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解密分布式账本设计——分类法、分类及区块链社区评估

Decrypting distributed ledger design-taxonomy, classification and blockchain community evaluation.

作者信息

Ballandies Mark C, Dapp Marcus M, Pournaras Evangelos

机构信息

Computational Social Science, ETH Zurich, Zürich, Switzerland.

School of Computing, University of Leeds, Leeds, UK.

出版信息

Cluster Comput. 2022;25(3):1817-1838. doi: 10.1007/s10586-021-03256-w. Epub 2021 Apr 24.

DOI:10.1007/s10586-021-03256-w
PMID:35601685
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9114082/
Abstract

UNLABELLED

More than 1000 distributed ledger technology (DLT) systems raising $600 billion in investment in 2016 feature the unprecedented and disruptive potential of blockchain technology. A systematic and data-driven analysis, comparison and rigorous evaluation of the different design choices of distributed ledgers and their implications is a challenge. The rapidly evolving nature of the blockchain landscape hinders reaching a common understanding of the techno-socio-economic design space of distributed ledgers and the cryptoeconomies they support. To fill this gap, this paper makes the following contributions: (i) A conceptual architecture of DLT systems with which (ii) a taxonomy is designed and (iii) a rigorous classification of DLT systems is made using real-world data and wisdom of the crowd. (iv) A DLT design guideline is the end result of applying machine learning methodologies on the classification data. Compared to related work and as defined in earlier taxonomy theory, the proposed taxonomy is highly comprehensive, robust, explanatory and extensible. The findings of this paper can provide new insights and better understanding of the key design choices evolving the modeling complexity of DLT systems, while identifying opportunities for new research contributions and business innovation.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10586-021-03256-w.

摘要

未标注

2016年有1000多个分布式账本技术（DLT）系统筹集了6000亿美元投资，展现出区块链技术前所未有的颠覆性潜力。对分布式账本的不同设计选择及其影响进行系统的、数据驱动的分析、比较和严格评估是一项挑战。区块链领域快速发展的特性阻碍了人们对分布式账本及其所支持的加密经济的技术 - 社会 - 经济设计空间达成共识。为填补这一空白，本文做出了以下贡献：（i）提出了DLT系统的概念架构，（ii）据此设计了一种分类法，（iii）利用实际数据和群体智慧对DLT系统进行了严格分类。（iv）将机器学习方法应用于分类数据，最终得到了一份DLT设计指南。与相关工作以及早期分类法理论中所定义的相比，所提出的分类法具有高度的全面性、稳健性、解释性和可扩展性。本文的研究结果能够为理解影响DLT系统建模复杂性的关键设计选择提供新的见解，同时为新的研究贡献和商业创新指明机会。

补充信息

在线版本包含可在10.1007/s10586 - 021 - 03256 - w获取的补充材料。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/5e479459c844/10586_2021_3256_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/0e228f78573b/10586_2021_3256_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/0e228f78573b/10586_2021_3256_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/93326699ab7e/10586_2021_3256_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/6c1b401ca050/10586_2021_3256_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/4d0f751d88bc/10586_2021_3256_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/5a81ff9f1042/10586_2021_3256_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/bea7dffb848c/10586_2021_3256_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/4dba6ed6d948/10586_2021_3256_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/d29fcbcdc0d5/10586_2021_3256_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/2562c2a5fd0c/10586_2021_3256_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/3b8ebfbb7dd5/10586_2021_3256_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/34d62d027b6b/10586_2021_3256_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ba4b/9114082/5e479459c844/10586_2021_3256_Fig12_HTML.jpg

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