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    "abstract": "Consumption of digital-media is shifting to Over-the-Top (OTT) delivery model and no longer requires involvement of multiple system operator in content distribution. These benefits are balanced by lack of standards for delivery platforms, audio-levels and advertising delivery. Inadequate industry standards, ad-block use, limited bandwidth and differing viewer-experience challenges content monetizing and delivery. With availability of inexpensive processing power and efficacy of real-time analysis technologies for big metadata, industry focus has shifted to content-recommendation and hyper-targeted advertising resulting in enhanced customer experience and efficient content distribution. The metadata managed by OTT platform can create partial-differential-equations containing unknown multivariable functions and partial-derivatives that can be analyzed in real-time. This paper presents the challenges and opportunities for a telco-grade OTT management-and-delivery-platforms as an ecosystem for collection of big-data from content-owner and client. This metadata could be fed to robust real-time analytic systems for augmenting end-user experience while generating revenue for content-owners and service-providers.",
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    "authors": [
      {
        "affiliation": "Viterbi School of Engineering, University of Southern California",
        "name": "Arnav Mendiratta"
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      {
        "affiliation": "Siemens CVC",
        "name": "Steve Wong"
      },
      {
        "affiliation": "Siemens CVC",
        "name": "Reinhard Grimm"
      },
      {
        "affiliation": "Hitachi Data Systems",
        "name": "Jay Yogeshwar"
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      {
        "affiliation": "Hitachi Data Systems",
        "name": "Shane Archiquette"
      }
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      "holder": "Society of Motion Picture and Television Engineers, Inc.",
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      "year": "2015",
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    "docTitle": "Big Data Analysis for Effective Monetization of over the Top TV Content",
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    "isbn": {
      "print": "978-1-61482-956-0",
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