In this work we propose a novel type of group pattern, called evolving group, which models the unusual group events of moving objects that travel together ...

Our comprehensive empirical study demonstrates that our discovery framework is effective and efficient on real-world high volume trajectory streams. Index Terms ...

Aug 12, 2022 · We propose a discovery method that efficiently supports online detection of evolving groups over massive-scale trajectory streams using a sliding window.

Sep 19, 2018 · They proposed a discovery framework that efficiently detects the evolving groups using sliding window technique [26] . Also, Chen et al. defined ...

This work proposes a novel pattern, called evolving group, which models the unusual group events of moving objects that travel together within density ...

Aug 12, 2022 · Hence the discovering algorithm should report the results simultaneously while receiving and processing the massive-scale trajectory streams.

Aug 24, 2014 · This method focuses on a totally different problem, namely discovering anomalous regions rather than abnormal moving objects. Trajectory ...

This work proposes classes of novel trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real time ...

In this work, we propose a rich taxonomy of novel classes of neighbor-based trajectory outlier definitions that model the anomalous behavior of moving objects.

Trajectory clustering techniques aim to find groups of moving object trajectories that are close to each other and have similar geometric shapes. Gaffney et al.