Query Evaluation In Deductive Databases Research Articles

Query evaluation in deductive databases with alternating fixpoint semantics

First-order formulas allow natural descriptions of queries and rules. Van Gelder's alternating fixpoint semantics extends the well-founded semantics of normal logic programs to general logic programs with arbitrary first-order formulas in rule bodies. However, an implementation of general logic programs through the standard translation into normal logic programs does not preserve the alternating fixpoint semantics. This paper presents a direct method for goal-oriented query evaluation of general logic programs. Every general logic program is first transformed into a normal form where the body of each rule is either an existential conjunction of literals or a universal disjunction of literals. Techniques of memoing and loop checking are incorporated so that termination and polynomial-time data complexity are guaranteed for deductive databases (or function-free programs). Results of the soundness and search space completeness are established.

Chain-split evaluation in deductive databases

Many popularly studied recursions in deductive databases can be compiled into one or a set of highly regular chain generating paths, each of which consists of one or a set of connected predicates. Previous studies on chain-based query evaluation in deductive databases take a chain generating path as an inseparable unit in the evaluation. However, some recursions, especially many functional recursions whose compiled chain consists of infinitely evaluable function(s), should be evaluated by chain-split evaluation, which splits a chain generating path into two portions in the evaluation: an immediately evaluable portion and a delayed-evaluation portion. The necessity of chain-split evaluation is examined from the points of view of both efficiency and finite evaluation, and three chain-split evaluation techniques: magic sets, buffered evaluation, and partial evaluation are developed. Our study shows that chain-split evaluation is a primitive recursive query evaluation technique for different kinds of recursions, and it can be implemented efficiently in deductive databases by extensions to the existing recursive query evaluation methods. >

Fixpoint iteration with subsumption in deductive databases

Declarative languages for deductive and object-oriented databases require some high-level mechanism for specifying semantic control knowledge. This paper proposes user-supplied subsumption information as a paradigm to specify desired, prefered or useful deductions at the meta level. For this purpose we augment logic programming by subsumption relations and succeed to extend the classical theorems for least models, fixpoints and bottom-up evaluation accordingly. Moreover, we provide a differential fixpoint operator for efficient query evaluation in deductive databases. This operator discards subsumed tuples on the fly. We also exemplify the ease of use of this programming methodology. In particular, we demonstrate how heuristic AI search procedures can be integrated into deductive databases in this way.

Constraint-based query evaluation in deductive databases

Constraints play an important role in the efficient query evaluation in deductive databases. Constraint-based query evaluation in deductive databases is investigated, with emphasis on linear recursions with function symbols. Constraints are grouped into three classes: rule constraints, integrity constraints, and query constraints. Techniques are developed for the maximal use of different kinds of constraints in rule compilation and query evaluation. The study on the roles of different classes of constraints in set-oriented evaluation of linear recursions shows the following: rule constraints should be integrated with their corresponding deduction rules in the compilation of recursions; integrity constraints, including finiteness constraints and monotonicity constraints, should be used in the analysis of finite evaluability and termination for specific queries; and query constraints, which are often useful in search space reduction and termination, should be transformed, when necessary, and should be pushed into the compiled chains as deeply as possible for efficient evaluation. The constraint-based query-processing technique integrates query-independent compilation and chain-based query evaluation methods and demonstrates its great promise in deductive query evaluation. >

Upside-down meta-interpretation of the model elimination theorem-proving procedure for deduction and abduction

Typical bottom-up, forward-chaining reasoning systems such as hyperresolution lack goaldirectedness, while typical top-down, backward-chaining reasoning systems like Prolog or model elimination repeatedly solve the same goals. Reasoning systems that are goal-directed and avoid repeatedly solving the same goals can be constructed by formulating the top-down methods meta-theoretically for execution by a bottom-up reasoning system (hence, we use the term upside-down meta-interpretation). This formulation also facilitates the use of flexible search strategies, such as merit-ordered search, that are common to bottom-up reasoning systems. The model elimination theorem-proving procedure, its extension by an assumption rule for abduction, and its restriction to Horn clauses are adapted here for such upside-down meta-interpretation. This work can be regarded as an extension of the magic-sets or Alexander method for query evaluation in deductive databases to both non-Horn clauses and abductive reasoning.

Inference by generating in deductive databases

A method, APEX, for query evaluation in deductive databases presented in this work is based on discovering of axioms and facts relevant to a given query. The notion of relevancy and migration of facts is derived from an analysis of data flow in the system. APEX is complete, and incorporates efficient query evaluation heuristics. Operation of APEX is illustrated by sample databases involving non-linear recursive axioms and cyclic relations. Main virtues of the method are its generality and adaptivity: it imposes no restrictions on the structure of axioms or the contents of relations, and it employs the knowledge of the actual data acquired at each step of a query evaluation.