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Featured article: A new approach to the space-time analysis of big data: application to subway traffic data in Seoul

In this paper from researchers at Seoul National University, CSEOF analysis is introduced as an efficient and valuable technique to interpret the space-time structure of variability in a big dataset. As an example, the authors apply this method to the hourly passenger traffic on Subway Line #2 of Seoul, South Korea during the period of 2010–2017. 


  1. Content type: Survey paper


    Authors: Chun-Wei Tsai, Chin-Feng Lai, Han-Chieh Chao and Athanasios V. Vasilakos

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Aims and scope

The Journal of Big Data publishes high-quality, scholarly research papers, methodologies and case studies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material.

Open Article Collection

Advanced Soft Computing Methodologies and Applications in Social Media Big Data Analytics

Lead Guest Editor

  • Zhiyong Zhang, Distinguished Professor, Henan University of Science & Technology, China

Guest Editors

  • Kun Hua, Lawrence Technology University, USA
  • Arun Kumar Sangaiah, VIT University, India


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Journal of Big Data Accepted into Scopus!

We are pleased to announce that the Journal of Big Data has been accepted into Scopus, the world's largest abstract and citation database of peer-reviewed literature. Read more about the journal's abstract and indexing on the 'About' page.

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