在大数据研究中，大数据集成的对象已经不仅仅是简单的数据，而是以实体为核心的数据和知识的复合体。通过对混杂数据中实体对象进行有效整合能够获得精准的实体信息与知识，这也为保证实体集成结果的精准性和高效性带来了新的挑战。要实现大数据中实体信息由混杂到精准的转变需要一系列的理论和技术。本项目研究大数据环境中面向实体的精准集成关键技术。项目的研究内容围绕实体模式缺失、语义异构和数据演化三个影响精准性的主要因素提出，包括面向实体精准集成的数据源管理，基于语义认知的实体精准集成技术，基于演化认知的实体精准集成技术，并结合高效处理策略保证实体集成的高效性。项目的研究方案基于模式匹配、实体链接和实体融合三个大数据集成的主要环节进行设计，结合医疗健康数据集成应用，提出相关理论模型、研究关键技术和进行实验验证，并构建数据精准集成平台。本项目的成果将解决大数据集成的多种科学问题，推动大数据管理技术的发展。

In research of Big Data, the object of Big data integration is not only simple data records but the complexes of data and knowledge in which entity is as the core. By integrating entities in miscellaneous data effectively, accurate information and knowledge about entities can be achieved. However, it also increases new challenges for ensuring the precision and efficiency of entity integration. To transform miscellaneous into precise, it requires a set of theories and techniques for big data. This project researches on the key techniques of precision integration for entities in big data environment. In the project, the research contents are proposed based on three major factors for precision which are losing schema of entities, semantic heterogeneous, and data evolving. Therefore, the research contents include data sources management for entity integration, techniques of precision entity integration based on semantic cognition, techniques of precision entity integration based on data evolving cognition, and strategy of efficient processing for entity integration. The research technology of this project will be proposed based on the three important steps of big data integration, which includes schema alignment, entity linkage and entity fusion. With analyzing requirements of medical information integration, the project will propose theories and key techniques for precision of data integration, and verify the performance of them based on experiments. Moreover, this project will develop a platform of precision integration for applications. The achievement of this project will provide the solution for addressing the science problems on big data integration, and will also improve the development of big data management in data integration.