Deductive Databases Research Articles

DrawCAD: using deductive object‐relational databases in CAD

AbstractComputer‐aided design (CAD) involves the use of computers in the various stages of engineering design. CAD has large volumes of data with complex structures that need to be stored and managed effectively and properly. Database systems provide general purpose programs that can be used to access and manipulate large amounts of data stored in the database. They also provide an independence between the program accessing data and the database. It is therefore important to use database systems to store CAD data in the most efficient and effective manner for easy retrieval and better management.Graphical objects can be created, in CAD, by reusing previously created objects. The data of these objects have references to the other objects they contain. Deductive object‐relational databases not only provide direct support for the effective storage and efficient access to large amounts of data with complex structures on disk, but also perform the inferences and computations to obtain the complete data of graphical objects that reuse other objects. They should be able to play a major role in CAD systems. This is the idea behind the development of the DrawCAD system. DrawCAD is a CAD system built on top of the Relationlog object‐relational deductive database system. It facilitates the creation of graphical objects by reusing previously created objects. The DrawCAD system illustrates how CAD systems can be developed, using database systems to store and manage data and also perform the inferences and computations that are normally performed by the application program. Copyright © 2003 John Wiley & Sons, Ltd.

Extending Datalog with Declarative Updates

The semantics of static deductive databases is well understood based on the work in logic programming. In the past decade, various methods to incorporate update constructs into logic programming and deductive databases have been proposed. However, there is still no consensus about the appropriate treatment of dynamic behavior in deductive databases. In this paper, we propose a language called DatalogU, which is a minimal but powerful extension of Datalog with updates to base relations. DatalogU allows the user to program set-oriented complex database transactions with concurrent, disjunctive and sequential update operations in a simple and direct way. It has a simple and intuitive declarative semantics that naturally accounts for set-oriented updates in deductive databases.

A Deductive Database Approach to A.I. Planning

In this paper, we show that the classical A.I. planning problem can be modelled using simple database constructs with logic-based semantics. The approach is similar to that used to model updates and nondeterminism in active database rules. We begin by showing that planning problems can be automatically converted to Datalog1S programs with nondeterministic choice constructs, for which we provide a formal semantics using the concept of stable models. The resulting programs are characterized by a syntactic structure (XY-stratification) that makes them amenable to efficient implementation using compilation and fixpoint computation techniques developed for deductive database systems. We first develop the approach for sequential plans, and then we illustrate its flexibility and expressiveness by formalizing a model for parallel plans, where several actions can be executed simultaneously. The characterization of parallel plans as partially ordered plans allows us to develop (parallel) versions of partially ordered plans that can often be executed faster than the original partially ordered plans.

Will database systems fail bioinformatics, too?

THE DATABASE SYSTEMS RESEARCH COMMUNITY has always been eager to try to extend database technology to address application areas not well served by current commercial products: Computer-Aided Design, expert systems, workflow, financial analysis, imagery. Such efforts have almost always led to innovative developments in the past: object-oriented databases, deductive databases, active databases, sequence data models, array algebras. Yet DBMS penetration into the motivating areas has been modest at best.

CWA Extensions to Multi-Valued Logics

The closed world assumption (CWA) plays a fundamental role in the theory of deductive databases. On the other hand, multi-valued logics occupy a vast field in non-classical logics. Some questions are better explained and expressed in terms of such logics. To enhance the expressive power and the declarative ability of a deductive database, we extend various CWA formalizations, including the naive CWA, the generalized CWA and the careful CWA, to multi-valued logics. The basic idea is to embed logic formulae into some polynomial ring. The extensions can be applied in a uniform manner to any finitely multi-valued logics. Therefore they have also computational interest.

Likelog for flexible query answering

Starting from unification based on similarity, a logic programming language, called LIKEness in LOGic (Likelog) is derived, thorougly relying on similarity. An operational semantics and a fix-point semantics of the language are defined, using an extension principle for fuzzy operators. The two approaches are proved to be related and a fuzzy extension of the least Herbrand model is given. One of the principal feature of such a logic programming language is to allow flexible query answering to deductive databases, which we show through an example. Moreover, we describe a system for web information retrieval through Likelog.

Protecting deductive databases from unauthorized retrieval and update requests

We show how deductive databases may be protected against unauthorized retrieval and update requests issued by authenticated users. To achieve this protection, a deductive database is expressed in a form that guarantees that only authorized access requests are permitted. Authorized retrieval and update requests are specified by using an access control theory that is expressed in normal clause logic. The approach has a number of attractive technical results associated with it, can be efficiently implemented, and can be used to protect the information in any normal deductive database.

