Establishing arc consistency for multiple database views

  • Steven Battle
Advanced Database and Information Systems Methods 1
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1134)


This paper introduces a class of problems where it is desirable to develop a number of potential search orders in advance. These problems emerge from a database environment in which the high volume of transactions means that pre-processing of the data can make a big difference to run-time performance. Furthermore, the kinds of queries that are made to this database are fairly stereotypical and are derived from a finite set of views of the database. The access path for each view may be expressed as a set of total variable orderings. Seen as a single partial ordering the question then arises as to how local consistency is to be established. Rather than enforcing consistency for each view separately the partial order is processed as a single structure. By organising the variables into groups of mutually dependent variables, this high level structure may be processed in a single DAC-like pass, while full arc-consistency is obtained for each sub-group.


Directed Graph Boolean Network Input Combination Constraint Graph Local Consistency 
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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  1. 1.
    Steven A. Battle. Generating database queries from a constraint network representation. In 5th Scandanavian Conference on AI, pages 343–347, Trondheim (Norway), May 1995.Google Scholar
  2. 2.
    Steven A. Battle and Richard H. McClatchey. A computerised reservation system using a relational database augmented by constraint based techniques. In DEXA 95 workshop proceedings, pages 315–321, London, September 1995.Google Scholar
  3. 3.
    Christian Bessiére. Arc-consistency and arc-consistency again. Artificial Intelligence, 65:179–190, 1994.CrossRefGoogle Scholar
  4. 4.
    Rina Dechter and Itay Meiri. Experimental evaluation of preprocessing algorithms for constraint satisfaction problems. Artificial Intelligence, 68:211–241, 1994.CrossRefGoogle Scholar
  5. 5.
    Rina Dechter and Judea Pearl. Network-based heuristics for constraint-satisfaction problems. Artificial Intelligence, 34:1–38, 1988.CrossRefGoogle Scholar
  6. 6.
    Rina Dechter and Judea Pearl. Tree clustering for constraint networks. Artificial Intelligence, 38:353–366, 1989.CrossRefGoogle Scholar
  7. 7.
    Eugene C. Freuder. Synthesizing constraint expressions. Communications of the ACM, 21(11):958–966, November 1978.CrossRefGoogle Scholar
  8. 8.
    Eugene C. Freuder. A sufficient condition for backtrack-free search. ACM, 29(1):24–32, 1982.CrossRefGoogle Scholar
  9. 9.
    Levy Leon S. Discrete Structures of Computer Science. John Wiley & Sons, New York, 1980.Google Scholar
  10. 10.
    A.K. Mackworth. Consistency in networks of relations. Artificial Intelligence, 8:99–118, 1977.CrossRefGoogle Scholar
  11. 11.
    D. Maier. The Theory of Relational Databases. Computer Science Press, 1983.Google Scholar
  12. 12.
    Richard H. McClatchey, Steven A. Battle, and Mary Percival. The architecture of a computerised reservation system integrated using an ‘active’ database. In Basque International Workshop on IT, San Sebastian (Spain), July 1995.Google Scholar
  13. 13.
    P.W. Purdom. Search rearrangement backtracking and polynomial average time. Artificial Intelligence, 21:117–177, 1983.Google Scholar
  14. 14.
    Edward Tsang. Foundations of Constraint Satisfaction. Academic Press, London, 1993.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Steven Battle
    • 1
  1. 1.University of the West of EnglandBristolUK

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