Real-time visualization tool reveals behavioral patterns in Bitcoin transactions
New Rochelle, July 5, 2016–A novel visualization method for exploring dynamic patterns in real-time Bitcoin transactional data can zoom in on individual transactions in large blocks of data and also detect meaningful associations between large numbers of transactions and recurring patterns such as money laundering. The information and insights made possible by this top-down visualization of Bitcoin cryptocurrency transactions are described in an article in Big Data, the highly innovative, peer-reviewed journal from Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/). The article is available free for download on the Big Data (http://online.liebertpub.com/doi/full/10.1089/big.2015.0056) website.
In the article "Visualizing Dynamic Bitcoin Transaction Patterns (http://online.liebertpub.com/doi/full/10.1089/big.2015.0056)," Dan McGinn, David Birch, David Akroyd, Miguel Molina-Solana, Yike Guo, and William Knottenbelt, Imperial College London, U.K., compare their visualization approach to previous bottom-up methods, which examine data from single-source transactions. Top-down system-wide visualization enables pattern detection, and it is then possible to drill down into any particular transaction for more detailed information. The researchers describe the successful deployment of their visualization tool in a high-resolution 64-screen data observatory facility.
"This is a bold attempt at a comprehensive visualization of bitcoin transactions for a lay audience," says Big Data Editor-in-Chief Vasant Dhar, Professor at the Stern School of Business and the Center for Data Science at New York University, "but should also be of great interest to regulators and bankers who are trying to make sense of blockchain and related methods that can work without a central trusted intermediary. There is a lot of confusion about these emerging methods and a real need for articles that cut through the clutter and explain them in simple terms. Visualization is a key to understanding them."
About the Journal
Big Data (http://www.liebertpub.com/big), published quarterly online with open access options and in print, facilitates and supports the efforts of researchers, analysts, statisticians, business leaders, and policymakers to improve operations, profitability, and communications within their organizations. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address the challenges and discover new breakthroughs and trends living within this information. Complete tables of content and a sample issue may be viewed on the Big Data (http://www.liebertpub.com/big) website.
About the Publisher
Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/) is a privately held, fully integrated media company known for establishing authoritative medical and biomedical peer-reviewed journals, including OMICS: A Journal of Integrative Biology, Journal of Computational Biology, New Space, and 3D Printing and Additive Manufacturing. Its biotechnology trade magazine, GEN (Genetic Engineering & Biotechnology News), was the first in its field and is today the industry's most widely read publication worldwide. A complete list of the firm's more than 80 journals, newsmagazines, and books is available on the Mary Ann Liebert, Inc., publishers (http://www.liebertpub.com/) website.