期刊
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE
卷 35, 期 4, 页码 819-845出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/13658816.2020.1834562
关键词
Data analytics; semantic enrichment; collective movement behaviour; co-movement patterns; evolving clusters
类别
资金
- project i4Sea - European Regional Development Fund of the EU [T1EDK-03268]
- project Track&Know - European Regional Development Fund of the EU [780754]
- Greek national funds (through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Research-Create-Innovate)
- EU Horizon 2020 RI Programme
The paper proposes a novel graph-based online co-movement pattern mining algorithm, which can discover different collective movement behaviors based on the activity of multiple concurrent objects through time and space.
The advent of GPS technologies generates location data-streams and accentuates the importance of developing practical tools that can process and analyze the vast amounts of location data at a given moment in a meaningful way. Profiling the trajectory of a moving object with respect to the trajectories of its surrounding objects, for example, can elicit its mobility behaviour and analyze it in order to inform domain experts with critical knowledge in real time. For instance, clustering multiple moving objects with respect to their spatial and temporal dimension to identify co-movement patterns. In this paper, we propose a novel graph-based online co-movement pattern mining algorithm, called EvolvingClusters, which can be used to discover different collective movement behaviours (like the well-known flocks and convoys) in a unified way based on the activity of multiple concurrent objects through time and space. We evaluate EvolvingClusters using real-world and synthetic datasets from multiple mobility domains. Our study demonstrates the effectiveness of the proposed algorithm as well as its value towards a tool to profile semantically rich behaviour and with capabilities to observe and categorize multiple moving objects in real-time.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据