On the complexity of data disjunctions

We study the complexity of data disjunctions in disjunctive deductive databases (DDDBs). A data disjunction is a disjunctive ground clause R( c ̄ 1)⋯R( c ̄ k) , k⩾2, which is derived from the database such that all atoms in the clause involve the same predicate R. We consider the complexity of deciding existence and uniqueness of a minimal data disjunction, as well as actually computing one, both for propositional (data) and nonground (program) complexity of the database. Our results extend and complement previous results on the complexity of disjunctive databases, and provide newly developed tools for the analysis of the complexity of function computation.

SPADA: A Spatial Association Discovery System*

This paper presents a spatial association discovery system, named SPADA, which has been developed according to the theoretical framework of inductive databases. Our approach considers inductive databases as deductive databases with an integrated inductive component and relies on techniques borrowed from the field of Inductive Logic Programming (ILP). In SPADA, an ILP module supports the processing of spatial association mining queries and accesses spatial databases via a middle-layer module for feature extraction. The resulting architecture is illustrated by commenting spatial association mining sessions in detail and showing that ILP is a viable and promising solution to the development of inductive databases.

불명확 연역 데이터베이스를 위한 포괄적 및 배타적 or 해석

In order to properly interpret and process or operators in deductive databases including indefinite clauses, we propose to use Lasez′s Strong Model Semantics(LSMS) which is yet powerful enough to support both exclusive and inclusive interpretations. Conventional semantics either fail to support both interpretations or simply too complex. Therefore, in this paper we study advantages and difficulties of representing indefinite information, and as for the solution to difficulties, we show how LSMS can be used to support both inclusive or and exclusive or interpretations. We also investigate and analyze it′s properties and show how it semantically differs from others. We believe that LSMS is the only reasonably simple semantics that supports both inclusive and exclusive interpretations.

객체지향 데이터베이스 시스템에서 내포적 답의 처리 기법

기존 데이터베이스 시스템에서 질의를 처리할 때, 질의문의 응답은 사실이나 튜플들의 집합이다. 객체지향 데이터베이스 시스템에서도 객체들의 집합이 질의문의 응답이다. 그러나 추론적 데이터베이스 시스템에서는 질의문에 대하여 식의 집합으로 응답할 수 있으므로, 질의문 처리 비용을 절감할 수 있으며, 데이터베이스 상태와는 독립적인 좀 더 간결한 "내포적 답"으로 표현할 수 있다. 본 논문에서는 객체지향 데이터베이스 시스템에 규칙을 도입하여 추론적 데이터베이스 시스템에서 사용되는 내포적 질의문 처리 방법과 결합하여, 기존의 객체지향 데이터베이스 시스템에서 답을 얻을 수 없었던 불완전한 질의문에 대한 답을 얻을 수 있게 하였을 뿐만 아니라, 답의 클래스의 이름으로 표현함으로써, 답을 보다 명료하게 이해할 수 있게 해준다. When processing a query in a conventional database systems, a set of facts or tuples are usually returned as an answer. This also applies to object -oriented database where a set of objects is returned. Deductive database systems, however, provide the opportunity to obtain the answer of a query as a set of formulas, thereby reduce the costs to process the query, and represent its "intensional answers" in a more compact way independently of the database state. In this paper, by introducing rules info the object-oriented database systems and integrating the intensional query processing of deductive database systems into talc object-oriented database systems, we make it possible not only to answer incomplete queries which are not able to be answered in conventional object-oriented database systems, but also to express the answer-set abstractly as the names of classes, which provides us better understanding of the answer.

Duality for Goal-Driven Query Processing in Disjunctive Deductive Databases

Bottom-up query-answering procedures tend to explore a much larger search space than what is strictly needed. Top-down processing methods use the query to perform a more focused search that can result in more efficient query answering. Given a disjunctive deductive database, i>DB, and a query, i>Q, we establish a strong connection between model generation and clause derivability in two different representations of i>DB and i>Q. This allows us to use a bottom-up procedure for evaluating i>Q against i>DB in a top-down fashion. The approach requires no extensive rewriting of the input theory and introduces no new predicates. Rather, it is based on a certain i>duality principle for interpreting logical connectives. The duality transformation is achieved by reversing the direction of implication arrows in the clauses representing both the theory and the negation of the query. The application of a generic bottom-up procedure to the transformed clause set results in top-down query answering. Under favorable conditions efficiency gains are substantial, as shown by our preliminary testing. We give the logical meaning of the duality transformation and point to the conditions and sources of improved efficiency. We show how the duality approach can be used for i>refined query answering by specifying the i>minimal conditions (weakest updates) to i>DB under which i>Q becomes derivable. This is shown to be useful for view updates in disjunctive deductive databases as well as for other interesting applications.

Knowledge representation of signal transduction pathways.

