Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB d...Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.展开更多
The ocean is a critical part of the global ecosystem.The marine ecosystem balance is crucial for human survival and sustainable development.However,due to the impacts of global climate change and human activities,the ...The ocean is a critical part of the global ecosystem.The marine ecosystem balance is crucial for human survival and sustainable development.However,due to the impacts of global climate change and human activities,the ocean is rapidly changing,which poses an enormous threat to human health and the econ-omy.“Conserve and sustainably use the oceans,seas and marine resources”is one of the 17 Sustainable Development Goals(SDGs).Therefore,it is urgent to construct a transformative marine scientific solution to promote sustainable development.Marine data is the basis of ocean cognition and governance.Marine science has ush-ered in the era of big data with continuous advances in modern marine data acquisition.While big data provides a large amount of data for SDG research,it simultaneously brings unprecedented challenges.This study introduces an overall framework of a system for solving the current problems faced by marine data serving SDGs from the perspective of marine data management and application.Also,it articulates how the system helps the SDGs through two application cases of managing fragmented marine data and developing global climate change data products.展开更多
基金This research was funded by the National Key Research and Development Plan(2018YFB0505300)the Guangxi Science and Technology Major Project(AA18118025)+1 种基金the Opening Foundation of Key Laboratory of Environment Change and Resources Use in Beibu Gulf,Ministry of Education(Nanning Normal University)Guangxi Key Laboratory of Earth Surface Processes and Intelligent Simulation(Nanning Normal University)(No.NNNU-KLOP-K1905).
文摘Marine big data are characterized by a large amount and complex structures,which bring great challenges to data management and retrieval.Based on the GeoSOT Grid Code and the composite index structure of the MongoDB database,this paper proposes a spatio-temporal grid index model(STGI)for efficient optimized query of marine big data.A spatio-temporal secondary index is created on the spatial code and time code columns to build a composite index in the MongoDB database used for the storage of massive marine data.Multiple comparative experiments demonstrate that the retrieval efficiency adopting the STGI approach is increased by more than two to three times compared with other index models.Through theoretical analysis and experimental verification,the conclusion could be achieved that the STGI model is quite suitable for retrieving large-scale spatial data with low time frequency,such as marine big data.
基金This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences[XDA19060101,XDA19060104,XDB42040401]the National Key R&D Program of China[2017YFA0603201]+4 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciencesthe Key R&D project of Shandong Province(2019JZZY010102)the Key deployment project of Center for Ocean Mega-Science,CAS(COMS2019R02)the Chinese Academy of Sciences(Y9KY04101L)National Natural Science Foundation of China[grant number U2006211].
文摘The ocean is a critical part of the global ecosystem.The marine ecosystem balance is crucial for human survival and sustainable development.However,due to the impacts of global climate change and human activities,the ocean is rapidly changing,which poses an enormous threat to human health and the econ-omy.“Conserve and sustainably use the oceans,seas and marine resources”is one of the 17 Sustainable Development Goals(SDGs).Therefore,it is urgent to construct a transformative marine scientific solution to promote sustainable development.Marine data is the basis of ocean cognition and governance.Marine science has ush-ered in the era of big data with continuous advances in modern marine data acquisition.While big data provides a large amount of data for SDG research,it simultaneously brings unprecedented challenges.This study introduces an overall framework of a system for solving the current problems faced by marine data serving SDGs from the perspective of marine data management and application.Also,it articulates how the system helps the SDGs through two application cases of managing fragmented marine data and developing global climate change data products.