Open Access

ARTICLE

Identification and Visualization of Spatial and Temporal Trends in Textile Industry

by Umair Yousaf¹, Muhammad Asif¹, Shahbaz Ahmed¹, Noman Tahir¹, Azeem Irshad², Akber Abid Gardezi³, Muhammad Shafiq^4,*, Jin-Ghoo Choi⁴, Habib Hamam^5,6,7,8

1 Department of Computer Science, National Textile University, Faisalabad, 38000, Pakistan
2 Department of Computer Science and Software Engineering, International Islamic University, Islamabad, 44000, Pakistan
3 Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan
4 Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38541, Korea
5 Faculty of Engineering, Uni de Moncton, Moncton, NB E1A3E9, Canada
6 International Institute of Technology and Management, Commune d'Akanda, BP, Libreville, 1989, Gabon
7 School of Electrical Engineering, Department of Electrical and Electronic Eng. Science, University of Johannesburg, Johannesburg, 2006, South Africa
8 Spectrum of Knowledge Production & Skills Development, Sfax, 3027, Tunisia

* Corresponding Author: Muhammad Shafiq. Email:

Computers, Materials & Continua 2023, 74(2), 4165-4181. https://doi.org/10.32604/cmc.2023.026607

Received 31 December 2021; Accepted 04 March 2022; Issue published 31 October 2022

Abstract

The research volume increases at the study rate, causing massive text corpora. Due to these enormous text corpora, we are drowning in data and starving for information. Therefore, recent research employed different text mining approaches to extract information from this text corpus. These proposed approaches extract meaningful and precise phrases that effectively describe the text's information. These extracted phrases are commonly termed keyphrases. Further, these key phrases are employed to determine the different fields of study trends. Moreover, these key phrases can also be used to determine the spatiotemporal trends in the various research fields. In this research, the progress of a research field can be better revealed through spatiotemporal bibliographic trend analysis. Therefore, an effective spatiotemporal trend extraction mechanism is required to disclose textile research trends of particular regions during a specific period. This study collected a diversified dataset of textile research from 2011–2019 and different countries to determine the research trend. This data was collected from various open access journals. Further, this research determined the spatiotemporal trends using quality phrase mining. This research also focused on finding the research collaboration of different countries in a particular research subject. The research collaborations of other countries’ researchers show the impact on import and export of those countries. The visualization approach is also incorporated to understand the results better.

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Keywords

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