Hilbert Curve Based Spatial Data Declustering Method for Parallel Spatial Database
Yan Jie Zhou, Ling Li Jiang · 2012
Parallel spatial database is an inevitable development trend of high performance spatial data management. A key problem to improve the performance of parallel spatial database is to achieve spatial data balancing distribution between parallel nodes especially in shared nothing parallel architecture. Considering the unique characteristics of spatial objects, the existing traditional data declustering methods are difficult to obtain well data declustering results when facing unstructured variable length spatial objects and intricate spatial locality relations. Aim at the status, this paper presents a Hilbert curve based spatial data declustering method, which use rectangular grids to partition data space, to assign the unique Hilbert code to each sub-grids according to the sequence of Hilbert curve going through each sub-grids, and use the Hilbert code to impose a linear ordering on each multidimensional spatial objects, and then declustering spatial objects based on their Hilbert code to keep spatial locality between spatial objects. In order to avoid code conflict problem of different sub-grids caused by hierarchically decomposing sub-grids, a fake Hilbert code strategy is given. Experimental results show that the proposed method can attain well spatial data balancing distribution results, and also keep well spatial locality of data objects in each declustering units.