Over the last couple of years, Bitcoin cryptocurrency and the Blockchain technology that forms the basis of Bitcoin have witnessed an unprecedented attention. Designed to facilitate a secure distributed platform without central regulation, Blockchain is heralded as a novel paradigm that will be as powerful as Big Data, Cloud Computing, and Machine Learning.
The Blockchain technology garners an ever increasing interest of researchers in various domains that benefit from scalable cooperation among trust-less parties. Some of these fields, such as graph analytics, have started analyzing Blockchain by using existing tools and algorithms, but have also offered novel approaches that are specifically tailored for Blockchain data. As Blockchain data analytics further proliferates, a need to glean successful approaches and to disseminate them among a diverse body of data scientists became a critical task. As an inter-disciplinary team of researchers, our aim is to fill this vital role.
We offer a holistic view on Blockchain Data Analytics. Starting with the core components of Blockchain, we will detail the state of art in Blockchain data analytics for graph, security, finance, and management domains. Beyond the cryptocurrency aspects of Blockchain, we will outline the frontier research approaches for data analyses from Blockchain platforms, such as Ethereum, Waves and Omni. Furthermore, we will discuss how the adoption of Blockchain will impact the future of data analytics and of human society, in general.
Cuneyt is an assistant professor in the Departments of Statistics and Computer Science at the University of Manitoba, Canada. He received his Ph.D. from University of Insubria, Italy and his M.S. from State University of New York at Buffalo, USA. His primary research interests are Data Science on complex networks and large scale graph analysis, with applications in social, biological, IoT and Blockchain networks. He is a Fulbright Scholarship recipient, and his research works have been published in leading conferences and journals including IJCAI, SDM, VLDB, ICDM and ICDE.
Yulia is Professor in the Department of Mathematical Science at the University of Texas at Dallas. Her research interests include statistical foundation of Data Science, inference for random graphs and complex networks, time series analysis, and predictive analytics. She holds a Ph.D in Mathematics, followed by a postdoctoral position in Statistics at the University of Washington. Prior to joining UT Dallas, she was a tenured faculty member at the University of Waterloo, Canada. She also held visiting positions at Johns Hopkins University, University of California, Berkeley, and the Isaac Newton Institute for Mathematical Sciences, Cambridge University, UK. She served as a Vice President of the International Society on Business and Industrial Statistics (ISBIS), and is a Fellow of the American Statistical Association.
Murat is a Professor in the Computer Science Department and Director of the UTD Data Security and Privacy Lab at the University of Texas at Dallas and a visiting scholar at Harvard University Data Privacy Lab. He is a recipient of NSF CAREER award, and Purdue CERIAS Diamond Award for Academic excellence. His research focuses on creating technologies that can efficiently extract useful information from any data without sacrificing privacy or security. Over the years, his research has been supported by grants from NSF, AFOSR, ONR, NSA, and NIH. In addition, he has published over 160 peer reviewed papers related to data security, privacy and privacy-preserving data mining. Some of his research work has been covered by the media outlets, such as Boston Globe, ABC News, and has received three best paper awards.