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A Scalable Data Stream Mining Methodology: Stream-Based Holistic Analytics and Reasoning in Parallel
Simon Fong1; Yan Zhuang1; Raymond Wong2; Sabah Mohammed3
2014
Conference Name2014 2nd International Symposium on Computational and Business Intelligence
Source PublicationProceedings - 2014 2nd International Symposium on Computational and Business Intelligence, ISCBI 2014
Pages110-115
Conference Date7-8 Dec. 2014
Conference PlaceNew Delhi, India
PublisherIEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Abstract

Big Data though it is a hype up-springing many technical challenges that confront both academic research communities and commercial IT deployment, the root sources of Big Data are founded on data streams. It is generally known that data which are sourced from data streams accumulate continuously making traditional batch-based model induction algorithms infeasible for real-time data mining or high-speed data analytics in a broad sense. In this paper, a novel data stream mining methodology, called Stream-based Holistic Analytics and Reasoning in Parallel (SHARP) is proposed. SHARP is based on principles of incremental learning which span across a typical data-mining model construction process, from lightweight feature selection, one-pass incremental decision tree induction, and incremental swarm optimization. Each one of these components in SHARP is designed to function together aiming at improving the classification/prediction performance to its best possible. SHARP is scalable, that depends on the available computing resources during runtime, the components can execute in parallel, collectively enhancing different aspects of the overall SHARP process for mining data streams. It is believed that if Big Data are being mined by incrementally learning a data mining model, one pass at a time on the fly, the large volume of such big data is no longer a technical issue, from the perspective of data analytics. Three computer simulation experimentations are shown in this paper, pertaining to three components of SHARP, for demonstrating its efficacy.

KeywordCache-based Data Stream Classifier Ccv Feature Selection Data Stream Mining Methodology Meta-heusristics
DOIhttps://doi.org/10.1109/ISCBI.2014.31
URLView the original
Indexed BySCI
Language英语
WOS Research AreaComputer Science
WOS SubjectComputer Science, Information Systems ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000393510400024
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Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Affiliation1.Department of Computer and Information Science, University of Macau, Macau SAR
2.School of Computer Science and Engineering, University of New South Wales, Kensington, NSW 2052, Australia
3.Department of Computer Science Lakehead University, 955 Oliver Road, Thunder Bay, ON, Canada P7B 5E1
First Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Simon Fong,Yan Zhuang,Raymond Wong,et al. A Scalable Data Stream Mining Methodology: Stream-Based Holistic Analytics and Reasoning in Parallel[C]:IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA,2014:110-115.
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