INTELLIGENT WEB SYSTEMS FOR AUTOMATION OF PROCESSING AND MONITORING OF INFORMATION FLOWS
DOI:
https://doi.org/10.26906/SUNZ.2025.3.087Keywords:
intelligent web systems, information flows, automation, artificial intelligence, optical character recognition (OCR), infrastructure monitoring, machine learning, natural language processing (NLP), security information and event management (SIEM), SemantiAbstract
In the conditions of rapid growth of digital data and information flows on the Internet, there is a growing need for automated methods of their processing and monitoring for effective data acquisition and making informed conclusions. In order to cope with this data flow, intelligent web systems are being created. They use artificial intelligence (AI), machine learning (ML) and other advanced technologies to automatically collect, analyze and present information. The main purpose of this article is a comprehensive review of intelligent web systems aimed at automating the processing and monitoring of information flows. The theoretical foundations of such systems are considered, including semantic technologies and artificial intelligence methods, as well as modern approaches to text recognition and analysis based on deep learning. An analysis of modern approaches to automated text recognition (OCR and deep learning methods) and modern server infrastructure monitoring tools is presented. A conceptual model of integration of components of text processing systems and infrastructure monitoring is proposed. Key challenges and limitations of this approach are highlighted, and promising directions for further research are outlined.Downloads
References
1. Liu, J., Zhong, N., Yao, Y. et al. The Wisdom Web: New Challenges for Web Intelligence (WI). Journal of Intelligent Information Systems 20, 5–9 (2003). https://doi.org/10.1023/A:1020945620934 DOI: https://doi.org/10.1023/A:1020945620934
2. Market Research Intellect: Online OCR Software Market Size And Forecast (2025); https://www.marketresearchintellect.com/product/global-online-ocr-software-market-size-and-forecast/
3. C. Stryker, J. Holdsworth: What is NLP (natural language processing)? (2024); https://www.ibm.com/think/topics/natural-language-processing
4. IBM Inc.: What is infrastructure monitoring? (2023): https://www.ibm.com/think/topics/infrastructure-monitoring
5. R. Guha, "Toward the Intelligent Web Systems," 2009 First International Conference on Computational Intelligence, Communication Systems and Networks, Indore, India, 2009, pp. 459-463, doi: 10.1109/CICSYN.2009.25. DOI: https://doi.org/10.1109/CICSYN.2009.25
6. Boudabous Maroua, Pappa Anna. WebT-IDC: A Web Tool for Intelligent Dataset Creation A Use Case for Forums and Blogs, Procedia Computer Science, Volume 192, 1051-1060 (2021). https://doi.org/10.1016/j.procs.2021.08.108. DOI: https://doi.org/10.1016/j.procs.2021.08.108
7. Noetzold, D., Rossetto, A. G. D. M., Leithardt, V. R. Q., & Costa, H. . J. de M.: Enhancing Infrastructure Observability: Machine Learning for Proactive Monitoring and Anomaly Detection. (2024); https://doi.org/10.5753/jisa.2024.4509 DOI: https://doi.org/10.5753/jisa.2024.4509
8. C. Garrido-Munoz, A. Rios-Vila, J. Calvo-Zaragoza. Handwritten Text Recognition: A Survey (2025). https://doi.org/10.48550/arXiv.2502.08417
9. S. Rakesh, P. Kushal Reddy, V. Prashanth and K. Srinath Reddy. Handwritten text recognition using deep learning techniques: A survey. International Conference on Multidisciplinary Research and Sustainable Development (2024). https://doi.org/10.1051/matecconf/202439201126 DOI: https://doi.org/10.1051/matecconf/202439201126
10. M. Li, T. Lv, J. Chen, L. Cui, Y. Lu, D. Florencio, C. Zhang, Z. Li, F. Wei. TrOCR: Transformer-based Optical Character Recognition with Pre-trained Models (2025) https://doi.org/10.48550/arXiv.2109.10282
11. J. Devlin, M.-W. Chang, K. Lee, K. Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding (2019). https://doi.org/10.48550/arXiv.1810.04805
