Taking Constraints out of Constraint Databases. Dina Goldin University of Connecticut Applications of Constraint Databases Paris, France, June 2004. queries. Table-based Logical Layer. Physical Layer. Relational Databases. Codd provided an additional level of abstraction
Dina GoldinUniversity of Connecticut
Applications of Constraint Databases
Paris, France, June 2004
A commercial success in the 1980s!
They are the latest commercial success.
Can constraint databases offer a better solution?
Goal: next commercial success (for GIS applications)
Physical LayerRevisiting the Logical Layer
Infinite semantics of finitely representable data imply an additional level of abstraction; we need to separate logical layer into two
(queries defined over this layer)
finite set-of-point semantics;table-based representation;
Abstract Logical Layer:(queries defined over this layer)
infinite set-of-point semantics
Concrete Logical Layer:
Finite data representation;
File-based data storage; indexing structures, data access methods; implementation-dependentAdditional Level of Abstraction
RDB to CDB: from two layers to three
Define query semantics (abstract level)
MAP lX [X.fname, px,y ( st=t1 (X.traj))] (Flight)
MAP lX [X.lname, X.geom ∩ Rect] (LandUse)
plname,geom (MAP lX [X.lname, X.geom, s(x,y) in Rect (X.geom)] (LandUse))
Output limited to 2 spatiotemporal dimensions (3 in case of interpolated attributes)
Pure constraint DB not practical