Space Vector Data Partition and Indexing Technology Based on Graph Complexity
The partitioning of vector space data has computational performance and cross regional issues.Although partitioning based on spatial location can meet the needs of spatial indexing and fast querying,it is difficult to achieve computational load balancing for parallel spatial analysis.This article proposes a space vector data partitioning and indexing technique based on graph complexity.This technique combines graph complexity with Hilbert space filling curves for vector data partitioning,and uses R-trees to establish distributed indexes.It not only improves data access speed,but also solves the problem of computational imbalance caused by data skewing,providing better support for load balancing of vector space computing tasks.
万方数据
维普
