Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911220781547520 |
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| author | Canay, Özkan Kocabıcak, {Ü}mit |
| author_facet | Canay, Özkan Kocabıcak, {Ü}mit |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_17253 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data Canay, Özkan Kocabıcak, {Ü}mit Human-Computer Interaction Artificial Intelligence 68T09, 68U35 H.5.2; H.3.3; I.2.6 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. |
| title | Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data |
| topic | Human-Computer Interaction Artificial Intelligence 68T09, 68U35 H.5.2; H.3.3; I.2.6 |
| url | https://arxiv.org/abs/2510.17253 |