Combining Relational Algebra, SQL, and Constraint Programming

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2309)


The goal of this paper is to provide a strong interaction between constraint programming and relational DBMSs. To this end we propose extensions of standard query languages such as relational algebra (RA) and SQL, by adding constraint solving capabilities to them. In particular, we propose non-deterministic extensions of both languages, which are specially suited for combinatorial problems. Non-determinism is introduced by means of a guessing operator, which declares a set of relations to have an arbitrary extension. This new operator results in languages with higher expressive power, able to express all problems in the complexity class NP. Some syntactical restrictions which make data complexity polynomial are shown. The effectiveness of both languages is demonstrated by means of several examples.


Combinatorial Problem Constraint Program Expressive Power Hamiltonian Path Relational Algebra 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    S. Abiteboul, R. Hull, and V. Vianu. Foundations of Databases. Addison Wesley Publ. Co., Reading, Massachussetts, 1995.zbMATHGoogle Scholar
  2. 2.
    J. Beasley, M. Krishnamoorthy, Y. Sharaiha, and D. Abramson. Scheduling aircraft landings-the static case. Transportation Science, 34:180–197, 2000.zbMATHCrossRefGoogle Scholar
  3. 3.
    M. Cadoli and L. Palopoli. Circumscribing datalog: expressive power and complexity. Theor. Comp. Sci., 193:215–244, 1998.zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    M. Cadoli and A. Schaerf. Compiling problem specifications into SAT. In Proceedings of the European Symposium On Programming (ESOP 2001), volume 2028 of LNAI, pages 387–401. Springer-Verlag, 2001.Google Scholar
  5. 6.
    R. Fagin. Generalized First-Order Spectra and Polynomial-Time Recognizable Sets. In R. M. Karp, ed., Complexity of Computation, pages 43–74. AMS, 1974.Google Scholar
  6. 7.
    R. Fourer, D. M. Gay, and B. W. Kernigham. AMPL: A Modeling Language for Mathematical Programming. International Thomson Publishing, 1993.Google Scholar
  7. 8.
    M. R. Garey and D. S. Johnson. Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman and Company, San Francisco, Ca, 1979.zbMATHGoogle Scholar
  8. 9.
    G. Gottlob, P. Kolatis, and T. Schwentick. Existential second-order logic over graphs: Charting the tractability frontier. In Proc. of FOCS 2000. IEEE CS Press, 2000.Google Scholar
  9. 12.
    P. G. Kolaitis and C. H. Papadimitriou. Why not negation by fixpoint? J. of Computer and System Sciences, 43:125–144, 1991.zbMATHCrossRefMathSciNetGoogle Scholar
  10. 13.
    C. H. Papadimitriou. Computational Complexity. Addison Wesley, Reading, MA, 1994.zbMATHGoogle Scholar
  11. 14.
    R. Ramakrishnan. Database Management Systems. McGraw-Hill, 1997.Google Scholar
  12. 15.
    P. Van Hentenryck. The OPL Optimization Programming Language. The MITPress, 1999.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  1. 1.Dipartimento di Informatica e SistemisticaUniversità di Roma “La Sapienza”RomaItaly

Personalised recommendations