所有最近的邻居查询,包括分数道路网络

Hyo-Kyun Kim, Tae-Sun Chung
{"title":"所有最近的邻居查询,包括分数道路网络","authors":"Hyo-Kyun Kim, Tae-Sun Chung","doi":"10.1109/CSCI51800.2020.00265","DOIUrl":null,"url":null,"abstract":"This paper introduces an improved ANN (All Nearest Neighbor) algorithm using the SCL (Standard Clustered Loop) algorithm to reduce the consumption of computing resources that can occur when searching for the data object nearest to the query object in the process of executing the algorithm. Additionally, a method to improve ANN algorithm is proposed. When the algorithm is executed, it is a situation in which the user finds a data object adjacent to the user. In this case, our technique applies the criteria set provided by users.","PeriodicalId":336929,"journal":{"name":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"All Nearest Neighbors Query Including Scores Road Network\",\"authors\":\"Hyo-Kyun Kim, Tae-Sun Chung\",\"doi\":\"10.1109/CSCI51800.2020.00265\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper introduces an improved ANN (All Nearest Neighbor) algorithm using the SCL (Standard Clustered Loop) algorithm to reduce the consumption of computing resources that can occur when searching for the data object nearest to the query object in the process of executing the algorithm. Additionally, a method to improve ANN algorithm is proposed. When the algorithm is executed, it is a situation in which the user finds a data object adjacent to the user. In this case, our technique applies the criteria set provided by users.\",\"PeriodicalId\":336929,\"journal\":{\"name\":\"2020 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"volume\":\"4 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 International Conference on Computational Science and Computational Intelligence (CSCI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CSCI51800.2020.00265\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 International Conference on Computational Science and Computational Intelligence (CSCI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCI51800.2020.00265","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种改进的ANN (All Nearest Neighbor,全近邻)算法,该算法采用标准集群循环(Standard Clustered Loop, SCL)算法,以减少在算法执行过程中搜索离查询对象最近的数据对象时可能产生的计算资源消耗。此外,还提出了一种改进人工神经网络算法的方法。当执行算法时,是用户找到与用户相邻的数据对象的情况。在这种情况下,我们的技术应用用户提供的标准集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
All Nearest Neighbors Query Including Scores Road Network
This paper introduces an improved ANN (All Nearest Neighbor) algorithm using the SCL (Standard Clustered Loop) algorithm to reduce the consumption of computing resources that can occur when searching for the data object nearest to the query object in the process of executing the algorithm. Additionally, a method to improve ANN algorithm is proposed. When the algorithm is executed, it is a situation in which the user finds a data object adjacent to the user. In this case, our technique applies the criteria set provided by users.
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