Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework

TL;DR

This paper introduces the CAWAL framework that combines enriched session and page view data with machine learning models to improve predictive accuracy and anomaly detection in large-scale web portals.

Contribution

It presents a novel integration of application logs with web analytics via the CAWAL framework, enhancing data quality and process efficiency in web usage mining.

Findings

Achieved over 92% accuracy in user behavior prediction

Enhanced anomaly detection capabilities

Improved data diversity and process efficiency

Abstract

This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining (WUM) applications. Traditional WUM methods often rely on web server logs, which limit data diversity and quality. Integrating application logs with web analytics, the CAWAL framework creates comprehensive session and page view datasets, providing a more detailed view of user interactions and effectively addressing these limitations. This integration enhances data diversity and quality while eliminating the preprocessing stage required in conventional WUM, leading to greater process efficiency. The enriched datasets, created by cross-integrating session and page view data, were applied to advanced machine learning models, such as Gradient Boosting and Random Forest, which are known for their effectiveness in capturing complex patterns and modeling non-linear relationships. These models achieved over 92% accuracy in predicting user behavior and significantly improved anomaly detection capabilities. The results show that this approach offers detailed insights into user behavior and system performance metrics, making it a reliable solution for improving large-scale web portals' efficiency, reliability, and scalability.