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Emerging intelligent big data analytics for cloud and edge computing
Author(s) -
Dong Fang,
Yong Jianming,
Fei Xiang
Publication year - 2020
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.5989
Subject(s) - big data , cloud computing , computer science , analytics , data science , exploit , artificial intelligence , edge computing , data analysis , computational intelligence , machine learning , enhanced data rates for gsm evolution , data mining , computer security , operating system
Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence, and it exploits how to use artificial intelligence to enhance big data analytics for various applications.1 As cloud computing cannot meet the strict computing time requirement in latency-critical big data analysis applications, edge computing has emerged as a solution to address the drawbacks of cloud-based solutions by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex intelligent big data analytics tasks. Consequently, this special issue is focused on exploiting key techniques of intelligent big data analytics by involving cloud and edge computing. This special issue presents manuscripts in the above mentioned topics with a focus on “Emerging Intelligent Big Data Analytics for Cloud and Edge Computing.” From the papers submitted to the 7th International Conference on Advanced Cloud and Big Data (CBD 2019) held in Suzhou, China on September 20-21, 2019, nine papers have been selected that address the following issues in cloud and edge computing: