Machine learning on big data: Opportunities and challenges

Zhou, L; Pan, S; Wang, J; Vasilakos, AV

Machine learning on big data: Opportunities and challenges

Zhou, L Pan, S Wang, J Vasilakos, AV

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Neurocomputing, 2017, 237, pp. 350-361
Issue Date:: 2017-05-10

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Field	Value	Language
dc.contributor.author	Zhou, L
dc.contributor.author	Pan, S
dc.contributor.author	Wang, J
dc.contributor.author	Vasilakos, AV
dc.date.accessioned	2022-08-24T04:26:25Z
dc.date.available	2022-08-24T04:26:25Z
dc.date.issued	2017-05-10
dc.identifier.citation	Neurocomputing, 2017, 237, pp. 350-361
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/160781
dc.description.abstract	Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advent of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges. The framework is centered on ML which follows the phases of preprocessing, learning, and evaluation. In addition, the framework is also comprised of four other components, namely big data, user, domain, and system. The phases of ML and the components of MLBiD provide directions for identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2017.01.026
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Machine learning on big data: Opportunities and challenges
dc.type	Journal Article
utslib.citation.volume	237
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-24T04:26:24Z
pubs.publication-status	Published
pubs.volume	237

Abstract:

Machine learning (ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the forefront in recent years partly owing to the advent of big data. ML algorithms have never been better promised while challenged by big data. Big data enables ML algorithms to uncover more fine-grained patterns and make more timely and accurate predictions than ever before; on the other hand, it presents major challenges to ML such as model scalability and distributed computing. In this paper, we introduce a framework of ML on big data (MLBiD) to guide the discussion of its opportunities and challenges. The framework is centered on ML which follows the phases of preprocessing, learning, and evaluation. In addition, the framework is also comprised of four other components, namely big data, user, domain, and system. The phases of ML and the components of MLBiD provide directions for identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas.

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http://hdl.handle.net/10453/160781