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A parametric approach to deductive databases with uncertainty

  • Laks V. S. Lakshmanan
  • Nematollaah Shiri
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1154)

Abstract

Numerous frameworks have been proposed in recent years for deductive databases with uncertainty. These frameworks differ in (i) their underlying notion of uncertainty, (ii) the way in which uncertainties are manipulated, and (iii) the way in which uncertainty is associated with the facts and rules of a program. On the basis of (iii), these frameworks can be classified into implication based (IB) and annotation based (AB) frameworks. In this paper, we develop a generic framework called the parametric framework as a unifying umbrella for IB frameworks. We develop the declarative, fixpoint, and proof-theoretic semantics of programs in the parametric framework and show their equivalence. Using this as a basis, we study the query optimization problem of containment of conjunctive queries in this framework, and establish necessary and sufficient conditions for containment for classes of parametric conjunctive queries. Our results yield tools for use in the query optimization for large classes of query programs in IB deductive databases with uncertainty.

Keywords

Logic Programming Predicate Symbol Query Optimization Conjunction Function Conjunctive Query 
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

  • Laks V. S. Lakshmanan
    • 1
  • Nematollaah Shiri
    • 1
  1. 1.Department of Computer ScienceConcordia UniversityMontrealCanada

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