Abstract
The aim of real-time co-movement pattern mining for streaming trajectories is to discover co-moving objects that satisfy specific spatio-temporal constraints in real time. This functionality serves a range of real-world applications, such as traffic monitoring and management. However, little work targets the visualization and interaction with such co-movement detection on streaming trajectories. To this end, we develop CoMing, a real-time co-movement pattern mining system, to handle streaming trajectories. CoMing leverages ICPE, a real-time distributed co-movement pattern detection framework, and thus, it has its capacity of good performance. This demonstration offers hands-on experience with CoMing's visual and user-friendly interface. Moreover, several applications in the traffic domain, including object monitoring and traffic statistics visualization, are also provided to users.
Originalsprog | Engelsk |
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Titel | Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data |
Antal sider | 4 |
Forlag | Association for Computing Machinery |
Publikationsdato | 2020 |
Sider | 2777-2780 |
ISBN (Trykt) | 978-1-4503-6735-6 |
ISBN (Elektronisk) | 9781450367356 |
DOI | |
Status | Udgivet - 2020 |
Begivenhed | ACM SIGMOD International Conference on Management of Data 2020 - Portland, USA Varighed: 1 jun. 2020 → 30 jun. 2020 |
Konference
Konference | ACM SIGMOD International Conference on Management of Data 2020 |
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Land/Område | USA |
By | Portland |
Periode | 01/06/2020 → 30/06/2020 |