Querying streaming point clusters as regions

Chengyang Zhang, Y. Huang
{"title":"Querying streaming point clusters as regions","authors":"Chengyang Zhang, Y. Huang","doi":"10.1145/1878500.1878510","DOIUrl":null,"url":null,"abstract":"This paper focuses on one important type of geo-streaming data - point geo-streams. Many interesting applications require selected discrete points with similar observations to be clustered according to spatial proximity and further elevated into higher-level spatial regions. Querying streaming point clusters as regions directly in a geo-stream database has many benefits, but is also very challenging. We propose two query optimization strategies, namely semantics-based optimization and incremental optimization for answering queries involving both point geo-streams and static data set. The experimental results on a streaming meteorological data set demonstrate the effectiveness and the efficiency of the query processing techniques. Compared with the baseline methods, our optimization methods can reduce the total execution time by more than an order of magnitude.","PeriodicalId":190366,"journal":{"name":"International Workshop on GeoStreaming","volume":"97 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on GeoStreaming","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1878500.1878510","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

This paper focuses on one important type of geo-streaming data - point geo-streams. Many interesting applications require selected discrete points with similar observations to be clustered according to spatial proximity and further elevated into higher-level spatial regions. Querying streaming point clusters as regions directly in a geo-stream database has many benefits, but is also very challenging. We propose two query optimization strategies, namely semantics-based optimization and incremental optimization for answering queries involving both point geo-streams and static data set. The experimental results on a streaming meteorological data set demonstrate the effectiveness and the efficiency of the query processing techniques. Compared with the baseline methods, our optimization methods can reduce the total execution time by more than an order of magnitude.
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
将流点集群查询为区域
本文重点研究了一种重要的地流数据类型——点地流。许多有趣的应用程序需要选择具有相似观测值的离散点,根据空间接近度进行聚类,并进一步提升到更高级别的空间区域。直接在地理流数据库中将流点集群作为区域查询有很多好处,但也非常具有挑战性。我们提出了两种查询优化策略,即基于语义的优化和增量优化,以回答涉及点地理流和静态数据集的查询。在一个流气象数据集上的实验结果验证了该查询处理技术的有效性和高效性。与基线方法相比,我们的优化方法可以将总执行时间减少一个数量级以上。
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
Clustering spatial data streams for targeted alerting in disaster response ADTOS: arrival departure tradeoff optimization system Mining robust neighborhoods for quality control of sensor data EHSTC: an enhanced method for semantic trajectory compression Towards window stream queries over continuous phenomena
×
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