Abstract not found

Advanced applications require construction, efficient access and management of large databases with rich data structures and inference mechanisms. However, such capabilities are not directly supported by the existing database systems. In this paper, we describe Relationlog, a persistent deductive database system that is able to directly support the storage, efficient access and inference of data with complex structures.

Many problems encountered when building applications of database systems involve the manipulation of models. By “model, ” we mean a complex structure that represents a design artifact, such as a relational schema, object-oriented interface, UML model, XML DTD, web-site schema, semantic network, complex document, or software configuration. Many uses of models involve managing changes in models and transformations of data from one model into another. These uses require an explicit representation of “mappings ” between models. We propose to make database systems easier to use for these applications by making “model ” and “model mapping ” first-class objects with special operations that simplify their use. We call this capability model management. In addition to making the case for model management, our main contribution is a sketch of a proposed data model. The data model consists of formal, object-oriented structures for representing models and model mappings, and of high-level algebraic operations on those structures, such as matching, differencing, merging, function application, selection, inversion and instantiation. We focus on structure and semantics, not implementation. 1

Abstract-Datalog is a database query language based on the logic programming paradigm; it has been designed and intensively studied over the last five years. We present the syntax and semantics of Datalog and its use for querying a relational database. Then, we classify optimization methods for achieving efficient evaluations of Datalog queries, and present the most relevant methods. Finally, we discuss various exhancements of Datalog, currently under study, and indicate what is still needed in order to extend Datalog’s applicability to the solution of real-life problems. The aim of this paper is to provide a survey of research performed on Datalog, also addressed to those members of the database community who are not too familiar with logic programming concepts. Zndex Terms-Deductive databases, logic programming, recursive queries, relational databases, query optimization. I.

CORAL is a modular declarative query language/programming language that supports general Horn clauses with complex terms, set-grouping, aggregation, negation, and relations with tuples that contain (universally quantified) variables. Support for persistent relations is provided by using the EXODUS storage manager. A unique feature of CORAL is that it provides a wide range of evaluation strategies and allows users to --- optionally --- tailor execution of a program through high-level annotations. A CORAL program is organized as a collection of modules, and this structure is used as the basis for expressing control choices. CORAL has an interface to C++, and uses the class structure of C++ to provide extensibility. Finally, CORAL supports a command sublanguage, in which statements are evaluated in a user-specified order. The statements can be queries, updates, production-system style rules, or any command that can be typed in at the CORAL system prompt.

A practical procedure for computing a regular approximation of a logic program is given. Regular approximations are useful in a variety of tasks in debugging, program specialisation and compile-time optimisation. The algorithm shown here incorporates optimisations taken from deductive database fixpoint algorithms and efficient bottom-up abstract interpretation techniques. Frameworks for defining regular approximations have been put forward in the past, but the emphasis has usually been on theoretical aspects. Our results contribute mainly to the development of effective analysis tools that can be applied to large programs. Precision of the approximation can be greatly improved by applying query-answer transformations to a program and a goal, thus capturing some argument dependency information. A novel technique is to use transformations based on computation rules other than left-to-right to improve precision further. We give performance results for our procedure on a range of programs. 1

Knowledge-base management systems (KBMS) based on description logics are being used in a variety of situations where access is needed to large amounts of data stored in existing relational databases. We present the architecture and algorithms of a system that converts most of the inferences made by the KBMS into a collection of SQL queries, thereby relying on the optimization facilities of existing DBMS to gain e#ciency, while maintaining an object-centered view of the world with a substantive semantics and significantly di#erent reasoning facilities than those provided by Relational DBMS and their deductive extensions. We address a number of optimization issues that arise in the translation process due to the fact that SQL queries with di#erent syntax (but identical semantics) are not treated uniformly by current database management systems.

Glue is a procedural language for deductive databases. It is designed to complement the purely declarative NAIL! language, firstly by performing system functions impossible to write in NAIL!, and secondly by allowing the procedural specification of algorithms for critical code sections. The two languages together are sufficient to write a complete application. Glue was designed to be as close to NAIL! as possible, hence minimizing the impedance mismatch problem. In this paper we concentrate on Glue. Pseudo-higher order programming is used in both languages, in the style of HiLog [1]. In particular Glue-Nail can handle set valued attributes (non1NF schemas) in a clean and efficient manner. NAIL! code is compiled into Glue code, simplifying the system design. An experimental implementation has been written, a more efficient version is under design.

Notallqueries inrelational calculus can beanswered sensibly when disjunction, negation, and universal quantification are allowed, The class of relation calculus queries or formulas that have sensible answers is called the domam independent class which is known to be undecidable. Subsequent research has focused on identifying large decidable subclasses of domain independent formulas. In this paper we investigate the properties of two such classes: the et,aluable formulas and the allowed formulas. Although both classes have been defined before, we give simplified definitions, present short proofs of their main properties, and describe a method to incorporate equality. Although evaluable queries have sensible answers, it is not straightforward to compute them efficiently or correctly, We introduce relational algebra normal form for formulas from which form the correct translation into relational algebra istrivlal. We give algorithms to transform anevaluable formula into an equivalent allowed formula and from there into relational algebra normal form, Our algorithms avoid use of the so-called Dom relation, consisting of all constants appearing in the database or the query. Finally, we describe a restriction under which every domain independent formula is evaluable

this paper. In particular, the paper reports on a very significant progress made recently in this area. It also presents some results which have not yet appeared in print. The paper is organized as follows. In the next two sections we define deductive databases and logic programs. Subsequently, in Sections 4 and 5, we discuss model theory and fixed points, which play a crucial role in the definition of semantics. Section 6 is the main section of the paper and is entirely devoted to a systematic exposition and comparison of various proposed semantics. In Section 7 we discuss the relationship between declarative semantics of deductive databases and logic programs and non-monotonic reasoning. Section 8 contains concluding remarks. 2 Deductive Databases