Publications from our Researchers
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research. To have a look at one of our most recent papers published, please click here, entitled Visualizing Dynamic Bitcoin Transaction Patterns. The abstract for the paper follows:
Soman, R. K., Birch D & Whyte, J. K. (2017). “Framework for shared visualization and real-time information flow to the construction site”, Proceedings of the 24th EG-ICE Workshop on Intelligent Computing in Engineering, 10-12 July 2017, Nottingham, UK
ABSTRACT: The aim of this paper is to develop a framework for shared visualization between design office and construction office using augmented reality as a platform with focus on the security of Building Information Model. The current paper is part of an ongoing study aimed at creating a real-time bi-directional information flow between the construction office and site and focuses on a shared visualisation context. A framework architecture for enabling shared visualisation with a stress on security of Building Information Model is discussed and a prototype was deployed on an Android device in a controlled environment for testing and the application augmented Building Information object dynamically to the real-world without any latency. Salient features of the prototype include dynamic loading of Building Information content during the runtime, data encapsulation based on user privileges, deployability on portable low-end computing devices etc. Using shared visualization would empower the construction engineers with real-time models updates and results in many near-optimal management solutions to narrow in on the best solution under given the constraints
Visualizing Dynamic Bitcoin Transaction Patterns
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.
Creating a Chemistry of Sciences with Big Data: Building the Data Science Institute at Imperial College London