云计算平台的海量数据知识提取框架

针对从海量数据中分析与提取知识计算时间高的问题，提出一种基于Hadoop的知识提取算法.本文结合Hadoop的并行处理能力与分布式存储特点，设计了一种知识提取框架，可兼容不同的原型约简方法.基于MapReduce编程方法将约简方法并行化处理，并且设计了分类准确率高、计算速度快的原型约简组合规则.最终基于真实UCI大数据集进行实验，本框架将最近邻分类器的分类时间提高两个数量级.

Massive Data Knowledge Extraction Framework Based on Cloud Computing Platform

Aimed at problem that analyzing and extracting knowledge form massive data is high computation cost, a Hadoop based knowledge extraction framework is proposed. We designe a knowledge exraction framework which combines with the parallel processing and distributed storage feature, and the framework is compatible different prototype reduction methods. Based on the MapReduce programming method the prototype reduction method is parallelly processed, and a prototype reduction combination rule with high classification accuracy and computational speed is designed. Finally, experiments results based on real UCI big data sets show that the proposed framework improves two orders of magnitude of the classification time of the nearest neighbor classifier.