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This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert queries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQL's superior performance in select operations. In primary tests, PostgreSQL's execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQL's ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQL's 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQL's performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQL's suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems. [ABSTRACT FROM AUTHOR]

Cada día en los hospitales cubanos se realizan cientos de estudios imagenológicos de diversas índoles que luego son almacenados en el sistema de imágenes médicas Xavia PACSServer. En la actualidad los desarrolladores del Centro de Informática Médica (CESIM) se han dado la tarea de lanzar una nueva versión del servidor en la nube con el objetivo de incrementar el tamaño de almacenamiento de las imágenes médicas. Esta actualización traerá grandes beneficios a los procesos que se realicen en los hospitales, pero a su vez puede ocurrir que se incrementen los altos tiempos de respuesta de consulta que se obtienen en la versión actual del servidor. Para evitar este problema, se identificó al sistema de recuperación de información ElasticSearch, el cual con su potente capacidad de búsqueda fue capaz de disminuir los tiempos de respuesta de las consultas en la búsqueda de las imágenes almacenadas.

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

While substantial efforts have been invested in the development of Discrete Global Grid Systems (DGGS) spatial operations and their potential applications in the geospatial domain, it has become evident that there is a demand for an efficient and scalable system to handle the visualization of large-scale DGGS data. This study demonstrated the potential of DGGS in conjunction with the serverless framework for dynamic visualization at various resolutions, which is based on data storage and effective querying using PostgreSQL integrated into Amazon Aurora Serverless. The use of Amazon Web Services (AWS) Lambda for on-the-fly generation of hexagon geometries significantly reduced the storage requirements and improved the speed of the visualization process. In addition, we implemented on-the-fly spatial operations including point binning, thresholding, aggregation, and neighborhood operations in the DGGS, highlighting the capabilities of DGGS in vector and raster processing. The proposed system has shown promising results in terms of efficiency, scalability, and adaptability, making it a viable solution for large-scale geospatial data processing and visualization. Case studies using flood risk data and terrain data further illustrate the system’s practical applicability in on-the-fly spatial operations and rapid visualization.

Objectives. The aim of the research is to investigate the possibilities of using the PgBouncer connection pool with various configurations in modern database installations by conducting load testing with diverse real-world like scenarios, identifying critical metrics, obtaining testing results, and interpreting them in the form of graphs.Methods. The research utilized methods of experimentation, induction, testing, and statistical analysis.Results. The main features, architecture and modes of operation of the PgBouncer service are considered. Load testing was carried out on a virtual machine deployed on the basis of an open cloud platform with different configurations of computing resources (CPU, RAM) and according to several scenarios with different configurations and different numbers of balancer connections to the database, during which the following main indicators were investigated: distribution of processor usage, utilization of RAM, disk space, and CPU. The interpretation of the data obtained and the analysis of the results obtained by highlighting critical parameters are performed. On the basis of results analysis, conclusions and recommendations are formulated on the use of a connection balancer in real high-load installations for optimizing the resources utilized by the server on which the database management system (DBMS) is located. A conclusion is presented on the usefulness of using the PgBouncer query balancer along with proposed configuration options for subsequent use in real installations.Conclusions. The degree of influence of the use of the PgBouncer connection balancer on the performance of the system as a whole deployed in a virtualized environment is investigated. The results of the work showed that the use of PgBouncer allows significantly optimization of the computing resources of a computing node for a DBMS server, namely, load on the CPU decreased by 15%, RAM—by 25–50%, disk subsystem—by 20%, depending on the test scenarios, the number of connections to the database, and the configuration of the connection balancer.

В данной статье рассмотрена база данных PostgreSQL и ее расширение TimescaleDB. Целью данной работы является сравнение производительности баз данных при работе с большими данными. Так как в настоящее время при разработке приложений одним из первых вопросов, над которым задумывается разработчик, является выбор базы данных и время работы с ней, то данная тема остается актуальной. Для развертывания обеих баз данных использовано облачное хранилище, так как оно имеет ряд преимуществ перед развертыванием на локальной машине, и пользовательский интерфейс pgAdmin 4. Для проведения тестов сгенерирован тестовый набор данных, представляющий из себя временные ряды со значениями климатических показателей за 2011-2023 года. В статье описана структура тестовых данных, представлены параметры настройки баз данных и листинги SQL-запросов. Для каждой базы данных представлены результаты времени выполнения одного и того же запроса. Эти результаты представлены в виде гистограмм для каждого SQL-запроса, загрузки и выгрузки всего набора данных. После их анализа, представлена их интерпретация. В заключении предложены рекомендации по выбору базы данных под определенные цели.

