Open Access

ARTICLE

Brief Talk About Big Data Graph Analysis and Visualization

Guang Sun^1,2, Fenghua Li^1,*, Wangdong Jiang¹

1 Hunan University of Finance and Economics, Changsha, 410001, China.
2 The University of Alabama, Tuscaloosa 35401, USA.

*Corresponding Author: FengHua Li. Email: .

Journal on Big Data 2019, 1(1), 25-38. https://doi.org/10.32604/jbd.2019.05800

Abstract

Graphical methods are used for construction. Data analysis and visualization are an important area of applications of big data. At the same time, visual analysis is also an important method for big data analysis. Data visualization refers to data that is presented in a visual form, such as a chart or map, to help people understand the meaning of the data. Data visualization helps people extract meaning from data quickly and easily. Visualization can be used to fully demonstrate the patterns, trends, and dependencies of your data, which can be found in other displays. Big data visualization analysis combines the advantages of computers, which can be static or interactive, interactive analysis methods and interactive technologies, which can directly help people and effectively understand the information behind big data. It is indispensable in the era of big data visualization, and it can be very intuitive if used properly. Graphical analysis also found that valuable information becomes a powerful tool in complex data relationships, and it represents a significant business opportunity. With the rise of big data, important technologies suitable for dealing with complex relationships have emerged. Graphics come in a variety of shapes and sizes for a variety of business problems. Graphic analysis is first in the visualization. The step is to get the right data and answer the goal. In short, to choose the right method, you must understand each relative strengths and weaknesses and understand the data. Key steps to get data: target; collect; clean; connect.

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