Abstract
In recent years, data has become a special kind of information commodity and promoted the development of information commodity economy through distribution. With the development of big data, the data market emerged and provided convenience for data transactions. However, the issues of optimal pricing and data quality allocation in the big data market have not been fully studied yet. In this paper, we proposed a big data market pricing model based on data quality. We first analyzed the dimensional indicators that affect data quality, and a linear evaluation model was established. Then, from the perspective of data science, we analyzed the impact of quality level on big data analysis (i.e., machine learning algorithms) and defined the utility function of data quality. The experimental results in real data sets have shown the applicability of the proposed quality utility function. In addition, we formulated the profit maximization problem and gave theoretical analysis. Finally, the data market can maximize profits through the proposed model illustrated with numerical examples.
Highlights
With the rapid development of information technology, big data has become the core resource of all walks of life
In this paper, we have proposed a pricing model based on quality utility to optimize data market pricing
(ii) We proposed a utility model based on the quality level and verified it with real-world datasets, using machine learning algorithms
Summary
With the rapid development of information technology, big data has become the core resource of all walks of life. There are generally three types of consumers who have demand for big data products, i.e., enterprises, government departments, and research institutions. These consumers need the data and corresponding services provided by the online market in order to innovate products, optimize decisions, or conduct research. In this paper, we have proposed a pricing model based on quality utility to optimize data market pricing. (ii) We proposed a utility model based on the quality level and verified it with real-world datasets, using machine learning algorithms.
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