A cognitive oriented framework for IoT big-data management prospective

Nilamadhab Mishra; Chung Chih Lin; Hsien Tsung Chang

doi:10.1109/ICCPS.2014.7062233

A cognitive oriented framework for IoT big-data management prospective

Nilamadhab Mishra
, Chung Chih Lin
, Hsien Tsung Chang

Chang Gung University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

26 Scopus citations

Abstract

The Internet-of-Things (IoT) environment continuously inspires any time-place-things connectivity of the smart objects in and around the universe. Day by day the rapid growth of enormous IoT objects and digital storage technology, lead to a large heterogeneous data depository, in which the IoT big-data are stored in the dissimilar database frameworks as a consequence of heterogeneous data sources. So due to large heterogeneous data sources, some incompatibilities like name, scale, structure, and level of abstraction are there in between the frameworks of IoT big-data that create threats to data management and knowledge discovery. 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 a data organization framework for effective data management and knowledge discovery that cop-up with the large scale industrial automation applications. The discussion and analysis shows that the proposed framework and architectures creates a feasible solution in implementing IoT big-data based smart industrial applications.

Original language	English
Title of host publication	2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	124-127
Number of pages	4
ISBN (Electronic)	9781479942466
DOIs	https://doi.org/10.1109/ICCPS.2014.7062233
State	Published - 17 03 2014
Event	2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014 - Beijing, China Duration: 05 12 2014 → 07 12 2014

Publication series

Name	2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014

Conference

Conference	2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014
Country/Territory	China
City	Beijing
Period	05/12/14 → 07/12/14

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

COIB-framework
IoT big-data
IoT object
data management and knowledge discovery
industrial automation

Access to Document

10.1109/ICCPS.2014.7062233

Cite this

Mishra, N., Lin, C. C., & Chang, H. T. (2014). A cognitive oriented framework for IoT big-data management prospective. In 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014 (pp. 124-127). Article 7062233 (2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCPS.2014.7062233

Mishra, Nilamadhab ; Lin, Chung Chih ; Chang, Hsien Tsung. / A cognitive oriented framework for IoT big-data management prospective. 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 124-127 (2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014).

@inproceedings{91bc73ead2064d669cec5824041d264a,

title = "A cognitive oriented framework for IoT big-data management prospective",

abstract = "The Internet-of-Things (IoT) environment continuously inspires any time-place-things connectivity of the smart objects in and around the universe. Day by day the rapid growth of enormous IoT objects and digital storage technology, lead to a large heterogeneous data depository, in which the IoT big-data are stored in the dissimilar database frameworks as a consequence of heterogeneous data sources. So due to large heterogeneous data sources, some incompatibilities like name, scale, structure, and level of abstraction are there in between the frameworks of IoT big-data that create threats to data management and knowledge discovery. 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 a data organization framework for effective data management and knowledge discovery that cop-up with the large scale industrial automation applications. The discussion and analysis shows that the proposed framework and architectures creates a feasible solution in implementing IoT big-data based smart industrial applications.",

keywords = "COIB-framework, IoT big-data, IoT object, data management and knowledge discovery, industrial automation",

author = "Nilamadhab Mishra and Lin, \{Chung Chih\} and Chang, \{Hsien Tsung\}",

note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014 ; Conference date: 05-12-2014 Through 07-12-2014",

year = "2014",

month = mar,

day = "17",

doi = "10.1109/ICCPS.2014.7062233",

language = "英语",

series = "2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "124--127",

booktitle = "2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014",

address = "美国",

}

Mishra, N, Lin, CC & Chang, HT 2014, A cognitive oriented framework for IoT big-data management prospective. in 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014., 7062233, 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014, Institute of Electrical and Electronics Engineers Inc., pp. 124-127, 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014, Beijing, China, 05/12/14. https://doi.org/10.1109/ICCPS.2014.7062233

A cognitive oriented framework for IoT big-data management prospective. / Mishra, Nilamadhab; Lin, Chung Chih ; Chang, Hsien Tsung.
2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 124-127 7062233 (2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A cognitive oriented framework for IoT big-data management prospective

AU - Mishra, Nilamadhab

AU - Lin, Chung Chih

AU - Chang, Hsien Tsung

PY - 2014/3/17

Y1 - 2014/3/17

N2 - The Internet-of-Things (IoT) environment continuously inspires any time-place-things connectivity of the smart objects in and around the universe. Day by day the rapid growth of enormous IoT objects and digital storage technology, lead to a large heterogeneous data depository, in which the IoT big-data are stored in the dissimilar database frameworks as a consequence of heterogeneous data sources. So due to large heterogeneous data sources, some incompatibilities like name, scale, structure, and level of abstraction are there in between the frameworks of IoT big-data that create threats to data management and knowledge discovery. 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 a data organization framework for effective data management and knowledge discovery that cop-up with the large scale industrial automation applications. The discussion and analysis shows that the proposed framework and architectures creates a feasible solution in implementing IoT big-data based smart industrial applications.

AB - The Internet-of-Things (IoT) environment continuously inspires any time-place-things connectivity of the smart objects in and around the universe. Day by day the rapid growth of enormous IoT objects and digital storage technology, lead to a large heterogeneous data depository, in which the IoT big-data are stored in the dissimilar database frameworks as a consequence of heterogeneous data sources. So due to large heterogeneous data sources, some incompatibilities like name, scale, structure, and level of abstraction are there in between the frameworks of IoT big-data that create threats to data management and knowledge discovery. 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 a data organization framework for effective data management and knowledge discovery that cop-up with the large scale industrial automation applications. The discussion and analysis shows that the proposed framework and architectures creates a feasible solution in implementing IoT big-data based smart industrial applications.

KW - COIB-framework

KW - IoT big-data

KW - IoT object

KW - data management and knowledge discovery

KW - industrial automation

UR - https://www.scopus.com/pages/publications/84957588295

U2 - 10.1109/ICCPS.2014.7062233

DO - 10.1109/ICCPS.2014.7062233

M3 - 会议稿件

AN - SCOPUS:84957588295

T3 - 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014

SP - 124

EP - 127

BT - 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014

Y2 - 5 December 2014 through 7 December 2014

ER -

Mishra N, Lin CC , Chang HT. A cognitive oriented framework for IoT big-data management prospective. In 2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014. Institute of Electrical and Electronics Engineers Inc. 2014. p. 124-127. 7062233. (2014 IEEE International Conference on Communication Problem-Solving, ICCP 2014). doi: 10.1109/ICCPS.2014.7062233

A cognitive oriented framework for IoT big-data management prospective

Abstract

Publication series

Conference

Bibliographical note

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this