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Data-Driven Approaches for Intelligent System Performance Optimization

Author(s) Prof. Dr. Jay Liebowitz
Country United States
Abstract The rapid development of intelligent digital systems has increased the need for effective approaches to monitor, evaluate, and optimize system performance. Traditional performance optimization methods often depend on predefined rules, static configurations, and periodic evaluations, which may be insufficient for dynamic computational environments. Data-driven approaches provide an alternative by utilizing operational data, performance indicators, behavioral patterns, and historical information to identify inefficiencies and support continuous optimization.
This study examines data-driven approaches for intelligent system performance optimization and develops a conceptual framework integrating data collection, preprocessing, performance monitoring, predictive analytics, optimization modeling, decision support, and continuous feedback. A conceptual and descriptive research design is adopted through the synthesis of secondary scholarly literature related to intelligent systems, machine learning, predictive analytics, performance engineering, optimization, and data management.
The analysis indicates that data-driven techniques can improve resource allocation, processing efficiency, response time, system reliability, scalability, and operational decision-making. However, challenges involving data quality, model accuracy, computational complexity, system integration, cybersecurity, interpretability, and changing operational conditions remain significant. The proposed framework emphasizes continuous performance measurement and adaptive optimization rather than one-time system improvement. The study concludes that integrating data-driven intelligence with performance optimization can support more responsive, efficient, reliable, and scalable intelligent systems across diverse technological environments.
Keywords Data-Driven Systems, Intelligent Systems, Performance Optimization, Predictive Analytics, Machine Learning, System Efficiency, Data Analytics, Intelligent Computing, Adaptive Optimization
Field Engineering
Published In Volume 1, Issue 6, November-December 2019
Published On 2019-11-18
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