\section{Introduction}
\label{sec:intro}

Recently, online video sharing systems have been increasing and gaining popularity quickly. Watch and publish video on the Internet is becoming a routine on the
daily lives of Web users. According to comScore, in may of 2008, 74\% of the North American Internet audience watched videos online, corresponding to 12
billions of videos streamed only in that month~\cite{comscore1}.

Part of this success is associated with the change on the perspective of the user, from simple spectator to an active content creator. Additionally, these
environments allow several kinds of interactions between users and videos, such as friendship relations, video evaluation and publication of comments.
Associated with this new perspective of the Web, also known as Web 2.0, there are several challenges that the providers of these services need to deal with such
as content storage, server performance and scalability, personalization and service differentiation, detection of illegal content, etc. 
Thus, understanding characteristics of the requests and the patterns of access of users as well as aspects of user navigation 
when they connect to these sites is important for two main reasons. First, studies of user navigation allow to evaluate the performance of existing systems 
and lead to better site design~\cite{eurosys2009benzhao,lento2009chi} and advertisement placement policies~\cite{emarketer}.  Second, understanding how the workload of
social media is re-shaping the Internet traffic is valuable in designing the next-generation Internet infrastructure and content distribution
systems~\cite{pablo-www09keynote,bala@comsnets09}. Despite some efforts that characterize user generated workloads~\cite{moon2007,arlitt2007,benevenuto@imc09}, there
is not a work that provides a characterization of user navigation from the point of view of a video server.

This work gives the first step in this direction. Through a large data set obtained from the video service of Universo OnLine (UOL)\footnote{http://videolog.uol.com.br}, the largest Latin American
content provider, we present an in-depth workload characterization of sessions and requests on the video server.  Our study uses traces such as clickstream data, which
capture \textit{all} activities of users~\cite{clickstream2003,benevenuto@imc09}. 
We obtained a clickstream dataset, which described session-level summaries of over 3.6 million 
HTTP requests from more than 1 million different IPs during a 26-day period.

Using the clickstream data, we conducted two sets of analyses. First, we characterized the traffic and session patterns of the workload. We examined how frequently people connect
to the video server and for how long. Based on the data, we provide best fit models of session inter-arrival times and session length distributions.  Second, we provide a
definition of user session in our system and we characterize user navigation within sessions.  Our analysis unveil dominant user activities and the transition rates between
activities.Our study provides many interesting findings, including: 

\begin{itemize}

\item A typical user session of online video sharing systems lasts about 40 minutes, a high value in comparison with traditional Web systems.

\item The popularity distributions of accesses of objects (videos and tags) follow long tails.

\item The rankings of user activity in terms of the number of requests sent and sessions created follow long tail and exponential distributions, respectively.

\item The arrival request rate at the system presents a periodic pattern with higher intensity during the day and smaller intensity during the night.

\item The distributions of inter-request time and inter-session time can be modeled by exponential distributions.

\item For longer sessions, users spend more time viewing videos than in short sessions. 

\item Our analysis reveals different user profiles who access the system, which can be used by system administrators to personalize services.


\end{itemize}

The remainder of this work is organized as follows. Next section describes related work. Section~\ref{sec:data} presents statistics about the workload of the UOL
video service. Section~\ref{sec:charac} discusses the characterization of requests and sessions. In Section~\ref{sub:models}, we present an analysis of the
profile of the users who navigate on the system. Finally, Section~\ref{sec:conc} concludes the paper and present directions for future work.

