一种高密度关联数据压缩方案  

A High Density Linked-Data Compression Method

作　　者：彭燊 杨璐 顾进广[1,2,3] PENG Shen YANG Lu GU Jinguang(College of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430065, Hubei, China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan 430065, Hubei, China Hubei Research Center for Language and Intelligent Information Processing （Wuhan University）, Wuhan 430072, Hubei, China)

机构地区：[1]武汉科技大学计算机科学与技术学院,湖北武汉430065 [2]智能信息处理与实时工业系统湖北省重点实验室,湖北武汉430065 [3]湖北语言与智能信息处理研究基地(武汉大学),湖北武汉430072

出　　处：《武汉大学学报（理学版）》2017年第5期453-458,共6页Journal of Wuhan University:Natural Science Edition

基　　金：国家自然科学基金(61100133;61272110);国家社会科学基金重大计划(11&ZD189);武汉市科技攻关计划(201110821225);软件工程国家重点实验室(武汉大学)开放基金SKLSE2012-09-07

摘　　要：随着语义网技术的标准化,网络上的关联数据爆炸式增长.海量的关联数据在网络上存储和交换变得越来越困难.本文提出了一种高密度关联数据压缩方案,将关联数据的三维关系矩阵分解成3个二维稀疏矩阵,再用K2-Tree压缩二维稀疏矩阵,提高了关联数据的压缩率和压缩效率.实验表明,本文提出压缩方案的压缩率相较于HDT++平均有12%的提高,压缩耗时相较于HDT++也有10%左右的降低.With the standardization of the semantic web technology, the linked-data is widely used in various fields, and grows explosively. The storage and exchange of mass linked-data on Internet become more and more difficult. In this paper, a high density compression method of linked-data is proposed, which splits three-dimensional matrix into three two-dimensional matrix and using K2-tree to compress the two-dimensional matrix. The experimental results show that the compression method proposed in this paper improves the compression rate by average 12% and the compression efficiency by average 10 % compared with HDT＋＋.

关 键 词：关联数据 压缩 降维 K2树 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]