Parallel Coordinates ◽  
Visual Knowledge ◽  
General Line

Preserving all multidimensional data in two-dimensional visualization is a long-standing problem in Visual Analytics, Machine Learning/Data Mining, and Multiobjective Pareto Optimization. While Parallel and Radial (Star) coordinates preserve all n-D data in two dimensions, they are not sufficient to address visualization challenges of all possible datasets such as occlusion. More such methods are needed. Recently, the concepts of lossless General Line Coordinates that generalize Parallel, Radial, Cartesian, and other coordinates were proposed with initial exploration and application of several subclasses of General Line Coordinates such as Collocated Paired Coordinates and Star Collocated Paired Coordinates. This article explores and enhances benefits of General Line Coordinates. It shows the ways to increase expressiveness of General Line Coordinates including decreasing occlusion and simplifying visual pattern while preserving all n-D data in two dimensions by adjusting General Line Coordinates for given n-D datasets. The adjustments include relocating, rescaling, and other transformations of General Line Coordinates. One of the major sources of benefits of General Line Coordinates relative to Parallel Coordinates is twice less number of point and lines in visual representation of each n-D points. This article demonstrates the benefits of different General Line Coordinates for real data visual analysis such as health monitoring and benchmark Iris data classification compared with results from Parallel Coordinates, Radvis, and Support Vector Machine. The experimental part of the article presents the results of the experiment with about 70 participants on efficiency of visual pattern discovery using Star Collocated Paired Coordinates, Parallel, and Radial Coordinates. It shows advantages of visual discovery of n-D patterns using General Line Coordinates subclass Star Collocated Paired Coordinates with n = 160 dimensions.

Using arced axes in parallel coordinates geometry for high dimensional BigData visual analytics in cloud computing

Computing ◽  
2014 ◽  
Vol 97 (4) ◽  
pp. 425-437 ◽  
Author(s):  
Mao Lin Huang ◽  
Liang Fu Lu ◽  
Xuyun Zhang
Keyword(s):  
Cloud Computing ◽  
Visual Analytics ◽  
High Dimensional ◽  
Parallel Coordinates

scholarly journals Visual Analytics e Outlying Aspect Mining: contextualização de anomalias considerando questões temporais e multidimensionais

2021 ◽  
Author(s):  
Felipe Marx Benghi ◽  
Luiz Gomes-Jr
Keyword(s):  
Visual System ◽  
Human Visual System ◽  
Visual Analytics ◽  
Fault Analysis ◽  
Point Of View ◽  
Abnormal Behavior ◽  
Multidimensional Data ◽  
Parallel Coordinates ◽  
Aspect Mining ◽  
Visual Approach

Outlying Aspect Mining (OAM) is a new way of handling outliers that, instead of focusing solely on the detection, also provides an explanation. This is done by presenting a subspace of attributes that had the most abnormal behavior. Acknowledging this group of attributes is important but only listing them is not sufficient for a human specialist to comprehend the situation and take the necessary actions. A higher-level, visual approach can improve the process, providing better cognitive clues to experts. Here we describe a Visual Analytics platform developed to present data and OAM outputs in a human-friendly interface. A novelty available on this platform is a parallel coordinates plot that also display temporal multidimensional data. Such representation overcome human visual system limitations and helps in the outlier investigation. To explore the applicability of the developed tool, a locomotive operation user case is employed with focus on fault analysis in an OAM point of view.

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