构造r树的一种新方法

Shaoxi Li, De-peng Zhao, K. Bai
{"title":"构造r树的一种新方法","authors":"Shaoxi Li, De-peng Zhao, K. Bai","doi":"10.1109/ICCIS.2012.24","DOIUrl":null,"url":null,"abstract":"R-tree is a crucial technique for spatial index. However, coverage and overlap produced by traditional methods are big side effects. In order to minimize the overlap and reduce the coverage, this paper proposes a new R-tree construction method based on clustering algorithm which can minimize the overlap and reduce the coverage to a great extent. This method resolves effectively the clustering storage for the adjacent data, and reduces the overlap between spatial nodes. Comparisons and experiments are conducted and performances are evaluated for this method. The results show that this method has high efficiency in querying.","PeriodicalId":269967,"journal":{"name":"2012 Fourth International Conference on Computational and Information Sciences","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A New Approach of R-tree Construction\",\"authors\":\"Shaoxi Li, De-peng Zhao, K. Bai\",\"doi\":\"10.1109/ICCIS.2012.24\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"R-tree is a crucial technique for spatial index. However, coverage and overlap produced by traditional methods are big side effects. In order to minimize the overlap and reduce the coverage, this paper proposes a new R-tree construction method based on clustering algorithm which can minimize the overlap and reduce the coverage to a great extent. This method resolves effectively the clustering storage for the adjacent data, and reduces the overlap between spatial nodes. Comparisons and experiments are conducted and performances are evaluated for this method. The results show that this method has high efficiency in querying.\",\"PeriodicalId\":269967,\"journal\":{\"name\":\"2012 Fourth International Conference on Computational and Information Sciences\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-08-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 Fourth International Conference on Computational and Information Sciences\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCIS.2012.24\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 Fourth International Conference on Computational and Information Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCIS.2012.24","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

r树是空间索引的关键技术。然而,传统方法产生的覆盖和重叠是很大的副作用。为了最小化重叠,降低覆盖,本文提出了一种新的基于聚类算法的r树构造方法,该方法可以在很大程度上最小化重叠,降低覆盖。该方法有效地解决了相邻数据的聚类存储问题,减少了空间节点间的重叠。对该方法进行了比较和实验,并对其性能进行了评价。结果表明,该方法具有较高的查询效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Approach of R-tree Construction
R-tree is a crucial technique for spatial index. However, coverage and overlap produced by traditional methods are big side effects. In order to minimize the overlap and reduce the coverage, this paper proposes a new R-tree construction method based on clustering algorithm which can minimize the overlap and reduce the coverage to a great extent. This method resolves effectively the clustering storage for the adjacent data, and reduces the overlap between spatial nodes. Comparisons and experiments are conducted and performances are evaluated for this method. The results show that this method has high efficiency in querying.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信