Behavior-Aware Operating System Optimization through Data Engineering: A Scalable Framework for Personalized UX Enhancements

With the exponential increase in digital interaction, modern operating systems (OS) must evolve from static utilities to intelligent platforms that adapt based on user behavior. This paper proposes a robust, scalable, and privacy-respecting data engineering framework to analyze user behavior—specifically application usage frequency, tool preferences, session durations, and feature interaction levels. The insights are used to drive adaptive operating system configurations in real time. We design and implement a behavioral telemetry pipeline using event stream processing, feature importance modeling, and unsupervised clustering to deliver personalized user experiences (UX). The framework aligns with the principles of human-computer interaction (HCI), data-driven design, and ambient intelligence. Empirical results show significant gains in UX efficiency, while compliance with privacy regulations is achieved through federated learning and anonymization strategies.