4.7 Article

A Survey of Spatio-Temporal Big Data Indexing Methods in Distributed Environment

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2022.3175657

Keywords

Indexes; Big Data; Indexing; Distributed databases; Trajectory; Three-dimensional displays; Scalability; Big Data; distributed system; spatio-temporal index

Funding

  1. National Key R&D Program of China [2020YFF0410947]
  2. National Natural Science Foundation of China [62103072]
  3. China Postdoctoral Science Foundation [2021M690502]
  4. Department of Science and Technology of Liaoning [2020-HYLH-50]

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This article summarizes the spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020, and proposes the hot issues that need to be paid attention to in the future according to changes in application requirements.
With the widespread use of mobile and sensing devices, and the popularity of online map-based services, such as navigation services, the volume of spatio-temporal data is growing rapidly. Conventional big data technologies in existing distributed systems cannot effectively process spatio-temporal big data with temporal continuity and spatial proximity. How to construct an effective index for the application requirements of spatio-temporal data in a distributed environment has become one of the hotspots of spatio-temporal big data research. Many spatio-temporal indexing methods have been proposed to support efficient query processing of spatio-temporal data. In this article, the various spatio-temporal big data indexing methods proposed by domestic and foreign researchers from 2010 to 2020 are classified and summarized according to the distributed environment and application background, and the hot issues that need to be paid attention to in the future are proposed according to the changes in application requirements

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