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Open AccessArticle

MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data

by Junghee Jo 1,* and Kang-Woo Lee 2
1
Busan National University of Education, Busan 46241, Korea
2
Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 475; https://doi.org/10.3390/ijgi8110475
Received: 15 August 2019 / Revised: 12 October 2019 / Accepted: 21 October 2019 / Published: 24 October 2019
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
The conventional extracting–transforming–loading (ETL) system is typically operated on a single machine not capable of handling huge volumes of geospatial big data. To deal with the considerable amount of big data in the ETL process, we propose D_ELT (delayed extracting–loading –transforming) by utilizing MapReduce-based parallelization. Among various kinds of big data, we concentrate on geospatial big data generated via sensors using Internet of Things (IoT) technology. In the IoT environment, update latency for sensor big data is typically short and old data are not worth further analysis, so the speed of data preparation is even more significant. We conducted several experiments measuring the overall performance of D_ELT and compared it with both traditional ETL and extracting–loading– transforming (ELT) systems, using different sizes of data and complexity levels for analysis. The experimental results show that D_ELT outperforms the other two approaches, ETL and ELT. In addition, the larger the amount of data or the higher the complexity of the analysis, the greater the parallelization effect of transform in D_ELT, leading to better performance over the traditional ETL and ELT approaches. View Full-Text
Keywords: ETL; ELT; big data; sensor data; IoT; geospatial big data; MapReduce ETL; ELT; big data; sensor data; IoT; geospatial big data; MapReduce
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Jo, J.; Lee, K.-W. MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data. ISPRS Int. J. Geo-Inf. 2019, 8, 475.

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