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SDPPF — A MapReduce based parallel processing framework for spatial data

Spatial data processing requires large scale calculation and parallel processing is necessary for accelerating. Popular parallel frameworks need lots of code development for parallelization, which are difficult for geo-scientists who are often beginners in programming. In this paper, an easy-to-use parallel processing framework is proposed and named SDPPF — Spatial Data Parallel Processing Framework. SDPPF is MapReduce based and can directly reuse existing binary executable program for parallel processing, especially when the source code of specific algorithm is not able to get. The parallel model, architecture and implementation of SDPPF are presented and the evaluation of SDPPF is analyzed by testing specific algorithms. Experiments show that SDPPF is a flexible, easy-to-use and scalable framework for spatial data parallel processing.