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The VLDB Journal

, Volume 27, Issue 3, pp 395–420 | Cite as

Parallel trajectory similarity joins in spatial networks

  • Shuo Shang
  • Lisi Chen
  • Zhewei Wei
  • Christian S. Jensen
  • Kai Zheng
  • Panos Kalnis
Regular Paper
  • 303 Downloads

Abstract

The matching of similar pairs of objects, called similarity join, is fundamental functionality in data management. We consider two cases of trajectory similarity joins (TS-Joins), including a threshold-based join (Tb-TS-Join) and a top-k TS-Join (k-TS-Join), where the objects are trajectories of vehicles moving in road networks. Given two sets of trajectories and a threshold \(\theta \), the Tb-TS-Join returns all pairs of trajectories from the two sets with similarity above \(\theta \). In contrast, the k-TS-Join does not take a threshold as a parameter, and it returns the top-k most similar trajectory pairs from the two sets. The TS-Joins target diverse applications such as trajectory near-duplicate detection, data cleaning, ridesharing recommendation, and traffic congestion prediction. With these applications in mind, we provide purposeful definitions of similarity. To enable efficient processing of the TS-Joins on large sets of trajectories, we develop search space pruning techniques and enable use of the parallel processing capabilities of modern processors. Specifically, we present a two-phase divide-and-conquer search framework that lays the foundation for the algorithms for the Tb-TS-Join and the k-TS-Join that rely on different pruning techniques to achieve efficiency. For each trajectory, the algorithms first find similar trajectories. Then they merge the results to obtain the final result. The algorithms for the two joins exploit different upper and lower bounds on the spatiotemporal trajectory similarity and different heuristic scheduling strategies for search space pruning. Their per-trajectory searches are independent of each other and can be performed in parallel, and the mergings have constant cost. An empirical study with real data offers insight in the performance of the algorithms and demonstrates that they are capable of outperforming well-designed baseline algorithms by an order of magnitude.

Keywords

Trajectory similarity join Parallel processing Spatial networks Spatiotemporal databases 

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Shuo Shang
    • 1
  • Lisi Chen
    • 2
  • Zhewei Wei
    • 3
  • Christian S. Jensen
    • 4
  • Kai Zheng
    • 5
  • Panos Kalnis
    • 1
  1. 1.CEMSEKing Abdullah University of Science and TechnologyThuwalSaudi Arabia
  2. 2.School of Computer and Information TechnologyUniversity of WollongongWollongongAustralia
  3. 3.School of InformationRenmin University of ChinaBeijingChina
  4. 4.Department of Computer ScienceAalborg UniversityAalborgDenmark
  5. 5.School of Computer Science and Engineering and Big Data Research CenterUniversity of Electronic Science and Technology of ChinaChengduChina

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