为提高网格环境下海量空间数据管理与并行化处理效率,将网格环境下的分布并行处理技术与空间索引相融合,提出了一种空间索引框架(grid slot and hash Rtree,GSHR-Tree).该索引树结构基于散列hash表和动态空间槽,结合R树结构的范围查询...为提高网格环境下海量空间数据管理与并行化处理效率,将网格环境下的分布并行处理技术与空间索引相融合,提出了一种空间索引框架(grid slot and hash Rtree,GSHR-Tree).该索引树结构基于散列hash表和动态空间槽,结合R树结构的范围查询优势和哈希表结构的高效单key查询,分析改进了索引结构的组织和存储.构造了适合于大规模空间数据的网格并行空间计算的索引结构,该索引树算法根据空间数据划分策略,动态分割空间槽,并将它们映射到多个节点机上.每个节点机再将其对应空间槽中的空间对象组织成R树,以大节点R树方式在多个节点上分布索引数据.以空间范围查询并行处理的系统响应时间为性能评估指标,通过模拟实验证明,该GSHR-Tree索引满足了当前网格环境空间索引的需要,并具有设计合理、性能高效的特点.展开更多
In this paper,we propose a novel spatial data index based on Hadoop:HQ-Tree.In HQ-Tree,we use PR QuadTrec to solve the problem of poor efficiency in parallel processing,which is caused by data insertion order and spac...In this paper,we propose a novel spatial data index based on Hadoop:HQ-Tree.In HQ-Tree,we use PR QuadTrec to solve the problem of poor efficiency in parallel processing,which is caused by data insertion order and space overlapping.For the problem that HDFS cannot support random write,we propose an updating mechanism,called "Copy Write",to support the index update.Additionally,HQ-Tree employs a two-level index caching mechanism to reduce the cost of network transferring and I/O operations.Finally,we develop MapReduce-based algorithms,which are able to significantly enhance the efficiency of index creation and query.Experimental results demonstrate the effectiveness of our methods.展开更多
文摘为提高网格环境下海量空间数据管理与并行化处理效率,将网格环境下的分布并行处理技术与空间索引相融合,提出了一种空间索引框架(grid slot and hash Rtree,GSHR-Tree).该索引树结构基于散列hash表和动态空间槽,结合R树结构的范围查询优势和哈希表结构的高效单key查询,分析改进了索引结构的组织和存储.构造了适合于大规模空间数据的网格并行空间计算的索引结构,该索引树算法根据空间数据划分策略,动态分割空间槽,并将它们映射到多个节点机上.每个节点机再将其对应空间槽中的空间对象组织成R树,以大节点R树方式在多个节点上分布索引数据.以空间范围查询并行处理的系统响应时间为性能评估指标,通过模拟实验证明,该GSHR-Tree索引满足了当前网格环境空间索引的需要,并具有设计合理、性能高效的特点.
基金This work is supported by the National Natural Science Foundation of China under Grant No.61370091and No.61170200, Jiangsu Province Science and Technology Support Program （industry） Project under Grant No.BE2012179, Program Sponsored for Scientific Innovation Research of College Graduate in Jiangsu Province under Grant No. CXZZ12_0229.
文摘In this paper,we propose a novel spatial data index based on Hadoop:HQ-Tree.In HQ-Tree,we use PR QuadTrec to solve the problem of poor efficiency in parallel processing,which is caused by data insertion order and space overlapping.For the problem that HDFS cannot support random write,we propose an updating mechanism,called "Copy Write",to support the index update.Additionally,HQ-Tree employs a two-level index caching mechanism to reduce the cost of network transferring and I/O operations.Finally,we develop MapReduce-based algorithms,which are able to significantly enhance the efficiency of index creation and query.Experimental results demonstrate the effectiveness of our methods.
