eISSN: 2791-173X editor@grpublishing.org
All Journals
Submit your article

Global Multidisciplinary Journal

Open Access Peer Review International
Open Access

Architecting Intelligent Real-Time Distributed Systems: Integrating Event Streaming, Approximate Nearest Neighbor Search, Machine Learning, Serverless Computing, And Neuroprosthetic Applications

DOI
PDF
Dr. Eleanor M. Whitaker 1 ,
4 Department of Computer Science, University of Edinburgh, United Kingdom

Abstract

The exponential growth of data-intensive applications has transformed the landscape of distributed systems, necessitating architectures that integrate real-time data ingestion, scalable computation, intelligent analytics, and adaptive resource management. This research synthesizes foundational and contemporary contributions in distributed messaging systems, MapReduce-based file management, serverless computing, complex event processing, resource-aware partitioning, approximate nearest neighbor search, and machine learning frameworks to propose a unified architectural paradigm for intelligent real-time distributed systems. Drawing upon seminal works on Apache Kafka (Kreps, Narkhede, & Rao, 2011), adaptive MapReduce storage (Tudoran, Costan, & Antoniu, 2014), MLlib in Apache Spark (Meng et al., 2016), serverless computing (Grier, 2019), complex event processing (Cugola & Margara, 2012), resource-aware partitioning (Kulkarni et al., 2015), and approximate nearest neighbor algorithms (Arya & Mount, 1993; Kleinberg, 1997; Indyk & Motwani, 1998; Kushilevitz, Ostrovsky, & Rabani, 1998), the study develops a comprehensive theoretical framework for scalable intelligent infrastructures. Furthermore, emerging AI-powered neuroprosthetic systems (Pulicharla & Premani, 2024) are examined as a high-impact application domain requiring ultra-low-latency analytics and adaptive distributed coordination. The study proposes an integrative architecture that leverages event streaming, distributed machine learning, approximate similarity search, and serverless orchestration while incorporating adaptive leader selection mechanisms for reliability (Sayyed, 2025). Through detailed conceptual modeling and scenario-based evaluation, the findings demonstrate that synergistic integration of these paradigms enhances scalability, resilience, responsiveness, and computational efficiency. The paper concludes by identifying research frontiers in hybrid cloud optimization, edge deployment, and cognitive cyber-physical systems.

 

Keywords

Distributed systems, Event streaming, Approximate nearest neighbor search, Serverless computing

References

📄 Arya, S., & Mount, D. (1993). Approximate nearest neighbor queries in fixed dimensions. Proceedings of the Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, 271–280.
📄 Cugola, G., & Margara, A. (2012). Processing flows of information: From data stream to complex event processing. ACM Computing Surveys, 44(3), 1–61.
📄 Grier, D. A. (2019). Serverless computing: A revolution in cloud architecture. IEEE Computer, 52(1), 15–17.
📄 Hasso, A., & Lutz, T. (2016). Optimizing real-time big data applications in hybrid cloud environments. International Journal of Cloud Computing and Big Data Analytics, 3(2), 78–88.
📄 Indyk, P., & Motwani, R. (1998). Approximate nearest neighbors: Towards removing the curse of dimensionality. Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, 604–613.
📄 Kleinberg, J. (1997). Two algorithms for nearest-neighbor search in high dimensions. Proceedings of the Twenty-ninth Annual ACM Symposium on Theory of Computing, 599–608.
📄 Kreps, J., Narkhede, N., & Rao, J. (2011). Kafka: A distributed messaging system for log processing. Proceedings of the 2011 NetDB Workshop, 1–7.
📄 Kulkarni, S. R., Sivathanu, M., Sridharan, K., & Govindarajan, R. (2015). Resource-aware data partitioning for distributed databases. IEEE Transactions on Parallel and Distributed Systems, 26(4), 1232–1244.
📄 Kushilevitz, E., Ostrovsky, R., & Rabani, Y. (1998). Efficient search for approximate nearest neighbor in high dimensional spaces. Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, 614–623.
📄 Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., et al. (2016). MLlib: Machine learning in Apache Spark. Journal of Machine Learning Research, 17(1), 1235–1241.
📄 Pulicharla, M. R., & Premani, V. (2024). AI-powered neuroprosthetics for brain-computer interfaces (BCIs). World Journal of Advanced Engineering Technology and Sciences, 12(1), 109–115.
📄 Sayyed, Z. (2025). Application Level Scalable Leader Selection Algorithm for Distributed Systems. International Journal of Computational and Experimental Science and Engineering, 11(3). https://doi.org/10.22399/ijcesen.3856
📄 Tudoran, R., Costan, A., & Antoniu, G. (2014). Adaptive file management in MapReduce: Efficiency vs. scalability trade-offs. Future Generation Computer Systems, 37, 62–77.

How to Cite

Dr. Eleanor M. Whitaker. (2026). Architecting Intelligent Real-Time Distributed Systems: Integrating Event Streaming, Approximate Nearest Neighbor Search, Machine Learning, Serverless Computing, And Neuroprosthetic Applications. Global Multidisciplinary Journal, 5(02), 54-59. https://www.grpublishing.org/journals/index.php/gmj/article/view/342

Most read articles by the same author(s)

1 2 3 4 5 6 7 8 9 10 > >> 

Similar Articles

51-60 of 83

You may also start an advanced similarity search for this article.