| CS-Chord:基于聚类分离的分布式高维向量索引 |
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| 引用本文： | 袁鑫攀,汪灿飞,龙军,彭成. CS-Chord:基于聚类分离的分布式高维向量索引[J]. 计算机科学, 2017, 44(Z11): 494-497 |
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| 作者姓名： | 袁鑫攀 汪灿飞 龙军 彭成 |
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| 作者单位： | 湖南工业大学计算机学院 株洲412007;湖南工业大学智能信息感知及处理技术湖南省重点实验室 株洲412007,湖南工业大学计算机学院 株洲412007;湖南工业大学智能信息感知及处理技术湖南省重点实验室 株洲412007,中南大学信息科学与工程学院 长沙410083,湖南工业大学计算机学院 株洲412007;湖南工业大学智能信息感知及处理技术湖南省重点实验室 株洲412007 |
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| 基金项目： | 本文受国家自然科学基金项目(61402165),湖南省自然科学基金项目(2015JJ3058),湖南省重点研发计划(2016JC2018),国家基金应急管理项目(S1651002),2016年湖南工业大学研究生校级创新基金项目(CX1606)资助 |
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| 摘 要： | M-Chord是一种基于P2P网络的高维向量索引,其聚类边缘的向量容易与搜索圆频繁相交,使得查找的区域增多,降低了M-Chord的效率。提出一种基于聚类分离的分布式高维向量索引(CS-Chord),将边缘区域的高频检索向量从Chord环中分离出来,集中存储在服务器上,中心区域的向量仍存储于Chord环中,节省了大量资源的定位时间,从而提高检索效率。实验结果表明:在查询半径为0.2时,CS-Chord距离计算次数约为2000,比M-Chord减少了约2500次；CS-Chord消息转发次数约降低150次,仅为M-Chord的50%。
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| 关 键 词： | 高维向量 聚类 Chord 分布式索引 |
CS-Chord:Distributed High Dimensional Vector Index Based on Clustering Separation |
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| YUAN Xin-pan,WANG Can-fei,LONG Jun and PENG Cheng. CS-Chord:Distributed High Dimensional Vector Index Based on Clustering Separation[J]. Computer Science, 2017, 44(Z11): 494-497 |
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| Authors: | YUAN Xin-pan WANG Can-fei LONG Jun PENG Cheng |
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| Affiliation: | School of Computer Science,Hunan University of Technology, Zhuzhou 412007,China;Key Laboratory of Intelligent Information Perception and Processing Technology,Hunan University of Technology,Zhuzhou 412007,China,School of Computer Science,Hunan University of Technology, Zhuzhou 412007,China;Key Laboratory of Intelligent Information Perception and Processing Technology,Hunan University of Technology,Zhuzhou 412007,China,School of Information Science and Engineering,Central South University,Changsha 410083,China and School of Computer Science,Hunan University of Technology, Zhuzhou 412007,China;Key Laboratory of Intelligent Information Perception and Processing Technology,Hunan University of Technology,Zhuzhou 412007,China |
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| Abstract: | M-Chord is a high dimensional vector index based on the P2P network,and the sparse vector of the clustering edge is easy to intersect with the query circle,which makes the area of the query increased,and the efficiency of M-Chord is reduced.Based on the idea of M-Chord,this paper proposed a distributed high dimensional vector index (CS-Chord) based on clustering,which separates the edge vectors from the dense vectors.The vector of the central region is still stored in the Chord loop,which saves a lot of resources locating time and improves the efficiency of retrieval.Experimental results show that when the query radius is 0.2,the number of CS-Chord distance computation 2000,reducing about 2500 times compared with M-Chord.The message forwarding times of CS-Chord decreases about 150 times,only 50% of M-Chord. |
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| Keywords: | High-dimensional vector Cluster Chord Distributed index |
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