Multi-module data acquisition and analysis system based on Python, C++ and Java

Abstract

This article is devoted to the issue of information acquisition and processing, which is a key element of modern data analysis, especially in the context of the growing volume of digital resources. The main objective of the work was to design and implement a multi-module IT system, each module using one of the Python, C++, and Java programming languages. The developed solution enables effective data retrieval from both network and local sources. As part of the project, algorithms responsible for data selection, classification, filtering, and transformation were implemented. To validate the scientific utility of the software, three distinct research experiments were conducted using real-world macroeconomic, financial, and meteorological datasets. The tests confirmed the correct operation of the implemented modules, the high computational performance of the compiled languages, and the usefulness of the system in the process of data visualization and interpretation.

https://doi.org/10.7862/ra.2026.4
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References

OpenWeatherMap API Documentation. https://openweathermap.org/api, access on 2025-12-19.

Eurostat Database - Economy and finance. https://ec.europa.eu/eurostat/data/database, access on 2025-12-19.

Stooq - Stock exchange quotes and data. https://stooq.pl, access on 2025-12-19.

Investopedia, Technical Analysis Indicators Guide. https://www.investopedia.com, access on 2025-12-19.

KNIME AG: KNIME Analytics Platform. https://www.knime.com/knime-analytics-platform, access on 2026-06-06.

Altair Engineering Inc.: Altair AI Studio. https://altair.com/altair-ai-studio/, access on 2026-06-06.

Demsar J., Curk T., Erjavec A., et al.: Orange: Data Mining Toolbox in Python. Journal of Machine Lear-ning Research, vol. 14, 2013, pp. 2349-2353.

Apache Software Foundation: Apache NiFi. https://nifi.apache.org/, access on 2026-06-06.

Fourment K., Gillings M. R.: A comparison of common programming languages used in bioinformatics. BMC Bioinformatics, vol. 9, article 82, 2008. https://doi.org/10.1186/1471-2105-9-82, access on 2026-06-10.

McKinney W.: Data Structures for Statistical Computing in Python. Proceedings of the 9th Python in Sci-ence Conference, 2010. https://doi.org/10.25080/Majora-92bf1922-00a/, access on 2026-06-10.

Treleaven P., Galas M., Lalchand V.: Algorithmic trading review. Communications of the ACM, 2013, 56(11), pp. 76-85.