A high performance web-based system for analyzing and visualizing spatiotemporal data for climate studies

  • Authors:

  • Zhenlong Li;Chaowei Yang;Min Sun;Jing Li;Chen Xu;Qunying Huang;Kai Liu

  • Affiliations:

  • Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA;Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA;Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA;Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA;Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA;Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA;Center of Intelligent Spatial Computing for Water/Energy Sciences, George Mason University, Fairfax, VA

  • Venue:
  • W2GIS'13 Proceedings of the 12th international conference on Web and Wireless Geographical Information Systems
  • Year:
  • 2013

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Abstract

Large amount of data are produced at different spatiotemporal scales by many sensors observing Earth and model simulations. Although advancements of contemporary technologies provide better solutions to access the spatiotemporal data, it is still a big challenge for researchers to easily extract information and knowledge from the data due to the data complexities of high dimensions, heterogeneity, distribution, large amount and frequently updating. This is especially true in climate studies, because climate data with coverage of the entire Earth and a long time period (such as 200 years) are often required to extract useful climate change information and patterns. A well-developed online visual analytical system has the potential to provide an efficient mechanism to bridge this gap. Using performance improving techniques for an online visual analytical system, we researched and developed a high performance Web-based system for spatiotemporal data visual analytics includes the following components: 1) a Spatial Data Registration Center for managing the big spatiotemporal data and enabling researchers to focus on analyses without worrying about data related issues such as format, management and storage; 2) a workflow for pre-generating and caching frequently requested data to reduce the server response time; and 3) a technique of "single data fetch, multiple analyses" to reduce both server response time and client response time; Finally, we demonstrate the effectiveness of the prototype through a few use cases.