This study highlights the necessity for efficient database management in continuous authentication systems, which rely on large-scale behavioral biometric data such as keystroke patterns. A benchmarking framework was developed to evaluate the PostgreSQL and MySQL databases, minimizing repetitive coding through configurable functions and variables. The methodology involved experiments assessing select and insert queries under primary and complex conditions, simulating real-world scenarios. Our quantified results show PostgreSQL’s superior performance in select operations. In primary tests, PostgreSQL’s execution time for 1 million records ranged from 0.6 ms to 0.8 ms, while MySQL’s ranged from 9 ms to 12 ms, indicating that PostgreSQL is about 13 times faster. For select queries with a where clause, PostgreSQL required 0.09 ms to 0.13 ms compared to MySQL’s 0.9 ms to 1 ms, making it roughly 9 times more efficient. Insert operations were similar, with PostgreSQL at 0.0007 ms to 0.0014 ms and MySQL at 0.0010 ms to 0.0030 ms. In complex experiments with simultaneous operations, PostgreSQL maintained stable performance (0.7 ms to 0.9 ms for select queries during inserts), while MySQL’s performance degraded significantly (7 ms to 13 ms). These findings underscore PostgreSQL’s suitability for environments requiring low data latency and robust concurrent processing capabilities, making it ideal for continuous authentication systems.

Ubiquitous digitalization and the development of big data technologies require a high level of availability (24/7) of information resources. Ensuring high availability of the database requires a significant amount of work on the design, testing and maintenance of several components and configurations. High availability can be achieved or maximized for the following deployment options: one database instance (with or without read replicas) and several main ones (with or without a coordinator). High availability ensures the smooth operation of the system by eliminating individual points of failure, while disaster recovery focuses on restoring normal operation after a failure or downtime. There are several deployment options because there is no universal solution. Choosing the best option depends on your specific use case and requirements. This article presents the main concepts of ensuring high availability and the most effective patterns for providing highly accessible database deployments. Purpose. definition of effective deployment patterns for database servers to ensure high availability. Methodology. The results of foreign and domestic scientific research are used in the work. The author uses theoretical research methods related to the search and analysis of information to identify connections and obtain unique conclusions. Results. The most effective deployment schemes for ensuring high availability and fault tolerance of databases are highlighted. Practical implications it is advisable to apply the results obtained in the field of DevOps development in order to optimize the process of developing and releasing applications by eliminating a known bottleneck: changes to the database code.

This study examines the methodology for creating a geographic information system of transport infrastructure objects - from obtaining initial data to storing the data itself. The article defines the classification of objects in the transportation system, the procedure for collecting and processing initial data, as well as the sequence of data processing for transmission to the GIS environment. A study was conducted on the dependence of response time on the current number of elements in a database table with indexed and un-indexed data. Until about the 1000th element, it was determined that the sampling rate is higher by "direct search", but after that, a smooth linear growth begins to a sharp peak and a nonlinear increase in search time. Thus, the support of database management system functions for working with spatial data is important for its functioning in the context of geometric data aggregation tasks.

