大数据时代的数据活动日益频繁，包括数据生产、数据流通、数据处理等过程，这与物品供应链的流程很相似。因此，我们拟根据数据的流通关系，结合数据本身的特性，构建数据供应链管理体系，监督和控制数据供应链中的每一个环节，提高效率，创造价值，提升企业的竞争优势。由于数据供应链动态性和结构复杂性的特点，传统的物流管理体系框架还不能很好的适用于数据供应链。本课题以数据供应链的管理和服务为目标，研究面向单/多数据平台数据供应链建模方法和高效的供应链数据生成与管理方案，探索数据供应链的多维度可伸缩可视化技术和基于关键转折点的数据供应链相似性检索技术，以便提升数据供应链的分析效率；进而针对数据供应链质量评价与优化问题，研究供应链的时效性、可靠性和可信性等多个绩效因子的成因与计算方法，从而准确、定量的表达数据供应链质量，为实现数据供应链的优化提供依据。最后在基于分布式的数据聚合和共享系统上验证理论结果。

In the age of big data, there have been increasingly frequent data activities, such as data production, data flow, and data processing. Actually, these activities are similar to the processes of item supply chain. Therefore, according to the relationship of data flow, we combine with the new features of data to build the management system of data supply chain. The powerful system can monitor and control every link of data supply chain. Given this, we increase efficiency and create value to improve the competitive edge of the enterprise. As we all know, the typical characteristics of data supply chain are dynamics and structure complexity. Certainly, the traditional logistics management system cannot work well in the data supply chain. In summary, the management and services of data supply chain is the goal of this topic. For it, we research the modeling method of data supply chain and efficient scheme for the production and management of the supply chain in the single or multiple platform. Meanwhile, we explore the multi-dimensional and scalable visualization for data supply chain as well as the similarity retrieval in the chain based on the key turning point. Accordingly, the analysis efficiency in data supply chain can be obviously improved. Furthermore, considering the quality evaluation and optimization in data supply chain, we design the origin and calculation method which includes timeliness, reliability, credibility and other performance factors. Based on this, we can accurately and quantitatively express the quality of data supply chain, which provides the foundation for the optimization of data supply chain. At last, we verify the theoretical results on the distributed data aggregation and sharing system.