Abstract: With continuous improvement of numerical weather prediction technology and resolution,meteorological data shows massive growth trend,resulting in less efficient meteorological archival and retrieval system (MARS) on large data service requests.Aiming at this issue,we carried out the research on optimization for region query based on retrie-val in MARS,and proposed an efficient method through complement transform range query(CTRQ) by utilizing the complement ideas of mathematics and calculating principle of multi array aggregation,which transforms “big data” to “small data” in extensive range query.The basic idea is to calculate the rest by comparing the size of aggregation dimension in hypercube with attribute values set in query service request when indexes have more than half,and to second calculate the complement of physically stored information of meteorological data in retrieval.Experiment results show that comparing with the original index calculation method,CTRQ can effectively reduce metadata index computation overhead in data retrieval.On this basis,combining with parallel processing method,we designed and implemented CTRQ parallel algorithm,and attracted 1.9 times at maximum speedup ratio compared with the improved serial algorithm,to further improve the retrieval efficiency of Mars.

Key words: MARS,Hypercube,Range query,Metadata index computation,Parallel processing