Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique
2019
Sangdeok Park | Daesik Ko | Seokil Song
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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