12. Popa, Sorin. "WEB Server monitoring." Annals of University of Craiova-Economic Sciences Series 2.36 (2008): 710-715. https://ideas.repec.org/a/aio/aucsse/v2y2008i11p710-715.html
13. Zeng, Wenxian, and Yue Wang. "Design and implementation of server monitoring system based on SNMP." 2009 International Joint Conference on Artificial Intelligence. IEEE, 2009. https://doi.org/10.1109/JCAI.2009.34 DOI: https://doi.org/10.1109/JCAI.2009.34
14. Mail, I. D. "The future of SIEM in a machine learning-driven cybersecurity landscape." Turkish Journal of Computer and Mathematics Education Vol 14.03 (2023): 1309-1314. https://doi.org/10.61841/turcomat.v14i03.14392 DOI: https://doi.org/10.61841/turcomat.v14i03.14392
15. Teggi, P., Malakreddy, B., Teggi, P. P., & Harivinod, N. (2021). AIOPS prediction for server stability based on ARIMA Model. Int. J. Eng. Res. Technol, 10, 128-134. https://doi.org/10.17577/IJERTV10IS120055
16. Pasquadibisceglie, V., Scaringi, R., Appice, A., Castellano, G., & Malerba, D. (2024). PROPHET: explainable predictive process monitoring with heterogeneous graph neural networks. IEEE Transactions on Services Computing. https://doi.org/10.1109/TSC.2024.3463487 DOI: https://doi.org/10.1109/TSC.2024.3463487
17. Sheeraz, M., Paracha, M. A., Haque, M. U., Durad, M. H., Mohsin, S. M., Band, S. S., & Mosavi, A. (2023). Effective security monitoring using efficient SIEM architecture. Hum.-Centric Comput. Inf. Sci, 13, 1-18. https://doi.org/10.22967/HCIS.2023.13.023
18. Tafti, Ahmad P., et al. "OCR as a service: an experimental evaluation of Google Docs OCR, Tesseract, ABBYY FineReader, and Transym." Advances in Visual Computing: 12th International Symposium, ISVC 2016, Las Vegas, NV, USA, December 12-14, 2016, Proceedings, Part I 12. Springer International Publishing, 2016. https://doi.org/10.1007/978-3-319-50835-1_66 DOI: https://doi.org/10.1007/978-3-319-50835-1_66
19. FERNOAGA, Vlad Paul, et al. "OCR-based solution for the integration of legacy and-or non-electric counters in cloud smart grids." 2018 IEEE 24th International Symposium for Design and Technology in Electronic Packaging (SIITME). IEEE, 2018. https://doi.org/10.1109/SIITME.2018.8599200 DOI: https://doi.org/10.1109/SIITME.2018.8599200
20. Koroteev, Mikhail V. "BERT: a review of applications in natural language processing and understanding." arXiv preprintarXiv:2103.11943 (2021). https://doi.org/10.48550/arXiv.2103.11943
21. Varghese, Blesson, et al. "Challenges and opportunities in edge computing." 2016 IEEE international conference on smart cloud (SmartCloud). IEEE, 2016. https://doi.org/10.1109/SmartCloud.2016.18 DOI: https://doi.org/10.1109/SmartCloud.2016.18
22. Barnum, Sean. "Standardizing cyber threat intelligence information with the structured threat information expression (stix)." Mitre Corporation 11 (2012): 1-22. https://www.mitre.org/sites/default/files/publications/stix.pdf
23. Sheta, Sagar Vishnubhai. "Developing efficient server monitoring systems using AI for real-time data processing." (2023). https://doi.org/10.2139/ssrn.5034125 DOI: https://doi.org/10.2139/ssrn.5034125
24. Kairatuly, Akhmetzhanov Batyrzhan, et al. "Development of a Security and Monitoring System for Server Equipment Utilizing IoT Technologies." 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2024. https://doi.org/10.1109/SIST61555.2024.10629465 DOI: https://doi.org/10.1109/SIST61555.2024.10629465
25. Massonet, Philippe, et al. "A monitoring and audit logging architecture for data location compliance in federated cloud infrastructures." 2011 IEEE international symposium on parallel and distributed processing workshops and PhD forum. IEEE, 2011. https://doi.org/10.1109/IPDPS.2011.304 DOI: https://doi.org/10.1109/IPDPS.2011.304
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Vladyslav Zaika, Oleh Chuiev, Olga Morozova, Tetyana Nikitina
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
