CoMing: A Real-time Co-Movement Mining System for Streaming Trajectories

Ziquan Fang, Yunjun Gao, Lu Pan, Lu Chen, Xiaoye Miao, Christian S. Jensen

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

2 Citations (Scopus)

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 languageEnglish
Title of host publicationProceedings of the 2020 ACM SIGMOD International Conference on Management of Data
Number of pages4
PublisherAssociation for Computing Machinery
Publication date2020
Pages2777-2780
ISBN (Print)978-1-4503-6735-6
ISBN (Electronic)9781450367356
DOIs
Publication statusPublished - 2020
EventACM SIGMOD International Conference on Management of Data 2020 - Portland, United States
Duration: 1 Jun 202030 Jun 2020

Conference

ConferenceACM SIGMOD International Conference on Management of Data 2020
CountryUnited States
CityPortland
Period01/06/202030/06/2020

Keywords

  • co-movement pattern
  • system
  • trajectory
  • visualization

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