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

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