.png){kind=icon}
返回
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
.png){kind=icon}
摘要
En 中文
Recent improvements in positioning technology make massive moving object data widely available. One important analysis is to find the moving objects that travel together. Existing methods put a strong constraint in defining moving object cluster, that they require the moving objects to stick together for
consecutive
timestamps. Our key observation is that the moving objects in a cluster may actually diverge temporarily and congregate at certain timestamps.
Motivated by this, we propose the concept of
swarm
which captures the moving objects that move within arbitrary shape of clusters for certain timestamps that are possibly non-consecutive. The goal of our paper is to find all discriminative swarms, namely
closed swarm
. While the search space for closed swarms is prohibitively huge, we design a method, ObjectGrowth, to efficiently retrieve the answer. In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly. Empirical studies on the real data as well as large synthetic data demonstrate the effectiveness and efficiency of our methods.
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
暂无期刊信息
机构
暂无机构信息
引用论文
暂无论文信息