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    SharkDB: an in-memory column-oriented storage for trajectory analysis

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    Type
    Article
    Authors
    Zheng, Bolong
    Wang, Haozhou
    Zheng, Kai
    Su, Han
    Liu, Kuien
    Shang, Shuo
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-05-05
    Online Publication Date
    2017-05-05
    Print Publication Date
    2018-03
    Permanent link to this record
    http://hdl.handle.net/10754/623667
    
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    Abstract
    The last decade has witnessed the prevalence of sensor and GPS technologies that produce a high volume of trajectory data representing the motion history of moving objects. However some characteristics of trajectories such as variable lengths and asynchronous sampling rates make it difficult to fit into traditional database systems that are disk-based and tuple-oriented. Motivated by the success of column store and recent development of in-memory databases, we try to explore the potential opportunities of boosting the performance of trajectory data processing by designing a novel trajectory storage within main memory. In contrast to most existing trajectory indexing methods that keep consecutive samples of the same trajectory in the same disk page, we partition the database into frames in which the positions of all moving objects at the same time instant are stored together and aligned in main memory. We found this column-wise storage to be surprisingly well suited for in-memory computing since most frames can be stored in highly compressed form, which is pivotal for increasing the memory throughput and reducing CPU-cache miss. The independence between frames also makes them natural working units when parallelizing data processing on a multi-core environment. Lastly we run a variety of common trajectory queries on both real and synthetic datasets in order to demonstrate advantages and study the limitations of our proposed storage.
    Citation
    Zheng B, Wang H, Zheng K, Su H, Liu K, et al. (2017) SharkDB: an in-memory column-oriented storage for trajectory analysis. World Wide Web. Available: http://dx.doi.org/10.1007/s11280-017-0466-9.
    Sponsors
    This work is partially supported by Natural Science Foundation of China (No. 61502324 and No. 61532018).
    Publisher
    Springer Nature
    Journal
    World Wide Web
    DOI
    10.1007/s11280-017-0466-9
    Additional Links
    http://link.springer.com/article/10.1007/s11280-017-0466-9
    ae974a485f413a2113503eed53cd6c53
    10.1007/s11280-017-0466-9
    Scopus Count
    Collections
    Articles; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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