Academic research trend analysis based on big data technology. (23rd October 2019)

• Record Type: Journal Article
• Authors: Lin, Weiwei; Zhang, Zilong; Peng, Shaoliang
• Is Part Of: International journal of computational science and engineering. Volume 20:Number 1(2019)
• Page Start: 31
• Page End: 39
• Publication Date: 2019-10-23
• Subjects: big data -- associate rule mining -- Spark -- apriori -- technology convergence; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.