The increasing complexity of managing modern database systems, particularly in terms of optimizing query performance for large datasets, presents significant challenges that traditional methods often fail to address. This paper proposes a comprehensive framework for integrating advanced machine learning (ML) models within the architecture of a database management system (DBMS), with a specific focus on PostgreSQL. Our approach leverages a combination of supervised and unsupervised learning techniques to predict query execution times, optimize performance, and dynamically manage workloads. Unlike existing solutions that address specific optimization tasks in isolation, our framework provides a unified platform that supports real-time model inference and automatic database configuration adjustments based on workload patterns. A key contribution of our work is the integration of ML capabilities directly into the DBMS engine, enabling seamless interaction between the ML models and the query optimization process. This integration allows for the automatic retraining of models and dynamic workload management, resulting in substantial improvements in both query response times and overall system throughput. Our evaluations using the Transaction Processing Performance Council Decision Support (TPC-DS) benchmark dataset at scale factors of 100 GB, 1 TB, and 10 TB demonstrate a reduction of up to 42% in query execution times and a 74% improvement in throughput compared with traditional approaches. Additionally, we address challenges such as potential conflicts in tuning recommendations and the performance overhead associated with ML integration, providing insights for future research directions. This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? (2) What performance improvements can be achieved through dynamic configuration tuning based on real-time workload patterns? Our results suggest that the proposed framework significantly reduces the need for manual database administration while effectively adapting to evolving workloads, offering a robust solution for modern large-scale data environments.

This study examines the methodology for creating a geographic information system of transport infrastructure objects - from obtaining initial data to storing the data itself. The article defines the classification of objects in the transportation system, the procedure for collecting and processing initial data, as well as the sequence of data processing for transmission to the GIS environment. A study was conducted on the dependence of response time on the current number of elements in a database table with indexed and un-indexed data. Until about the 1000th element, it was determined that the sampling rate is higher by "direct search", but after that, a smooth linear growth begins to a sharp peak and a nonlinear increase in search time. Thus, the support of database management system functions for working with spatial data is important for its functioning in the context of geometric data aggregation tasks. [ABSTRACT FROM AUTHOR]

There is a growing number of frail patients whose health conditions require constant monitoring by physicians. Unfortunately, the budget restrictions of hospitals and the concomitant decision that patients should stay home require that this monitoring is to be carried out remotely. Today, Internet of Things (IoT) wearables are the most promising technology solution for sensing patients' physiological values 24/7. These measurements need to be stored permanently and then processed in order to provide support to physicians in charge of making real-time clinical decisions consistent with the patient's health status. This paper elaborates on the combination of the IoT and DBMS technologies. In detail, a PostgreSQL relational database stores patient-generated health data (PGHD), while a set of SQL views generates standard summary statistical indicators. [ABSTRACT FROM AUTHOR]

Innovative technologies have been incorporated into intelligent transportation systems (ITS) to improve sustainability, safety, and efficiency, hence revolutionising traditional transportation. The combination of three-dimensional (3D) indoor building mapping and navigation is a groundbreaking development in the field of ITS. A novel methodology, the "Three-Dimensional Routing Information Framework "(3D RIF), is designed to improve indoor navigation systems in the field of ITS. By leveraging the Quantum Geographic Information System (QGIS), this framework can produce three-dimensional routing data and incorporate sophisticated routing algorithms to handle the complexities associated with indoor navigation. The paper provides a detailed examination of how the framework can be implemented in transport systems in urban environments, with a specific focus on optimising indoor navigation for various applications, including emergency services, tourism, and logistics. The framework includes real-time updates and point-of-interest information, thereby enhancing the overall indoor navigation experience. The 3D RIF's framework boosts the efficiency and effectiveness of intelligent transportation services by optimising the utilisation of internal resources. The research outcomes are emphasised, demonstrating a mean enhancement of around 25.51% in travel. The measurable enhancement highlighted in this statement emphasises the beneficial influence of ITS on the efficiency of travel, hence underscoring the significance of the ongoing progress in this field. [ABSTRACT FROM AUTHOR]

In this work, we apply the Logistic Regression (LR) model for predicting the outcome of tennis matches and providing win probability for participating/competing players. Prediction models are classified as machine learning methods, which generate predictions for future scenaria exploiting existing data collections. With the objective to maximize the accuracy of our predictions, we apply the Logistic Regression model under various parameter configurations seeking for an optimal combination of independent variables (features). We present and discuss promising results obtained via the holdout-validation method and the cross-validation method. Furthermore, as a proof of concept of our approach, we designed and implemented a web platform, where users can obtain real-time predictions for tennis matches. Our LR-based framework combined with Artificial Intelligence (AI) can serve as a paradigm in the field of sports analysis for predicting outcomes and evaluating athletic performance. [ABSTRACT FROM AUTHOR]

