Trajectory Data Pattern Mining

  • Elio MasciariEmail author
  • Gao Shi
  • Carlo Zaniolo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8399)


In this paper, we study the problem of mining for frequent trajectories, which is crucial in many application scenarios, such as vehicle traffic management, hand-off in cellular networks, supply chain management. We approach this problem as that of mining for frequent sequential patterns. Our approach consists of a partitioning strategy for incoming streams of trajectories in order to reduce the trajectory size and represent trajectories as strings. We mine frequent trajectories using a sliding windows approach combined with a counting algorithm that allows us to promptly update the frequency of patterns. In order to make counting really efficient, we represent frequent trajectories by prime numbers, whereby the Chinese reminder theorem can then be used to expedite the computation.


Frequent Pattern Minimum Support Pattern Mining Frequent Itemsets Trajectory Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ICAR-CNRNaplesItaly
  2. 2.UCLALos AngelesUSA

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