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    Parallel trajectory similarity joins in spatial networks

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    sigproc-sp-1006.pdf
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    Type
    Article
    Authors
    Shang, Shuo
    Chen, Lisi
    Wei, Zhewei
    Jensen, Christian S.
    Zheng, Kai
    Kalnis, Panos cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2018-04-04
    Online Publication Date
    2018-04-04
    Print Publication Date
    2018-06
    Permanent link to this record
    http://hdl.handle.net/10754/627421
    
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    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 θ, the Tb-TS-Join returns all pairs of trajectories from the two sets with similarity above θ. 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.
    Citation
    Shang S, Chen L, Wei Z, Jensen CS, Zheng K, et al. (2018) Parallel trajectory similarity joins in spatial networks. The VLDB Journal. Available: http://dx.doi.org/10.1007/s00778-018-0502-0.
    Publisher
    Springer Nature
    Journal
    The VLDB Journal
    DOI
    10.1007/s00778-018-0502-0
    Additional Links
    http://link.springer.com/article/10.1007/s00778-018-0502-0
    ae974a485f413a2113503eed53cd6c53
    10.1007/s00778-018-0502-0
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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