Datalog: Bases de datos Deductivas

Este artículo muestra un breve estudio sobre Datalog, el cual es considerado como una  extensión de Prolog que es uno de los sofware más utilizados en la inteligencia artificial. Sistemas importantes como SWI-Prolog[22], Ciao Prolog[4], Sictus-Prolog[20], han sido compiladores utilizados para hacer uso de la funcionalidad del lenguaje lógico para bases de datos deductivas y han logrado la implementación de consultas recursivas  sobre las bases de datos relacionales. En este estudio también se presentan conceptos básicos de Datalog, así como algunos sistemas que se han desarrollado para trabajar con este lenguaje.Palabra(s) Clave(s): Programación lógica, Prolog, Datalog

Taking I/O seriously: resolution reconsidered for disk

Journal ArticleModern compilation techniques can give Prolog programs, in the best cases, a speed comparable to C. However, Prolog has proven to be unacceptable for data-oriented queries for two major reasons: its poor termination and complexity properties for Datalog, and its tuple-at-a-time strategy. A number of tabling frameworks and systems have addressed the first problem, including the XSB system which has achieved Prolog speeds for tabled programs. Yet tabling systems such as XSB continue to use the tuple-at-a-time paradigm. As a result, these systems are not amenable to a tight interconnection with disk-resident data. However, in a tabling framework the difference between tuple-at-a-time behavior and set-at-a-time can be viewed as one of scheduling. Accordingly, we define a breadth-first set-at-a-time tabling strategy and prove it iteration equivalent to a form of semi-naive magic evaluation. That is, we extend the well-known asymptotic results of Seki [10] by proving that each iteration of the tabling strategy produces the same information as semi-naive magic. Further, this set-at-a-time scheduling is amenable to implementation in an engine that uses Prolog compilation. We describe both the engine and its performance, which is comparable with the tuple-at-a-time strategy even for in-memory Datalog queries. Because of its performance and its fine level of integration of Prolog with a database-style search, the set-at-a-time engine appears as an important key to linking logic programming and deductive databases

Making logic programs reactive

Logic programming languages based on linear logic have been of recent interest, particularly as such languages provide a logical basis for programs which execute within a dynamic environment. Most of these languages are implemented using standard resolution or backward-chaining techniques. However, there are applications for which the use of forward-chaining techniques within a dynamic environment are appropriate, such as genetic algorithms, active databases and agent-based systems, and for which it is difficult or impossible to specify an appropriate goal in advance. In this paper we discuss the foundations for a forward-chaining approach (or in logic programming parlance, a bottom-up approach) to the execution of linear logic programs, which thus provides forward-chaining within a dynamic environment. In this way it is possible not only to execute programs in a forward-chaining manner, but also to combine forward- and backward-chaining execution. We describe and discuss the appropriate inference rules for such a system, the formal results about such rules, the role of search strategies, and applications

JELLY VIEWS : EXTENDING RELATIONAL DATABASE SYSTEMS TOWARD DEDUCTIVE DATABASE SYSTEMS

This paper regards the Jelly View technology, which provides a new, practical methodology for knowledge decomposition, storage, and retrieval within Relational Database Management Systems (RDBMS). Intensional Knowledge clauses (rules) are decomposed and stored in the RDBMS founding reusable components. The results of the rule-based processing are visible as regular views, accessible through SQL. From the end-user point of view the processing capability becomes unlimited (arbitrarily complex queries can be constructed using Intensional Knowledge), while the most external queries are expressed with standard SQL. The RDBMS functionality becomes extended toward that of the Deductive Database

Three Denerations of DBMS

This paper describes the evolution of data base technology from early computing to the sophisticated systems of today. It presents an overview of the most popular data base management systems architectures such as hierarchical, network, relational and object-oriented. The last section of this paper presents a view of the factors that will influence the future of data base technology

Depth-bounded bottom-up evaluation of logic programs

AbstractWe present here a depth-bounded bottom-up evaluation algorithm for logic programs. We show that it is sound, complete, and terminating for finite-answer queries if the programs are syntactically restricted to DatalognS, a class of logic programs with limited function symbols. DatalognS is an extension of Datalog capable of representing infinite phenomena. Predicates in DatalognS can have arbitrary unary and limited n-ary function symbols in one distinguished argument. We precisely characterize the computational complexity of depth-bounded evaluation for DatalognS and compare depth-bounded evaluation with other evaluation methods, top-down and Magic Sets among others. We also show that universal safety (finiteness of query answers for any database) is decidable for DatalognS