Abstract
Tracking and recording users' browsing behaviors on the web down to individual mouse clicks can create massive web session logs. While such web session data contains valuable information about user behaviors, the ever-increasing data size has placed a big challenge to analyzing and visualizing the data. An efficient data analysis framework requires both powerful computational analysis and interactive visualization. Following the visual analytics mantra "Analyze first, show the important, zoom, filter and analyze further, details on demand", we introduce a two-tier visual analysis system, TrailExplorer2, to discover knowledge from massive log data. The system supports a visual analysis process iterating between two steps: querying web sessions and visually analyzing the retrieved data. The query happens at the lower tier where terabytes of web session data are processed in a cluster. At the upper tier, the extracted web sessions with much smaller scale are visualized on a personal computer for interactive exploration. Our system visualizes a sorted list of web sessions' temporal patterns and enables data exploration at different levels of details. The query-visualization-exploration process iterates until a satisfactory conclusion is achieved. We present two case studies of TrailExplorer2 using real world session data from eBay to demonstrate the system's effectiveness.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings |
| Pages | 65-72 |
| Number of pages | 8 |
| DOIs | |
| State | Published - Dec 1 2012 |
| Event | 2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 - Seattle, WA, United States Duration: Oct 14 2012 → Oct 19 2012 |
Other
| Other | 2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 |
|---|---|
| Country | United States |
| City | Seattle, WA |
| Period | 10/14/12 → 10/19/12 |
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Keywords
- H.5.m [Information Interfaces and presentation (e.g., HCI)]: Miscellaneous
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition
- Information Systems
Cite this
Visual analysis of massive web session data. / Shen, Zeqian; Wei, Jishang; Sundaresan, Neel; Ma, Kwan-Liu.
IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings. 2012. p. 65-72 6378977.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Visual analysis of massive web session data
AU - Shen, Zeqian
AU - Wei, Jishang
AU - Sundaresan, Neel
AU - Ma, Kwan-Liu
PY - 2012/12/1
Y1 - 2012/12/1
N2 - Tracking and recording users' browsing behaviors on the web down to individual mouse clicks can create massive web session logs. While such web session data contains valuable information about user behaviors, the ever-increasing data size has placed a big challenge to analyzing and visualizing the data. An efficient data analysis framework requires both powerful computational analysis and interactive visualization. Following the visual analytics mantra "Analyze first, show the important, zoom, filter and analyze further, details on demand", we introduce a two-tier visual analysis system, TrailExplorer2, to discover knowledge from massive log data. The system supports a visual analysis process iterating between two steps: querying web sessions and visually analyzing the retrieved data. The query happens at the lower tier where terabytes of web session data are processed in a cluster. At the upper tier, the extracted web sessions with much smaller scale are visualized on a personal computer for interactive exploration. Our system visualizes a sorted list of web sessions' temporal patterns and enables data exploration at different levels of details. The query-visualization-exploration process iterates until a satisfactory conclusion is achieved. We present two case studies of TrailExplorer2 using real world session data from eBay to demonstrate the system's effectiveness.
AB - Tracking and recording users' browsing behaviors on the web down to individual mouse clicks can create massive web session logs. While such web session data contains valuable information about user behaviors, the ever-increasing data size has placed a big challenge to analyzing and visualizing the data. An efficient data analysis framework requires both powerful computational analysis and interactive visualization. Following the visual analytics mantra "Analyze first, show the important, zoom, filter and analyze further, details on demand", we introduce a two-tier visual analysis system, TrailExplorer2, to discover knowledge from massive log data. The system supports a visual analysis process iterating between two steps: querying web sessions and visually analyzing the retrieved data. The query happens at the lower tier where terabytes of web session data are processed in a cluster. At the upper tier, the extracted web sessions with much smaller scale are visualized on a personal computer for interactive exploration. Our system visualizes a sorted list of web sessions' temporal patterns and enables data exploration at different levels of details. The query-visualization-exploration process iterates until a satisfactory conclusion is achieved. We present two case studies of TrailExplorer2 using real world session data from eBay to demonstrate the system's effectiveness.
KW - H.5.m [Information Interfaces and presentation (e.g., HCI)]: Miscellaneous
UR - http://www.scopus.com/inward/record.url?scp=84872190817&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84872190817&partnerID=8YFLogxK
U2 - 10.1109/LDAV.2012.6378977
DO - 10.1109/LDAV.2012.6378977
M3 - Conference contribution
AN - SCOPUS:84872190817
SN - 9781467347334
SP - 65
EP - 72
BT - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
ER -