Name
Abstract | ||
|---|---|---|
This paper introduces a new interpolation method that fills the gap in missing solar filament big data that are captured every day from numerous ground-based and space-based observatories. It proposes a new algorithm that takes two filament event instances and interpolates between them given a cadence. The method combines K-means clustering algorithm, time series shape representation, and linear interpolation to generate the missing filament polygons. This is the first research of this kind that attempts to address the problem of solar big data interpolation. We evaluate the proposed method using area, shape, and distance accuracy criteria. Finally, we outline future improvements and opportunities for solar data interpolation. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/BDCloud-SocialCom-SustainCom.2016.25 | 2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom) |
Keywords | Field | DocType |
Spatiotemporal visualization,Spatiotemporal interpolation,Heterogeneous data visualization,Dynamic Time Warping,Time Series | Data mining,Time series,Polygon,Computer science,Interpolation,Solar prominence,Linear interpolation,Cluster analysis,Big data | Conference |
ISBN | Citations | PageRank |
978-1-5090-3937-1 | 1 | 0.36 |
References | Authors | |
9 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Soukaina Filali Boubrahimi | 1 | 1 | 6.10 |
| Berkay Aydin | 2 | 40 | 10.75 |
| dustin kempton | 3 | 12 | 6.54 |
| Sushant S. Mahajan | 4 | 1 | 1.03 |
| Rafal A. Angryk | 5 | 271 | 45.56 |