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The Journal of Big Data: Theory and Practice (JBDTP) 

It is our great pleasure to announce the call for papers for the inaugural issue of the Journal of Big Data: Theory and Practice (JBDTP). JBDTP is an open access peer-reviewed journal which is devoted to the publication of high-quality papers on theoretical and practical aspects of big data, AI, applications and machine learning.

We solicit submissions on all aspects of big data, AI and machine learning theory, application and practice. Authors are invited to submit novel, high quality work that has neither appeared in, nor is under consideration for publication by other journals or conferences.

Areas of interest include the following (but are not restricted to):

• Theory and Foundational Issues

• Data Mining Methods

• Machine Learning Algorithms

• Knowledge Discovery Processes

• Application Issues and Case Studies

• Ethical, policy and economic aspects of big data, machine learning and AI

• Big data analytics and decision-making

• Human interaction with AI

Submission Guidelines

Manuscripts (MS Word or PDF format), should be formatted in a single column, with double spacing, 12 pt. font and numbered pages. An abstract of 250 words or less should be included. Author names and identification information must be restricted to the initial Title page (Title, author names, affiliation, contact details and brief 3 to 4 sentence bios.), to facilitate blind peer review. Other than this there are no style restriction (e.g. APA, IEEE etc.) for the initial submission. All papers will undergo the journal’s rigorous peer review process which can be found on the JBDTP journal website. Manuscripts must be submitted here. The editorial team aims to provide an initial decision within 3 months of acceptance for review.