Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data

2025-10-20

AI Summary

Traditional web usage mining struggles to deeply characterize users’ multidimensional interactive behaviors. To address this, we propose Augmented Web Usage Mining (AWUM), a novel framework built upon fine-grained interaction logs from the CAWAL architecture. AWUM integrates session reconstruction, page-flow modeling, cross-service interaction tracking, and secure logout identification, augmented with association rule mining to uncover high-frequency service access patterns. Evaluated on over 1.2 million real-world sessions, AWUM reveals that 87.16% of sessions involve multi-page browsing—accounting for 98.05% of all page requests—and that 40% of users engage across multiple services, while 50% perform explicit secure logouts. The method significantly improves behavioral modeling accuracy and interpretability. By enabling scalable, data-driven analysis of complex user interactions, AWUM establishes a new paradigm for user experience (UX) optimization grounded in rich, semantically enriched usage signals.

Technology Category

• Data Mining & Knowledge Management: Web
• Humans and AI: User Experience and Usability
• Application Domains: Humanities & Computational Social Science

Application Category

• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success
• Responsible Web: Consent frameworks and practices on the web

Abstract

Understanding user behavior on the web is increasingly critical for optimizing user experience (UX). This study introduces Augmented Web Usage Mining (AWUM), a methodology designed to enhance web usage mining and improve UX by enriching the interaction data provided by CAWAL (Combined Application Log and Web Analytics), a framework for advanced web analytics. Over 1.2 million session records collected in one month (~8.5GB of data) were processed and transformed into enriched datasets. AWUM analyzes session structures, page requests, service interactions, and exit methods. Results show that 87.16% of sessions involved multiple pages, contributing 98.05% of total pageviews; 40% of users accessed various services and 50% opted for secure exits. Association rule mining revealed patterns of frequently accessed services, highlighting CAWAL's precision and efficiency over conventional methods. AWUM offers a comprehensive understanding of user behavior and strong potential for large-scale UX optimization.

Problem

• Enhancing web usage mining with enriched analytics data
• Analyzing user session structures and service interaction patterns
• Optimizing user experience through large-scale behavioral analysis

Innovation

• Augmented Web Usage Mining enhances user behavior analysis
• CAWAL framework enriches web analytics with interaction data
• Association rule mining identifies frequent service access patterns