Web traffic: analysis of navigation data and modeling at single user level.
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1 Web traffic: analysis of navigation data and moling at single user level. José Javier Ramasco 1 Santanr Octubre 2006 Marta Sánchez La Lama
2 Outline Internet and the Web Navigation traces Data analysis at an aggregate level Individual-level data: navigation trees Mols of Web navigation 2 Santanr Octubre 2006 Marta Sánchez La Lama
3 Internet and the WWW (Web) 3 Santanr Octubre 2006 Marta Sánchez La Lama
4 Internet and the WWW (Web) 4 Santanr Octubre 2006 Marta Sánchez La Lama
5 Internet and the Web Friendster.com 5 Santanr Octubre 2006 Marta Sánchez La Lama
6 Internet and the Web 6 Santanr Octubre 2006 Marta Sánchez La Lama
7 Web navigation & navigation traces Santanr Octubre 2006 Marta Sánchez La Lama
8 Navigation traces 8 Santanr Octubre 2006 Marta Sánchez La Lama
9 Navigation traces (Web requests) Source MAC: 03:5a:66:17:0:5e Dest. MAC: 10::1:3f:51:2f Source IP: Dest. IP: Source Port: 421 Dest. Port: 80 GET /inx.html HTTP/1.1 Agent: SuperCrawler-200/beta Referer: Host: Santanr Octubre 2006 Marta Sánchez La Lama
10 Why to study navigation traces? 10 Santanr Octubre 2006 Marta Sánchez La Lama
11 Why to study navigation traces? 11 Santanr Octubre 2006 Marta Sánchez La Lama
12 Databases Emory University Stunts: 12,300 Faculty: ~ 3,200 Population: 70 k 5,6 Indiana University, Bloomington 12 Stunts: 42,000 Faculty: ~ 5,000 Santanr Octubre 2006 Population of the metro area: 5,6 million Marta Sánchez La Lama
13 Databases (Emory University) The database is formed by the weblogs of Emory University from Apr. 1st 2005 to Jan. 17th 2006 (41 weeks). Each click in a web of the university is registered at the time resolution of 1 second. 13 Santanr Octubre 2006 Marta Sánchez La Lama
14 Databases (Indiana University) The database is formed by the Web requests from a dorm of the University. Data collected from March 5, 2008 through May 3, million HTTP requests 1083 unique MAC addresses (Computers). 2.8 million page requests 67 unique users 630,000 Web servers 110,000 referring hosts 14 Santanr Octubre 2006 Marta Sánchez La Lama
15 Aggregate results 15 Santanr Octubre 2006 Marta Sánchez La Lama
16 Aggregate results 16 Santanr Octubre 2006 Marta Sánchez La Lama
17 Aggregate results 17 Santanr Octubre 2006 Marta Sánchez La Lama
18 Aggregate results 18 Santanr Octubre 2006 Marta Sánchez La Lama
19 Aggregate results 1 Santanr Octubre 2006 Marta Sánchez La Lama
20 Aggregate results 20 Santanr Octubre 2006 Marta Sánchez La Lama IP
21 Individual users results 21 Santanr Octubre 2006 Marta Sánchez La Lama
22 Individual users results (Sessions) 22 Santanr Octubre 2006 Marta Sánchez La Lama
23 Mols: PageRank 23 Santanr Octubre 2006 Marta Sánchez La Lama
24 Mols: BookRank 24 Santanr Octubre 2006 Marta Sánchez La Lama
25 Mols: bookmarks + topicality (ABC) 25 Santanr Octubre 2006 Marta Sánchez La Lama
26 Simulation vs empirical data 26 Santanr Octubre 2006 Marta Sánchez La Lama
27 Simulation vs empirical data 27 Santanr Octubre 2006 Marta Sánchez La Lama
28 Simulation vs empirical data 28 Santanr Octubre 2006 Marta Sánchez La Lama
29 Simulation vs empirical data 2 Santanr Octubre 2006 Marta Sánchez La Lama
30 Conclusions We have studied the Web navigation traces of a large number of users. Some of the features seem to be relatively universal spite natural user-user variability. We have proposed a family of mols able to reproduce eper and eper characteristics of the users navigation patterns. 30 How far should we go? Do this last simple mol implement topicality satisfactorily? And what about real time dynamics? Santanr Octubre 2006 Marta Sánchez La Lama
31 Collaborators & papers 31 Santanr Octubre 2006 Marta Sánchez La Lama
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