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

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Title: Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data.
Authors: Canay, Özkan1,2 (AUTHOR) canay@sakarya.edu.tr, Kocabıçak, Ümit3,4 (AUTHOR)
Source: International Journal of Human-Computer Interaction. Jun2025, Vol. 41 Issue 11, p7152-7171. 20p.
Subjects: Web analytics, Association rule mining, Blogs, Behavioral assessment, User experience
Abstract: A detailed understanding of user behavior on the web is crucial for optimizing user experience (UX) through data-driven analysis. This study introduces Augmented Web Usage Mining (AWUM), an approach that enhances web usage mining by enriching interaction data collected through the CAWAL (Combined Application Log and Web Analytics) framework. Over 1.2 million session records gathered within one month were transformed into 8.5 GB of enriched data and analyzed using AWUM to investigate session structures, page requests, service interactions, and exit behaviors across user segments. Results revealed that 87.16% of sessions involved multiple page visits, accounting for 98.05% of total pageviews. Furthermore, 76.2% of users accessed multiple services, and 57.2% of sessions involved secure exits during sensitive transactions. Association rule mining identified frequent service usage patterns, demonstrating AWUM's superiority in precision and efficiency compared to traditional web usage mining methods, thereby supporting the development of more effective UX strategies. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Augmented Web Usage Mining and User Experience Optimization with CAWAL's Enriched Analytics Data.
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  Data: <searchLink fieldCode="AR" term="%22Canay%2C+Özkan%22">Canay, Özkan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> canay@sakarya.edu.tr</i><br /><searchLink fieldCode="AR" term="%22Kocabıçak%2C+Ümit%22">Kocabıçak, Ümit</searchLink><relatesTo>3,4</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Human-Computer+Interaction%22">International Journal of Human-Computer Interaction</searchLink>. Jun2025, Vol. 41 Issue 11, p7152-7171. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Web+analytics%22">Web analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Association+rule+mining%22">Association rule mining</searchLink><br /><searchLink fieldCode="DE" term="%22Blogs%22">Blogs</searchLink><br /><searchLink fieldCode="DE" term="%22Behavioral+assessment%22">Behavioral assessment</searchLink><br /><searchLink fieldCode="DE" term="%22User+experience%22">User experience</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: A detailed understanding of user behavior on the web is crucial for optimizing user experience (UX) through data-driven analysis. This study introduces Augmented Web Usage Mining (AWUM), an approach that enhances web usage mining by enriching interaction data collected through the CAWAL (Combined Application Log and Web Analytics) framework. Over 1.2 million session records gathered within one month were transformed into 8.5 GB of enriched data and analyzed using AWUM to investigate session structures, page requests, service interactions, and exit behaviors across user segments. Results revealed that 87.16% of sessions involved multiple page visits, accounting for 98.05% of total pageviews. Furthermore, 76.2% of users accessed multiple services, and 57.2% of sessions involved secure exits during sensitive transactions. Association rule mining identified frequent service usage patterns, demonstrating AWUM's superiority in precision and efficiency compared to traditional web usage mining methods, thereby supporting the development of more effective UX strategies. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Human-Computer Interaction is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/10447318.2025.2495839
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        Text: English
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        Type: general
      – SubjectFull: Association rule mining
        Type: general
      – SubjectFull: Blogs
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      – SubjectFull: Behavioral assessment
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      – SubjectFull: User experience
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              Text: Jun2025
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