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.

翻译：理解用户在网页上的行为对于优化用户体验（UX）日益关键。本研究介绍了增强型网络使用挖掘（AWUM），这是一种旨在通过丰富CAWAL（组合应用日志与网络分析）框架提供的高级网络分析交互数据，来增强网络使用挖掘并改善用户体验的方法论。我们对一个月内收集的超过120万条会话记录（约8.5GB数据）进行了处理，并将其转化为增强型数据集。AWUM分析了会话结构、页面请求、服务交互以及退出方式。结果显示，87.16%的会话涉及多个页面，贡献了总页面浏览量的98.05%；40%的用户访问了多种服务，50%的用户选择了安全退出。关联规则挖掘揭示了频繁访问服务的模式，凸显了CAWAL相较于传统方法在精确性和效率上的优势。AWUM提供了对用户行为的全面理解，并为大规模用户体验优化展现出巨大潜力。