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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 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.

Authors are invited to submit novel, high quality work that has neither appeared in, nor is under consideration for publication elsewhere.

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), formatted in a single column, with double spacing, 12 pt. font and numbered pages. An abstract of 250 words or fewer 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 these, there are no style restrictions (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. Final paper needs to be no more than 10 pages in length, please refer to author guidelines.

Important Dates

Manuscript submission is on a rolling basis, and will remain open perpetually.

The submission window for the next issue will close on September 15, 2023.