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    Searching Trajectories by Regions of Interest

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    08509449.pdf
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    Type
    Conference Paper
    Authors
    Shang, Shuo
    Chen, Lisi
    Jensen, Christian S.
    Wen, Ji-Rong
    Kalnis, Panos cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2018-10-25
    Online Publication Date
    2018-10-25
    Print Publication Date
    2018-04
    Permanent link to this record
    http://hdl.handle.net/10754/630374
    
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    Abstract
    We propose and investigate a novel query type named trajectory search by regions of interest (TSR query). Given an argument set of trajectories, a TSR query takes a set of regions of interest as a parameter and returns the trajectory in the argument set with the highest spatial-density correlation to the query regions. This type of query is useful in applications such as trip planning and recommendation. To process the TSR query, a set of new metrics are defined to model spatial-density correlations. An efficient trajectory search algorithm is developed that exploits upper and lower bounds to prune the search space and that adopts a query-source selection strategy, as well as integrates a heuristic search strategy based on priority ranking to schedule multiple query sources. The performance of TSR query processing is studied in extensive experiments based on real and synthetic spatial data.
    Citation
    Shang S, Chen L, Jensen CS, Wen J-R, Kalnis P (2018) Searching Trajectories by Regions of Interest. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00228.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE 34th International Conference on Data Engineering (ICDE)
    Conference/Event name
    34th IEEE International Conference on Data Engineering, ICDE 2018
    DOI
    10.1109/ICDE.2018.00228
    Additional Links
    https://ieeexplore.ieee.org/document/8509449
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDE.2018.00228
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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