A Stackelberg Game Pricing Through Balancing Trilateral Profits in Big Data Market

With the popularity of the Internet of Things (IoT) and large-scale deployment of sensors, data have exploded. Big data is utilized to extract useful knowledge and information and served as data services to consumers. While most of the current researches in the field of big data services focus on developing and improving algorithms for data mining and information extraction, and rarely studies "big data" from an economic perspective. This article thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then, we build an economic model of the data market, which consists of three parties: 1) data vendor; 2) service provider; and 3) service users. The data vendor gathers various raw data and sells them to the service provider. The raw data are further processed into data services by a service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of the data market can achieve the maximum profit through the proposed pricing mechanism and economic model.