A Pricing Model for Data Markets | Semantic Scholar (2024)

A first of its kind measurement study of the growing DM ecosystem and shed light on several totally unknown facts about it, showing that products about telecommunications, manufacturing, automotive, and gaming command the highest prices.

This paper presents the results and conclusions from a comprehensive survey covering 190 commercial data trading entities, the types of data that their trade, as well as their business models and the technologies that they rely upon, and point to promising open research questions in the areas of data marketplace federation, pricing, and data ownership protection.

This paper presents a first of its kind measurement study of the growing DM ecosystem, focused on understanding which features of data are actually driving their prices in the market.

A first of its kind measurement study of the growing DM ecosystem, shedding light on several totally unknown facts about it, showing that data products listed in commercial DMs may cost from few to hundreds of thousands of US dollars.

The paper refers to other discussions and projects on defining data as consumable goods and market mechanisms that can be applied to data exchange, such as data markets, data exchange and industrial data space.

A unified, interdisciplinary and comprehensive overview of the economics of data pricing is presented and the development and evolution of pricing models according to a series of fundamental principles are reviewed.

This work conducted semi-structured expert interviews to gain insights on data valuation methods and seven value-driving quality criteria and several challenges for future data valuation are derived from the empirical results.

This paper divides the data pricing solutions into three major strategies and thirteen models, including query Pricing strategy, feature-based data pricing strategy, and pricing strategy in machine learning, which is so far the first attempt to classify so many existing data pricing models.

A new ecosystem of ML model trading over a trusted Blockchain-based network is proposed, where the buyer can acquire the model of interest from the ML market, and interested sellers spend local computations on their data to enhance that model's quality.

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An approach is proposed which adapts existing asset valuation methods to reflect the unique characteristics of information as an asset, and which may be used to measure IT effectiveness, increase organisational awareness of the value of information, guide IT strategic planning and cost-justify DSS/EIS developments.

This chapter discusses how Wiki-Government and other open-source technologies can make government decisionmaking more expert and more democratic.

The ability to capture, warehouse, and understand massive amounts of data is changing science, medicine, business, and technology, and so will the opportunity to find answers to fundamental questions.

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