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Machine Learning on Big Data: Opportunities and Challenges
Information Systems Department, UMBC, Baltimore.
Information Systems Department, UMBC, Baltimore.
Information Systems Department, UMBC, Baltimore.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
Number of Authors: 4
2017 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286, Vol. 237, 350-361 p.Article in journal (Refereed) Published
Abstract [en]

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 advert 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 the identification of associated opportunities and challenges and open up future work in many unexplored or under explored research areas.

Place, publisher, year, edition, pages
2017. Vol. 237, 350-361 p.
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-61412DOI: 10.1016/j.neucom.2017.01.026ISI: 000397356700032Scopus ID: 2-s2.0-85011371254OAI: oai:DiVA.org:ltu-61412DiVA: diva2:1064623
Note

Validerad; 2017; Nivå 2; 2017-03-08 (andbra)

Available from: 2017-01-12 Created: 2017-01-12 Last updated: 2017-11-24Bibliographically approved

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