Query processing of large-scale vector spatial data selection based on Mapredue

Query processing of large-scale vector spatial data selection based on Mapredue

Tao Daiju Zheng Zezhong Liu Shuai

In order to efficiently deal with large scale vector spatial data, a distributed query processing method for vector spatial data selection is implemented based on the parallel Computing framework of Hadoop (Mapredue). First, analyze the OGC simple factor Standard and Hadoop's Key/value data model, A vector file format can be stored in Hadoop HDFs, secondly, according to the two-stage filtering-refining strategy, the key steps of map input data fragmentation, selection query processing and reduce result merging are expounded in detail. Finally, based on the above techniques, The proposed method is validated by the Hadoop cluster environment, and the method has good feasibility and high efficiency.

Query processing of large-scale vector spatial data selection based on Mapredue