TY - GEN
T1 - A Big Aurora Data Management Framework Toward Aurora Classification
AU - Wang, Yuhang
AU - Zhao, Hui
AU - Zhang, Xian
AU - Liang, Jimin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - Aurora is the only geophysical phenomenon observed directly with the naked eye in the high latitude regions. The systematic observation of aurora plays an important role in the magnetosphere and solar-terrestrial electromagnetic activity. With the observed aurora data growing quickly, aurora morphology research becomes data intensive. This paper presents a big aurora data management framework toward aurora classification analysis considering the scalability and performance. The framework integrates aurora data acquisition and management, aurora data preprocessing, data query and explore, and aurora classification experiment management. The NoSQL database is employed to manage the aurora data and the classification experiment data. The in-memory index is constructed to support the in-situ analysis.
AB - Aurora is the only geophysical phenomenon observed directly with the naked eye in the high latitude regions. The systematic observation of aurora plays an important role in the magnetosphere and solar-terrestrial electromagnetic activity. With the observed aurora data growing quickly, aurora morphology research becomes data intensive. This paper presents a big aurora data management framework toward aurora classification analysis considering the scalability and performance. The framework integrates aurora data acquisition and management, aurora data preprocessing, data query and explore, and aurora classification experiment management. The NoSQL database is employed to manage the aurora data and the classification experiment data. The in-memory index is constructed to support the in-situ analysis.
KW - aurora classification
KW - big aurora data
KW - data management
KW - data service framework
UR - https://www.scopus.com/pages/publications/85048077651
U2 - 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.206
DO - 10.1109/DASC-PICom-DataCom-CyberSciTec.2017.206
M3 - 会议稿件
AN - SCOPUS:85048077651
T3 - Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
SP - 1284
EP - 1287
BT - Proceedings - 2017 IEEE 15th International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Dependable, Autonomic and Secure Computing, 2017 IEEE 15th International Conference on Pervasive Intelligence and Computing, 2017 IEEE 3rd International Conference on Big Data Intelligence and Computing and 2017 IEEE Cyber Science and Technology Congress, DASC-PICom-DataCom-CyberSciTec 2017
Y2 - 6 November 2017 through 11 November 2017
ER -