Queries In Deductive Databases Research Articles

Алгоритмы обработки запросов к дедуктивным базам данных и реализация алгоритма QSQ

Алгоритмы обработки запросов к дедуктивным базам данных и реализация алгоритма QSQ

Preference queries in deductive databases

Traditional database query languages such as datalog and SQL allow the user to specify only mandatory requirements on the data to be retrieved from a database. In many applications, it may be natural to express not only mandatory requirements but also preferences on the data to be retrieved. Lacroix and Lavency10) extended SQL with a notion of preference and showed how the resulting query language could still be translated into the domain relational calculus. We explore the use of preference in databases in the setting of datalog. We introduce the formalism of preference datalog programs (PDPs) as preference logic programs without uninterpreted function symbols for this purpose. PDPs extend datalog not only with constructs to specify which predicate is to be optimized and the criterion for optimization but also with constructs to specify which predicate to be relaxed and the criterion to be used for relaxation. We can show that all of the soft requirements in Reference10) can be directly encoded in PDP. We first develop anaively-pruned bottom-up evaluation procedure that is sound and complete for computing answers to normal and relaxation queries when the PDPs are stratified, we then show how the evaluation scheme can be extended to the case when the programs are not necessarily stratified, and finally we develop an extension of themagic templates method for datalog14) that constructs an equivalent but more efficient program for bottom-up evaluation.

OLDTNF-based evaluation method for handling recursive queries in deductive databases

OLDTNF resolution is an important mechanism used in a Prolog interpreter. This mechanism is extended and improved for evaluating recursive queries in deductive databases. The key idea of the refinement is to distinguish between two classes of lookup nodes in an OLDTNF derivation and to handle them differently. First, reduce the search space by cutting of any subtree rooted at a lookup node of the first class. Further, speed up the evaluation by processing the second class in a second phase and generate many solutions directly from the solutions already produced (and the corresponding keys of solution lists) instead of evaluating them by expanding the corresponding subtrees in terms of the new solutions stored in solution lists.

Deductive query languages for recursively typed complex objects

Deductive database query languages for recursively typed complex objects based on the set and tuple constructs are studied. A fundamental characteristic of such complex objects is that in them, sets may contain members with arbitrarily deep nesting of tuple and set constructs. Relative to mappings from flat relations to flat relations, two extensions of COL in this context (with stratified semantics and inflationary semantics, respectively) are shown to have the expressive power of computable queries. Although the deductive calculus of Bancilhon and Khoshafian has the ability to simulate Turing machines, when restricted to flat input and output its expressive power is characterized by a weak variant of the conjunctive queries.

A deductive environment for dealing with objects and nonmonotonic reasoning

The Bottom-up Query machine (BQM)-the role played by our system in the framework of the KIWIS system (K. Apt et al., 1987)-extends deductive database technology with knowledge structuring capabilities to provide an advanced environment for the development of data and knowledge based applications. The system relies on a knowledge representation language that combines the declarativeness of logic programming with the notions of object, inheritance with exceptions, and message passing. Exceptions are supported by allowing rules with negated heads. The use of exceptions inside the inheritance mechanism makes the language inherently nonmonotonic. The paper contains a comprehensive description of both the language and the implementation principles of the BQM system. It begins by providing a model theoretic semantics of the language based on the notion of least model. A fixpoint semantics, providing a constructive definition of the least model, is given as well. Then, a number of implementation techniques for efficient query evaluation are described. Such techniques significantly extend "traditional" deductive database query evaluation strategies to deal with monotonic reasoning. A description of the architecture of the current prototype of the BQM system is also given.

Advanced query formulation in deductive databases

A novel operation-oriented framework for deductive databases is introduced. Several types of end-user queries to deductive databases are classified, and sample queries are used to demonstrate how they can be expressed in our framework. The sample queries and their sample evaluations show essentially reduced user effort and essentially increased user capability in the expression of advanced queries in deductive databases. These are also necessary features in modern information retrieval from fact databases. We also establish criteria for advanced query formulation in deductive databases from the end-user viewpoint and analyze failures of conventional and present deductive DBMSs in meeting them.

Classification and compilation of linear recursive queries in deductive databases

The authors present a graph model which is powerful in classifying and compiling linear recursive formulas in deductive databases. The graph model consists of two kinds of graphs: I-graph and resolution graph. Essential properties of a recursive formula can be extracted from its I-graph, and the compiled formula and the query evaluation plan of the recursive formulas can be determined from its resolution graph. It is demonstrated that based on the graph model all the linear recursive formulas can be classified into a taxonomy of classes and each class shares common characteristics in query compilation and query processing. The compiled formulas and the corresponding query evaluation plans can be derived based on the study of the compilation of each class. >

Partial evaluation of queries in deductive databases

This paper presents some applications of partial evaluation method to a query optimization in deductive database. A Horn clause transformation is used for the partial evaluation of a query in an intensional database, and its application to multiple query processing is discussed. Three strategies are presented for the compatible case, ordered case and crossed case. In each case, partial evaluation is used to preprocess the intensional database in order to obtain subqueries which direct access to an extensional database.

Non-deterministic modelling of logical queries in deductive databases

We propose a technique based on Petri Nets formalism to model logic queries in deductive databases. The model is called PNLP (Petri Net model for Logic Programs), and it has a simple formal description and a graphical representation. The PNLP model explicitly represents the relationships between rules and predicates. It is general and flexible enough to demonstrate the flow of control in different algorithms used to evaluate recursive logic queries. In fact the model unifies the level of description of these algorithms, and facilitates identifying similarities and differences between them. The inherent non-determinism in the PNLP model may also be useful in recognizing the parallelism within Horn-clause logic programs. In this paper, the PNLP model is described, and its functionality is demonstrated by modeling several existing algorithms for recursive query evaluation.