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
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Taking Constraints out of Constraint Databases
Dina GoldinUniversity of Connecticut
Applications of Constraint Databases
Paris, France, June 2004
Codd provided an additional level of abstraction
between physical data and queries
data layout for
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)
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-dependent
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