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

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.
https://doi.org/10.1109/ACCESS.2021.3127335

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IEEE_Access_Special_Section_Editorial_Big_Data_Learning_and_Discovery.pdf (Publisher version), 4MB
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IEEE_Access_Special_Section_Editorial_Big_Data_Learning_and_Discovery.pdf
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 Creators:
Gao, Zhong-Ke 1, Author
Liu, An-An 1, Author
Wang, Yanhui 1, Author
Small, Michael 1, Author
Chang, Xiaojun 1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: 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.

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 Dates: 2021-12-082021-12-08
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1109/ACCESS.2021.3127335
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Model / method: Machine Learning
Model / method: Nonlinear Data Analysis
OATYPE: Gold Open Access
 Degree: -

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Title: IEEE Access
Source Genre: Journal, SCI, Scopus, p3
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Pages: - Volume / Issue: 9 Sequence Number: - Start / End Page: 158064 - 158073 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/1803142
Publisher: Institute of Electrical and Electronics Engineers (IEEE)