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Big Data in Ocean Observation: Opportunities and Challenges

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Big Data Computing and Communications (BigCom 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9784))

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Abstract

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 spatio-temporal scales. Data collected by these platforms help answer a range of fundamental and applied research questions. 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. This paper discusses some possible new strategies to solve marine big data challenges in the phases of data storage, data computing and analysis. A geological example illustrates the significant use of marine big data. Finally, we highlight some challenges and key issues in marine big data.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (NSFC) under Grant No. 61572448, No. 61379127, and by the Shandong Provincial Natural Science Foundation, China under Grant No. ZR2014JL043.

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Authors and Affiliations

  1. Department of Computer Science and Technology, Ocean University of China, Qingdao, 266100, China

    Yingjian Liu, Meng Qiu, Chao Liu & Zhongwen Guo

Authors
  1. Yingjian Liu
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  2. Meng Qiu
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  3. Chao Liu
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  4. Zhongwen Guo
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Corresponding author

Correspondence to Yingjian Liu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of N. Carolina at Charlotte, Charlotte, North Carolina, USA

    Yu Wang

  2. Northeastern University, College of Information Science and Engineering, Shenyang, Liaoning, China

    Ge Yu

  3. Department of Electrical & Computer Engineering, Rutgers University, Piscataway, New Jersey, USA

    Yanyong Zhang

  4. Department of Electrical and Computer Engineering, University of Houston Department of Engineering, Houston, Texas, USA

    Zhu Han

  5. College of Information Science and Engineering, Northeastern University, Shenyang , Liaoning, China

    Guoren Wang

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Liu, Y., Qiu, M., Liu, C., Guo, Z. (2016). Big Data in Ocean Observation: Opportunities and Challenges. In: Wang, Y., Yu, G., Zhang, Y., Han, Z., Wang, G. (eds) Big Data Computing and Communications. BigCom 2016. Lecture Notes in Computer Science(), vol 9784. Springer, Cham. https://doi.org/10.1007/978-3-319-42553-5_18

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  • DOI: https://doi.org/10.1007/978-3-319-42553-5_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42552-8

  • Online ISBN: 978-3-319-42553-5

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