This work presents a systemic top-down visualization of Bitcoin transaction activity to explore dynamically generated patterns of algorithmic behavior. Bitcoin dominates the cryptocurrency markets and presents researchers with a rich source of real-time transactional data. The pseudonymous yet public nature of the data presents opportunities for the discovery of human and algorithmic behavioral patterns of interest to many parties such as financial regulators, protocol designers, and security analysts. However, retaining visual fidelity to the underlying data to retain a fuller understanding of activity within the network remains challenging, particularly in real time. We expose an effective force-directed graph visualization employed in our large-scale data observation facility to accelerate this data exploration and derive useful insight among domain experts and the general public alike. The high-fidelity visualizations demonstrated in this article allowed for collaborative discovery of unexpected high frequency transaction patterns, including automated laundering operations, and the evolution of multiple distinct algorithmic denial of service attacks on the Bitcoin network.
Scalable resolution display environments, including immersive data observatories, are emerging as equitable and socially engaging platforms for collaborative data exploration and decision making. These environments require specialized middleware to drive them, but, due to various limitations, there is still a gap in frameworks capable of scalable rendering of data visualizations. To overcome these limitations, we introduce a new modular open-source middleware, the Open Visualization Environment (OVE). This framework uses web technologies to provide an ecosystem for visualizing data using web browsers that span hundreds of displays. In this paper, we discuss the key design features and architecture of our framework as well as its limitations. This is followed by an extensive study on performance and scalability, which validates its design and compares it to the popular SAGE2 middleware. We show how our framework solves three key limitations in SAGE2. Thereafter, we present two of our projects that used OVE and show how it can extend SAGE2 to overcome limitations and simplify the user experience for common data visualization use-cases.
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