Session Viewer: Visual Exploratory Analysis of Web Session Logs
Abstract
Large-scale session log analysis typically includes statistical methods and detailed log examinations. While both methods have merits, statistical methods can miss previously unknown sub- populations in the data and detailed analyses may have selection biases. We therefore built Session Viewer, a visualization tool to facilitate and bridge between statistical and detailed analyses. Taking a multiple-coordinated view approach, Session Viewer shows multiple session populations at the Aggregate, Multiple, and Detail data levels to support different analysis styles. To bridge between the statistical and the detailed analysis levels, Session Viewer provides fluid traversal between data levels and side-by-side comparison at all data levels. We describe an analysis of a large-scale web usage study to demonstrate the use of Session Viewer, where we quantified the importance of grouping sessions based on task type.
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There are no precedents for representation of continuous data in log-data analysis using exploratory sequential data analysis.
“While this approach has been applied to log-data analysis for session events grouped into a small number of distinct categories [32], there are no precedents for representation of continuous data.”
From Analyzing Engagement in a Web-Based Intervention Platform Through Visualizing Log-Data (2014) · cites this paper as [32]
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- Authors
- Lam, H., Russell, D., Tang, D., & Munzner, T.
- Affiliations
- University of British Columbia (CA)
- Google (United States) (US)
- Venue
- 2007 IEEE Symposium on Visual Analytics Science and Technology
- Publisher
- IEEE
- Published
- 2007-10
- Pages
- 147-154
- OpenAlex
- W2156017999
- Cited by
- 62
- References
- 38
- Citations by year
- 2026: 1 · 2024: 1 · 2023: 3 · 2022: 2 · 2021: 1 · 2020: 2 · 2019: 5 · 2018: 4 · 2017: 6 · 2016: 4 · 2015: 4 · 2014: 3 · 2013: 1 · 2012: 5
- FWCI
- 4.37
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Conference on Visual Analytics Science and Technology (VAST) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Data Visualization and Analytics
- Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- visualization tools, exploratory data analysis, side-by-side comparison, web usage mining, user behavior analysis
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