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Self-adaptive pre-processing methodology for big data stream mining in internet of things environmental sensor monitoring
Kun Lan1; Simon Fong1; Wei Song2; Athanasios V. Vasilakos3; Richard C. Millham4
2017-10-21
Source PublicationSymmetry
ISSN2073-8994
Volume9Issue:10
Abstract

Over the years, advanced IT technologies have facilitated the emergence of new ways of generating and gathering data rapidly, continuously, and largely and are associated with a new research and application branch, namely, data stream mining (DSM). Among those multiple scenarios of DSM, the Internet of Things (IoT) plays a significant role, with a typical meaning of a tough and challenging computational case of big data. In this paper, we describe a self-adaptive approach to the pre-processing step of data stream classification. The proposed algorithm allows different divisions with both variable numbers and lengths of sub-windows under a whole sliding window on an input stream, and clustering-based particle swarm optimization (CPSO) is adopted as the main metaheuristic search method to guarantee that its stream segmentations are effective and adaptive to itself. In order to create a more abundant search space, statistical feature extraction (SFX) is applied after variable partitions of the entire sliding window. We validate and test the effort of our algorithm with other temporal methods according to several IoT environmental sensor monitoring datasets. The experiments yield encouraging outcomes, supporting the reality that picking significant appropriate variant sub-window segmentations heuristically with an incorporated clustering technique merit would allow these to perform better than others.

KeywordData Stream Pre-processing Self-adaptive Segmentation Clustering-based Particle Swarm Optimization (Cpso) Internet Of Things (Iot) Datasets
DOI10.3390/sym9100244
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000414911000047
PublisherMDPI, ST ALBAN-ANLAGE 66, CH-4052 BASEL, SWITZERLAND
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Cited Times [WOS]:7   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Corresponding AuthorSimon Fong
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa 999078, Macau
2.School of Computer Science, North China University of Technology, Beijing 100144, China
3.Department of Computer Science, Electrical and Space Engineering, Lulea University of Technology, SE-97187 Lulea, Sweden
4.Department of Information Technology, Durban University of Technology, Ritson Campus, Durban P.O. BOX 1334, South Africa
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Kun Lan,Simon Fong,Wei Song,et al. Self-adaptive pre-processing methodology for big data stream mining in internet of things environmental sensor monitoring[J]. Symmetry,2017,9(10).
APA Kun Lan,Simon Fong,Wei Song,Athanasios V. Vasilakos,&Richard C. Millham.(2017).Self-adaptive pre-processing methodology for big data stream mining in internet of things environmental sensor monitoring.Symmetry,9(10).
MLA Kun Lan,et al."Self-adaptive pre-processing methodology for big data stream mining in internet of things environmental sensor monitoring".Symmetry 9.10(2017).
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