Big data challenges in ocean observation: a survey

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• 作者：Yingjian Liu ; Meng Qiu ; Chao Liu ; Zhongwen Guo
• 关键词：Ocean observation ; Marine big data ; Data storage ; Data computing ; Data analysis
• 刊名：Personal and Ubiquitous Computing
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：21
• 期：1
• 页码：55-65
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：User Interfaces and Human Computer Interaction; Computer Science, general; Personal Computing; Mobile Computing;
• 出版者：Springer London
• ISSN：1617-4917
• 卷排序：21

文摘

Ocean observation plays an essential role in ocean exploration. Ocean science is entering into big data era with the exponentially growth of information technology and advances in ocean observatories. Ocean observatories are collections of platforms capable of carrying sensors to sample the ocean over appropriate spatiotemporal scales. Data collected by these platforms help answer a range of fundamental and applied research questions. Many countries are spending considerable amount of resources on ocean observing programs for various purposes. Given the huge volume, diverse types, sustained measurement, and potential uses of ocean observing data, it is a typical kind of big data, namely marine big data. The traditional data-centric infrastructure is insufficient to deal with new challenges arising in ocean science. New distributed, large-scale modern infrastructure backbone is urgently required. This paper discusses some possible strategies to solve marine big data challenges in the phases of data storage, data computing, and analysis. Some applications in physics, chemistry, geology, and biology illustrate the significant uses of marine big data. Finally, we highlight some challenges and key issues in marine big data.