The study is devoted to the analysis of optimization features and mechanisms available in Oracle Database, SQL Server, and PostgreSQL. The purpose of the study was to identify differences in the available functionalities and to assess the impact of selected mechanisms on SQL query performance. The research included a theoretical analysis based on database system documentation and a practical part conducted in a prepared test environment. The research scenarios examined the impact of indexes, materialized views, data partitioning, and query optimizer behaviour. The analysis indicated that Oracle Database offers the most extensive set of optimization functionalities compared with the other examined systems. The results of the practical tests showed that the use of appropriate optimization mechanisms can significantly reduce query execution time, while the scale of improvement depends on the type of query and the way in which a given mechanism is implemented.
The modern digital age led to the creation of a field called digital genealogy. To this day there seems to be little consensus on which database approach is most suited for such environment; genealogical data remains overlooked in database research. This study examines whether the popular relational SQL database systems prove themselves to be more efficient than graph databases when operating on graph-like genealogical structures. The article analyses PostgreSQL and Neo4j database engines’ efficiency, comparing them across different sizes of genealogical datasets and types of operations on artificially generated data. Results show that an optimized SQL approach is generally faster and uses less memory than the graph-based counterpart, while requiring more disk space, in all tested cases.
In times of growing global tensions, attacks on ICT infrastructure are becoming increasingly common. The field of IT systems security is becoming even more important. Various types of network vulnerability testing tools allow for the examination and improvement of the security of such systems. This article aims to compare and analyze selected network vulnerability testing tools. The systems that were examined are Nessus by Tenable, which is an enterprise-class tool, OpenVAS by Greenbone, which is an open-source project, and Nmap, a standard network mapping utility explicitly utilized in this study as a baseline tool.
This paper presents an empirical comparison of two leading frameworks for automated UI testing of Android applications: Espresso and Appium. An identical suite of 65 test cases was implemented in both frameworks for the MediTrack medication management application and executed in ten independent runs. Analysis of stable runs showed that Espresso completed the test suite in 437.4 s on average, while Appium required 727.7 s, a slowdown factor of 1.66. Per-module ratios ranged from 0.71 to 2.78, depending on test complexity. Appium achieved higher run stability (80% vs 60%), while standard deviations of stable runs were comparable. The results provide empirical guidance for choosing between the frameworks based on project requirements.
This study presents the performance of seven betting strategies in European roulette via Monte Carlo simulations across 48 scenarios. Performance metrics included mean and median net profit, win and ruin probability, maximum drawdown, VaR and CVaR. Results confirmed that all strategies have negative expected value. Progressive strategies had higher short-term win rates but incurred much heavier loss tails. Fractional Kelly and Fixed-fractional showed moderate profiles with no long-term advantage. The fixed-stake strategy proved to be the safest, effectively limiting the risk of ruin. The study can be used to modify new betting strategies and serve as a warning to casino users.
