Characterizing User Behavior and Items in Multimedia Streaming Services

Recently, Multimedia Web content has gotten so much popular worldwide (e.g., YouTube and Netflix). Traditional corporations are also contracting online multimedia platforms to provide content to their users. These services are called non-UGC (non User Generated Content), where these companies produce and publish content for client consumption. In this work we present a methodology for characterizing and analyzing multimedia streaming services. We validate it using actual data from one of the most popular online video streaming provider of Latin American, called Samba Tech. Our methodology is divided in four modules that allow evaluating and understanding different views and data characteristics.

[1]  Pablo Rodriguez,et al.  I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system , 2007, IMC '07.

[2]  Balachander Krishnamurthy,et al.  Key differences between Web 1.0 and Web 2.0 , 2008, First Monday.

[3]  Flavio Figueiredo,et al.  Understanding video-ad consumption on YouTube: a measurement study on user behavior, popularity, and content properties , 2016, WebSci.

[4]  Padmini Srinivasan,et al.  What's trending?: mining topical trends in UGC systems with YouTube as a case study , 2011, MDMKDD '11.

[5]  Tim Brecht,et al.  Characterizing the workload of a netflix streaming video server , 2016, 2016 IEEE International Symposium on Workload Characterization (IISWC).

[6]  Luam C. Totti,et al.  ROCK: A Characterization Methodology for Web Multimedia Services Based on an Informational Hierarchy , 2011, Webmedia 2011.

[7]  Touradj Ebrahimi,et al.  Multimedia content analysis for emotional characterization of music video clips , 2013, EURASIP J. Image Video Process..

[8]  Almeida Junior,et al.  Avaliação de transmissão ao vivo de grandes eventos pela internet , 2015 .

[9]  Daniel Lewis,et al.  What is web 2.0? , 2006, CROS.

[10]  Dorgival Guedes,et al.  Caracterização do Comportamento dos Espectadores em Transmissões de Vı́deo ao Vivo Geradas por Usuários , 2009 .

[11]  Virgílio A. F. Almeida,et al.  A hierarchical characterization of a live streaming media workload , 2006, TNET.

[12]  Adriano M. Pereira,et al.  Modeling, Characterization and Recommendation of Multimedia Web Content Services , 2013, 2013 IEEE International Symposium on Multimedia.

[13]  Alec Wolman,et al.  Measurement and Analysis of a Streaming Media Workload , 2001, USITS.