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Today, the world is undergoing a digital transformation of society. Most of human activities are gradually being transferred to the digital dimension. In this regard, the digitalization of the peer review process is a very urgent task. The description of the implementation of automated peer review systems is not publicly available, because such systems are the intellectual property of the respective journals. The aim of the work is to develop an automated system for double-blind reviewing of articles for the professional Informatics and Mathematical Methods in Simulation journal. The paper discusses the process of reviewing in an automated system for reviewing articles. Described actions of reviewer, editor and authors. The sequence of these actions is considered. Designed the database that ensures the functioning of the developed system. An algorithm has been developed that removes the personal information of authors from the file. The algorithm allows you to delete: personal information of the author from the first page, headers of even pages, annotations in Russian and English at the end of the file. The article has developed an automated system for double-blind reviewing of articles, which provides double-blind reviewing, automates the workflow of the journal editor and reviewer, and minimizes the possibility of a third-party reviewing. Due to the introduction of an automated system for reviewing articles, the speed of reviewing will significantly increase and a single database of reviews will appear. The developed automated review system is used by the editors of the Informatics and Mathematical Methods in Simulation journal to improve the efficiency of the review process. The results of this work can be used to build a similar system for reviewing articles. [ABSTRACT FROM AUTHOR]

In the paper the associative analysis as a Data Mining technique is investigated. The properties of the algorithms for extracting the associative rules is analyzed and compared. The associative analysis is used for discovering and exploring hidden and interesting relationships between items in large amounts of data. The extracted patterns are presented in the form of association rules or sets of frequent items. The associative rules are represent in the form IF A then B. For studying of various algorithms is used open source software RapidMiner. [ABSTRACT FROM AUTHOR]