Getting to the truth requires crazy machine learning
Semantic brand detection and hierarchy is important
Simple things, like merging data from different sources, turn out to not be so simple.
+ We identify true brands from POI with merely similar names (e.g. Lee’s Sandwiches vs. Lee’s Deli)
+ Understanding hierarchical relationships like native substores (e.g. Walmart Vision Center) vs. foreign substores (e.g. CVS inside Target) enables us to better filter or keep POI.
Spatial transformation and interpolation is important (and really hard).
We intelligently partition an overall building shape into substores (think of a strip mall).
We also strive to understand spatial relationships of substores within malls, stadiums, airports, and more.
