面向多源异构数据源的实际范围索引树索引方法

在发电、输配电、新能源接入及新型能源消费等各个环节产生的大数据需要高效、可靠的数据存储和管理。文中以能源互联网作为虚拟应用场景,提出了集群式多源异构数据存储方案。为满足集群存储中高效查询需求,构建了实际范围索引树(AR-tree)分层索引模型,并提出了面向多源异构数据源的AR-tree索引方法,该方法针对在双层索引模型的基础上,以局部数据索引的实际索引范围为对象,建立全局索引。同时,对所提AR-tree索引方法操作开销进行了综合分析。最后,仿真结果表明AR-tree索引方法能提高查询命中率并提升查询等操作效率。

An AR-tree Indexing Scheme for Multi-source Heterogeneous Data Sources

Reliable data storage and highly efficient management are needed when big data is generated from power generation, transmission, distribution, and new energy access and consumption. Taking the Energy Internet as a virtual application scenario, this paper proposes a cluster storage scheme for multi-source heterogeneous data. In order to meet the needs of efficient query in the cluster storage, a hierarchical indexing model based on the actual range tree (AR-tree) is developed. And the AR-tree indexing method for multi-source heterogeneous data sources is proposed. This method is based on the double layer index model, in which the global index is developed by taking the actual indexing range of local data as the object. Also, the operation cost of the AR-tree indexing method is comprehensively analyzed. Finally, the simulation results show that the AR-tree indexing method is able to improve the query hit ratio and efficiency. This work is supported by National Natural Science Foundation of China (No. 51507063).