This decade has been marked with the rise of blockchain based technologies. A blockchain is a distributed public ledger that stores transactions between two parties without requiring a trusted central authority. On a Blockchain, two unacquainted parties can create an unmodifiable transaction that is permanently recorded on the ledger to be seen by the public.
Many applications built with this technology are already having a wide array of social and economic impacts on society. As these applications proliferate, so does the complexity and volume of data stored by blockchains. Analyzing this data has emerged as an important research topic, already leading to methodological advancements in statistics, computer and information sciences. This website serves as the central repository on research associated with all forms of blockchain data analytics.
The premier IEEE International Conference for researchers in Data Engineering and data-intensive systems.
April 20-24 2020 - Dallas, Texas.
IEEE International Conference on Data Engineering - ICDE 2020
SDM has established itself as a leading conference in the field of data mining.
May 7–9, 2020 in Hilton Netherland Plaza - Cincinnati, Ohio.
SIAM International Conference on Data Mining - SDM 2020
ICDM Workshop on Blockchain Data Analytics
November 8, 2019 in Beijing, China
The Statistical and Applied Mathematical Sciences Institute
Workshop on Blockchain Data Analytics
October 6-7, 2019 in Research Triangle Park, NC.
Foundations for Blockchain Data Analytics - SAMSI 2019
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) is one of the longest established and leading international conferences in the areas of data mining and knowledge discovery.
April 14-17, 2019 in Macau, China.
Pacific-Asia Conference on Knowledge Discovery and Data Mining - PaKDD 2019
The conference seeks to advance the state-of-the-art in data mining by promoting novel, high quality research findings addressing complex data mining problems.
Nov 17-20, 2018 in Singapore
IEEE International Conference on Data Mining - ICDM 2018
Proposes an efficient and tractable data analytics framework to automatically detect new malicious addresses in a ransomware family, given only a limited records of previous transactions. Furthermore, our proposed techniques exhibit high utility to detect the emergence of new ransomware families, that is, ransomware with no previous records of transactions.
Reviews the research on combining blockchain and machine learning technologies and demonstrates that they can collaborate efficiently and effectively.
In this tutorial, we offer a holistic view on applied Data Science on Blockchains. Starting with the core components of Blockchain, we will detail the state of art in Blockchain data analytics for graph, security and finance domains.
Created and Maintained by:
Dr. Cuneyt Akcora, School of Computer Science, University of Manitoba
About Dr. Cuneyt Akcora
Dr. Yulia R. Gel, School of Mathematical Sciences, University of Texas at Dallas
About Dr. Yulia R. Gel
Dr. Murat Kantarcioglu, Erik Jonsson School of Cumputer Science, University of Texas at Dallas
About Dr. Murat Kantarcioglu
Please send questions and comments to:
Sudhanva Purushotham, Erik Jonsson School of Computer Science, University of Texas at Dallas