Query evaluation as constraint search; an overview of early results

  • Daniel P. Miranker
  • Roberto J. BayardoJr.
  • Vasilis Samoladas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1191)


We present early results on the development of database query evaluation algorithms that have been inspired by search methods from the domain of constraint satisfaction. We define a mapping between these two specialties and discuss how the differences in problem domains have instigated new results.

It appears that contemporary problems in databases which lead to queries requiring many-way joins (such as active and deductive databases) will be the primary beneficiaries of this approach. Object-oriented queries and queries which are not intended to return all solutions also benefit. Some obvious CSP interpretations of certain semantic database properties suggest open research opportunities.


Constraint Satisfaction Problem Query Evaluation Constraint Graph Query Plan Query Graph 
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.
    Bayardo, R. J. and Miranker, D. P. 1994. An Optimal Backtrack Algorithm for Tree-Structured Constraint Satisfaction Problems. Artificial Intelligence 71(1): 159–181.Google Scholar
  2. 2.
    Bayardo, R. J. and Miranker, D. P. 1995. On the Space-Time Trade-Off in Solving Constraint Satisfaction Problems. In Proc. 14th Intl. Joint Conf. on Artificial Intelligence, 558–562.Google Scholar
  3. 3.
    Bayardo, R. J. and Miranker, D. P. 1996. A complexity analysis of space-bounded learning algorithms for the constraint satisfaction problem. In Proc. of the 13th National Conf. on Artificial Intelligence, 298–304.Google Scholar
  4. 4.
    Bayardo, R. J. and Miranker, D. P. 1996. Processing queries for first few answers. In Proc. of the Fifth International Conference on Information and Knowledge Management, 45–52.Google Scholar
  5. 5.
    Bernstein, P. A. and Chiu, D.-M. W. 1981. Using semijoins to solve relational queries, J. ACM 28(1), 25–30.Google Scholar
  6. 6.
    Dechter, R. 1990. Enhancement Schemes for Constraint Processing: Backjumping, Learning, and Cutset Decomposition. Artificial Intelligence 41(3):273–312.Google Scholar
  7. 7.
    Gyssens, M., Jeavons, P. G. and Cohen, D. A. 1994. Decomposing constraint satisfaction problems using database techniques, Artificial Intelligence 66, 57–89.Google Scholar
  8. 8.
    Samoladas, V. and Miranker, D. P. 1996. Loop optimizations for acyclic object-oriented queries. Technical Report TR96-10, Dept. of Computer Sciences, University of Texas at Austin Available at Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Daniel P. Miranker
    • 1
  • Roberto J. BayardoJr.
    • 1
  • Vasilis Samoladas
    • 1
  1. 1.Dept. of Computer Sciences and Applied Research LaboratoriesUniversity of Texas at AustinAustin

Personalised recommendations