Methods of data transfer from the Oracle environment to the PostgreSQL environment with stage-by-stage organization of actions and determination of the sequence of migration are considered. The main directions for the transformation of the structure of databases, data formats and executed objects are identified. Methods for converting stored procedures and triggers written in procedural languages (PL/SQL and PL/pgSQL) are specified. Software change on nodes directly connected (associated) with the database server and minimization of downtimes during migration are considered, which allows the transfer to be performed for geographically distributed and round-the-clock functioning objects. Examples of changing the speed of query execution before and after migration are given for systems of various types. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Facultad de Ingeniería - UPTC is the property of Universidad Pedagogica y Tecnologica de Colombia, Facultad de Ingenieria and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The article discusses an efficient and automated way to import large amounts of data from Excel tables into a database. In various projects, there are tasks in which the flow of huge data, such as logs of program operations or manual operations performed at work sites, is vital for effective analysis. Purpose – development of a module for automatic import of a large amount of data from Excel format into a database. Method or methodology of work: the article discusses a method that implements automatic import of data from Excel tables into a Postgresql database. Result: developed its own unique module that is able to process huge Excel tables and import them into a Postgresql database without manual operations. Scope of the results: the data obtained, which are stored in the database, should be used to identify high-yield accounts for subsequent investment.

Copyright of Revista Cubana de Ciencias Informáticas is the property of Universidad de las Ciencias Informaticas (UCI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Foodborne diseases and outbreaks are significant threats to public health, resulting in millions of illnesses and deaths worldwide each year. Traditional foodborne disease surveillance systems rely on data from healthcare facilities, laboratories, and government agencies to monitor and control outbreaks. Recently, there is a growing recognition of the potential value of incorporating social media data into surveillance systems. This paper explores the use of social media data as an alternative surveillance tool for foodborne diseases by collecting large-scale Twitter data, building food safety data storage models, and developing a novel frontend foodborne illness surveillance system. Descriptive and predictive analyses of the collected data were conducted in comparison with ground truth data reported by the U.S. Centers for Disease Control and Prevention (CDC). The results indicate that the most implicated food categories and the distributions from both Twitter and the CDC were similar. The system developed with Twitter data could complement traditional foodborne disease surveillance systems by providing near-real-time information on foodborne illnesses, implicated foods, symptoms, locations, and other information critical for detecting a potential foodborne outbreak. [ABSTRACT FROM AUTHOR]

This study evaluates mechanisms to secure relational databases against security vulnerabilities and utilized PostgreSQL and Microsoft SQL Server due to data breach incidences reported across the world. Emulation experiments with documentary review were employed to collect necessary study data. Identification of security features and vulnerabilities that are found to affect the data tier of the web applications were examined. The findings from the study have shown that Microsoft SQL Server is more security feature-rich in terms of Confidentiality, Integrity, and Availability compared to PostgreSQL and Microsoft SQL Server is more resilient to security attacks in its default behavior compared to PostgreSQL. [ABSTRACT FROM AUTHOR]

Database replication is a way to improve system throughput or achieve high availability. In most cases, the use of an active-active replica architecture is efficient and easy to deploy. Such a system has CP properties (from the CAP theorem: consistency, availability, and network-partition tolerance). Creating an AP (available and partition-tolerant) system requires the use of multiprimary replication. Because of the many difficulties in its implementation, this approach is not widely used; however, ALICE's deployment of CCDB (experiment conditions and calibration database) needs to be an AP system in two locations. This necessity became the inspiration for examining the state-of-theart methods in this field and testing the available solutions. The tests that were performed evaluated the performance of the chosen replication tools: Bucardo, and EDB Replication Server; these showed that the tested tools could be successfully used for the continuous synchronization of two independent database instances. [ABSTRACT FROM AUTHOR]

