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

arXiv: https://arxiv.org/abs/2510.17253 · HTML: https://mlsanity.com/paper/2510.17253/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 68 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [An innovative data collection method to eliminate the preprocessing phase in web usage mining](https://mlsanity.com/paper/2501.04364/) (2025)
- [CAWAL: A novel unified analytics framework for enterprise web applications and multi-server environments](https://mlsanity.com/paper/2503.23244/) (2025)

## Abstract (arXiv)

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.