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 paper 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.

翻译：数据市场是一种在线平台，汇集数据所有者、数据经纪人和数据消费者，并促进数据在这些主体间的商品化。数据定价作为数据市场的核心功能，要求量化数据的货币价值。文献中可查有大量关于数据定价的研究。本文旨在全面综述现有数据定价研究的最新进展，以提供对该新兴研究领域的总体认知。我们的主要贡献在于提出一种新的数据定价研究分类体系，该体系统一了决定数据价格的不同属性。这一框架的基础是根据市场结构类型对研究进行分类，即卖方市场、买方市场或双边市场。在卖方市场中，研究进一步按查询类型划分——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，则根据隐私概念划分——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均作为分类标准。我们系统审视了分类体系中每个类别下的相关研究。最后，我们讨论了现有研究存在的空白，并定义了未来的研究方向。