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

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.

KEYWORDS:

• big spatiotemporal data
• query analytics
• climate
• Hadoop

Acknowledgments

We thank the anonymous reviewers for their insightful and constructive comments that significantly improved the manuscript. The earlier development of SOVAS was in part supported by the University of South Carolina Office of the Vice President for Research and Federation of Earth Science Information Partners.

Disclosure statement

No potential conflict of interest was reported by the authors.

Supplementary material

Supplemental data for the article can be accessed here.

Notes

1. Detailed descriptions of these functions and usage examples can be found at https://gidbusc.github.io/SCOVAS/#functions.

Additional information

Funding

This work was in part supported by the University of South Carolina and the Federation of Earth Science Information Partners.

Notes on contributors

Zhenlong Li

Dr. Zhenlong Li is an Assistant Professor with the Department of Geography at the University of South Carolina, where he leads the Geoinformation and Big Data Research Laboratory (GIBD). His primary research field is GIScience with a focus on geospatial big data analytics, high performance computing, spatiotemporal analysis/modelling, and geospatial cyberinfrastructure/CyberGIS with applications to disaster management, climate analysis, and human mobility.

Qunying Huang

Dr. Qunying Huang is an assistant professor in the Department of Geography at University of Wisconsin-Madison (UW-Madison). Her main fields of expertise include Spatial Computing, Spatiotemporal Data Fusion and Mining, Social Media Analytics, Human Mobility, and Natural Hazards.

Yuqin Jiang

Yuqin Jiang is a Ph.D. student at the Department of Geography, University of South Carolina. Her research field is GIScience with a focus on spatial network, geo-spatial big data analytics and visualization, cyberGIS with applications to disaster management, human dynamics, and transportation.

Fei Hu

Dr. Fei Hu is an Software Engineer at the Center for Open-Source Data and AI Technologies, IBM. His work focuses on contributing code to the open-source projects (e.g. TensorFlow), meanwhile developing the large-scale machine learning/deep learning approach to get a better understanding of big data and discover the underlying value.