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Open AccessArticle

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

1
Department of Information Convergence, Korea National University of Transportation, Daehakro 50, Chungju, Chungbuk 27469, Korea
2
Department of Electronic Engineering, Mokwon University, Doanbukro 88, Daejeon 35349, Korea
3
School of Computer Engineering & Information Technology, Korea National University of Transportation, Daehakro 50, Chungju, Chungbuk 27469, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(20), 4261; https://doi.org/10.3390/app9204261
Received: 24 July 2019 / Revised: 27 September 2019 / Accepted: 30 September 2019 / Published: 11 October 2019
(This article belongs to the Special Issue Artificial Intelligence for Smart Systems)
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. View Full-Text
Keywords: spatiotemporal; index; parallel; ingestion spatiotemporal; index; parallel; ingestion
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Park, S.; Ko, D.; Song, S. Parallel Insertion and Indexing Method for Large Amount of Spatiotemporal Data Using Dynamic Multilevel Grid Technique. Appl. Sci. 2019, 9, 4261.

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