Article: Academic research trend analysis based on big data technology Journal: International Journal of Computational Science and Engineering (IJCSE) 2019 Vol.20 No.1 pp.31 - 39 Abstract: Big data technology can well support the analysis of academic research trends, which requires the ability to process an enormous amount of metadata efficiently. On this point, we propose an academic trend analysis method that exploits a popular topic model for paper feature extraction and an influence propagation model for field influence evaluation. We also propose a parallel association rule mining algorithm based on Spark to accelerate trend analysis process. The algorithm can take the advantages of Spark memory architecture to enhance the iterative speed of traditional algorithms. Experimentally, a vast amount of paper metadata was collected from four popular digital libraries: ACM, IEEE, Science Direct and Springer, serving as the raw data for our final feature dataset. Focusing on the hotspot of cloud computing, our result demonstrates that the most relevant topics to cloud computing have been changing these years from basic research to applied research, and from a microscopic point of view, the development of cloud computing related fields presents a certain periodicity. Inderscience Publishers - linking academia, business and industry through research

Title: Academic research trend analysis based on big data technology

Authors: Weiwei Lin; Zilong Zhang; Shaoliang Peng

Addresses: School of Computer Engineering and Science, South China University of Technology, Guangdong, Guangzhou, 510640, China; Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, 510006, China ' School of Computer Engineering and Science, South China University of Technology, Guangdong, Guangzhou, 510640, China ' Department of Computer Science, National University of Defense Technology, Changsha, 410073, China

Abstract: Big data technology can well support the analysis of academic research trends, which requires the ability to process an enormous amount of metadata efficiently. On this point, we propose an academic trend analysis method that exploits a popular topic model for paper feature extraction and an influence propagation model for field influence evaluation. We also propose a parallel association rule mining algorithm based on Spark to accelerate trend analysis process. The algorithm can take the advantages of Spark memory architecture to enhance the iterative speed of traditional algorithms. Experimentally, a vast amount of paper metadata was collected from four popular digital libraries: ACM, IEEE, Science Direct and Springer, serving as the raw data for our final feature dataset. Focusing on the hotspot of cloud computing, our result demonstrates that the most relevant topics to cloud computing have been changing these years from basic research to applied research, and from a microscopic point of view, the development of cloud computing related fields presents a certain periodicity.

Keywords: big data; associate rule mining; Spark; apriori; technology convergence.

DOI: 10.1504/IJCSE.2017.10016151

International Journal of Computational Science and Engineering, 2019 Vol.20 No.1, pp.31 - 39

Received: 02 Nov 2016
Accepted: 23 Apr 2017
Published online: 23 Oct 2019 *

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Title: Academic research trend analysis based on big data technology

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