![]() Figure 2 shows a treemap of a collection of choices for streaming music and video tracks in a social network community. The size of each sub-rectangle represents one measure, while color is often used to represent another measure of data. Treemap is an effective method for visualizing hierarchies. Parallel coordinate is very useful when to display multidimensional data. Parallel coordinates is used to plot individual data elements across many dimensions. The additional methods are: parallel coordinates, treemap, cone tree, and semantic network, etc. ![]() In addition, some data visualization methods have been used although they are less known compared the above methods. ![]() They are: table, histogram, scatter plot, line chart, bar chart, pie chart, area chart, flow chart, bubble chart, multiple data series or combination of charts, time line, Venn diagram, data flow diagram, and entity relationship diagram, etc. Many conventional data visualization methods are often used. The authors focused on big data visualization challenges as well as new methods, technology progress, and developed tools for big data visualization. The extension of some conventional visualization approaches to handling big data is far from enough in functions. At this stage, authors found that most conventional data visualization methods do not apply to big data. Most of these papers were published in the past three years because big data is a newer area. Next, authors searched for papers that are related to big data visualization. At this stage, authors mainly summarized traditional data visualization methods and new progress in this area. In this study, authors first searched for papers that are related to data visualization and were published in recent years through the university library system. The goal and the objectives of this paper are to present new methods and advances of Big Data visualization through introducing conventional visualization methods and the extension of some them to handling big data, discussing the challenges of big data visualization, and analyzing technology progress in big data visualization. Choosing proper data representation is also very important when visualizing big data. In large-scale data visualization, many researchers use feature extraction and geometric modeling to greatly reduce data size before actual data rendering. The extension of traditional visualization approaches have already been emerged but far from enough. Big Data visualization is not as easy as traditional small data sets. Visualization approaches are used to create tables, diagrams, images, and other intuitive display ways to represent data. Visualization can be manipulated with different effects. Visualization will lead to certainty: Data is visualized doesn’t mean it shows an accurate picture of what is important.Visualization will always manifest the right decision or action: Visualization cannot replace critical thinking. ![]()
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