Applied Sciences (Oct 2019)

Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique

  • Sangdeok Park,
  • Daesik Ko,
  • Seokil Song

DOI
https://doi.org/10.3390/app9204261
Journal volume & issue
Vol. 9, no. 20
p. 4261

Abstract

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In this paper, we propose a method to ingest big spatiotemporal data using a parallel technique in a cluster environment. The proposed method includes an indexing method for effective retrieval in addition to the parallel ingestion method of spatiotemporal data. In this paper, a dynamic multilevel grid index scheme is proposed to maximize parallelism and to adapt to the skewed spatiotemporal data. Finally, through experiments in a cluster environment, it is shown that the ingestion and query throughput increase as the number of nodes increases.

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