A Multidimensional Data Utility Evaluation and Pricing Scheme in the Big Data Market
A Multidimensional Data Utility Evaluation and Pricing Scheme in the Big Data Market
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DOI：
10.1155/2023/6217495
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发表时间：
2023-02
影响因子：
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通讯作者：
Yuling Chen;Rui Bai;Yongtang Wu;Tao Li;Hui Zhou
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文献类型：
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作者：
Yuling Chen;Rui Bai;Yongtang Wu;Tao Li;Hui Zhou
大数据作为信息技术的衍生物，促成了数据交易的诞生。围绕大数据商业价值的技术已经成为焦点。然而，目前的研究大多集中在提高大数据分析算法的性能上。数据定价仍然是数据交易的主要问题之一。因此，我们的目标是解决在大数据交易市场中评估数据效用的问题，以及使数据交易中涉及的各个角色的利润最大化的问题。为此，我们提出了一个多维数据效用评估（MDDUE）方法，通过三个数据质量维度，即数据大小，可用性和完整性。接下来，我们提出了一个包括数据提供者、服务提供者和服务使用者的大数据交易市场模型。提出了一种基于三方Stackelberg博弈的最优数据定价方案，使参与者的利润最大化。最后，通过机器学习模型验证了MDDUE的合理性和有效性。结果表明，MDDUE可以更准确地评估数据的效用比以前的工作。通过数值实验证明了纳什均衡解的存在唯一性。
Big data as a derivative of information technology facilitates the birth of data trading. The technology surrounding the business value of big data has come into focus. However, most of the current research focuses on improving the performance of big data analytics algorithms. Data pricing is still one of the main issues in data trading. Therefore, we aim to tackle the problem of evaluating the utility of data in the big data trading market and the problem of maximizing the profits of the various roles involved in data trading. To this end, we propose a Multidimensional Data Utility Evaluation (MDDUE) method through three data quality dimensions, namely, data size, availability, and completeness. Next, we propose a big data trading market model including data providers, service providers, and service users. An optimal data-pricing scheme based on a three-party Stackelberg game is proposed to maximize the participants’ profits. Finally, a machine learning model is used to verify the rationality and validity of the MDDUE. The results show that MDDUE can evaluate the utility of data more accurately than previous work. The existence and uniqueness of the Nash equilibrium are demonstrated through numerical experiments.