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Indexing Moving Objects Using Query Oriented Parallel Grids

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Published:22 October 2018Publication History

ABSTRACT

To1 improve updating efficiency and querying accuracy for spatial moving object data, we propose a parallel data structure for indexing moving objects. The structure contains a main index and an auxiliary index, which are used for supporting range based and identity based spatial object query operations, respectively. It also utilizes a query index which hooks updating operations to querying operations that may be influenced. Thus, it avoids locking relevant spatial objects and indexing structures as existing approaches do when range query operations are processed. At the same time, it also supports time slice semantics for parallel operations. Experimental results show that, under high working load, the structure can not only guarantee querying accuracy, the throughput is also obviously higher than that of the existing methods. The index improves the degree of system parallelism, makes it possible for object updating and querying operations in same ranges be processed in parallel, and therefore improves the overall efficiency of the system.

References

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    • Published in

      cover image ACM Other conferences
      CSAE '18: Proceedings of the 2nd International Conference on Computer Science and Application Engineering
      October 2018
      1083 pages
      ISBN:9781450365123
      DOI:10.1145/3207677

      Copyright © 2018 Owner/Author

      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 22 October 2018

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      • research-article
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      • Refereed limited

      Acceptance Rates

      CSAE '18 Paper Acceptance Rate189of383submissions,49%Overall Acceptance Rate368of770submissions,48%

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