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
R. Uddin, Mehnaz Tabassum Mahin, Payas Rajan, C. Ravishankar, V. Tsotras
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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。
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.
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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.
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