Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT

Sushil Kumar Singh1, Jeonghun Cha1, Tae Woo Kim1 and Jong Hyuk Park1

  1. Department of Computer Science and Engineering, Seoul National University of Science and Technology
    (SeoulTech) Seoul 01811, Korea
    {sushil.sngh001007, ckwjdgns, tang_kim, jhpark1}@seoultech.ac.kr

Abstract

For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.

Key words

machine learning, big data analysis, extreme learning machine, IoT, security, and privacy

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200330012S

Publication information

Volume 18, Issue 2 (April 2021)
Special Issue on Emerging Services in the Next-Generation Web: Human Meets Artificial Intelligence
Year of Publication: 2021
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Singh, S. K., Cha, J., Kim, T. W., Park, J. H.: Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. Computer Science and Information Systems, Vol. 18, No. 2, 597–618. (2021), https://doi.org/10.2298/CSIS200330012S