关键词: 图数据库, 分布式存储, ETL（数据提取、转换和加载）, 子模式

Abstract: For addressing problems such as multi-layer relational query and community detection, graph database outperforms relational database. However, most data of existing applications have stored in the form of relationship. Therefore, how to extract-transform-load （ETL） relational data to graph data efficiently and absolutely is still an important problem of deploying graph database applications. Existing researches suffer from three major limitations：（1） The quality of converted graph data are poor; （2） the efficiency of transforming is low; （3） the transformed results are not suitable for distributed storage. To overcome these limitations, a sub-schema-based ETL method for transforming relational data to graph data is proposed in this paper. By splitting schema of relational database to several sub-schemas, this method improves the algorithm and procedure of previous ETLs and provides an efficient way for parallel ETL. The transformed results can satisfy the requirements of distributed storage, and conduct to be the basis data for Spark GraphX computing framework. Finally, Java EE and Neo4j are applied to implement the prototype system for experimental verification. The comparative results show that the improved ETL method yields better performance than previous methods.

Key words: graph database, distributed storage, extract-transform-load（ETL）, sub-schema