Circle-Covering Algorithm for Multiple-POI Searching

X. Yu, Shengling Wang, Chun-Chi Liu, Chenyu Wang, Weiman Sun
{"title":"Circle-Covering Algorithm for Multiple-POI Searching","authors":"X. Yu, Shengling Wang, Chun-Chi Liu, Chenyu Wang, Weiman Sun","doi":"10.1109/IIKI.2016.16","DOIUrl":null,"url":null,"abstract":"Nowadays, there is a tendency that the Location-based service (LBS) is becoming more and more popular. That is because LBS can provide customized services according to user's current location information. What is more, it can easily recommend the information of a point of interest (POI) to the user. When a user queries a series of POIs, LBS can return a cluster of these POIs. This paper aims to find a given-sized circle that can cover the maximum total weights of POIs for which people require. The weight of a POI reflects its quality, hence, our aim is to help people find the best POIs nearby. In this paper, every cluster is considered as a point. We stipulate an order to traverse these points and update the maximum weight. The challenge of our proposed algorithm lies in the \"cast effect\", which means the most of the returned POI clusters may be discarded when some hot POIs are blocked. To overcome this challenge, our proposed algorithm will reduce the impact of the cast effect when calculating the weight of a circle. The simulation and the conclusion are presented at the end of this paper.","PeriodicalId":371106,"journal":{"name":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IIKI.2016.16","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Nowadays, there is a tendency that the Location-based service (LBS) is becoming more and more popular. That is because LBS can provide customized services according to user's current location information. What is more, it can easily recommend the information of a point of interest (POI) to the user. When a user queries a series of POIs, LBS can return a cluster of these POIs. This paper aims to find a given-sized circle that can cover the maximum total weights of POIs for which people require. The weight of a POI reflects its quality, hence, our aim is to help people find the best POIs nearby. In this paper, every cluster is considered as a point. We stipulate an order to traverse these points and update the maximum weight. The challenge of our proposed algorithm lies in the "cast effect", which means the most of the returned POI clusters may be discarded when some hot POIs are blocked. To overcome this challenge, our proposed algorithm will reduce the impact of the cast effect when calculating the weight of a circle. The simulation and the conclusion are presented at the end of this paper.
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
多点搜索的圆覆盖算法
目前,基于位置的服务(LBS)有越来越流行的趋势。这是因为LBS可以根据用户当前的位置信息提供定制化服务。更重要的是,它可以很容易地向用户推荐兴趣点(POI)的信息。当用户查询一系列poi时,LBS可以返回这些poi的集群。本文的目的是找到一个给定大小的圆,它可以覆盖人们需要的poi的最大总权重。POI的权重反映了它的质量,因此,我们的目标是帮助人们找到附近最好的POI。本文将每个聚类视为一个点。我们规定遍历这些点并更新最大权重的顺序。我们提出的算法的挑战在于“投射效应”,这意味着当一些热点POI被阻塞时,大多数返回的POI簇可能会被丢弃。为了克服这一挑战,我们提出的算法将在计算圆的重量时减少铸造效应的影响。最后给出了仿真结果和结论。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Research on the Evaluation of Product Quality Perceived Value Based on Text Mining and Fuzzy Comprehensive Evaluation A New Pre-copy Strategy for Live Migration of Virtual Machines Hbase Based Surveillance Video Processing, Storage and Retrieval Mutual Information-Based Feature Selection and Ensemble Learning for Classification Implicit Correlation Intensity Mining Based on the Monte Carlo Method with Attenuation
×
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