Optimized Algorithmic Models for Large-Scale Data Analytics and Intelligent Database

Abstract

The exponential growth of digital information has made data a crucial asset for decision-making and innovation. However, traditional database systems often face challenges managing the volume, speed, and complexity of modern data. This paper examines optimized algorithmic approaches that enhance the efficiency, scalability, and intelligence of data analytics and database systems. The study focuses on three key areas: computational optimization, intelligent data management, and system scalability. Computational optimization applies parallel algorithms, advanced indexing, and machine learning–based query tuning to improve processing speed. Intelligent data management introduces adaptive storage, automated schema updates, and AI-driven workload prediction for dynamic system adjustment. Scalability emphasizes cloud-native architectures, stream processing, and fault-tolerant infrastructures that maintain high performance under growing data loads. This research contributes by showing how algorithmic models can be integrated into modern database architectures to support big data analytics with minimal latency and optimal resource use. It also underscores the role of intelligent databases in enabling advanced decision-making in healthcare, finance, e-commerce, and research. The findings indicate that future database ecosystems will depend on the convergence of AI, distributed computing, and real-time analytics. Such integration will bridge the gap between theoretical algorithm design and practical applications, leading to next-generation intelligent database platforms capable of self-optimization, autonomous learning, and adaptive performance in dynamic environments.