居住区域:流和存档轨迹数据的广义停留区域

IF 1.2 Q4 REMOTE SENSING ACM Transactions on Spatial Algorithms and Systems Pub Date : 2022-06-13 DOI:10.1145/3543850
R. Uddin, Mehnaz Tabassum Mahin, Payas Rajan, C. Ravishankar, V. Tsotras
{"title":"居住区域:流和存档轨迹数据的广义停留区域","authors":"R. Uddin, Mehnaz Tabassum Mahin, Payas Rajan, C. Ravishankar, V. Tsotras","doi":"10.1145/3543850","DOIUrl":null,"url":null,"abstract":"A region ℛ is a dwell region for a moving object O if, given a threshold distance rq and duration τq, every point of ℛ remains within distance rq from O for at least time τq. Points within ℛ are likely to be of interest to O, so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ-Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ-Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude.","PeriodicalId":43641,"journal":{"name":"ACM Transactions on Spatial Algorithms and Systems","volume":null,"pages":null},"PeriodicalIF":1.2000,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory Data\",\"authors\":\"R. Uddin, Mehnaz Tabassum Mahin, Payas Rajan, C. Ravishankar, V. Tsotras\",\"doi\":\"10.1145/3543850\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A region ℛ is a dwell region for a moving object O if, given a threshold distance rq and duration τq, every point of ℛ remains within distance rq from O for at least time τq. Points within ℛ are likely to be of interest to O, so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ-Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ-Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude.\",\"PeriodicalId\":43641,\"journal\":{\"name\":\"ACM Transactions on Spatial Algorithms and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2022-06-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ACM Transactions on Spatial Algorithms and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3543850\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"REMOTE SENSING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACM Transactions on Spatial Algorithms and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3543850","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0

摘要

一个地区ℛ 是移动对象O的停留区域,如果给定阈值距离rq和持续时间τqℛ 在距离O的距离rq内保持至少时间τq。内的点ℛ O可能感兴趣,因此驻留区域的识别具有监测和监视等应用。我们首先提出了一种对数时间在线算法,以在物体位置的输入流中找到停留区域。我们的方法保持了包围对象位置的最小圆的半径的上限和下限,从而大大减少了评估查询所需的轨迹点的数量。它近似于在任意小的用户定义因子内包围给定子域的最小圆的半径,并且还能够有效地回答询问是否存在驻留区域的决策查询。对于停留区问题的离线版本,我们首先扩展了我们的在线方法来开发ρ-索引,该索引使用查询半径范围对子表进行索引。然后,我们对这种方法进行了改进,以获得τ-索引,该索引使用查询半径范围和停留持续时间对子表进行索引。我们使用真实世界和合成数据集进行的实验表明,在线方法可以扩展到数十万个移动对象。对于存档的轨迹,我们的索引方法将查询速度提高了许多数量级。
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
Dwell Regions: Generalized Stay Regions for Streaming and Archival Trajectory Data
A region ℛ is a dwell region for a moving object O if, given a threshold distance rq and duration τq, every point of ℛ remains within distance rq from O for at least time τq. Points within ℛ are likely to be of interest to O, so identification of dwell regions has applications such as monitoring and surveillance. We first present a logarithmic-time online algorithm to find dwell regions in an incoming stream of object positions. Our method maintains the upper and lower bounds for the radius of the smallest circle enclosing the object positions, thereby greatly reducing the number of trajectory points needed to evaluate the query. It approximates the radius of the smallest circle enclosing a given subtrajectory within an arbitrarily small user-defined factor and is also able to efficiently answer decision queries asking whether or not a dwell region exists. For the offline version of the dwell region problem, we first extend our online approach to develop the ρ-Index, which indexes subtrajectories using query radius ranges. We then refine this approach to obtain the τ-Index, which indexes subtrajectories using both query radius ranges and dwell durations. Our experiments using both real-world and synthetic datasets show that the online approach can scale up to hundreds of thousands of moving objects. For archived trajectories, our indexing approaches speed up queries by many orders of magnitude.
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
CiteScore
4.40
自引率
5.30%
发文量
43
期刊介绍: ACM Transactions on Spatial Algorithms and Systems (TSAS) is a scholarly journal that publishes the highest quality papers on all aspects of spatial algorithms and systems and closely related disciplines. It has a multi-disciplinary perspective in that it spans a large number of areas where spatial data is manipulated or visualized (regardless of how it is specified - i.e., geometrically or textually) such as geography, geographic information systems (GIS), geospatial and spatiotemporal databases, spatial and metric indexing, location-based services, web-based spatial applications, geographic information retrieval (GIR), spatial reasoning and mining, security and privacy, as well as the related visual computing areas of computer graphics, computer vision, geometric modeling, and visualization where the spatial, geospatial, and spatiotemporal data is central.
期刊最新文献
Cross- and Context-Aware Attention Based Spatial-Temporal Graph Convolutional Networks for Human Mobility Prediction (Vision Paper) A Vision for Spatio-Causal Situation Awareness, Forecasting, and Planning Mobility Data Science: Perspectives and Challenges Graph Sampling for Map Comparison Latent Representation Learning for Geospatial Entities
×
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