【目的】分析關聯大數據的概念、內涵與特征,針對關聯大數據管理的技術挑戰,探討關聯大數據管理技術的對策和解決思路。【方法】結合NoSQL數據管理技術、分布式圖計算技術、大數據流水線技術等給出應對挑戰的思路,并基于此思路形成大規模圖數據倉庫加工系統gETL。【結果】該方法和系統在NSFC-KBMS和WDCM項目中得到了應用,實現了大規模知識型數據和生物數據的有效管理,滿足了多元化的數據管理需求。【局限】需要結合應用的情況,進一步完善方法與系統。【結論】通過采用NoSQL數據存儲技術、分布式圖計算技術、大數據流水線技術以及gETL系統,可以很好地解決關聯大數據的管理問題。

[Objective] This article analyzed the concept, connotation and characteristics of the big linked data, aiming to explore possible solutions for technical challenges facing its management. [Methods] We proposed a new model based on NoSQL data management, distributed graph computing and big data pipeline technologies, which designed and develop gETL, a large-scale graph data warehouse processing system. [Results] The proposed system was used in NSFC-KBMS and WDCM projects, which effectively manages large-scale knowledge-data and biological data. [Limitations] The proposed system could be improved with new applications. [Conclusions] The NoSQL data storage, distributed graph computing, and big data pipeline technologies, as well as the gETL system, help us address the challenges facing linked big data management.