ABES Engineering College Crossings Republik, Ghaziabad, Uttar Pradesh 201009
DOI:
https://doi.org/10.36676/urr.v12.i1.1469Keywords:
Intelligence, Network Optimization, Telecommunications, Capacity Planning, Predictive Analytics, Resource Allocation,Abstract
In today’s rapidly evolving telecommunications landscape, optimizing network performance and managing resources efficiently have become essential for maintaining service quality and maximizing profitability. Business Intelligence (BI) is increasingly being leveraged to gain actionable insights from vast amounts of network data, enabling operators to enhance network performance, improve capacity planning, and optimize resource allocation. This paper explores the integration of Business Intelligence frameworks into the telecommunications sector and their role in optimizing network operations.
References
• Khan, S. A., & Memon, Z. A. (2017). Business Intelligence for Telecom Industry: A Review. Journal of Computer Networks and Communications, 2017, 1-11.
• Cheng, W., Zhang, Q., & He, J. (2019). Network Capacity Planning and Optimization with Big Data in Telecommunications. Telecommunications Systems, 70(3), 461-472.
• Tariq, A., Ali, S., & Zafar, M. (2018). Data-Driven Optimization Techniques in Network Resource Management. IEEE Transactions on Network and Service Management, 15(4), 470-485.
• Guan, X., & Zhang, Y. (2019). Optimizing Network Performance in Telecommunication Systems Using Business Intelligence. International Journal of Communication Systems, 32(8), 1-11.
• Siddiqui, S., & Ahmed, R. (2020). Machine Learning for Optimizing Telecommunication Networks: A Business Intelligence Approach. Journal of Network and Computer Applications, 164, 102702.
• Liu, W., & Lin, C. (2019). Data Analytics for Network Traffic Management and Optimization. Telecommunication Systems, 71(1), 45-60.
• Ranjan, R., & Prasad, N. (2018). Resource Allocation in Telecommunication Networks: A Machine Learning Approach. Journal of Communications and Networks, 20(5), 527-537.
• Kumar, S., & Bhushan, B. (2021). Leveraging Business Intelligence for Network Traffic Optimization. IEEE Access, 9, 78547-78557.
• Nguyen, T., & Kim, D. (2020). Real-Time Data-Driven Network Optimization for Telecom Networks. Journal of Network and Systems Management, 28(4), 1018-1028.
• Bashir, M., & Raza, M. (2018). Business Intelligence Models for Predictive Network Resource Management. International Journal of Advanced Computer Science and Applications, 9(7), 56-63.
• Pérez, M. A., & Gonzalez, A. (2017). Capacity Planning in Telecommunications: Integrating Big Data and Business Intelligence. Computer Networks, 119, 11-21.
• Sharma, P., & Srivastava, S. (2021). Data-Driven Solutions for Network Capacity Management in Telecom. Telecommunications Policy, 45(5), 102122.
• Arvind, P., & Venkatesan, S. (2019). AI-Based Network Optimization for Telecom Service Providers. Journal of Artificial Intelligence Research, 68, 225-247.
• Zhang, X., & Liu, H. (2020). A Review of Network Optimization Techniques Using Machine Learning in Telecommunications. Telecommunications Science, 38(4), 100-113.
• Lee, J., & Hong, S. (2018). Integrating Predictive Analytics in Telecom Networks for Capacity and Resource Allocation. Journal of Communications and Networks, 20(6), 664-674.
• Xie, L., & Wang, C. (2019). Business Intelligence and Data Analytics for Improving Telecom Network Efficiency. International Journal of Networking and Computing, 9(2), 75-89.
• Feng, Y., & Chen, J. (2020). Dynamic Network Resource Allocation Using Machine Learning and Business Intelligence. IEEE Transactions on Mobile Computing, 19(3), 478-490.
• Zhou, Z., & Yang, G. (2017). Intelligent Resource Management in Telecommunication Networks Using Business Intelligence. Journal of Network and Computer Applications, 85, 48-59.
• Choi, K., & Lee, J. (2021). Data-Driven Decision Making in Telecom Networks: A Business Intelligence Perspective. Telecommunication Systems, 73(2), 413-429.
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