IEEE Access Special Section Editorial: Big Data Learning and Discovery 
[Editorial]

Gao, Zhong-Ke; Liu, An-An; Wang, Yanhui; Small, Michael; Chang, Xiaojun; Kurths, Jürgen

doi:10.1109/ACCESS.2021.3127335

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学術論文

IEEE Access Special Section Editorial: Big Data Learning and Discovery [Editorial]

Authors

Gao, Zhong-Ke
External Organizations;

Liu, An-An
External Organizations;

Wang, Yanhui
External Organizations;

Small, Michael
External Organizations;

Chang, Xiaojun
External Organizations;

/persons/resource/Juergen.Kurths

Kurths, Jürgen
Potsdam Institute for Climate Impact Research;

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フルテキスト (公開)

IEEE_Access_Special_Section_Editorial_Big_Data_Learning_and_Discovery.pdf
(出版社版), 4MB

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引用

Gao, Z.-K., Liu, A.-A., Wang, Y., Small, M., Chang, X., & Kurths, J. (2021). IEEE Access Special Section Editorial: Big Data Learning and Discovery [Editorial]. IEEE Access, 9, 158064-158073. doi:10.1109/ACCESS.2021.3127335.

引用: https://publications.pik-potsdam.de/pubman/item/item_26466

要旨

We are now witnessing a dramatic growth of heterogeneous data, consisting of a complex set of cross-media content, such as texts, images, videos, audio, graphics, spatio-temporal data, and multivariate time series. The inception of modern techniques from computer science have offered very robust and hi-tech solutions for data and information analysis, collection, storage, and organization, as well as product and service delivery to customers. Recently, technological advancements, particularly in the form of big data, have resulted in the storage of enormous amounts of potentially valuable data in a wide variety of formats. This situation is creating new challenges for the development of effective algorithms and frameworks to meet the strong requirements of big data representation and analysis, knowledge understanding, and discovery.