1. CLUSTER BY: A NEW SQL EXTENSION FOR SPATIAL DATA AGGREGATION
Chengyang Zhang and Yan Huang [chengyang,
Department of Computer Science & Engineering
University of North Texas
Our Contributions
Enables queries over spatial clusters in spatial databases
Clusters are elevated into spatial objects on which spatial functions and predicates can be directly applied
Challenges
Efficient aggregation/elevation algorithms
Query processing and optimization
Extension to spatial streaming data
v.s.
Group By
Cluster By
Only classifies tuples with the same values
Allow spatial points to be clustered based on spatial proximity
Future Work: implementations
Spatial functions do not accept discrete points as operands
Clusters are elevated to spatial objects as the input to spatial functions
Motivating Scenario
Spatial Data
Selection
Clustering
Polygonization
Identify low income neighborhoods
Clusters of homes with low income.
House information is stored in a spatial table House
SELECT Area(Polygon(location))
FROM House
WHERE household_income < 30K
CLUSTER BY location
SELECT Area(Polygon(location))
FROM House
WHERE household_income < 30K
CLUSTER BY location
SELECT AREA(Polygon(location))
FROM House
WHERE household_income < 30K
CLUSTER BY location