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.
Original language | English |
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Title of host publication | Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data |
Number of pages | 4 |
Publisher | Association for Computing Machinery |
Publication date | 2020 |
Pages | 2777-2780 |
ISBN (Print) | 978-1-4503-6735-6 |
ISBN (Electronic) | 9781450367356 |
DOIs | |
Publication status | Published - 2020 |
Event | ACM SIGMOD International Conference on Management of Data 2020 - Portland, United States Duration: 1 Jun 2020 → 30 Jun 2020 |
Conference
Conference | ACM SIGMOD International Conference on Management of Data 2020 |
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Country | United States |
City | Portland |
Period | 01/06/2020 → 30/06/2020 |
Keywords
- co-movement pattern
- system
- trajectory
- visualization