UM
Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring
Lan, Kun; Fong, Simon; Song, Wei; Vasilakos, Athanasios V.; Millham, Richard C.
2017-10
Source PublicationSYMMETRY-BASEL
ISSN2073-8994
Volume9Issue:10
AbstractOver 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 BySCI
Language英语
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000414911000047
PublisherMDPI AG
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:4   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionUniversity of Macau
Recommended Citation
GB/T 7714
Lan, Kun,Fong, Simon,Song, Wei,et al. Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring[J]. SYMMETRY-BASEL,2017,9(10).
APA Lan, Kun,Fong, Simon,Song, Wei,Vasilakos, Athanasios V.,&Millham, Richard C..(2017).Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring.SYMMETRY-BASEL,9(10).
MLA Lan, Kun,et al."Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring".SYMMETRY-BASEL 9.10(2017).
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Lan, Kun]'s Articles
[Fong, Simon]'s Articles
[Song, Wei]'s Articles
Baidu academic
Similar articles in Baidu academic
[Lan, Kun]'s Articles
[Fong, Simon]'s Articles
[Song, Wei]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Lan, Kun]'s Articles
[Fong, Simon]'s Articles
[Song, Wei]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.