Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework
This work addresses efficiency and reliability issues for large-scale web portal operators, though it appears incremental as it builds on existing machine learning methods with enhanced data integration.
The study tackled the problem of limited data diversity and quality in traditional web usage mining by integrating application logs with web analytics through the CAWAL framework, achieving over 92% accuracy in predicting user behavior and improving anomaly detection.
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.