大型数据库中树状结构对象的高效相似性搜索

K. Murthy, H. Kriegel, Stefan Schönauer, T. Seidl
{"title":"大型数据库中树状结构对象的高效相似性搜索","authors":"K. Murthy, H. Kriegel, Stefan Schönauer, T. Seidl","doi":"10.1109/ICDE.2004.1320066","DOIUrl":null,"url":null,"abstract":"We implemented our new approach for efficient similarity search in large databases of tree structures. Our experiments show that filtering significantly accelerates the complex task of similarity search for tree-structured objects. Moreover, they show that no single feature of a tree is sufficient for effective filtering, but only the combination of structural and content-based filters yields good results.","PeriodicalId":358862,"journal":{"name":"Proceedings. 20th International Conference on Data Engineering","volume":"93 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2004-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Efficient similarity search in large databases of tree structured objects\",\"authors\":\"K. Murthy, H. Kriegel, Stefan Schönauer, T. Seidl\",\"doi\":\"10.1109/ICDE.2004.1320066\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We implemented our new approach for efficient similarity search in large databases of tree structures. Our experiments show that filtering significantly accelerates the complex task of similarity search for tree-structured objects. Moreover, they show that no single feature of a tree is sufficient for effective filtering, but only the combination of structural and content-based filters yields good results.\",\"PeriodicalId\":358862,\"journal\":{\"name\":\"Proceedings. 20th International Conference on Data Engineering\",\"volume\":\"93 6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2004-03-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings. 20th International Conference on Data Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDE.2004.1320066\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings. 20th International Conference on Data Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDE.2004.1320066","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

摘要

我们在大型树状结构数据库中实现了高效的相似性搜索。我们的实验表明,过滤显著加快了树状结构对象的复杂相似性搜索任务。此外,他们还表明,树的单个特征不足以进行有效的过滤,只有结构过滤器和基于内容的过滤器相结合才能产生良好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Efficient similarity search in large databases of tree structured objects
We implemented our new approach for efficient similarity search in large databases of tree structures. Our experiments show that filtering significantly accelerates the complex task of similarity search for tree-structured objects. Moreover, they show that no single feature of a tree is sufficient for effective filtering, but only the combination of structural and content-based filters yields good results.
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
期刊最新文献
ContextMetrics/sup /spl trade//: semantic and syntactic interoperability in cross-border trading systems EShopMonitor: a Web content monitoring tool A probabilistic approach to metasearching with adaptive probing Simple, robust and highly concurrent b-trees with node deletion Substructure clustering on sequential 3d object datasets
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1