Network data contain a large number of target objects,resulting in a large number of metadata description entries and dense labels,which increases the difficulty of querying.Therefore,a network massive metadata query algorithm based on re-gion partitioning and parallel scanning is proposed.This algorithm applies sub forest theory to partition massive metadata into regions to solve the problem of label density in metadata.Based on the results of metadata region partitioning,this paper de-signs a metadata parallel scanning algorithm that simultaneously scans multiple metadata partitions to improve query speed and overall efficiency.The Internet massive metadata query program is developed based on parallel scanning algorithm,and the query results are obtained according to query request parameters,and achieve precise querying of Internet massive metadata.The experimental data show that the proposed algorithm has a minimum metadata query response time of 7.25 ms and a mini-mum metadata query error rate of 4.8％,indicating good metadata query performance.
维普
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