Since the volume of data generated by a scientific data experiment has grown exponentially, new scientific methods to analyze and organize the data are required. Hence, these methods need to be used effective infrastructure composed of computing resources that are used for pre-processing and post-processing data. The demanding requirement has led to development of methods to reduce the size of dataset and to apply a new programming model and its implementation like MapReduce. In this paper, we describe an empirical study for handling the dataset of a scientific data experiment to support data transformation, which is an essential phase to handling large-scale data in scientific data experiments. In this experiment we show a way to optimize the dataset written in netCDF by a data reduction as a sub-setting method and to process the dataset about tornado outbreak in the US by Hadoop, a MapReduce framework. These methods can be applied to pre-processing and post-processing in scientific data experiments.
목차
Abstract 1. Introduction 2. Related Works 2.1. PolarGrid: Scientific Data Project 2.2 MapReduce 3. Scientific Data Experiment Framework 4. Examples of Scientific Data Experiment 4.1 Data Reduction of Dataset 4.2 Data Transformation for MapReduce Application 5. Conclusion References
키워드
MapReduceScientific Data ExperimentSub-SettingData Transformation
저자
Yunhee Kang [ Baekseok University, Samsung Advanced Institute of Technology ]
Heeyoul Choi [ Baekseok University, Samsung Advanced Institute of Technology ]
보안공학연구지원센터(IJGDC) [Science & Engineering Research Support Center, Republic of Korea(IJGDC)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Grid and Distributed Computing
간기
격월간
pISSN
2005-4262
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Grid and Distributed Computing Vol.5 No.3