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Research Paper | Computer Science & Engineering | United States of America | Volume 13 Issue 11, November 2024 | Popularity: 6.1 / 10
Data-Driven Decision Making: Advanced Database Systems for Business Intelligence
Maria Anurag Reddy Basani, Anudeep Kandi
Abstract: Data-driven decision-making is integral to modern businesses, relying heavily on Business Intelligence (BI) systems to analyze vast and complex datasets for actionable insights. Traditional BI systems often struggle with high query latency, limited scalability, insufficient predictive capabilities, and inadequate security measures, hindering their effectiveness in dynamic data environments. This paper presents an AI augmented database system designed to address these challenges within BI applications. The proposed system integrates AI driven query optimization, real-time data processing, advanced predictive analytics, and enhanced security protocols. The methodology involves implementing machine learning models for dynamic query prediction and caching, utilizing recurrent neural networks for improved time-series forecasting, and employing isolation forests for robust anomaly detection. Security is reinforced through role-based access control and AES-256 encryption. Experimental evaluations demonstrate a significant reduction in query latency by 77% for high-frequency queries, achieving an average response time of 15 ms compared to 65 ms in traditional systems. The predictive analytics model reduces the Mean Absolute Error to 0.54, outperforming conventional ARIMA models. Anomaly detection achieves a precision of 0.89 and recall of 0.92, indicating high reliability.
Keywords: Business Intelligence, AI-driven query optimization, real-time data processing, predictive analytics, anomaly detection, role-based access control, machine learning, data security, scalability, data-driven decision-making
Edition: Volume 13 Issue 11, November 2024
Pages: 844 - 850
DOI: https://www.doi.org/10.21275/SR241114034006
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