The blockchain technology became familiar simultaneously with the Bitcoin ecosystem. It furnishes the eccentric chance to document the record of digital transactions and events on the basis of the consensus system that is found to be publicly accessible to all. The publicly accessible transaction data stockpiled in the blockchain gives the research group the potentiality to examine the succeeding and preceding cash flows.

Influencing Visualizing Approaches for Big Data

The elevated price increase [2] of Bitcoins in the last few years has resulted in the increase in the intrigue between laypeople and academic persons. Accordingly, the fields of visualization, security, and privacy became of considerable significance for the investigation and investigation of this virtual cryptocurrency under several facets and situations. Several visualization approaches are said to be divided into three types, based on the utilization and objectives. They are:

  • • Economic visualization for big data
  • • Real-time visualization for big data
  • • Security and analytic-related visualization for big data

The following sections look at these approaches.

Economic Visualization for Big Data

From the point of view of economic visualization, the perspective of economists regarding the Bitcoin payment system is measured from different angle. From this perspective, the major objective remained in using the Bitcoins, providing solutions to the question of whether it has to be used as a currency or a commodity. To visualize the economic angle, several graphical patterns were evolved without any association and exclusively concentrate on widespread com- pendiums. Several network and conversation analysis were made regarding the economic visualization for big data and they were able to understand those consequences to Bitcoin. Due to their economic visual representation for big data, they were accomplished to ascertain an association between discussion sentiment and the prevailing Bitcoin price. Their economic visualizations in connection to the prototype, however, concentrated only on economic states regarding visualization for big data [14,15].

Real-Time Visualization for Big Data

Real-time visualizations for big data of transaction flows are usually published as web applications. The two major goals for real-time visualization for big data are the values of the prevailing transaction and frequencies and their association between each other. The real-time visualizations for big data span from blob visualization and simple graphical patterns to more complicated world maps and city blocks. This real-time visualization for big data mostly has a very unforgettable dispensing of the prevailing blockchain transaction. However, the objective information that can be extracted of those is found to be very less velocity while visualizing the big data [16,17].

Security and Analytic-Related Visualization for Big Data

Finally, the security and analytic-related visualizations for big data has the major influential factors [11,12,19-26].

Volume and Flow Visualization Patterns

Three types of volume and flow visualization patterns are said to exist. Figure 7.6 shows the types of patterns.

The three types of views are said to exist in the volume and flow visualization patterns and they are discussed in the following sections.

Filter View

The first step in the volume and flow visualization analysis is to filter out elements with certain attribute levels. For example, it may be of interest to filter out elements with only one transaction or one-time user or elements that have not been in the enrollment anymore for a longer period of time. Here, with the aid of a decision tree, the filtering functionality is said to be applied to filter out the unnecessary elements based on a decision making process.

Different volume and flow visualization patterns

FIGURE 7.6 Different volume and flow visualization patterns.

Cluster View

The second step in the volume and flow visualization analysis is to obtain the filtered set of elements. These filtered set of elements are then said to be clustered. This is performed with the objective of clustering the similar elements into a single group. For this specific purpose, the data analysis identifies the number of attributes that are of high relevant and also the total number of clusters. In this manner, the elements with unique higher amounts of Bitcoin transferred are said to be clustered with the objective of making fair comparisons between elements transferring high amounts of Bitcoin.

Timeline View

The final and the last step in the volume and flow visualization analysis is the timeline view. This timeline view comprises a number of horizontal timelines and vertical timelines. Here, two axes representing the horizontal and vertical axis are said to be formed using different types of available graphs. The data analyst now performs the task of extracting the temporal distribution of transactions belonging to each cluster. From this, further analyses are said to be made, and final information is said to be obtained in the timeline.

Information Visualizations

The main objective of information visualization is to validate the user to traverse enormous amount of abstract data. A straightforward differentiation and glance via this immense amount of data is impracticable for a human being. Therefore, a visual representation is requisite to obtain a feeling of the organized and the transaction process that are stored in a public manner in the blockchain.

Big Data Visual Analytics

Big data visual analytics is a comparatively new area of study that concentrates on the tight combination of visualization and analytics concerning big data. The name, visual analytics for big data was formed by the research report Illuminating the Path: The Research and Development Agenda for Visual Analytics, by the National Visualization and Analytics Center (NVAC) in 2005 [3]. NVAC is concerned with sponsoring the research unit with the objective of defining a long-term research memorandum in visual analytics with the purpose of enhancing the analytic potentialities.

One of the important approaches for analysis is the visual analytics. With heavily complicated issues, a combination of higher amount of analysis mechanisms is heavily required to analyze and provide a clear knowledge about single problem. To state, for example, machine learning methods and mechanisms are said to be applied with the objective of training analytics to find the patterns pertaining to specific data; intelligence-value-estimation algorithms have found their place in ranking or scoring the results and in all these streams; and visual analytics have found their place in throughout this entire process to boost these algorithms or to present the results for several interpretation. Some of the applications are shown in Figure 7.7.

As illustrated, having a visual presentation permits a user to observe hidden relationships not identified via algorithmic steps. The inception of big data affects all these analytic methods and elements of the analytic process. Modifying and registering visual analytics to big data issues new advantages and opens new research perspectives.

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