Distributed Data Science Architectures for Large Scale Processing and Knowledge Discovery in Heterogeneous Networks
Keywords:
Distributed computing, data science architectures, large-scale processing, heterogeneous networks, edge computing, federated learning, graph processing, knowledge discovery, big data analytics, network intelligenceAbstract
The growing complexity and scale of heterogeneous networks, spanning domains such as telecommunications, social media, sensor systems, and cyber-physical infrastructures, necessitates distributed data science architectures capable of large-scale processing and knowledge discovery. Traditional centralized processing models are ill-suited for handling vast, diverse, and dynamic datasets. This paper explores modern distributed data science frameworks that enable efficient computation, knowledge inference, and real-time analytics in such networks. Key architectural paradigms, including edge-cloud collaboration, distributed graph processing, and federated analytics, are examined in light of scalability, fault tolerance, and performance. Emphasis is placed on data locality, heterogeneity-aware modeling, and adaptive learning algorithms. Furthermore, the paper evaluates architectural trade-offs and proposes a modular reference architecture for integrating distributed learning workflows in heterogeneous environments.
References
Dean J, Ghemawat S. MapReduce: Simplified data processing on large clusters. Commun. ACM 51, 107–113 (2004).
Zaharia M, Chowdhury M, Franklin MJ, Shenker S, Stoica I. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In: NSDI (2012).
Oleti, C.S. (2022). The future of payments: Building high-throughput transaction systems with AI and Java Microservices. World Journal of Advanced Research and Reviews, 16(03), 1401-1411. https://doi.org/10.30574/wjarr.2022.16.3.1281
Zhou X, Liu W, Zhang Y. Challenges in data integration for heterogeneous networks. IEEE Network 24, 12–18 (2010).
Abadi M et al. TensorFlow: A system for large-scale machine learning. In: OSDI (2016).
Oleti, C. S. (2022). Serverless intelligence: Securing J2EE-based federated learning pipelines on AWS. International Journal of Computer Engineering and Technology, 13(3), 163-180. https://doi.org/10.34218/IJCET_13_03_017
Li M et al. Scaling distributed machine learning with the parameter server. In: OSDI (2014).
McMahan HB et al. Communication-efficient learning of deep networks from decentralized data. In: AISTATS (2017).
Bonawitz K et al. Towards federated learning at scale: System design. In: SysML (2019).
Malewicz G et al. Pregel: A system for large-scale graph processing. In: SIGMOD (2010).
Gujjala, P.K.R. (2022). Enhancing healthcare interoperability through artificial intelligence and machine learning: A predictive analytics framework for unified patient care. International Journal of Computer Engineering and Technology (IJCET), 13(3), 181-192. https://doi.org/10.34218/IJCET_13_03_018
Gonzalez JE et al. PowerGraph: Distributed graph-parallel computation on natural graphs. In: OSDI (2012).
Satyanarayanan M, Bahl P, Caceres R, Davies N. The case for VM-based cloudlets in mobile computing. IEEE Pervasive Comput. 8, 14–23 (2015).
Kairouz P et al. Advances and open problems in federated learning. Found. Trends Mach. Learn. 14, 1–210 (2021).
Ghemawat S, Gobioff H, Leung ST. The Google file system. SOSP (2003).
Chen T et al. MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems. In: NIPS Workshop (2015).
Wang S, Tuor T, Salonidis T, Leung K, Makaya C, He T, Chan KC. Adaptive federated learning in resource constrained edge computing systems. IEEE J. Sel. Areas Commun. 37, 1205–1221 (2019).
Gujjala, P.K.R. (2023). Advancing Artificial Intelligence and Data Science: A Comprehensive Framework for Computational Efficiency and Scalability. International Journal of Research in Computer Applications and Information Technology, 6(1), 155–166. https://doi.org/10.34218/IJRCAIT_06_01_012
Zhang Y, Chen X, Lin X, Li H. A survey on federated learning systems: Vision, hype and reality. IEEE Trans. Knowl. Data Eng. (2021).
Shi W, Cao J, Zhang Q, Li Y, Xu L. Edge computing: Vision and challenges. IEEE Internet Things J. 3, 637–646 (2016).
