The Journal of Big Data: Theory and Practice (JBDTP) (ISSN 2692-7977) is an open access peer-reviewed journal devoted to the publication of high-quality papers on theoretical and practical aspects of big data, AI and machine learning. The goal of this journal is to publish the latest contributions from academia, practitioners and industry to advance these fields. Original research papers, state-of-the-art reviews, innovative case studies and tutorials are invited for publication.

Areas of interest include (but not restricted to):
• Theory and Foundational Issues
• Data Mining Methods
• Machine Learning Algorithms
• Knowledge Discovery Processes
• Application Issues
• Ethical, policy and economic aspects of big data, machine learning and AI
• Big data analytics and decision-making

Publication Frequency
The Journal of Big Data: Theory and Practice (JBDTP) publishes two issues per year on a continuous basis. That is, articles submitted, reviewed and accepted between January and June each year will be published in the first issue, while articles submitted, reviewed and accepted between July and December each year will be published in the second issue. The accepted article ready for publication at any time is immediately made available online.