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Self-Adaptive Pre-Processing Methodology for Big Data Stream Mining in Internet of Things Environmental Sensor Monitoring
Department of Computer and Information Science, University of Macau.
Department of Computer and Information Science, University of Macau.
School of Computer Science, North China University of Technology, Beijing .
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
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2017 (English)In: Symmetry, ISSN 2073-8994, E-ISSN 2073-8994, Vol. 9, no 10, 244Article in journal (Refereed) Published
Abstract [en]

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

Place, publisher, year, edition, pages
MDPI AG , 2017. Vol. 9, no 10, 244
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-66368DOI: 10.3390/sym9100244ISI: 000414911000047OAI: oai:DiVA.org:ltu-66368DiVA: diva2:1154507
Note

Validerad;2017;Nivå 2;2017-11-02 (andbra)

Available from: 2017-11-02 Created: 2017-11-02 Last updated: 2017-12-01Bibliographically approved

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CiteExportLink to record
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  • apa
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