Abstract: Aiming at the characteristics of huge data volume, rich types and abundant inspection contents, based on MapReduce framework, this paper applies the distributed parallel computing technology and multi-thread technology to the quality control system to build the parallel quality control framework of multi-source and multi-temporal mass data. The algorithm structure of one operation per cycle is transformed into the parallel algorithm of multiple operations per cycle to solve fundamentally the problem of multiple operations and slow calculation. Then we select core vector elements, DOM results and DEM results as the experimental data. The results show that the built quality control technology based on parallel processing increases the efficiency by 2~3 times, effectively compresses the task execution time, saves the cost, realizes the rapid quality check and ensures the timely quality control of the geographic information resources.

Key words: mass geographic information data, quality control, distributed parallel computing, multi-thread, MapReduce framework