Efficient mining of platoon patterns in trajectory databases

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• 作者：Yuxuan Li ; ^{yuxuan.li@unimelb.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; James Bailey ^{baileyj@unimelb.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Lars Kulik ^{lkulik@unimelb.edu.au" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Spatial clustering ; Trajectory database ; Moving object cluster ; Spatial pattern mining ; Data mining
• 刊名：Data & Knowledge Engineering
• 出版年：2015
• 出版时间：November 2015
• 年：2015
• 卷：100
• 期：part_PA
• 页码：167-187
• 全文大小：2154 K

文摘

The widespread use of localization technologies produces increasing quantities of trajectory data. An important task in the analysis of trajectory data is the discovery of moving object clusters, i.e., moving objects that travel together for a period of time. Algorithms for the discovery of moving object clusters operate by applying constraints on the consecutiveness of timestamps. However, existing approaches either use a very strict timestamp constraint, which may result in the loss of interesting patterns, or a very relaxed timestamp constraint, which risks discovering noisy patterns. To address this challenge, we introduce a new type of moving object pattern called the platoon pattern.

We propose a novel algorithm to efficiently retrieve platoon patterns in large trajectory databases, using several pruning techniques. Our experiments on both real data and synthetic data evaluate the effectiveness and efficiency of our approach and demonstrate that our algorithm is able to achieve several orders of magnitude improvement in running time, compared to an existing method for retrieving moving object clusters.