Division of training and test datasets.

The issue of data quality has emerged as a critical concern, as low-quality data can impede data sharing, diminish intrinsic value, and result in economic losses. Current research on data quality assessment primarily focuses on four dimensions: intrinsic, contextual, presentational, and accessibility quality, with intrinsic and presentational quality mainly centered on data content, and contextual quality reflecting data usage scenarios. However, existing approaches lack consideration for the behavior of data within specific application scenarios, which encompasses the degree of participation and support of data within a given scenario, offering valuable insights for optimizing resource deployment and business processes. In response, this paper proposes a data contribution assessment method based on maximal sequential patterns of behavior paradigms (DecentralDC). DecentralDC is composed of three steps: (1) mining the maximal sequential patterns of sharing and exchange behavior paradigms; (2) determining the weights of these paradigms; (3) calculating the contribution of sharing and exchange databases combined with data volume. To validate our approach, two sharing and exchange scenarios of different scales are established. The experimental results in two scenarios validate the effectiveness of our method and demonstrate a significant reduction in cumulative regret and regret rate in data pricing due to the introduction of data contribution. Specifically, compared to the most competitive baseline, the improvements of mean average precision in two scenarios are 6% and 8%. The code and simulation scenarios have been open-sourced and are available at https://github.com/seukgcode/DecentralDC.数据质量问题已成为至关重要的关切焦点，低质量数据会阻碍数据共享、削弱数据固有价值，并造成经济损失。当前关于数据质量评估的研究主要聚焦于四大维度：内在质量（intrinsic quality）、情境质量（contextual quality）、表现质量（presentational quality）与可访问性质量（accessibility quality），其中内在质量与表现质量主要围绕数据内容展开，而情境质量则反映数据的应用场景。然而，现有方法未考虑特定应用场景下的数据行为——该行为涵盖数据在对应场景中的参与程度与支撑力度，可为资源部署与业务流程优化提供宝贵参考。为此，本文提出一种基于行为范式的最大序列模式（maximal sequential patterns of behavior paradigms）的数据贡献度评估方法（DecentralDC）。DecentralDC包含三个步骤：(1) 挖掘共享与交换行为范式的最大序列模式；(2) 确定各行为范式的权重；(3) 结合数据体量计算共享与交换数据库的贡献度。为验证所提方法的有效性，本文构建了两种不同规模的共享与交换场景。两种场景下的实验结果验证了本文方法的有效性，同时表明，引入数据贡献度后，数据定价中的累计遗憾值与遗憾率均得到显著降低。具体而言，与当前最具竞争力的基准方法相比，两种场景下的平均精度均值（mean average precision）提升幅度分别为6%与8%。本文的代码与仿真场景已开源，可通过https://github.com/seukgcode/DecentralDC获取。