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학술지 MapReduce-Based D_ELT Framework to Address the Challenges of Geospatial Big Data
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저자
조정희, 이강우
발행일
201911
출처
ISPRS International Journal of Geo-Information, v.8 no.11, pp.1-15
ISSN
2220-9964
출판사
MDPI
DOI
https://dx.doi.org/10.3390/ijgi8110475
협약과제
19GH1700, 공간 빅데이터 저장관리 인프라 기술 개발 , 이강우
초록
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.
KSP 제안 키워드
Different sizes, ETL process, Geospatial big data, Internet of thing(IoT), IoT environment, Overall performance, Sensor Big Data, data preparation, single machine
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