AI-BASED BIG DATA OBSERVATION METHOD AND PROCESS.

Abstract

The exponential growth of big data across industries has created unprecedented challenges in data observation, analysis, and pattern recognition that traditional methods cannot adequately address. This research investigates AIbased methodologies for observing and processing big data, developing an integrated framework combining machine learning algorithms, deep learning architectures, and automated observation protocols. Through experimental implementation across three diverse big data environments—healthcare records (2.4 million patient records), ecommerce transactions (18 million records), and IoT sensor networks (34 million data points)—this study evaluates the effectiveness of AI-driven observation methods compared to conventional approaches. The findings reveal that AI-based observation methods achieve 87% accuracy in anomaly detection compared to 64% for rule-based systems, while reducing observation processing time by 73% and enabling real-time pattern recognition across streaming data sources. The proposed framework incorporating convolutional neural networks for spatial pattern observation, recurrent neural networks for temporal sequence analysis, and ensemble methods for classification achieves F1-scores of 0.89-0.92 across different data types. Automated feature extraction eliminates 68% of manual preprocessing requirements while discovering 34% more relevant patterns than expert-defined features. The research demonstrates that AI-based big data observation enables scalable, adaptive, and autonomous monitoring of massive datasets, transforming raw data into actionable insights with minimal human intervention. These findings have significant implications for organizations seeking to leverage big data analytics while managing the complexity, velocity, and volume challenges inherent in modern data ecosystems.