Big Data Analysis for Effective Monetization of over the Top TV Content

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Publisher: SMPTE — White Plains, NY, USA
Doc Type: Conference Paper
Content Type: Original Research
Volume: 2015, No. 10, pp. 1–6
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.
Publication Date: 2015-10-26
DOI: 10.5594/M001648
ISBN: Print: 978-1-61482-956-0
Link: https://doi.org/10.5594/M001648
Author(s): Arnav MendirattaViterbi School of Engineering, University of Southern California
Steve WongSiemens CVC
Reinhard GrimmSiemens CVC
Jay YogeshwarHitachi Data Systems
Shane ArchiquetteHitachi Data Systems
Keyword(s): OTT, Advertising, Electronic Commerce, Big Data
Copyright: © 2015 Society of Motion Picture and Television Engineers, Inc.

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Arnav Mendiratta, Steve Wong, Reinhard Grimm, Jay Yogeshwar, and Shane Archiquette; Big Data Analysis for Effective Monetization of over the Top TV Content, SMPTE Meetings and Conferences ( October 2015); SMPTE, 2015, ISBN: 978-1-61482-956-0. Available at https://doi.org/10.5594/M001648

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Arnav Mendiratta, Steve Wong, Reinhard Grimm, Jay Yogeshwar, and Shane Archiquette; Big Data Analysis for Effective Monetization of over the Top TV Content, SMPTE Meetings and Conferences ( October 2015); SMPTE, 2015, ISBN: 978-1-61482-956-0. Available at https://doi.org/10.5594/M001648

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<span class="citation">Arnav Mendiratta, Steve Wong, Reinhard Grimm, Jay Yogeshwar, and Shane Archiquette; <cite>Big Data Analysis for Effective Monetization of over the Top TV Content</cite>, SMPTE Meetings and Conferences ( October 2015); SMPTE, 2015, ISBN: 978-1-61482-956-0. Available at <a href="https://doi.org/10.5594/M001648" target="_blank" rel="noopener">https://doi.org/10.5594/M001648</a></span>

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<li>
Arnav Mendiratta, Steve Wong, Reinhard Grimm, Jay Yogeshwar, and Shane Archiquette; <cite id="bib-10-5594-m001648">Big Data Analysis for Effective Monetization of over the Top TV Content</cite>, SMPTE Meetings and Conferences ( October 2015); SMPTE, 2015, ISBN: 978-1-61482-956-0
<span class="doi">10.5594/M001648</span>
</li>