Machine learning on big data: Opportunities and challenges

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 uncove...

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Vydáno v:Neurocomputing (Amsterdam) Ročník 237; s. 350 - 361
Hlavní autoři: Zhou, Lina, Pan, Shimei, Wang, Jianwu, Vasilakos, Athanasios V.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 10.05.2017
Témata:
Big data
Data preprocessing
Distribuerade datorsystem
Evaluation
Machine learning
Parallelization
Pervasive Mobile Computing
Evaluation
Big data
Data preprocessing
Parallelization
Machine learning
ISSN:0925-2312, 1872-8286, 1872-8286
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Shrnutí: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.
ISSN:0925-2312
1872-8286
1872-8286
DOI:10.1016/j.neucom.2017.01.026