chapter  7
22 Pages

Visualizing Bitcoin Using Big Data

Mempool Visualization, Visualization, Peer Visualization, Attack Visual Analysis, High-Resolution Visualization of Bitcoin Systems, Effectiveness
WithBasetty Mallikarjuna, T. V. Ramana, Suresh Kallam, Rizwan Patan, R. Manikandan

Bitcoin symbolizes cryptocurrency following peer-to-peer payment model. Here, the transactions are said to take place between pseudo-anonymous users, without any centralized authority. In other words, Bitcoin refers to digital currency with transactions being stored into a public ledger, called blockchain. Among the different types of entities, Bitcoin exchange differentiates by including a governing trading framework where most habitual clients barter fiat currency for Bitcoins and vice versa. Certain open standards are followed to ensure transparency in Bitcoins. Some of them are, transacting through public ledger, integrity being achieved via cryptographic mechanisms and privacy being attained by correlating Bitcoin owners with opaque cryptographic identifiers hiding the actual identities behind them. The blooming interest in Bitcoin has resulted to emergence of hundreds of cryptocurrency exchanges of differing sizes across the globe since its inception. Hence, Bitcoin and cryptocurrencies with huge volumes of data present several challenges.

Visualization, on the other hand, empowers clients to utilize the visual senses [12] and intuition with respect to Bitcoin blockchain data. Visualization is considered to be an important area which is used in different domains and fields, to name a few are, healthcare, security, privacy, image processing and so on that provide clients with visual information[11]. The technology behind blockchain became popular with the existence of Bitcoin ecosystem [26]. Unique opportunities are being provided here to store the digital transaction history according to consensus that is publicly available to everyone. This publicly available digital transaction history in blockchain provides the probability in analyzing both the current and previous cash flows. Different visualization approaches are split into three types based on the objectives and applications, ranging from, economic visualizations to transaction visualizations and security visualizations [19–22]. Besides, visualization are classified into three different areas, where huge data pertaining to abstract data are viewed (i.e., information visualization), forming analytical reasoning (i.e., visual analytics), and information pertaining to scientific aspects (i.e., scientific visualization).

Within the Bitcoin network, different types of protocol compatible data structures are generated throughout the peer-to-peer network using several algorithms. With these compatible data structures, Bitcoin system generate, transmit, establish and record corresponding data structures called as transactions. The transaction refers to the atomic record wherein the ownership of an amount of Bitcoin is conveyed by the existing client to the new client. The transactions in turn are broadcasted across the globe and each client obtains a copy of valid transactions in a data structure kept in volatile memory also referred to as the mempool or memory pool. The peer visualization aims in simply rotating globe visualization, or in other words that demonstrates the global scope of peer-to-peer network. As far as peer visualization is concerned, not only the requirement of network topology comes into existence to provide robustness but also ensure to determine the nodes possessing advantage over other in terms of system feasibility. Therefore, visualizations employed in transaction data have already grown in several contexts, to name a few being blockchain data visualization, dynamic graph visualization, and so on.

Finally, to measure the efficiency and effectiveness of the Bitcoin visualization, several methods are used and conducted over different number of clients, ranging from, general public to executives from companies, researchers in various fields and so on. Novel visualization models for obtaining dynamic patterns in real time Bitcoin transaction can obtain individual transactions in a more reliable manner. Besides, meaningful associations are also found to be detected between huge transactions in analyzing fraudulent activities. The effectiveness of Bitcoin visualization remains in recognizing both the memory visualization and peer visualization, as representing all global transactions, rather than a limited subset. With the aid of these two peer and memory visualizations, understandability linking between transactions found to be more precise than the raw data.