A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective

In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-...

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Main Authors: Nilamadhab Mishra, Chung-Chih Lin, Hsien-Tsung Chang
Format: Article
Language:English
Published: Wiley 2015-10-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2015/718390
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author Nilamadhab Mishra
Chung-Chih Lin
Hsien-Tsung Chang
author_facet Nilamadhab Mishra
Chung-Chih Lin
Hsien-Tsung Chang
author_sort Nilamadhab Mishra
collection DOAJ
description In future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.
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institution OA Journals
issn 1550-1477
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record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-75a5b140fe2e4f3ea935a449197bf2ba2025-08-20T02:07:01ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-10-011110.1155/2015/718390718390A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery ProspectiveNilamadhab MishraChung-Chih LinHsien-Tsung ChangIn future IoT big-data management and knowledge discovery for large scale industrial automation application, the importance of industrial internet is increasing day by day. Several diversified technologies such as IoT (Internet of Things), computational intelligence, machine type communication, big-data, and sensor technology can be incorporated together to improve the data management and knowledge discovery efficiency of large scale automation applications. So in this work, we need to propose a Cognitive Oriented IoT Big-data Framework (COIB-framework) along with implementation architecture, IoT big-data layering architecture, and data organization and knowledge exploration subsystem for effective data management and knowledge discovery that is well-suited with the large scale industrial automation applications. The discussion and analysis show that the proposed framework and architectures create a reasonable solution in implementing IoT big-data based smart industrial applications.https://doi.org/10.1155/2015/718390
spellingShingle Nilamadhab Mishra
Chung-Chih Lin
Hsien-Tsung Chang
A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective
International Journal of Distributed Sensor Networks
title A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective
title_full A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective
title_fullStr A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective
title_full_unstemmed A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective
title_short A Cognitive Adopted Framework for IoT Big-Data Management and Knowledge Discovery Prospective
title_sort cognitive adopted framework for iot big data management and knowledge discovery prospective
url https://doi.org/10.1155/2015/718390
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