Survey of data factor market: value, pricing, and trading

With the rise of computing power networks, a novel infrastructure centered around computational power has emerged as a key driver in the development of the information economy. Data, recognized as a crucial production factor in the digital era, serves as a vital pillar for constructing new developme...

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Main Authors: YANG Ming, FENG Honglin, WANG Xin, HUO Jidong, JIAO Xuguo, ZHANG Heng
Format: Article
Language:English
Published: POSTS&TELECOM PRESS Co., LTD 2024-06-01
Series:网络与信息安全学报
Subjects:
Online Access:http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024036
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author YANG Ming
FENG Honglin
WANG Xin
HUO Jidong
JIAO Xuguo
ZHANG Heng
author_facet YANG Ming
FENG Honglin
WANG Xin
HUO Jidong
JIAO Xuguo
ZHANG Heng
author_sort YANG Ming
collection DOAJ
description With the rise of computing power networks, a novel infrastructure centered around computational power has emerged as a key driver in the development of the information economy. Data, recognized as a crucial production factor in the digital era, serves as a vital pillar for constructing new development paradigms. The data factor market, acting as the link between data and computational power networks, is particularly critical. Its robust operation directly affects the sustainable development of computational power networks and is provided with crucial support for the information economy system. Therefore, the data products were treated as the focal point, while the processes involved in the data factor market and the inherent connections among these components were introduced. Subsequently, the existing work on three key aspects of the data factor market was studied, including data value, data pricing, and data transactions. Regarding data value assessment, key aspects such as relative data value evaluation were discussed, introducing indicator systems, indicator weights, and value indices. For data asset value assessment, traditional methods such as the cost method, income method, and market method were discussed, as well as emerging methods based on machine learning. In terms of data pricing models, existing methods were categorized into two main types: pricing for dataset products and pricing for data service products. Seven pricing methods were summarized, and the advantages, disadvantages, and applicable scenarios of each method were further analyzed. Regarding data transaction systems, the applications of centralized and distributed architectures in the data trading market were analyzed from the perspective of system architecture. The current status and prospects of both on-exchange and over-the-counter trading modes were then introduced.
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institution Kabale University
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spelling doaj-art-f87aec60cf4047b39db303fd6ce081b12025-01-15T03:17:14ZengPOSTS&TELECOM PRESS Co., LTD网络与信息安全学报2096-109X2024-06-011011967188757Survey of data factor market: value, pricing, and tradingYANG MingFENG HonglinWANG XinHUO JidongJIAO XuguoZHANG HengWith the rise of computing power networks, a novel infrastructure centered around computational power has emerged as a key driver in the development of the information economy. Data, recognized as a crucial production factor in the digital era, serves as a vital pillar for constructing new development paradigms. The data factor market, acting as the link between data and computational power networks, is particularly critical. Its robust operation directly affects the sustainable development of computational power networks and is provided with crucial support for the information economy system. Therefore, the data products were treated as the focal point, while the processes involved in the data factor market and the inherent connections among these components were introduced. Subsequently, the existing work on three key aspects of the data factor market was studied, including data value, data pricing, and data transactions. Regarding data value assessment, key aspects such as relative data value evaluation were discussed, introducing indicator systems, indicator weights, and value indices. For data asset value assessment, traditional methods such as the cost method, income method, and market method were discussed, as well as emerging methods based on machine learning. In terms of data pricing models, existing methods were categorized into two main types: pricing for dataset products and pricing for data service products. Seven pricing methods were summarized, and the advantages, disadvantages, and applicable scenarios of each method were further analyzed. Regarding data transaction systems, the applications of centralized and distributed architectures in the data trading market were analyzed from the perspective of system architecture. The current status and prospects of both on-exchange and over-the-counter trading modes were then introduced.http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024036data tradingdata valuedata pricingdata market
spellingShingle YANG Ming
FENG Honglin
WANG Xin
HUO Jidong
JIAO Xuguo
ZHANG Heng
Survey of data factor market: value, pricing, and trading
网络与信息安全学报
data trading
data value
data pricing
data market
title Survey of data factor market: value, pricing, and trading
title_full Survey of data factor market: value, pricing, and trading
title_fullStr Survey of data factor market: value, pricing, and trading
title_full_unstemmed Survey of data factor market: value, pricing, and trading
title_short Survey of data factor market: value, pricing, and trading
title_sort survey of data factor market value pricing and trading
topic data trading
data value
data pricing
data market
url http://www.cjnis.com.cn/thesisDetails#10.11959/j.issn.2096-109x.2024036
work_keys_str_mv AT yangming surveyofdatafactormarketvaluepricingandtrading
AT fenghonglin surveyofdatafactormarketvaluepricingandtrading
AT wangxin surveyofdatafactormarketvaluepricingandtrading
AT huojidong surveyofdatafactormarketvaluepricingandtrading
AT jiaoxuguo surveyofdatafactormarketvaluepricingandtrading
AT zhangheng surveyofdatafactormarketvaluepricingandtrading