In terms of spatial online aggregation, traditional stand-alone serial methods gradually become limited. Although parallel computing is widely studied nowadays, there scarcely has research conducted on the index-based parallel online aggregation methods, specifically for spatial data. In this letter, a parallel multilevel indexing method is proposed to accelerate spatial online aggregation analyses, which contains two steps. In the first step, a parallel aR tree index is built to accelerate aggregate query locally. In the second step, a multilevel sampling data pyramid structure is built based on the parallel aR tree index, which contribute to the concurrent returned query results with certain confidence degree. Experimental and analytical results verify that the methods are capable of handling billion-scale data.

The challenges of providing a divide-and-conquer strategy for tackling large geospatial raster data input/output (I/O) are longstanding. Solutions need to change with advances in the technology and hardware. After analyzing the reason for the problems of traditional parallel raster I/O mode, a parallel I/O strategy using file view is proposed to solve these problems. Message Passing Interface I/O (MPI-IO) is used to implement this strategy. Experimental results show how a file view approach can be effectively married to General Parallel File System (GPFS). A suitable file view setting provides an efficient solution to parallel geospatial raster data I/O.