1. BIG DATA AT TELENAV
USING DATA TO IMPROVE YOUR LIFE
Mark Watkins, general manager, entertainment content
@viking2917
2/21/2012 © 2012 Telenav, Proprietary and Confidential 1

2. A PIONEER IN LOCATION SERVICES
OUR GPS
Public company: $200M+ revenue, 11 NAVIGATION PARTNERS
years in business
Leader in Personalized Mobile Navigation:
30MM+ subscribers
Leader in Drive To Mobile Advertising:
750K local advertisers
Leader in Mobile Distribution Platforms:
900+ devices
Growing Global Carrier Audience Reach:
14 carriers in 29 countries
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3. KEY PROBLEMS WE ARE WORKING ON
Traffic & Mapping
Local Search for businesses, events, points of interest
Lifestyle content & recommendation engine
Combination of “traditional” big data processing, machine
learning and proprietary algorithms
People are drowning in information
– use “big data” signals to condense to something manageable

4. TRAFFIC & MAPS
Traffic-aware routing engine
– Navigation is core competency
– 1.3B routes/trips since 2007
Routes generate traffic/motion data
– “probe data” from app (billions/month)
– Anonymized & summarized to power routing
– Persisted in aggregate form for historical
traffic metrics
Used to augment Open Street Map
– Turn restrictions, stop signs, road geometry
– Deduced from probe patterns
Technology set
– Hadoop + Hive

5. AUTOMATED DEVELOPMENT OF RICH LOCAL CONTENT
(YOU MAY KNOW THIS AS GOBY)
Categorized to
taxonomy (“blues”,
“hiking trails”)
all entities geotagged
OTHER FEATURES WORTH NOTING
• automatic entity/place creation
• aggregated ratings & reviews
• proprietary result ranking formula
venues automatically
recognized; events • domain-specific metadata extraction
mapped to venues • sorting by metadata (e.g. price, rating)

6. AUTOMATED DEVELOPMENT OF RICH LOCAL DATA
Data space is large, but not immense
– Tens or Hundreds of millions (or smaller), not billions
But very complex
– Thousands of data sources
– attribute space is 10,000 wide
– E.g. how many holes in the golf course; how long is the hiking
trail?
Generates a large, sparse matrix
– Ambiguous, conflicting data
– Unstructured or semi-structured data
– Need to recognize entities & merge/dedup

7. SOME LEARNINGS
Lots of data sources / signals generate “goodness”
– Ranking, Confidence, importance, comprehensiveness
“Interesting” ≠ “Most Popular”
Frequency of occurrence
Museum of Bad Art
The Middle East Nightclub
Fred’s dry cleaners
Museum of Science
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8. COMPOSITE, STRUCTURED LOCAL DATA
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9. PERSONALIZED RECOMMENDATIONS
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10. RECOMMENDATIONS – WORK IN PROGRESS
Key signals
– Personalized “interest graph”
– “Drive to” data (where are people driving to?)
– Entity-level “page rank”
– Web/mobile clickstream data
Integrated with social media
– Facebook actions influencing recommendations
Key technology enablers
– Large amounts of user-generated data
– Proprietary algorithms; machine learning / SVM

11. TELENAV.COM – SCOUT
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