XBlock-EOS: Extracting and exploring blockchain data from EOSIO

Weilin Zheng, Zibin Zheng*, Hong-Ning Dai, Xu Chen, Peilin Zheng

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

37 Citations (Scopus)

Abstract

Blockchain-based cryptocurrencies and applications have flourished in blockchain research community. Massive data generated from diverse blockchain systems bring not only huge business values but also technological challenges in data analytics of heterogeneous blockchain data. Different from Bitcoin and Ethereum, EOSIO has richer diversity and a higher volume of blockchain data due to its unique architectural design in resource management, consensus scheme and high throughput. Despite its popularity (e.g., 89,800,000 blocks generated till November 14, 2019 since its launch on June 8, 2018), few studies have been made on data analysis of EOSIO. To fill this gap, we collect and process the up-to-date on-chain data from EOSIO. We name these well-processed EOSIO datasets as XBlock-EOS, which consists of 7 well-processed datasets: (1) Block, Transaction and Action, (2) Internal and External EOS Transfer Action, (3) Contract Information, (4) Contract Invocation, (5) Token Action, (6) Account Creation, (7) Resource Management. It is challenging to process and analyze a high volume of raw EOSIO data and establish the mapping from original raw data to the well-grained datasets since it requires substantial efforts in extracting various types of data as well as sophisticated knowledge on software engineering and data analytics. Meanwhile, we present statistics and exploration on these datasets. Moreover, we also outline the possible research opportunities based on XBlock-EOS.
Original languageEnglish
Article number102477
Number of pages19
JournalInformation Processing and Management
Volume58
Issue number3
Early online date5 Feb 2021
DOIs
Publication statusPublished - May 2021

User-Defined Keywords

  • Blockchain
  • EOSIO
  • Big data
  • Data acquisition
  • Data analysis
  • Security

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