• Partitioning - distribute queries across multiple machines
• Striping - distribute computations across multiple machines
• Custom traversal using server side iterators
• Custom analytics embedded in server side iterators
• Ad-hoc interactive MapReduce
  

• Any computation that is associative can be parallelized
  
• Compute sparse density matrix per partition/stripe
• Reduce via associative operation in client (Geoserver)

1. Fix bbox2. While results.size == 0
      Compute knn per stripe/partition
      if results.size == 0
         loop (expand bbox and take symmetric difference with unioned previous bounds)
3. Reduce client side

• Brute force map-reduce
• Faster than best-first search using R-trees in most cases

• Find features that intersect in both space and time
  
• Deployed in Geoserver as WPS process
• Use path-prediction and gap-filling for sparse datasets
  

• Examples:
• Find other people traveling the same route at the same time as you are (Who was stuck in that traffic jam?)
  
• Find people you may have interacted with on a recent road-trip from DC to NYC