Green and Sustainable AI
Submission Deadline: 30 September 2023Big Data in Human Behaviour Research: A contextual turn?
Submission Deadline: 1 October 2023
Featured Collections on Computationally Intensive Problems in General Math and Engineering
This two-part special issue covers computationally intensive problems in engineering and focuses on mathematical mechanisms of interest for emerging problems such as Partial Difference Equations, Tensor Calculus, Mathematical Logic, and Algorithmic Enhancements based on Artificial Intelligence. Applications of the research highlighted in the collection include, but are not limited to: Earthquake Engineering, Spatial Data Analysis, Geo Computation, Geophysics, Genomics and Simulations for Nature Based Construction, and Aerospace Engineering. Featured lead articles are co-authored by three esteemed Nobel laureates: Jean-Marie Lehn, Konstantin Novoselov, and Dan Shechtman.
Open Special Issues
Articles
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Hajj pilgrimage abnormal crowd movement monitoring using optical flow and FCNN
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Improved content recommendation algorithm integrating semantic information
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Time series big data: a survey on data stream frameworks, analysis and algorithms
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Healthcare knowledge graph construction: A systematic review of the state-of-the-art, open issues, and opportunities
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A survey on Image Data Augmentation for Deep Learning
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Big data in healthcare: management, analysis and future prospects
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Deep learning applications and challenges in big data analytics
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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
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Short-term stock market price trend prediction using a comprehensive deep learning system
Aims and scope
The Journal of Big Data publishes open-access original research on data science and data analytics. Deep learning algorithms and all applications of big data are welcomed. Survey papers and case studies are also considered.
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.
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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Annual Journal Metrics
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Citation Impact
10.835 - 2 Year Impact Factor (2021)
4.661 - Source Normalized Impact per Paper (SNIP)
2.592 - SCImago Journal Rank (SJR)
14.4 - CiteScoreSpeed
94 days to first decision for all manuscripts (Median)
106 days to first decision for reviewed manuscripts only (Median)Usage
2,250,289 downloads (2022)
982 Altmetric mentions (2021)
- ISSN: 2196-1115 (electronic)