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Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
Use of Big Data Technology in the area of Video Analytics
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Use of Big Data Technology in the area of Video Analytics

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2013 한국데이터사이언스학회 학술대회 및 정기총회(2013. 11. 29) - Ong Beng Hui …

2013 한국데이터사이언스학회 학술대회 및 정기총회(2013. 11. 29) - Ong Beng Hui
“Use of Big Data Technology in the area of Video Analytics” 발표 자료입니다.

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  • 1. Use of Big Data Technology in the area of Video Analytics
  • 2. WHO WE ARE EXPERIENCED TEAM Founded in 2007 300+ employees worldwide Global footprint of 200M unique users in 130 countries Ooyala works with the most successful broadcast and media companies in the world BACKING INNOVATION Ooyala was first to innovate: Integrated content discovery engine Real-time analytics Integrated OTT delivery Adaptive bit rate streaming for Flash Integrated paywall solution Services-based Android video runtime 2
  • 3. THE FUTURE IS HERE 10X Growth in mobile and tablet viewing in the past two years. 2.2 Billion people will be watching online video by 2017. 27% of adults watch videos on non-TV devices every day. 59% of adults in the United States watch OTT content weekly. 3
  • 4. BEYOND SIMPLE ENABLEMENT… Deliver video content Scheduled, linear learning Static system Bolt-on analytics Limited monetization options Broadcast blast 4
  • 5. …TO SMART ENGAGEMENT Real-time consumer intelligence 1:1 personalization Automated, self-improving system Multiple monetization strategies 5
  • 6. …TO SMART ENGAGEMENT REAC H Bigger, increasingly fragmented audiences MEASURE Engagement and optimize programming MONETIZE Content to generate maximum return 6
  • 7. OOYALA SOLUTION • Make smarter programming and promotion decisions • Streamline, centralize workflows • Support multiple monetization strategies including ads, subscriptions, PPV • Maximize ad revenues, subscribers, transactions • Deliver to the right viewer, on the right device, at the right time • Premium playback, content protection, performance across devices • Provide personalized viewer experiences; expose your whole library, including live and VOD • Acquire and retain larger, more engaged audiences 7
  • 8. DISCOVERY INNOVATION Algorithm Profiles Goal-based Recommendation Engine* Collaborative Filtering (freshness weighted) Completion Rate CTR Real-time Recommendations Collaborative Filtering Trending Popular Historical Recommendation Performance Real-time Analytics Feedback Loop Player / API Session Intent User Intent *Ooyala US Patent 8,260,117 “Automatically Recommending Content” Ooyala Analytics Ooyala NOW 8
  • 9. OOYALA analytics DELIVERING BUSINESS INSIGHTS 9
  • 10. PROCESSING STRATEGY Scalable architecture Storage of discrete events + Data Storage • Store discrete events, including custom events & attributes State-of-the-art computational technologies = Data Aggregation • Aggregate events across dimensions (e.g. geography) Both pre-aggregation of metrics and ad-hoc computation possible Multidimensional Analysis • Slice and dice across multiple dimensions (e.g. geography and device and label) 10
  • 11. DATA FLOW: ingestion INGESTION AND LOGGING MULTIPLE PLATFORMS (WEB/DEVICE/TV) RAW EVENTS STORAGE 10101011111010101 01110101011111010 10101110101011111 010 010101 010 10 1010 1010 101 INGESTION API REPORTING COMPUTED DATA PRE-DEFINED COMPUTATIO N (SPARK) AGGREGAT ION & QUERY DATA CUBES REAL-TIME DASHBOARD AD HOC ANALYSIS (SHARK) REAL-TIME DATA STORE CUSTOM DASHBOARDS PRE-DEFINED COMPUTATION (SPARK) PINGS 1010 01 010 1010 101010 101 AGGREGATED STORAGE REAL-TIME ANALYSIS (STORM) RAW EVENTS LOGGERS PINGS COMPUTATION RAW EVENTS STORE (CASSANDRA) BENEFITS: • HTTP API — RESTful API simplifies ingestion from any device & enables on-thefly ingestion as well as bulk ingestion via XML feed • Turn-key Solution — very simple implementation of analytics pings • Distributed Infrastructure — a cloud-based, distributed infrastructure enables fault-tolerant scaling 11
  • 12. DATA FLOW: processing INGESTION AND LOGGING MULTIPLE PLATFORMS (WEB/DEVICE/TV) RAW EVENTS STORAGE 10101011111010101 01110101011111010 10101110101011111 010 010101 010 10 1010 1010 101 INGESTION API REPORTING PRE-DEFINED COMPUTATIO N (SPARK) COMPUTED DATA PRE-DEFINED COMPUTATIO N (SPARK) AGGREGAT ION & QUERY DATA CUBES REAL-TIME DASHBOARD AD HOC ANALYSIS (SHARK) REAL-TIME DATA STORE CUSTOM DASHBOARDS PINGS 1010 01 010 1010 101010 101 AGGREGATED STORAGE REAL-TIME ANALYSIS (STORM) RAW EVENTS LOGGERS PINGS COMPUTATION RAW EVENTS STORE (CASSANDRA) BENEFITS: • Flexible — as raw events are stored, ad-hoc reporting is possible • Fast — pre-defined computation using SPARK & STORM technologies will enable real-time, in-memory reporting • Applied Data Science — machine learning and data science applied to generate actionable insights 12
  • 13. DATA FLOW: reporting INGESTION AND LOGGING MULTIPLE PLATFORMS (WEB/DEVICE/TV) RAW EVENTS STORAGE 10101011111010101 01110101011111010 10101110101011111 010 AGGREGATED STORAGE REPORTING COMPUTED DATA PRE-DEFINED COMPUTATIO N (SPARK) REAL-TIME ANALYSIS (STORM) RAW EVENTS LOGGERS PINGS COMPUTATION AGGREGATION & QUERY DATA CUBES REAL-TIME DASHBOARD AD HOC ANALYSIS (SHARK) REAL-TIME DATA STORE PRE-DEFINED COMPUTATION (SPARK) PINGS 1010 01 010 1010 101010 101 010101 010 10 1010 1010 101 INGESTION API RAW EVENTS STORE (CASSANDRA) CUSTOM DASHBOARDS BENEFITS: • Flexible APIs — create custom dashboards using reporting APIs • Multi-dimensional Analysis — storage or raw events combined with state-of-theart reporting topologies enables queries across multiple dimensions • Actionable Insights — combining content, monetization, and audience data unveils actionable and insightful analytics 13
  • 14. REAL-TIME ANALYTICS Ooyala Now provides up-to-the-second analysis of network traffic and content trends for live and VOD content: Continually updated within SECONDS: Viewer Stats by Asset: • Most Popular Content • Top 10 Performing GEOs • Trending Content • Completion Rate • Average Time Spent 14
  • 15. THANK YOU

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