Data Mining Systems and Platforms: Efficiency, Scalability, and Privacy

Abstract

Joshua Adiele

Modern data mining systems face increasing demands for performance, scalability, and privacy preservation. As data volumes grow exponentially, platforms must evolve to support distributed architectures, real-time analytics, and secure processing. This paper presents a comprehensive study of current data mining platforms, evaluating their efficiency, scalability strategies, and privacy-preserving mechanisms. We propose a modular framework that integrates parallel processing, federated learning, and differential privacy to enhance system robustness. Experimental results on benchmark datasets demonstrate significant improvements in throughput and privacy compliance, offering a roadmap for next-generation data mining platforms.

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