Big Data, Big Local


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Where 2.0 2011 presentation discussion the use of local businesses and POI as topographical nodes in a complex network

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  • These coordinates can map to the US, which has its own array of contextual associations…
  • They also map to California, which has its own, different context…
  • And of course to San Francisco, which also has its own context independent of others…
  • The coordinates actual map directly onto an Adult ‘Novelty’ shop, which of course has entirely different associations… Google streetview Image
  • Diff. between grid and graph Coordinates provide location, Businesses and POI provide context Semantic hooks on which we hang activity
  • So we got that going for us…
  • Nomalization and canonicalization are huge problem Across all attributes, varying by country. 10 core attributes, 35 countries = 350 rule sets
  • Same store on 8 different sites
  • Uniform Resource Identifiers accessible via HTTP Dereference: to obtain a copy or representation of the resource it identifies.
  • Large-scale data engineering is a royal PITA We address this so that your efforts go on the application layer – where differentiation counts
  • Big Data, Big Local

    1. 1. Big Data, Big Local Tyler Bell @twbell
    2. 2. 37.7632,-122.4213 Great for machines Coordinates: For people, less so
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    7. 7. While coordinates are regular and convenient, they lack context and character Square
    8. 8.
    9. 9. The Evolving Local Use Case Yellow Pages Local Search Recommendations Social Engagement Brand Engagement Commercial Engagement Navigation Interaction
    10. 10. Local Businesses and POI – An irregular , but extremely rich topographic network
    11. 11. Employing POI and Business Listings as topographical nodes brings its own problems…
    12. 12. Subway Restaurants Subway Sandwich and Salad Subway Sandwich and Salad Shop Subway Subs and Salads Subway Restaurants Subway Subs Subway Shop Subway Sandwich Shops Subway Sandwichs Subway Sandwiches and Salads Subway Restaurant Subway Sandwiches Subway Sandwiches and Salads Subway Sandwich Shop Subway Subway Sandwiches and Salad Subway Sandwich and Salads Subway Sandwich Poor/Absent Canonicalization
    13. 13. Multiple Electronic Representations of one physical entity
    14. 15. Webpage URLs have become URIs Identifiers for people, places, things
    15. 17. 14.5m entities pointing to over… 1.5b references found across… 4.7m domains US Local Dataset
    16. 18. We need more STP [Straight Through Processing] for the web so that we have fewer stove pipe services and can move to a seamless web instead. The obstacle is no longer a lack of APIs […] the problem is a lack of data mapping/unification services. - Albert Wenger!/cdixon/status/49906284492881920
    17. 19. We are able to focus on our core vision of geotagging the web’s content and information while also providing our developers with a great Places Database that is open and free to use.
    18. 20. How easily men could make things much better than they are -- if they only all tried together - Winston Churchill
    19. 21. Tyler Bell @twbell