Chuyển đổi số đang tác động mạnh mẽ trên mọi lĩnh vực, đóng vai trò quan trọng, là động lực chủ đạo cho sự phát triển của đất nước [1]. Đề án chuyển đổi số ngành Giao thông vận tải (GTVT) của thành phố Đà Nẵng đã nêu nhiệm vụ số hóa toàn bộ hạ tầng giao thông (HTGT) trên bản đồ số phục vụ công tác xây dựng và duy tu bảo dưỡng công trình giao thông, quản lý quy hoạch, chia sẻ cho các cơ quan có liên quan khai thác sử dụng [1, 2]. Tuy nhiên, dữ liệu HTGT rất đặc thù về độ lớn, cách thức lưu trữ, biến động theo thời gian, nếu biểu diễn dữ liệu và lưu trữ theo mô hình các hệ quản trị cơ sở dữ liệu truyền thống như các phần mềm quản lý hay sử dụng thì sẽ không đáp ứng được nhu cầu. Trong bài báo này, nhóm tác giả tiến hành nghiên cứu và đề xuất giải pháp tổ chức dữ liệu HTGT kết hợp bản đồ số nhằm đáp ứng nhu cầu ứng dụng tại sở GTVT thành phố Đà Nẵng.

El desarrollo de las tecnologías y la informatización de las empresas ha incrementado el cúmulo de información almacenada en las bases de datos dificultando el proceso de análisis de estas manualmente, por lo cual es necesario utilizar técnicas como la minería de datos que faciliten obtener conocimientos a partir de los datos recopilados. En la presente investigación se analizó la técnica de minería de reglas de asociación para integrar un algoritmo al sistema gestor de bases de datos (SGBD) PostgreSQL, debido a las deficiencias de las herramientas libres existentes para realizar el análisis de la información. En ella se desarrollan mecanismos para optimizar el rendimiento del algoritmo implementado con el objetivo de aprovechar las ventajas de PostgreSQL. Además, se comprobó la solución de la investigación mediante la aplicación de las técnicas a una base de datos ficticia y se realizó un experimento para comprobar que el algoritmo integrado al gestor permite aprovechar las potencialidades que brindan otros algoritmos del minado de reglas.

The relevance of the topic under consideration is since in one of the Discord channel communities there was a need to collect the activity of channel members for a certain time and duration. To solve this problem, it was decided to develop a database and a special bot to interact with this database and the Discord API to collect information. The goal: development of a system for analyzing, collecting, and displaying information on the activity of participants, developing a database to store all information. The method or methodology of the work: the article considers a project for collecting, storing, and displaying information about the activity of participants. Results: a Python discord bot was developed that keeps statistics on the activity of channel members, a PostgreSQL database was developed that stores data on all participants and their activity. Scope of the results: the functionality of the bot can be used to manage channel members, highlight the most active and allocate incentives for greater activity

The article discusses the issues of making effective decisions about investing in commercial accounts and currencies. In order to know how much these solutions will be beneficial in the long term, specialized information is needed: data on the dynamics of growth, decline in the value of accounts and currencies, analytical information. But there is no single source where this data will be accumulated, systematized and stored. Purpose – create a tool for collecting and storing the parameters of investment accounts for their subsequent analysis in order to make an effective investment decision. Method or methodology of work: the article discusses a method that can implement a tool for collecting specialized information of investment accounts, two tools can be used for this: Postgresql databases and Python programming language Result: a tool has been developed that performs the function of collecting and storing information on investment accounts. Scope of the results: the data obtained, which are stored in the database, should be used to identify high-yield accounts for subsequent investment.

This paper aims to demonstrate the potential of relational databases for archaeological purposes in storing, organizing, managing, querying, and analysis of various types of data. This results in exploration of the long-term settlement history of a regional landscape. The organization and analysis of spatial and non-spatial archaeological datasets pose significant challenges for archaeologists, particularly when it comes to integrating data from multiple sources, representing spatial relationships among data elements, and accommodating multiple scales of analysis. In recent years, advances in database technologies have enabled archaeologists to organize these datasets more effectively through the application of relational databases. This study focuses on the design of a relational database for the eastern Rhodope region in Bulgaria, with the further goal of investigating settlement patterns through the identification, mapping, and analysis of archaeological sites and their spatial relationships. The aim of this research is to explore how archaeologists can enhance their analysis of complex datasets by adopting relational database design, which enables effective organization and integration of data from various sources.