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