Comparative Analysis of Tools for Big Data Visualization and Challenges

Abstract

The equivalent term for data visualization is ‘visual communication,’ representing data visually. The theme of data visualization is to convey information clearly, efficiently to users by using graphics, and it helps to have an inner view of data. Data visualization mesmerizes users by changing tedious data into a visually colorful tale. For this, we need data visualization tools that are popular in understanding data easily and visually. When analyzing data, data visualization is one of the steps that aroused to present the data to the users. Big data has come up considerably in recent times and has become an integral part of modern database research. In fact, there is a need for analyzing large amount of data which is temporal. Other characteristics of these datasets are that these are dynamic, noisy and heterogeneous in nature. As a result, to transform these different types of datasets into an accessible and understandable format, we need big data visualization tools. Traditionally, in the beginning, visualization in big data area was done by traditional systems but this approach was not capable of handling variety in datasets. Also, these traditional systems are restricted to deal with the datasets of small size. So, here comes the need for modern systems to analyze variety of datasets in big data, and this modern system gives awareness of modern data visualization tools that supports a variety of big datasets. Henceforth, the comparative analysis on visualization tools and challenges allows user to go with the best visualization tool for analyzing the big data based on the nature of the dataset. Since their inception, several tools have been proposed to model and analyze the process of data visualization in big datasets. In this chapter, we propose to analyze these tools in the form of their strengths and weaknesses. Also, we have planned to discuss in detail their applications and suitability in dealing with different situations (Caldarola, Picariello, & Rinaldi in Experiences in wordnet visualization with labeled graph database. Berlin: Springer, pp. 80–99, [1], Caldarola, Picariello, Rinaldi, & Sacco in Exploration and visualization of big graphs—The DBpedia case study. KDIR (IC3K 2016), pp. 257–264, [2], Caldarola, & Rinaldi in Improving the visualization of wordnet large lexical database through semantic tag clouds. IEEE, pp. 34–41 [3]).