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

Özkan Canay, {Ü}mit Kocabıcak

TL;DR

The paper tackles the limitations of traditional WUM, notably session misdefinition, data quality issues, and privacy concerns, in large-scale, multi-service web portals. It introduces CAWAL, a framework that fuses application logs with web analytics to produce enriched analytics data, and AWUM, a methodology that leverages these data to perform WUM with reduced preprocessing and higher accuracy. On a dataset of over 1.2 million sessions (~8.5 GB), AWUM reveals that 87.16% of sessions are multi-page and that 98.05% of pageviews come from these sessions, while analyzing exit methods and cross-service transitions to derive strong association rules via Apriori. The approach demonstrates improved data quality and UX insights, enabling scalable, privacy-conscious user behavior analysis that can inform service design and security decisions in large-scale portals.

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.

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

TL;DR

The paper tackles the limitations of traditional WUM, notably session misdefinition, data quality issues, and privacy concerns, in large-scale, multi-service web portals. It introduces CAWAL, a framework that fuses application logs with web analytics to produce enriched analytics data, and AWUM, a methodology that leverages these data to perform WUM with reduced preprocessing and higher accuracy. On a dataset of over 1.2 million sessions (~8.5 GB), AWUM reveals that 87.16% of sessions are multi-page and that 98.05% of pageviews come from these sessions, while analyzing exit methods and cross-service transitions to derive strong association rules via Apriori. The approach demonstrates improved data quality and UX insights, enabling scalable, privacy-conscious user behavior analysis that can inform service design and security decisions in large-scale portals.

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.
Paper Structure (20 sections, 5 equations, 5 figures, 6 tables)

This paper contains 20 sections, 5 equations, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related work
  3. Web usage mining process and methods
  4. The role of web usage mining in user experience design
  5. Challenges in existing WUM tools and UX integration
  6. Methodology
  7. CAWAL model and framework
  8. Integrating CAWAL into web usage mining
  9. Augmented web usage mining approach
  10. Data reliability and privacy
  11. Generation of enriched analytics data
  12. Schemas of data files
  13. Preparation of time-frame-based datasets
  14. User experience-driven analysis and results
  15. Analysis of bounce rate and client attributes
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Application logging and web analytics processes combined by CAWAL.
  • Figure 2: Usage of web access data collected with CAWAL as data source in web usage mining.
  • Figure 3: Standard WUM process and CAWAL's Augmented WUM.
  • Figure 4: Service-based analysis of portal exit methods.
  • Figure 5: Transition paths and weights between portal services.