The C3 constraint object-oriented database system: An overview

  • Alexander Brodsky
  • Victor E. Segal
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1191)


Constraints provide a flexible and uniform way to conceptually represent diverse data capturing spatio-temporal behavior, complex modeling requirements, partial and incomplete information etc, and have been used in a wide variety of application domains. Constraint databases have recently emerged to deeply integrate data captured by constraints in databases. This paper reports on the development of the first constraint object-oriented database system, C3, and describes its specification, design and implementation. The C3 system is designed to be used for both implementation and optimization of high-level constraint object-oriented query languages such as \(\mathcal{L}yri\mathcal{C}\)or constraint extensions of OQL, and for directly building software systems requiring extensible use of constraint database features. The C3 data manipulation language, Constraint Comprehension Calculus, is an integration constraint calculus for extensible constraint domains within monoid comprehensions, which serve as an optimization-level language for object-oriented queries. The data model for constraint calculus is based on constraint spatio-temporal (CST) objects that may hold spatial, temporal or constraint data, conceptually represented by constraints. New CST objects are constructed, manipulated and queried by means of constraint calculus. The model for monoid comprehensions, in turn, is based on the notion of monoids, which is a generalization of collection and aggregation types to structures over which one can iterate and apply merge operator; this includes disjunctions and conjunctions of constraints. The focal point of our work is achieving the right balance between expressiveness, complexity and representation usefulness, without which the practical use of the system would not be possible. To that end, C3 constraint calculus guarantees polynomial time data complexity, and, furthermore, is tightly integrated with monoid comprehensions to allow deep global optimization.


Free Variable Query Language Quantifier Elimination Constraint Object Atomic Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Alexander Brodsky
    • 1
  • Victor E. Segal
    • 1
  1. 1.Department of Information and Software Systems EngineeringGeorge Mason UniversityFairfaxUSA

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