Journal of Big Data

Featured article: Horizontally scalable PGST-based route prediction using map data and GPS traces

Route prediction is an essential requirement for many intelligent transport system services that are a vital facet of modern life. This work focuses on building an end-to-end horizontally scalable route prediction application based on statistical modelling of user travel data.

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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.

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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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ISSN: 2196-1115

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.