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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