J. Space Weather Space Clim.
Volume 6, 2016
Statistical Challenges in Solar Information Processing
|Number of page(s)||18|
|Published online||04 May 2016|
A large-scale dataset of solar event reports from automated feature recognition modules
Dept. of Computer Science, Montana State University, Bozeman, MT
2 Dept. of Computer Science, Georgia State University, Atlanta, GA 30303, USA
3 Dept. of Physics and Astronomy, Georgia State University, Atlanta, GA 30303, USA
* Corresponding author: firstname.lastname@example.org
Accepted: 18 March 2016
The massive repository of images of the Sun captured by the Solar Dynamics Observatory (SDO) mission has ushered in the era of Big Data for Solar Physics. In this work, we investigate the entire public collection of events reported to the Heliophysics Event Knowledgebase (HEK) from automated solar feature recognition modules operated by the SDO Feature Finding Team (FFT). With the SDO mission recently surpassing five years of operations, and over 280,000 event reports for seven types of solar phenomena, we present the broadest and most comprehensive large-scale dataset of the SDO FFT modules to date. We also present numerous statistics on these modules, providing valuable contextual information for better understanding and validating of the individual event reports and the entire dataset as a whole. After extensive data cleaning through exploratory data analysis, we highlight several opportunities for knowledge discovery from data (KDD). Through these important prerequisite analyses presented here, the results of KDD from Solar Big Data will be overall more reliable and better understood. As the SDO mission remains operational over the coming years, these datasets will continue to grow in size and value. Future versions of this dataset will be analyzed in the general framework established in this work and maintained publicly online for easy access by the community.
Key words: Solar activity / Data analysis / Data mining / Validation / Statistics and probability
© M.A. Schuh et al., Published by EDP Sciences 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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