Advancements on Automated Data Platform Management, Orchestration, and Optimization
Submission Deadline: 30 September 2024
Emergent architectures and technologies for big data management and analysis
Submission Deadline: 1 October 2024
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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Examining ALS: reformed PCA and random forest for effective detection of ALS
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Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis
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Exploring AI-driven approaches for unstructured document analysis and future horizons
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New custom rating for improving recommendation system performance
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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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Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
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Deep learning applications and challenges in big data analytics
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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 2023
Journal Impact Factor: 8.6
5-year Journal Impact Factor: 12.4
Source Normalized Impact per Paper (SNIP): 3.853
SCImago Journal Rank (SJR): 2.068Speed 2023
Submission to first editorial decision (median days): 56
Submission to acceptance (median days): 205Usage 2023
Downloads: 2,559,548
Altmetric mentions: 280
- ISSN: 2196-1115 (electronic)