High Indexing Compression for Spatial Databases

The KDB-tree is a traditional point access method for retrieving multidimensional data. Many literatures frequently address the low storage utilization and insufficient retrieval performance as two bottlenecks for KDB-tree family of structures. A large amount of unnecessary splits caused by data insertion orders and data skewness is the fatal reason for these two bottlenecks. Compressing KDB-trees still has high appeal for practical applications. In this paper, dynamic-tuning partition (DT-partition) and leaf replication(l-replication) methods are proposed to mend the sufferings of data insertion orders and data skewness. Without loss the quantity of data selectivity, a better dynamic indexing scheme is presented for accommodating data to leaf nodes as many as possible. Moreover, the degradation of retrieval performance in heavily skewed spaces are carefully investigated and solved. Analytical and experimental results show our indexing method out performs the traditional methods.