Parallel Coordinates Version of Time-Tunnel (PCTT) and Its Combinatorial Use for Macro to Micro Level Visual Analytics of Multidimensional Data

2015 ◽  
pp. 231-255 ◽  
Author(s):  
Yoshihiro Okada
Keyword(s):  
Visual Analytics ◽  
Multidimensional Data ◽  
Parallel Coordinates ◽  
Micro Level

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.

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.

scholarly journals Collaborative visual analytics of radio surveys in the Big Data era

2016 ◽  
Vol 12 (S325) ◽  
pp. 311-315 ◽  
Author(s):  
Dany Vohl ◽  
Christopher J. Fluke ◽  
Amr H. Hassan ◽  
David G. Barnes ◽  
Virginia A. Kilborn
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Research Process ◽  
Multidimensional Data ◽  
Asynchronous Collaboration ◽  
Parallel Data ◽  
Display Ecology ◽  
Data Inquiry ◽  
Large Survey ◽  
2D And 3D

AbstractRadio survey datasets comprise an increasing number of individual observations stored as sets of multidimensional data. In large survey projects, astronomers commonly face limitations regarding: 1) interactive visual analytics of sufficiently large subsets of data; 2) synchronous and asynchronous collaboration; and 3) documentation of the discovery workflow. To support collaborative data inquiry, we present encube, a large-scale comparative visual analytics framework. encube can utilise advanced visualization environments such as the CAVE2 (a hybrid 2D and 3D virtual reality environment powered with a 100 Tflop/s GPU-based supercomputer and 84 million pixels) for collaborative analysis of large subsets of data from radio surveys. It can also run on standard desktops, providing a capable visual analytics experience across the display ecology. encube is composed of four primary units enabling compute-intensive processing, advanced visualisation, dynamic interaction, parallel data query, along with data management. Its modularity will make it simple to incorporate astronomical analysis packages and Virtual Observatory capabilities developed within our community. We discuss how encube builds a bridge between high-end display systems (such as CAVE2) and the classical desktop, preserving all traces of the work completed on either platform – allowing the research process to continue wherever you are.

scholarly journals Using of Parallel Coordinates in Finding Minimum Distance in Time-Space

2019 ◽  
Vol 21 (3) ◽  
pp. 3-7
Author(s):  
Olga Blazekova ◽  
Maria Vojtekova
Keyword(s):  
Coordinate System ◽  
Minimum Distance ◽  
Three Dimensional ◽  
Multidimensional Data ◽  
Fourth Dimension ◽  
Geometric Representation ◽  
Two Dimensional ◽  
Parallel Coordinates ◽  
Time Space ◽  
Cartesian Coordinate

Airspace domain may be represented by a time-space consisting of a three-dimensional Cartesian coordinate system and time as the fourth dimension. A coordinate system provides a scheme for locating points given its coordinates and vice versa. The choice of coordinate system is important, as it transforms data to geometric representation. Visualization of the three and more dimensional data on the two-dimensional drawing - computer monitor is usually done by projection, which often can restrict the amount of information presented at a time. Using the parallel coordinate system is one of possibilities to present multidimensional data. The aim of this article is to describe basics of parallel coordinate system and to investigate lines and their characteristics in time-space.

scholarly journals Comprehensible Visualization of Multidimensional Data: Sum of Ranking Differences-Based Parallel Coordinates

Mathematics ◽  
2021 ◽  
Vol 9 (24) ◽  
pp. 3203
Author(s):  
Ádám Ipkovich ◽  
Károly Héberger ◽  
János Abonyi
Keyword(s):  
Multidimensional Scaling ◽  
Statistical Data ◽  
Multidimensional Data ◽  
Use Case ◽  
Iterative Approach ◽  
Visualization Technique ◽  
Parallel Coordinates ◽  
Source Codes ◽  
Sum Of Ranking Differences ◽  
Degree Of Similarity

A novel visualization technique is proposed for the sum of ranking differences method (SRD) based on parallel coordinates. An axis is defined for each variable, on which the data are depicted row-wise. By connecting data, the lines may intersect. The fewer intersections between the variables, the more similar they are and the clearer the figure becomes. Therefore, the visualization depends on what techniques are used to order the variables. The key idea is to employ the SRD method to measure the degree of similarity of the variables, establishing a distance-based order. The distances between the axes are not uniformly distributed in the proposed visualization; their closeness reflects similarity, according to their SRD value. The proposed algorithm identifies false similarities through an iterative approach, where the angles between the SRD values determine which side a variable is plotted. Visualization of the algorithm is provided by MATLAB/Octave source codes. The proposed tool is applied to study how the sources of greenhouse gas emissions can be grouped based on the statistical data of the countries. A comparison to multidimensional scaling (MDS)-based ordering is also given. The use case demonstrates the applicability of the method and the synergies of the incorporation of the SRD method into parallel coordinates.

Authorship Attribution of Noisy Text Data With a Comparative Study of Clustering Methods

2018 ◽  
Vol 9 (2) ◽  
pp. 45-69
Author(s):  
Zohra Hamadache ◽  
Halim Sayoud
Keyword(s):  
Visual Analytics ◽  
High Performance ◽  
Average Length ◽  
Recognition Rate ◽  
Authorship Attribution ◽  
Multidimensional Data ◽  
Clustering Methods ◽  
Noise Levels ◽  
Text Data ◽  
Noisy Text

Through the fast development and intensification of the large volume of data via the internet, visual analytics (VA) comes out with the intention of visualizing multidimensional data in different ways, which reveals interesting information about the data, making them clearer and more intelligible. In this investigation, the authors focused on the VA based Authorship Attribution (AA) task, applied on noisy text data. Furthermore, this article proposes 3D Visual Analytics technique based on sphere implementation. The used dataset contains several text documents written by 5 American Philosophers, with an average length of 850 words per text, which were scanned and then corrupted with different noise levels. The obtained results show that the hierarchical clustering technique using a fully-automated threshold, presents high performance in terms of authorship attribution accuracy, especially with character trigrams and ending bigrams, where the clustering recognition rate (CRR) reaches an accuracy of 100% at noise levels: from 0% to 7%. In addition, the proposed 3D sphere technique appears quite interesting by showing high clustering performances, mainly with Words.

scholarly journals DataMeadow: A Visual Canvas for Analysis of Large-Scale Multivariate Data

2008 ◽  
Vol 7 (1) ◽  
pp. 18-33 ◽  
Author(s):  
Niklas Elmqvist ◽  
John Stasko ◽  
Philippas Tsigas
Keyword(s):  
Visual Analytics ◽  
Large Scale ◽  
Multidimensional Data ◽  
Data Sets ◽  
Data Set ◽  
Data Dependencies ◽  
Expert Review ◽  
History Of ◽  
Multidimensional Data Sets ◽  
High Degree

Supporting visual analytics of multiple large-scale multidimensional data sets requires a high degree of interactivity and user control beyond the conventional challenges of visualizing such data sets. We present the DataMeadow, a visual canvas providing rich interaction for constructing visual queries using graphical set representations called DataRoses. A DataRose is essentially a starplot of selected columns in a data set displayed as multivariate visualizations with dynamic query sliders integrated into each axis. The purpose of the DataMeadow is to allow users to create advanced visual queries by iteratively selecting and filtering into the multidimensional data. Furthermore, the canvas provides a clear history of the analysis that can be annotated to facilitate dissemination of analytical results to stakeholders. A powerful direct manipulation interface allows for selection, filtering, and creation of sets, subsets, and data dependencies. We have evaluated our system using a qualitative expert review involving two visualization researchers. Results from this review are favorable for the new method.

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