TY - GEN
T1 - DataEther: Data exploration framework for ethereum
AU - Chen, Ting
AU - Hu, Teng
AU - Chen, Jiachi
AU - Zhang, Xiaosong
AU - Li, Zihao
AU - Zhang, Yufei
AU - Luo, Xiapu
AU - Chen, Ang
AU - Yang, Kun
AU - Hu, Bin
AU - Zhu, Tong
AU - Deng, Shifang
PY - 2019/7
Y1 - 2019/7
N2 - Ethereum is the largest blockchain platform supporting smart contracts with the second biggest market capitalization. Ethereum data can yield many useful insights because of the large volume of transactions, accounts and blocks as well as the popular applications developed as smart contracts. Studying Ethereum data can also reveal many new attacks to the platform and its smart contracts. Unfortunately, it is non-trivial to systematically explore Ethereum because it involves massive heterogeneous data, which are produced and stored in different ways. Although a few recent studies report some interesting observations about Ethereum, they are limited by their data acquisition methods which cannot provide comprehensive and precise data. In this paper, to fill the gap, we propose DataEther, a systematic and high-fidelity data exploration framework for Ethereum by exploiting its internal mechanisms. Besides supporting the analyses in existing studies, DataEther further empowers users to explore unknown phenomena and obtain in-depth understandings. We first describe how we tackle the challenging issues in developing DataEther, and then use four data-centric applications to demonstrate its usage and report many new observations.
AB - Ethereum is the largest blockchain platform supporting smart contracts with the second biggest market capitalization. Ethereum data can yield many useful insights because of the large volume of transactions, accounts and blocks as well as the popular applications developed as smart contracts. Studying Ethereum data can also reveal many new attacks to the platform and its smart contracts. Unfortunately, it is non-trivial to systematically explore Ethereum because it involves massive heterogeneous data, which are produced and stored in different ways. Although a few recent studies report some interesting observations about Ethereum, they are limited by their data acquisition methods which cannot provide comprehensive and precise data. In this paper, to fill the gap, we propose DataEther, a systematic and high-fidelity data exploration framework for Ethereum by exploiting its internal mechanisms. Besides supporting the analyses in existing studies, DataEther further empowers users to explore unknown phenomena and obtain in-depth understandings. We first describe how we tackle the challenging issues in developing DataEther, and then use four data-centric applications to demonstrate its usage and report many new observations.
KW - Data exploration
KW - Data-centric applications
KW - Ethereum
KW - Instrumentation
UR - http://www.scopus.com/inward/record.url?scp=85074844811&partnerID=8YFLogxK
U2 - 10.1109/ICDCS.2019.00137
DO - 10.1109/ICDCS.2019.00137
M3 - Conference article published in proceeding or book
AN - SCOPUS:85074844811
T3 - Proceedings - International Conference on Distributed Computing Systems
SP - 1369
EP - 1380
BT - Proceedings - 2019 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Conference on Distributed Computing Systems, ICDCS 2019
Y2 - 7 July 2019 through 9 July 2019
ER -