Journal of Big Data Research

ISSN: 2768-0207


Aims and Scope


Journal of Big Data Research is open accesspeer-reviewed journal that publishes high-quality, scholarly research papers, methodologies covering a broad range of topics, from big data analytics to data-intensive computing and all applications of big data research. JBR focuses on the challenges that big data facing today to move forward encompassing but not restricted to: big data technologies, big data analytics, data storage, data capture, and data mining, machine learning algorithms for big data, cloud computing platforms, data visualization, architectures for massively parallel processing, distributed file systems, and databases and scalable storage systems.


JBR accepts and publishes contributions in the form of Original Research, Review, Literature review, Conference proceedings, Case reports, Short communication, Thesis, Letter to the editor, and Editorials.


Few keywords were outlined, which defines the scope of the journal. If you have any queries, do contact us at [email protected]


  • Algorithms and systems for big data search
  • Autonomic computing and cyber-infrastructure, system architectures, design, and deployment
  • Big data
  • Big data analytics in government, public sector, and society in general
  • Big data analytics in small business enterprises (SMEs)
  • Big data as a service
  • Big data industry standards
  • Big data open platforms
  • Big data search architectures, scalability, and efficiency
  • Big data analysis
  • Big data analytics
  • Big data analytics in healthcare
  • Big data analytics in healthcare promise and potential
  • Big data cancer
  • Big data ethics
  • Big data health
  • Big data healthcare
  • Big data medicine
  • Cloud/grid/stream computing for big data
  • Cloud/grid/stream data mining- big velocity data
  • Cloud/grid/streamdata mining- big velocity data
  • Complex big data applications in science, engineering, medicine, healthcare, finance, business, law, education, transportation, retailing, telecommunication
  • Computational modeling and data integration
  • Data acquisition, integration, cleaning, and best practices
  • Data and information quality for big data
  • Distributed, and peer-to-peer search
  • Energy-efficient computing for big data
  • Experiences with big data project deployments
  • High performance/parallel computing platforms for big data
  • Large-scale recommendation systems and social media systems
  • Link and graph mining
  • Medical big data
  • Mobility and big data
  • Multimedia and multi-structured data- big variety data
  • New computational models for big data
  • New data standards
  • New programming models for big data beyond Hadoop/MapReduce, storm
  • Novel theoretical models for big data
  • Programming models and environments for cluster, cloud, and grid computing to support big data
  • Real-life case studies of value creation through big data analytics
  • Search and Mining of a variety of data including scientific and engineering, social, sensor/IoT/ioe, and multimedia data
  • Semantic-based data mining and data pre-processing
  • Social web search and mining
  • Software systems to support big data computing
  • Software techniques and architectures in cloud/grid/stream computing
  • Visualization analytics for big data

Journals By Subject

Life Sciences
Medical Sciences
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