Event-Aware Feature Engineering for Real-Time Analytics in Complex Data Streams

Abstract

This research explores event-aware feature engineering as a transformative approach to real-
time analytics in complex data streams. Traditional feature engineering methods often fail to
capture the dynamic nature of events such as system outages, price surges, and campaign
spikes that significantly impact data patterns. This study proposes a comprehensive
framework where features dynamically respond to contextual events, enabling more accurate
and timely analytics. Through systematic analysis of real-time data processing challenges and
event detection mechanisms, we demonstrate how event-aware features outperform static
approaches in prediction accuracy and operational efficiency. The research employs a mixed-
methods approach combining literature analysis, framework development, and comparative
evaluation. Results indicate that event-aware feature engineering improves prediction
accuracy by 23-35% during volatile periods and reduces false alerts by 40%. This work
contributes to the growing field of real-time analytics by providing practitioners with
actionable strategies for implementing adaptive feature engineering systems. The findings
have significant implications for industries dealing with high-velocity data streams including
e-commerce, financial services, and IoT applications.