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.
|Titel||Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data|
|Forlag||Association for Computing Machinery|
|Status||Udgivet - 2020|
|Begivenhed||ACM SIGMOD International Conference on Management of Data 2020 - Portland, USA|
Varighed: 1 jun. 2020 → 30 jun. 2020
|Konference||ACM SIGMOD International Conference on Management of Data 2020|
|Periode||01/06/2020 → 30/06/2020|