Gedetector: Early detection of gathering events based on cluster containment join in trajectory streams

Bin Zhao, Zhaoyuan Yu, Genlin Ji, Xintao Liu, Yu Yang, Ningfang Mi

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)


Existing trajectory patterns, such as flock, convoy, swarm, and gathering, are to detect moving clusters staying or travelling together for a certain time period. But these patterns model group movement behaviors after moving objects' gathering together. This may result in loosing golden opportunities to detect emergency incidents earlier, such as traffic congestion and serious stampedes. In this work, we propose a novel group pattern, called converging, which can model converging behaviors of moving objects. As a proof-of-concept, we implemented a visual analytic system GEDetector based on trajectory streams to detect gathering events as early as possible. A user-friendly interface is designed to help users gain insights into gathering events from spatial and temporal aspects. Finally, we demonstrate the effectiveness and efficiency of our system by using a real world dataset.

Original languageEnglish
Title of host publicationAdvances in Database Technology - EDBT 2018
Subtitle of host publication21st International Conference on Extending Database Technology, Proceedings
EditorsMichael Bohlen, Reinhard Pichler, Norman May, Erhard Rahm, Shan-Hung Wu, Katja Hose
Number of pages4
ISBN (Electronic)9783893180783
Publication statusPublished - 1 Jan 2018
Event21st International Conference on Extending Database Technology, EDBT 2018 - Vienna, Austria
Duration: 26 Mar 201829 Mar 2018

Publication series

NameAdvances in Database Technology - EDBT
ISSN (Electronic)2367-2005


Conference21st International Conference on Extending Database Technology, EDBT 2018

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Science Applications

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