ПРОБЛЕМЫ ИНТЕГРАЦИИ ИНФОРМАЦИОННЫХ СИСТЕМ В УСЛОВИЯХ БОЛЬШИХ ДАННЫХ И ПУТИ ИХ РЕШЕНИЯ

1. Chen, M., Mao, S., & Liu, Y. (2019). Big data: A survey. Mobile Networks and Applications, 24(1), 7–28. https://doi.org/10.1007/s11036-018-1233-8 2. Hashem, I. A. T., Yaqoob, I., Anuar, N. B., et al. (2019). The rise of “big data” on cloud computing. Information Systems, https://doi.org/10.1016/j.is.2014.07.006 47, 98–115. 3. Gandomi, A., & Haider, M. (2020). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 50, 104 116. https://doi.org/10.1016/j.ijinfomgt.2014.07.006 4. Bousdekis, A., & Mentzas, G. (2021). Enterprise integration and interoperability for big data–driven processes. Frontiers in Big Data, 4, 644651. https://doi.org/10.3389/fdata.2021.644651 5. Dave, D. M. K., & Mittapally, B. K. (2024). Data integration and interoperability in IoT: Challenges and strategies. International Journal of Computer Engineering & Technology, https://doi.org/10.34218/IJCET.15.1.2024.006 15(1), 55–70. 6. Park, J. (2019). Information systems interoperability: What lies beneath? ACM SIGMIS Database, 50(2), 31–47. https://doi.org/10.1145/1028099.1028103 7. Zikria, Y. B., Yu, H., Afzal, M., et al. (2020). Internet of Things (IoT): Operating system, applications and protocols design, and quality of service issues. IEEE Internet of Things https://doi.org/10.1109/JIOT.2018.2878639 Journal, 6(3), 4722–4735. 8. Kambatla, K., Kollias, G., Kumar, V., & Grama, A. (2019). Trends in big data analytics. Journal of Parallel and Distributed Computing, 74(7), 2561–2573. https://doi.org/10.1016/j.jpdc.2014.01.003 9. Serrano, M., Kamel, M., Rey, I., et al. (2020). A review of semantic data integration in distributed computing environments. Future Generation Computer Systems, 104, 120–137. https://doi.org/10.1016/j.future.2019.10.010 10. Saha, B., & Srivastava, D. (2019). Data quality: The other face of Big Data. Data Engineering Bulletin, 42(4), 47–59. 11. Stonebraker, M., & Hong, C. (2018). Data curation and integration in the era of big data. Communications https://doi.org/10.1145/3185594 of the ACM, 61(10), 36–42. 12. Villari, M., Celesti, A., & Fazio, M. (2019). Efficient real-time data integration in cloud–edge environments. IEEE Transactions on Cloud Computing, 7(3), 776–789. https://doi.org/10.1109/TCC.2017.2682098

13. Zhao, J., Zhang, X., & Xue, L. (2021). Ontology-driven semantic interoperability in distributed environments. Knowledge-Based Systems, 214, 106728. https://doi.org/10.1016/j.knosys.2020.106728 14. Ardagna, D., Casale, G., Ciavotta, M., Perez, J. F., & Wang, W. (2018). Quality-of-service in cloud computing: Modeling techniques and their applications. Journal of Internet Services https://doi.org/10.1186/s13174-018-0079-3 and Applications, 9(1), 1–23. 15. Li, W., & Choi, B. (2020). Data integration challenges in distributed systems: A systematic review. Journal of Systems and Software, 162, 110–118. https://doi.org/10.1016/j.jss.2019.110484 16. Tichenor, G., & Henson, T. (2022). Microservices-based architectures for scalable big data integration. IEEE https://doi.org/10.1109/ACCESS.2022.3172345 Access, 10, 45401–45415. 17. Wang, Y., Kung, L., & Byrd, T. A. (2018). Big Data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting & Social Change, https://doi.org/10.1016/j.techfore.2015.12.019 126, 3–13. 18. Naik, N., & Jenkins, P. (2020). Cloud-native containers for big data analytics: A performance https://doi.org/10.3390/fi12080129 analysis. Future Internet, 12(8), 129. 19. Ramanathan, V. (2021). Semantic enrichment for large-scale heterogeneous data integration. Information Processing & Management, 58(5), 102689. https://doi.org/10.1016/j.ipm.2021.102689 20. Kwon, O., Lee, N., & Shin, B. (2020). Data integration approaches in the era of big data and cloud computing: A taxonomy and survey. Journal of Information Science, 46(3), 313–332. https://doi.org/10.1177/0165551519844022.