Signal transduction is the common term used to define a diverse topic that encompasses a large body of knowledge about the biochemical mechanisms. Since most of the knowledge of signal transduction resides in scientific articles and is represented by texts in natural language or by diagrams, there is the need of a knowledge representation model for signal transduction pathways that can be as readily processed by a computer as it is easily understood by humans. A signal transduction pathway representation model is presented. It is based on a compound graph structure and is designed to handle the diversity and hierarchical structure of pathways. A prototype knowledge base was implemented on a deductive database and a number of biological queries are demonstrated on it.

Constructing interpretations of deductive databases using reduction and dynamic stratification

The family of dynamic interpretations for unstratified deductive databases is introduced and studied. Such interpretations are defined from a base semantics using dynamic stratification (which in turn relies upon any natural stratification of the database), and reduction operators (which eliminate rules and dependencies which spuriously affect the natural stratification). Dynamic interpretations coincide with perfect models in the stratified case, and can also be employed to construct perfect models of disjunctive stratified databases. We characterise precisely those dynamic interpretations that are consistent with the well-founded model, and show that a certain class of dynamic interpretations coincides with stable models. We also present briefly a class of dynamic interpretations that can be regarded as being analogous to extensions of the well-founded model such as WF S , GWFS, EWFS and WFS +.

Closed world assumption for disjunctive reasoning

In this paper, the relationship between argumentation and closed world reasoning for disjunctive information is studied. In particular, the authors propose a simple and intuitive generalization of the closed world assumption (CWA) for general disjunctive deductive databases (with default negation). This semantics called DCWA, allows a natural argumentation-based interpretation and can beused to represent reasoning for disjunctive information. We compare DCWA with GCWA and prove that DCWA extends Minker’s GCWA to the class of disjunctive databases with default negation. Also we compare our semantics with some related approaches. In addition, the computational complexity of DCWA is investigated.

A Hybrid Fragmentation Approach for Distributed Deductive Database Systems

Fragmentation of base relations in distributed database management systems increases the level of concurrency and therefore system throughput for query processing. Algorithms for horizontal and vertical fragmentation of relations in relational, object-oriented and deductive databases exist; however, hybrid fragmentation techniques based on variable bindings appearing in user queries and query-access-rule dependency are lacking for deductive database systems. In this paper, we propose a hybrid fragmentation approach for distributed deductive database systems. Our approach first considers the horizontal partition of base relations according to the bindings imposed on user queries, and then generates vertical fragments of the horizontally partitioned relations and clusters rules using affinity of attributes and access frequency of queries and rules. The proposed fragmentation technique facilitates the design of distributed deductive database systems.

CWA formalizations in multi-valued logics

To enhance the expressive power and the declarative ability of a deductive database, various CWA (Closed World Assumption) formalizations including the naive CWA, the generalized CWA and the careful CWA are extended to multi-valued logics. The basic idea is to embed logic formulas into some polynomial ring. The extensions can be applied in a uniform manner to any finitely multi-valued logics. Therefore they are also of computational significance.

On the equivalence of the static and disjunctive well-founded semantics and its computation

In recent years, much work was devoted to the study of theoretical foundations of Disjunctive Logic Programming and Disjunctive Deductive Databases. While the semantics of non-disjunctive programs is fairly well understood, the declarative and computational foundations of disjunctive logic programming proved to be much more elusive and difficult. Recently, two new and promising semantics have been proposed for the class of disjunctive logic programs. The first one is the static semantics STATIC, proposed by Przymusinski, and, the other is the disjunctive well-founded semantics D-WFS, proposed by Brass and Dix. Although the two semantics are based on very different ideas, both of them have been shown to share a number of natural and intuitive properties. In particular, both of them extend the well-founded semantics of normal logic programs. Nevertheless, since the static semantics employs a much richer underlying language than the D-WFS semantics, in general, the two semantics are different. The main result of this paper shows, however, that, when restricted to a common language, the two semantics in fact coincide. This important result provides additional and powerful argument in favor of the two semantics. It also allows us to use a recently developed minimal model theorem prover for an efficient implementation of the two semantics.

The Relationlog system prototype

AbstractThe Relationlog system is a novel persistent deductive database system for advanced data and knowledge‐based applications. It directly supports the storage and inference of data with complex structures, especially data supported in nested relational and complex‐object models. The Relationlog system supports the Relationlog query language, which is a typed extension of Datalog with tuples and sets and stands in the same relationship to the nested relational and complex‐object models as Datalog stands to the relational model. It also supports an SQL‐like data definition language and a declarative data manipulation language. This article introduces the Relationlog language, discusses the system architecture, the design decisions incorporated within its implementation, and our experience in developing the system. Copyright © 2001 John Wiley & Sons, Ltd.

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.