R
Rui Liu
Researcher at University of Chicago
Publications - 27
Citations - 2056
Rui Liu is an academic researcher from University of Chicago. The author has contributed to research in topics: Participatory sensing & Information privacy. The author has an hindex of 12, co-authored 26 publications receiving 1447 citations. Previous affiliations of Rui Liu include National University of Singapore & Northeastern University.
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Journal ArticleDOI
Untangling Blockchain: A Data Processing View of Blockchain Systems
TL;DR: This paper conducts a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity, and Hyperledger Fabric, and discusses several research directions for bringing blockchain performance closer to the realm of databases.
Proceedings ArticleDOI
BLOCKBENCH: A Framework for Analyzing Private Blockchains
TL;DR: Blockbench as mentioned in this paper is an evaluation framework for analyzing private blockchains, which can be used to assess blockchains' viability as another distributed data processing platform, while helping developers to identify bottlenecks and accordingly improve their platforms.
Posted Content
Untangling Blockchain: A Data Processing View of Blockchain Systems
TL;DR: In this article, the authors present a benchmarking framework for understanding performance of private blockchains against data processing workloads, and conduct a comprehensive evaluation of three major blockchain systems based on BLOCKBENCH, namely Ethereum, Parity and Hyperledger Fabric.
Posted Content
BLOCKBENCH: A Framework for Analyzing Private Blockchains
TL;DR: BLOCKBENCH is described, the first evaluation framework for analyzing private blockchains and it serves as a fair means of comparison for different platforms and enables deeper understanding of different system design choices, and is released for public use.
Proceedings ArticleDOI
Research on anti-money laundering based on core decision tree algorithm
TL;DR: A core decision tree algorithm to identify money laundering activities and the clustering algorithm is the combination of BIRCH and K-means, which can identify abnormal transaction data more effectively.