Blockchain networks: Data structures of Bitcoin, Monero, Zcash, Ethereum, Ripple, and Iota.

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2022-01-01 Epub Date: 2021-11-17 DOI:10.1002/widm.1436

Cuneyt Gurcan Akcora, Yulia R Gel, Murat Kantarcioglu

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引用次数: 19

Abstract

Blockchain is an emerging technology that has enabled many applications, from cryptocurrencies to digital asset management and supply chains. Due to this surge of popularity, analyzing the data stored on blockchains poses a new critical challenge in data science. To assist data scientists in various analytic tasks for a blockchain, in this tutorial, we provide a systematic and comprehensive overview of the fundamental elements of blockchain network models. We discuss how we can abstract blockchain data as various types of networks and further use such associated network abstractions to reap important insights on blockchains' structure, organization, and functionality. This article is categorized under:Technologies > Data PreprocessingApplication Areas > Business and IndustryFundamental Concepts of Data and Knowledge > Data ConceptsFundamental Concepts of Data and Knowledge > Knowledge Representation.

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区块链网络:比特币、门罗币、Zcash、以太坊、Ripple和Iota的数据结构。

区块链是一种新兴技术，已经实现了从加密货币到数字资产管理和供应链的许多应用。由于这种普及程度的激增，分析存储在区块链上的数据对数据科学提出了新的关键挑战。为了帮助数据科学家完成区块链的各种分析任务，在本教程中，我们对区块链网络模型的基本元素进行了系统和全面的概述。我们讨论了如何将区块链数据抽象为各种类型的网络，并进一步使用这些相关的网络抽象来获得关于区块链结构、组织和功能的重要见解。本文分类如下:技术>数据预处理>应用领域>商业和工业数据和知识的基本概念>数据概念>数据和知识的基本概念>知识表示。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.