数据市场的数据定价调查

IF 7.5 3区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Mengxiao Zhang;Fernando Beltrán;Jiamou Liu
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引用次数: 2

摘要

数据市场是一个在线场所,它将数据所有者、数据代理和数据消费者聚集在一起,并促进他们之间的数据商品化。数据定价作为数据市场的一项关键功能,要求对数据的货币价值进行量化。文献中有相当多关于数据定价的研究。本文试图全面回顾现有数据定价研究的最新进展,以提供对这一新兴研究领域的总体理解。我们的主要贡献在于数据定价研究的新分类,统一了决定数据价格的不同属性。我们框架的基础是根据市场结构的类型对这些研究进行分类,无论是卖方、买方还是双边。然后,在卖方市场中,研究进一步按照查询类型进行划分,查询类型定义了数据消费者访问数据的方式,而在买方市场中,研究根据隐私概念进行划分,隐私概念定义了量化数据所有者隐私的方式。在双边市场中,隐私概念和查询类型都被用作标准。我们系统地检查属于我们分类法中每一类的研究。最后,讨论了现有研究的不足,并明确了未来的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Survey of Data Pricing for Data Marketplaces
A data marketplace is an online venue that brings data owners, data brokers, and data consumers together and facilitates commoditisation of data amongst them. Data pricing, as a key function of a data marketplace, demands quantifying the monetary value of data. A considerable number of studies on data pricing can be found in literature. This article attempts to comprehensively review the state-of-the-art on existing data pricing studies to provide a general understanding of this emerging research area. Our key contribution lies in a new taxonomy of data pricing studies that unifies different attributes determining data prices. The basis of our framework categorises these studies by the kind of market structure, be it sell-side, buy-side, or two-sided. Then in a sell-side market, the studies are further divided by query type, which defines the way a data consumer accesses data, while in a buy-side market, the studies are divided according to privacy notion, which defines the way to quantify privacy of data owners. In a two-sided market, both privacy notion and query type are used as criteria. We systematically examine the studies falling into each category in our taxonomy. Lastly, we discuss gaps within the existing research and define future research directions.
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来源期刊
CiteScore
11.80
自引率
2.80%
发文量
114
期刊介绍: The IEEE Transactions on Big Data publishes peer-reviewed articles focusing on big data. These articles present innovative research ideas and application results across disciplines, including novel theories, algorithms, and applications. Research areas cover a wide range, such as big data analytics, visualization, curation, management, semantics, infrastructure, standards, performance analysis, intelligence extraction, scientific discovery, security, privacy, and legal issues specific to big data. The journal also prioritizes applications of big data in fields generating massive datasets.
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