The problem of transferring data from a database of one type to a database of another type is relevant today due to the increase in data volumes and the complexity of tasks solved in various fields of human activity. The existing databases are less and less satisfying the needs of users. New types of databases appear that are more suitable for solving large-scale problems. This article proposes an algorithm for solving the problem of optimizing the document structure of a database based on metadata about the structure of a distributed relational database from which data are transferred. The approach also takes into account information about the structure of the priority database queries. The priority of database queries is user-defined. The system of automatic translation of the database, taking into account these metadata, allows the user to create a distributed document database that is optimal in two parameters: in terms of the volume of stored data and in terms of the execution time of priority database queries.

Проблема трансляции базы данных из одного формата в другой периодически появляется в разных организациях по разным причинам. Сегодня хорошо отработан механизм смены формата реляционных баз данных. Но с появлением новых типов баз данных, таких как NoSql, проблема трансляции вновь стала очень актуальной в связи с радикальным отличием способов организации данных в различных базах данных. В статье рассматривается формализованный метод, основанный на теории множеств, по выбору числа и состава коллекций для базы данных типа ключ-документ. Исходными данными являются свойства объектов, информация о которых хранится в базе данных, и совокупность запросов, которые наиболее часто выполняются. Рассмотренный метод можно применять не только при создании новой базы данных типа ключ-документ, но также при трансформации существующей, при переходе от реляционных баз данных к NoSql, при консолидации баз данных.

Fatigue in high-stress work environments poses significant risks to employee performance and safety. This study introduces a non-invasive fatigue detection system utilizing facial parameters processed via a Long Short-Term Memory (LSTM) neural network, coupled with a human–machine interaction interface via a Telegram chatbot. The system analyzes eye blink patterns and facial expression changes captured through a webcam, achieving an accuracy of 92.35% on the UTA-RLDD dataset. An interactive feedback mechanism allows users to verify predictions, enhancing system adaptability. We further propose a multimodal AI framework to integrate physiological and environmental data, laying the groundwork for broader applications. This approach provides an effective solution for early fatigue detection and adaptive collaboration between humans and machines in real-time settings.

In this article, we describe a new formalized method for constructing the NoSQL document database of MongoDB, taking into account the structure of queries planned for execution to the database. The method is based on set theory. The initial data are the properties of objects, information about which is stored in the database, and the set of queries that are most often executed or whose execution speed should be maximum. In order to determine the need to create embedded documents, our method uses the type of relationship between tables in a relational database. Our studies have shown that this method is in addition to the method of creating collections without embedded documents. In the article, we also describe a methodology for determining in which cases which methods should be used to make working with databases more efficient. It should be noted that this approach can be used for translating data from MySQL to MongoDB and for the consolidation of these databases.