SOVAS: a scalable online visual analytic system for big climate data analysis
Big climate data offers great opportunities for scientific discovery but demands efficient and effective analytics to investigate unknown and complex patterns. Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These...
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| Veröffentlicht in: | International journal of geographical information science : IJGIS 2020-06, Vol.34 (6), p.1188-1209 |
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| container_issue | 6 |
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| container_title | International journal of geographical information science : IJGIS |
| container_volume | 34 |
| creator | Li, Zhenlong Huang, Qunying Jiang, Yuqin Hu, Fei |
| description | Big climate data offers great opportunities for scientific discovery but demands efficient and effective analytics to investigate unknown and complex patterns. Most existing online processing and analytics systems for climate studies only support fixed user interface with predefined functions. These systems are often not scalable to handle massive climate data that could easily accumulate terabytes daily. To address the major limitations of existing online systems for climate studies, this paper presents a scalable online visual analytic system, known as SOVAS, to balance both usability and flexibility. SOVAS, enabled by a set of key techniques, supports large-scale climate data analytics and knowledge discovery in a scalable and sharable environment. This research not only contributes to the community an efficient tool for analyzing big climate data but also contributes to the literature by providing valuable technical references for tackling spatiotemporal big data challenges. |
| doi_str_mv | 10.1080/13658816.2019.1605073 |
| format | Article |
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| identifier | ISSN: 1365-8816 |
| ispartof | International journal of geographical information science : IJGIS, 2020-06, Vol.34 (6), p.1188-1209 |
| issn | 1365-8816 1362-3087 1365-8824 |
| language | eng |
| recordid | cdi_crossref_primary_10_1080_13658816_2019_1605073 |
| source | Taylor & Francis Journals Complete; Alma/SFX Local Collection |
| subjects | big spatiotemporal data Climate Climate studies Climatic data Data analysis Hadoop Internet Mathematical analysis Meteorological satellites On-line systems query analytics |
| title | SOVAS: a scalable online visual analytic system for big climate data analysis |
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