Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing

Nowadays, researchers are interested on granular computing in order to solve the big data problem. The volume of Big Scholarly Data (BSD) is rapidly growing. In order to evaluate the research performance, it’s becoming essential to evaluate the impact of BSD. Traditionally, journals have been ranked...

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Main Authors:	Ahmed, M. M., Kader, Md. Abdul, Kamal Z., Zamli
Format:	Article
Language:	English
Published:	American Scientific Publisher 2018
Subjects:	QA75 Electronic computers. Computer science
Online Access:	http://umpir.ump.edu.my/id/eprint/20134/ http://umpir.ump.edu.my/id/eprint/20134/ http://umpir.ump.edu.my/id/eprint/20134/ http://umpir.ump.edu.my/id/eprint/20134/1/Comparative%20Study%20to%20Measure%20the%20Quality%20of%20Big%20Scholarly.pdf

id	ump-20134
recordtype	eprints
spelling	ump-201342018-11-29T02:13:21Z http://umpir.ump.edu.my/id/eprint/20134/ Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing Ahmed, M. M. Kader, Md. Abdul Kamal Z., Zamli QA75 Electronic computers. Computer science Nowadays, researchers are interested on granular computing in order to solve the big data problem. The volume of Big Scholarly Data (BSD) is rapidly growing. In order to evaluate the research performance, it’s becoming essential to evaluate the impact of BSD. Traditionally, journals have been ranked by their journal impact factor (JIF). However, several impact evaluation methods have been used by different BSD digital systems, such as the citation analysis, G-Index, H-index, i10-index, jurnal impact (JIF), and the Eigenfactor. In this paper, a detailed study of these different impact evaluation methods is shown along with their advantages and disadvantages. From this study, we can say that although the evaluation methods appear highly correlated but they lead to large differences in BSD impact evaluation. We conclude that no one evaluation method is superior and the present research gap is to develop standard rubrics and standard benchmarks in order to evaluate these existing methods. Furthermore, we have hypothetically modeled a new fuzzy granular approach as evolving structural fuzzy model (ESFM) which consider the concept of granular computing. Therefore, information granules exhibit the expressive and functional depiction of the global concept. American Scientific Publisher 2018-11 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/20134/1/Comparative%20Study%20to%20Measure%20the%20Quality%20of%20Big%20Scholarly.pdf Ahmed, M. M. and Kader, Md. Abdul and Kamal Z., Zamli (2018) Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing. Advanced Science Letters, 24 (10). pp. 7810-7814. ISSN 1936-6612 https://doi.org/0.1166/asl.2018.13022 doi: 0.1166/asl.2018.13022
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA75 Electronic computers. Computer science
spellingShingle	QA75 Electronic computers. Computer science Ahmed, M. M. Kader, Md. Abdul Kamal Z., Zamli Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing
description	Nowadays, researchers are interested on granular computing in order to solve the big data problem. The volume of Big Scholarly Data (BSD) is rapidly growing. In order to evaluate the research performance, it’s becoming essential to evaluate the impact of BSD. Traditionally, journals have been ranked by their journal impact factor (JIF). However, several impact evaluation methods have been used by different BSD digital systems, such as the citation analysis, G-Index, H-index, i10-index, jurnal impact (JIF), and the Eigenfactor. In this paper, a detailed study of these different impact evaluation methods is shown along with their advantages and disadvantages. From this study, we can say that although the evaluation methods appear highly correlated but they lead to large differences in BSD impact evaluation. We conclude that no one evaluation method is superior and the present research gap is to develop standard rubrics and standard benchmarks in order to evaluate these existing methods. Furthermore, we have hypothetically modeled a new fuzzy granular approach as evolving structural fuzzy model (ESFM) which consider the concept of granular computing. Therefore, information granules exhibit the expressive and functional depiction of the global concept.
format	Article
author	Ahmed, M. M. Kader, Md. Abdul Kamal Z., Zamli
author_facet	Ahmed, M. M. Kader, Md. Abdul Kamal Z., Zamli
author_sort	Ahmed, M. M.
title	Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing
title_short	Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing
title_full	Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing
title_fullStr	Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing
title_full_unstemmed	Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing
title_sort	comparative study to measure the quality of big scholarly data and its hypothetical mapping towards granular computing
publisher	American Scientific Publisher
publishDate	2018
url	http://umpir.ump.edu.my/id/eprint/20134/ http://umpir.ump.edu.my/id/eprint/20134/ http://umpir.ump.edu.my/id/eprint/20134/ http://umpir.ump.edu.my/id/eprint/20134/1/Comparative%20Study%20to%20Measure%20the%20Quality%20of%20Big%20Scholarly.pdf
first_indexed	2023-09-18T22:28:51Z
last_indexed	2023-09-18T22:28:51Z
_version_	1777416150023405568

Comparative Study to Measure the Quality of Big Scholarly Data and Its Hypothetical Mapping towards Granular Computing

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