SOVAS: a scalable online visual analytic system for big climate data analysis

Zhenlong Li, Qunying Huang, Yuqin Jiang, Fei Hu

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1188-1209
Number of pages22
JournalInternational Journal of Geographical Information Science
Volume34
Issue number6
DOIs
StatePublished - Jun 2 2020

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development
  • Library and Information Sciences

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