\section {Conclusions and Future Work}
\label{sec:conc}

In this work we use a real and representative workload to characterize access patterns of an online video sharing social network and study the user navigation profiles of this
system. As results, we provide several statistical models to various system characteristics, such as popularity of videos, users, and tags, inter-request and inter-session time
distributions, etc. Our analyses provide new and useful insights about user of online video sharing systems, which may help the design of future 
synthetic workload generation as well as drive the development of new infra-structures for this kind of 
service.
 
We model the navigation patterns of user sessions using the concept of UBMG. Using a clustering technique we provide an analysis of different user profiles accessing the system.
Our results can be used to drive service personalization policies as well as content recommendation for users.
 
As future work, we plan to characterize new workloads of the UOL video service, including aspects of content creation and social interactions. More importantly, we intend to study
the aspects that influence the popularity of videos, which are key to an emergent market, the association of advertisements to videos.

