时空数据流中的特征轨迹检测

R. Badretdinov, E. Takhavova, M. Shleimovich
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引用次数: 2

摘要

本文提出了一种解决识别物体频繁迁移轨迹任务的方法。这项任务可能发生在鸟类、动物的迁徙轨迹的研究中;在一段时间内研究一个物体或一组物体的运动轨迹。所描述的方法面向时空数据流的处理。考虑了识别特征轨迹的两个阶段。第一步是解决分析数据的聚类问题。第二阶段是确定最常遇到的群集集。应该选择要处理的信息的时间段。在考虑求解速度和质量要求的基础上,提出了求解这些问题的方法和算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Characteristic Trajectories Detection in Spatio-Temporal Data Streams
This article represents the approach for solving task of identifying objects' frequent migration trajectories. This task may take place in the study of migration trajectories of birds, animals; in the study of the trajectories of an object or a group of objects during a period of time. The described method is oriented to processing of spatio-temporal data streams. Two stages for identifying characteristic trajectories are considered. The first step is to solve the clustering problem for the analyzed data. The second stage is identifying the most frequently encountered sets of clusters. The time period of information to be processed should be chosen. Ways and algorithms to solve these tasks are offered which take into consideration requirements for speed and quality of problem solving.
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