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The 3rd edition of the International Conference on Big Data and Machine Learning (BML’22) is a major gathering of researchers, engineers, and practitioners within the fields of Big Data, Machine Learning, and Artificial Intelligence, from both theoretical and practical perspectives. We strongly encourage contributions that describe real-world problems, interdisciplinary research, and experimental and/or theoretical studies that yield new insights to advance Big Data and Machine Learning. Submitted papers should be original and relevant to any of the areas listed below. All accepted papers will be presented at the conference by one of the authors and published in the proceedings with an ISBN. The acceptance will be based on their quality, relevance, and originality. Presentation options include both oral and poster sessions. We have planned special sessions for case studies and commercial presentations, as well as tutorials focusing on technical or scientific matters. Companies interested in presenting their products or methodologies, as well as researchers wishing to demonstrate a demo or present a tutorial are encouraged to contact the conference secretariat. We cordially invite all speakers and participants to present their research and/or experiences at BML’22concerning the subjects listed below. While each of the main topic areas is further elaborated below, the sub-topics list provided is not exhaustive. Authors may address one or more of the listed sub-topics, while also being free to explore additional ones that align with the following main topic areas: BIG DATA MACHINE LEARNING ARTIFICIAL INTELLIGENCE Registration Fees Open Access Licence These conference proceedings provide immediate open access to its content under the CC-BY https://creativecommons.org/licenses/by/4.0/. Authors who will publish with these proceedings retain all copyrights and agree to the terms of the above-mentioned CC-BY 4.0 licence Open Access Statement This conference proceedings is an Open Access proceedings that allows a free unlimited access to all its contents without any restrictions upon publication to all users. Editorial Policy Instructions for authors National Scientific Committee International Scientific Committee

COMPUTERS / Computer Science.