Efficient complete local tests for conjunctive query constraints with negation

  • Nam Huyn
Contributed Papers Session 1: Conjunctive Queries in Heterogeneneous Databases
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1186)


We consider the problem of incrementally checking global integrity constraints without using all the relations under constraint. In many application areas such as collaborative design, mobile computing and enterprise information systems, total data availability cannot be assumed. Even if all base data is available, some of it may incur such a high cost that its use should only be considered as a last resort. Without looking at all the base data, how can one meaningfully check a constraint for violation? When the constraint is known to be satisfied prior to the update, the state of the relations that are available (aka local) can in principle be used to infer something about the relations that are not available (aka remote). This observation is the basis for the existence of tests that guarantee that data integrity is preserved under a given update, without looking at all the base data.

In order to make integrity maintenance practical, the challenge is to find those tests that are most general (we call them Complete Local Tests or CLT's in short) and that are efficient to generate and execute. This paper addresses the problem of finding efficient CLT's for an important class of constraints that are very common in practice: constraints expressible as conjunctive queries with negated subgoals (abbreviated CQC¬.) We show that for single updates, all CQC¬ constraints admit a CLT that can be expressed in nonrecursive Datalog¬ when the predicates for the remote relations are not repeated in the constraint query. We then extend this result to a larger class of constraints and to certain sets of updates.


Integrity Constraint Collaborative Design Local Cover Conjunctive Query Complete Test 
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

  • Nam Huyn
    • 1
  1. 1.Stanford UniversityUSA

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