• DocumentCode
    721064
  • Title

    Multimedia Big Data Computing for In-Depth Event Analysis

  • Author

    Tous, Ruben ; Torres, Jordi ; Ayguade, Eduard

  • Author_Institution
    Barcelona Supercomput. Center (BSC), Univ. Politec. de Catalunya, Barcelona, Spain
  • fYear
    2015
  • fDate
    20-22 April 2015
  • Firstpage
    144
  • Lastpage
    147
  • Abstract
    While the most part of "big data" systems target text-based analytics, multimedia data, which makes up about2/3 of internet traffic, provide unprecedented opportunities for understanding and responding to real world situations and challenges. Multimedia Big Data Computing is the new topic that focus on all aspects of distributed computing systems that enable massive scale image and video analytics. During the course of this paper we describe BPEM (Big Picture Event Monitor), a Multimedia Big Data Computing framework that operates over streams of digital photos generated by online communities, and enables monitoring the relationship between real world events and social media user reaction in real-time. As a case example, the paper examines publicly available social media data that relate to the Mobile World Congress 2014 that has been harvested and analyzed using the described system.
  • Keywords
    Big Data; Internet; multimedia computing; text analysis; Internet traffic; Mobile World Congress; big data systems; big picture event monitor; digital photos; distributed computing systems; in-depth event analysis; massive scale image; multimedia Big Data computing; multimedia big data computing; multimedia data; online communities; social media data; social media user reaction; text-based analytics; video analytics; Big data; Mobile communication; Multimedia communication; Multimedia computing; Real-time systems; Sparks; Streaming media; analysis; barcelona; big data; image; movile congress; multimedia; multimodal; spark;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Multimedia Big Data (BigMM), 2015 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-8687-3
  • Type

    conf

  • DOI
    10.1109/BigMM.2015.39
  • Filename
    7153868