Abstract
In data-driven world, organizations face challenges in managing and analyzing large volumes of data in real-time to make informed decisions. This paper proposes a scalable and efficient big data management and analytics framework for real-time deep decision support. The framework leverages advanced technologies such as distributed computing, parallel processing, and machine learning algorithms to enable organizations to process and analyze massive amounts of data quickly and accurately. By combining real-time data processing with deep decision support capabilities, the framework empowers decision-makers with timely insights and actionable intelligence to make informed decisions. The scalability and efficiency of the framework are demonstrated through experimental evaluations using real-world big data sets. The results show that the proposed framework outperforms existing solutions in terms of processing speed, resource utilization, and decision accuracy, making it an ideal choice for organizations seeking to harness the power of big data analytics for real-time decision support.
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Alla, K.R., Thangarasu, G. (2024). Scalable and Efficient Big Data Management and Analytics Framework for Real-Time Deep Decision Support. In: Hassan, F., Sunar, N., Mohd Basri, M.A., Mahmud, M.S.A., Ishak, M.H.I., Mohamed Ali, M.S. (eds) Methods and Applications for Modeling and Simulation of Complex Systems. AsiaSim 2023. Communications in Computer and Information Science, vol 1912. Springer, Singapore. https://doi.org/10.1007/978-981-99-7243-2_24
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