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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/20134/1/Comparative%20Study%20to%20Measure%20the%20Quality%20of%20Big%20Scholarly.pdf
http://umpir.ump.edu.my/id/eprint/20134/
https://doi.org/0.1166/asl.2018.13022
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spelling my.ump.umpir.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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/20134/1/Comparative%20Study%20to%20Measure%20the%20Quality%20of%20Big%20Scholarly.pdf
http://umpir.ump.edu.my/id/eprint/20134/
https://doi.org/0.1166/asl.2018.13022
_version_ 1643668796513189888
score 13.211869

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