Investigation into the Use of Quantum Computing for Big Data Analytics in Federal University, Wukari, Taraba State
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- NGN 5000
Background of the Study
Big data analytics has become an essential tool in various sectors, including education, for analyzing large datasets and deriving meaningful insights. Federal University, Wukari, Taraba State, is increasingly reliant on data-driven decision-making to enhance its academic and administrative processes. However, traditional data processing methods often struggle with the complexity and volume of big data, leading to slower analysis times and inaccurate predictions (Dixon et al., 2023). Quantum computing, with its potential to process exponentially large datasets faster and more efficiently than classical computers, could provide a transformative solution for big data analytics. By leveraging quantum algorithms and quantum machine learning, educational institutions can analyze big data more effectively, providing timely insights into student performance, resource allocation, and institutional planning.
The application of quantum computing to big data analytics at Federal University, Wukari could offer new opportunities to address challenges such as real-time data analysis, predictive modeling, and pattern recognition. This research aims to explore how quantum computing can be harnessed to enhance the university’s ability to analyze big data more effectively and efficiently.
Statement of the Problem
Despite the potential benefits of big data analytics, Federal University, Wukari faces significant challenges in processing and analyzing large datasets due to the limitations of classical computing methods. As the volume and complexity of data increase, traditional data analysis techniques become less effective and slower, hindering decision-making in key areas such as student performance prediction, resource allocation, and academic planning. Quantum computing has the potential to address these challenges by offering faster data processing and the ability to uncover patterns that classical computers may miss. However, its implementation in big data analytics at the university remains underexplored.
Objectives of the Study
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To explore the potential of quantum computing for big data analytics at Federal University, Wukari.
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To design a quantum-based model for enhancing big data analytics in the university’s academic and administrative processes.
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To evaluate the feasibility and impact of using quantum computing for big data analytics at Federal University, Wukari.
Research Questions
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How can quantum computing be applied to improve big data analytics at Federal University, Wukari?
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What advantages does quantum computing offer over traditional data processing methods in the context of big data analytics?
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What challenges may arise when implementing quantum computing for big data analytics at the university?
Significance of the Study
This study will contribute to the university’s ability to utilize big data more effectively by integrating quantum computing into its data analytics processes. The research will provide insights into how quantum computing can enhance decision-making, improve academic performance prediction, and support efficient resource allocation. The findings will also inform other universities considering the use of quantum computing in big data analytics.
Scope and Limitations of the Study
The study will focus on the application of quantum computing for big data analytics at Federal University, Wukari, Taraba State. The research will not address other applications of quantum computing outside of big data analytics and will be limited to academic and administrative data processing at the university.
Definitions of Terms
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Quantum Computing: A field of computing that uses principles of quantum mechanics to process information, offering exponential speed-up for certain types of problems.
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Big Data Analytics: The process of examining large and varied datasets to uncover hidden patterns, correlations, and insights.
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Predictive Modeling: A statistical technique used to predict future outcomes based on historical data.
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