Big Data Visualization Tools and Techniques

2022 ◽  
pp. 590-621
Author(s):  
Obinna Chimaobi Okechukwu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Data Sets ◽  
New Paradigm ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Data Analysis Methods

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

Big Data Visualization Tools and Techniques

2018 ◽  
pp. 465-496
Author(s):  
Obinna Chimaobi Okechukwu
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Data Sets ◽  
New Paradigm ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Tools And Techniques ◽  
Data Analysis Methods

In this chapter, a discussion is presented on the latest tools and techniques available for Big Data Visualization. These tools, techniques and methods need to be understood appropriately to analyze Big Data. Big Data is a whole new paradigm where huge sets of data are generated and analyzed based on volume, velocity and variety. Conventional data analysis methods are incapable of processing data of this dimension; hence, it is fundamentally important to be familiar with new tools and techniques capable of processing these datasets. This chapter will illustrate tools available for analysts to process and present Big Data sets in ways that can be used to make appropriate decisions. Some of these tools (e.g., Tableau, RapidMiner, R Studio, etc.) have phenomenal capabilities to visualize processed data in ways traditional tools cannot. The chapter will also aim to explain the differences between these tools and their utilities based on scenarios.

Design and Implementation of Financial Big Data Visualization Analysis Platform

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Xi Chen ◽  
Bo Fan ◽  
Jie Zheng ◽  
Hongyan Cui
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Visualization ◽  
Production Efficiency ◽  
Big Data Analysis ◽  
Research Field ◽  
Visualization Analysis ◽  
Front End ◽  
Big Data Visualization ◽  
Analysis Platform

At present, it has become a hot research field to improve production efficiency and improve life experience through big data analysis. In the process of big data analysis, how to vividly display the results of the analysis is crucial. So, this paper introduces a set of big data visualization analysis platform based on financial field. The platform adopts the MVC system architecture, which is mainly composed of two parts: the background and the front end. The background part is built on the Django framework, and the front end is built with html5, css3, and JavaScript. The chart is rendered by Echarts. The platform can realize the classification of customers' savings potential through bank data, and make portraits of customers with different savings levels. The data analysis results can be dynamically displayed and interact wit

scholarly journals Graph structures for data visualizations

2021 ◽  
Vol 6 (2) ◽  
pp. 24-31
Author(s):  
Stefana Janićijević ◽  
Vojkan Nikolić
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Data Types ◽  
Current State ◽  
Graph Structures ◽  
Big Data Visualization ◽  
Visualization Tools ◽  
Graph System ◽  
Data Visualizations

Networks are all around us. Graph structures are established in the core of every network system therefore it is assumed to be understood as graphs as data visualization objects. Those objects grow from abstract mathematical paradigms up to information insights and connection channels. Essential metrics in graphs were calculated such as degree centrality, closeness centrality, betweenness centrality and page rank centrality and in all of them describe communication inside the graph system. The main goal of this research is to look at the methods of visualization over the existing Big data and to present new approaches and solutions for the current state of Big data visualization. This paper provides a classification of existing data types, analytical methods, techniques and visualization tools, with special emphasis on researching the evolution of visualization methodology in recent years. Based on the obtained results, the shortcomings of the existing visualization methods can be noticed.

scholarly journals Human-Centric Situational Awareness and Big Data Visualization

10.29007/mq54 ◽  
2019 ◽  
Author(s):  
Sri Teja Bodempudi ◽  
Sharad Sharma ◽  
Atma Sahu ◽  
Rajeev Agrawal
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Situation Awareness ◽  
Situational Awareness ◽  
Research Effort ◽  
Data Sets ◽  
Big Data Visualization ◽  
Immersive Environment ◽  
The Given ◽  
Model Graph

Human-centric situational awareness and visualization are needed for analyzing the big data in an efficient way. One of the challenges is to create an algorithm to analyze the given data without any help of other data analyzing tools. This research effort aims to identify how graphical objects (such as data-shapes) developed in accordance with an analyst's mental model can enhance analyst's situation awareness. Our approach for improved big data visualization is two-fold, focusing on both visualization and interaction. This paper presents the developed data and graph technique based on force- directed model graph in 3D. It is developed using Unity 3D gaming engine. Pilot testing was done with different data sets for checking the efficiency of the system in immersive environment and non-immersive environment. The application is able to handle the data successfully for the given data sets in data visualization. The currently graph can render around 200 to 300 linked nodes in real-time.

Research and Application of Responsive Big Data Visualization Based on HTML5

2014 ◽  
Vol 631-632 ◽  
pp. 1075-1079
Author(s):  
Pei Yang ◽  
Hai Yun Han
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Mobile Platform ◽  
Technology Research ◽  
Mass Data ◽  
Volume Velocity ◽  
Big Data Visualization ◽  
Visualization Technology ◽  
Special Points ◽  
Meaningful Data

Research the responsive visualization technology of big data based on HTML5. Big data has 4 special points: volume, velocity, variety and value. Our purpose is to mine the value of big data with the visualization technology. There are many platforms such as desktop and mobile platform, and each kind of device may have different resolution, based on HTML5, CSS3 and JavaScript technology, research the responsive visualization technology to fit all platforms, then we can mine meaningful data of the mass data, guide the development of related forecasting and strategy.

Sign in / Sign up

Export Citation Format

Share Document