Journal of Big Data Research

Aims and Scope
 

Journal of Big Data Research (JBR) is an open-access, peer-reviewed journal dedicated to advancing the science, technology, and applications of big data. It serves as a global platform for researchers, practitioners, and decision-makers to share innovative research, methodologies, and real-world applications of big data technologies.  
 

Scope and Focus Areas:

• Data mining, machine learning, and artificial intelligence applications  
• Predictive modeling, pattern recognition, and deep learning for big data  
• Real-time data processing and decision-making systems  
• Cloud computing, distributed systems, and scalable storage solutions  
• High-performance computing, data warehousing, and database management  
• Architectures for massively parallel processing and distributed file systems  
• Big data in business, finance, healthcare, public administration, and engineering  
• Smart cities, IoT, cybersecurity, and digital transformation  
• Data-driven innovations in medical and life sciences  
• Data privacy, security, and governance in big data ecosystems  
• Responsible AI and ethical implications of large-scale data collection  
• Bias mitigation and fairness in algorithmic decision-making  
   

Submitting your manuscript is simple:

• Method 1: Register as an author, enter manuscript details (title, article type, abstract), and upload on Manuscriptzone.
• Method 2: Send the submission files to the Editorial Office via email.
• Method 3: Use the online submission page to upload your files.

A few keywords were outlined, which define the scope of the journal. If you have any queries, do contact us at info@openaccesspub.org

• Big data
• Health care
• E-Commerce
• Visualization and design
• Data capture and storage
• Databases
• Social networking
• Parallel processing
• Big data analytics
• Machine learning algorithms
• Geoscience
• Data protection
• Data acquisition
• New technologies
• Big data analysis
• Data mining tools
• Big data technologies
• Deep learning algorithms
• Physics and Astronomy
• Big data analytics in healthcare
• Big data healthcare
• Big data as a service
• Big data research
• Big data ethics
• Big data industry
• Big data health
• Data-intensive computing
• Big data analytics in healthcare promise and potential
• Big data cancer
• Medical big data
• Environment and Climate
• Big data industry standards
• Big data medicine
• Mobility and big data
• Big data analytics in small business enterprises (smes)
• Big data search architectures scalability and efficiency
• Data visualization
• Data acquisition integration cleaning and best practices
• Distributed and peer-to-peer search
• Energy-efficient computing for big data
• Multimedia and multi-structured data- big variety data
• Social web search and mining
• Visualization analytics for big data
• Big Data And Analytics In Healthcare
• Big Data Analytics In Transportation
• Artificial Intelligence And Big Data