CAJ | 학술논문

为了克服单机串行不确定性传播算法处理大规模数据集的局限，采用MapReduce编程模型对算法进行并行化实现。将单机算法按照算法流程进行拆分，每一步对应一个MapReduce程序。每一步的输入及输出数据都存储在Hadoop分布式文件系统上。用命中率对比并行化的不确定性传播算法与全局排名算法的性能。对比不同数据量、不同节点数时并行化的不确定性传播算法的加速比。试验结果表明，不确定性传播算法MapReduce并行化后部署在Hadoop集群上运行，命中率显著高于全局排名算法，且有着较好的并行性，扩大了单机算法所能处理的数据规模且提高了算法的运算速度。
위료극복단궤천행불학정성전파산법처리대규모수거집적국한，채용MapReduce편정모형대산법진행병행화실현。장단궤산법안조산법류정진행탁분，매일보대응일개MapReduce정서。매일보적수입급수출수거도존저재Hadoop분포식문건계통상。용명중솔대비병행화적불학정성전파산법여전국배명산법적성능。대비불동수거량、불동절점수시병행화적불학정성전파산법적가속비。시험결과표명，불학정성전파산법MapReduce병행화후부서재Hadoop집군상운행，명중솔현저고우전국배명산법，차유착교호적병행성，확대료단궤산법소능처리적수거규모차제고료산법적운산속도。
In order to overcome the limitations of the serial probabilistic spreading algorithm in dealing with large-scale dataset,a parallelization of the algorithm was put forth by using MapReduce.The complex computing tasks were de-composed into a series of MapReduce job flow for distributed parallel processing on Hadoop.The input and output data of every step were stored in the Hadoop distributed file system.Hit ratio was used to compare the parallelizable probabi-listic spreading algorithm versus the global ranking method performance.Speedups of the parallelizable algorithm were compared while the amount of data and the number of nodes was different.Experiment results showed that the probabi-listic spreading algorithm based on MapReduce had good parallelism and had higher hit ratio than the global ranking method.Data scale that can be handled by the serial algorithm was expanded,and the operation speed of the algorithm was raised.