Pierce Vollucci and Benjamin Le and Jefferson Lai and Wenxuan Cai and Yaohui Ye

EECS Department, University of California, Berkeley

Technical Report No. UCB/EECS-2015-71

May 13, 2015

http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-71.pdf

This capstone project report covers the research and development of Smart Anomaly Detection and Subscriber Analysis in the domain of Online Video Data Analytics. In the co-written portions of this document, we discuss the projected commercialization success of our products by analyzing worldwide trends in online video, presenting a competitive business strategy, and describing several approaches towards the management of our intellectual property. In the individually written portion of this document, we discuss our investigation into smart anomaly detection techniques as well as pursue experiments using nonseasonal data to evaluate the effectiveness of the most viable techniques.

Advisors: George Necula

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BibTeX citation:

@mastersthesis{Vollucci:EECS-2015-71,
    Author= {Vollucci, Pierce and Le, Benjamin and Lai, Jefferson and Cai, Wenxuan and Ye, Yaohui},
    Editor= {Necula, George and Wroblewski, Don},
    Title= {Online Video Data Analytics},
    School= {EECS Department, University of California, Berkeley},
    Year= {2015},
    Month= {May},
    Url= {http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-71.html},
    Number= {UCB/EECS-2015-71},
    Abstract= {This capstone project report covers the research and development of Smart Anomaly Detection and Subscriber Analysis in the domain of Online Video Data Analytics. In the co-written portions of this document, we discuss the projected commercialization success of our products by analyzing worldwide trends in online video, presenting a competitive business strategy, and describing several approaches towards the management of our intellectual property. In the individually written portion of this document, we discuss our investigation into smart anomaly detection techniques as well as pursue experiments using nonseasonal data to evaluate the effectiveness of the most viable techniques.},
}

EndNote citation:

%0 Thesis
%A Vollucci, Pierce 
%A Le, Benjamin 
%A Lai, Jefferson 
%A Cai, Wenxuan 
%A Ye, Yaohui 
%E Necula, George 
%E Wroblewski, Don 
%T Online Video Data Analytics
%I EECS Department, University of California, Berkeley
%D 2015
%8 May 13
%@ UCB/EECS-2015-71
%U http://www2.eecs.berkeley.edu/Pubs/TechRpts/2015/EECS-2015-71.html
%F Vollucci:EECS-2015-71