1. BIG DATA AT TELENAVUSING DATA TO IMPROVE YOUR LIFEMark Watkins, general manager, entertainment content@viking2917 2/21/2012 2012 Telenav, Proprietary and Confidential 1

2. A PIONEER IN LOCATION SERVICES OUR GPSPublic company: $200M+ revenue, 11 NAVIGATION PARTNERS years in businessLeader in Personalized Mobile Navigation: 30MM+ subscribersLeader in Drive To Mobile Advertising: 750K local advertisersLeader in Mobile Distribution Platforms: 900+ devicesGrowing Global Carrier Audience Reach: 14 carriers in 29 countries 2

3. KEY PROBLEMS WE ARE WORKING ONTraffic & MappingLocal Search for businesses, events, points of interestLifestyle content & recommendation engineCombination of traditional big data processing, machine learning and proprietary algorithmsPeople are drowning in information use big data signals to condense to something manageable

4. TRAFFIC & MAPSTraffic-aware routing engine Navigation is core competency 1.3B routes/trips since 2007Routes generate traffic/motion data probe data from app (billions/month) Anonymized & summarized to power routing Persisted in aggregate form for historical traffic metricsUsed to augment Open Street Map Turn restrictions, stop signs, road geometry Deduced from probe patternsTechnology 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 DATAData space is large, but not immense Tens or Hundreds of millions (or smaller), not billionsBut 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 LEARNINGSLots of data sources / signals generate goodness Ranking, Confidence, importance, comprehensivenessInteresting Most PopularFrequency of occurrence Museum of Bad Art The Middle East NightclubFreds dry cleaners Museum of Science 2/21/2012 2012 Telenav, Proprietary and Confidential 7

8. COMPOSITE, STRUCTURED LOCAL DATA 2/21/2012 2012 Telenav, Proprietary and Confidential 8

9. PERSONALIZED RECOMMENDATIONS 2/21/2012 2012 Telenav, Proprietary and Confidential 9

10. RECOMMENDATIONS WORK IN PROGRESSKey signals Personalized interest graph Drive to data (where are people driving to?) Entity-level page rank Web/mobile clickstream dataIntegrated with social media Facebook actions influencing recommendationsKey technology enablers Large amounts of user-generated data Proprietary algorithms; machine learning / SVM

11. TELENAV.COM SCOUT 2/21/2012 2012 Telenav, Proprietary and Confidential 11