Supporting location-based approximate-keyword queries

Sattam Alsubaiee, Alexander Behm, Chen Li
{"title":"Supporting location-based approximate-keyword queries","authors":"Sattam Alsubaiee, Alexander Behm, Chen Li","doi":"10.1145/1869790.1869802","DOIUrl":null,"url":null,"abstract":"Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"49 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"46","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1869790.1869802","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 46

Abstract

Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. We focus on a natural index structure that augments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resulting index to find answers. We develop three algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets.
支持基于位置的近似关键字查询
许多Web站点支持对其空间数据(如企业列表和照片)进行关键字搜索。在这些系统中,查询和数据中都可能存在不一致和错误。为了弥合查询和数据之间的差距,在空间数据上支持近似关键字搜索是很重要的。本文研究了如何有效地回答这类查询。我们专注于一个自然索引结构,它增强了基于树的空间索引的近似关键字搜索能力。我们系统地研究了如何有效地将这两类索引结合起来,以及如何搜索得到的索引来寻找答案。我们开发了三种构建索引的算法,通过利用数据的文本和空间特性,先后提高了时间和空间效率。我们通过实验证明了我们的技术在真实的大型数据集上的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:604180095
Book学术官方微信