.png){kind=icon}
.png){kind=icon}
Return
A parametric approach to deductive databases with uncertainty
.png){kind=icon}
PRE
AI
DOI：10.1109/69.940732.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Numerous frameworks have been proposed in recent years for deductive databases with uncertainty. On the basis of how uncertainty is associated with the facts and rules in a program, we classify these frameworks into implication-based (IB) and annotation-based (AB) frameworks. In this paper, we take the IB approach and propose 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 our framework and show their equivalence. Using the framework as a basis, we then study the query optimization problem of containment of conjunctive queries in this framework and establish necessary and sufficient conditions for containment for several 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:
conjunctive query containment
deductive databases
fixpoint computation
multisets
proof theory
query optimization
semantics
uncertainty
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.4
Papers:
6.8K
Citations:
3.2W
Organization
No organization information available