Deductive Databases Research Articles

Tail recursion elimination in deductive databases

We consider an optimization technique for deductive and relational databases. The optimization technique is an extension of the magic templates rewriting, and it can improve the performance of query evaluation by not materializing the extension of intermediate views. Standard relational techniques, such as unfolding embedded view definitions, do not apply to recursively defined views, and so alternative techniques are necessary. We demonstrate the correctness of our rewriting. We define a class of “nonrepeating” view definitions, and show that for certain queries our rewriting performs at least as well as magic templates on nonrepeating views, and often much better. A syntactically recognizable property, called “weak right-linearity”, is proposed. Weak right-linearity is a sufficient condition for nonrepetition and is more general than right-linearity. Our technique gives the same benefits as right-linear evaluation of right-linear views, while applying to a significantly more general class of views.

Performance of recursive query processing methods in the presence of cycles

An experimental performance analysis of three recursive query processing methods [the magic-sets method (MAG), the magic-counting method (MCS), and the synchronized-counting method (SCM)] in deductive databases in the presence of cycles is presented. We define a set of characteristics that affects the performance of query processing methods for cyclic data. Although there are other experimental performance analysis studies of recursive query processing methods in the literature, these studies have covered only acyclic databases. There are only some bounds for the worst case and best case analysis of recursive query processing methods on cyclic data. This paper introduces a set of graph characteristics on which similar performance studies can be based, and it is, to our knowledge, the first comparative experimental study on the performance of recursive query processing methods for cyclic data.

A functional database system for road accident analysis

A database system for a road accident analysis application has been developed using the new functional database language PFL. This application requires extensive data validation and restructuring, and queries (and hence data retrieval patterns) tend to be of a complex and ad hoc nature. PFL adapts functional programming to deductive databases and possesses features that are desirable for applications characterised by large volumes of both data and programs, and by complex structures of both data and queries. In this paper we describe the application domain and provide an overview of the salient features of PFL. We then discuss the development of a road accident database using PFL and comment upon the insight this has provided in terms of both the application and the language.

ON TEMPORAL DEDUCTIVE DATABASES

This article introduces a temporal deductive database system featuring a logic programming language and an algebraic front‐end. The language, called Temporal DATALOG, is an extension of DATALOG based on a linear‐time temporal logic in which the flow of time is modeled by the set of natural numbers. Programs of Temporal DATALOG are considered as temporal deductive databases, specifying temporal relationships among data and providing base relations to the algebraic front‐end. The minimum model of a given Temporal DATALOG program is regarded as the temporal database the program models intensionally. The algebraic front‐end, called TRA, is a point‐wise extension of the relational algebra upon the set of natural numbers. When needed during the evaluation of TRA expressions, slices of temporal relations over intervals can be retrieved from a given temporal deductive database by bottom‐up evaluation strategies.A modular extension of Temporal DATALOG is also proposed, through which temporal relations created during the evaluation of TRA expressions may be fed back to the deductive part for further manipulation. Modules therefore enable the algebra to have full access to the deductive capabilities of Temporal DATALOG and to extend it with nonstandard algebraic operators. This article also shows that the temporal operators of TRA can be simulated in Temporal DATALOG by program clauses.

COMPUTING THE WELL-FOUNDED MODEL OF DEDUCTIVE DATABASES

The well-founded model is one of the most popular models of general logic programs, i.e. logic programs with negation in the bodies of clauses. We present a method for constructing this model for general deductive databases, which are logic programs without any function symbols. The method adopts paraconsistent relations as the semantic objects associated with the predicate symbols of the database. Paraconsistent relations are a generalization of ordinary relations in that they allow manipulation of incomplete as well as inconsistent information. The first step in the model construction method is to transform the database clauses into paraconsistent relation definitions involving these operators. The second step is to build the well-founded model iteratively. Algorithms for both steps are presented and their termination and correctness is also established.

Matching of bounded set terms in the logic language [formula omitted]++

This paper is concerned with the problem of matching bounded set terms, i.e., terms of the form [ e 1, …, e n ], where e i is a constant or a variable. Such simplified types of set terms are much used in deductive database systems such as LDL . There are two main results: (a) the detailed complexity analysis of the problem by providing a formula for determining the number of matchers and (b) the invention of an optimal matching algorithm. This algorithm is also extended to handle bound set terms whose elements are not restricted to be constants or variables.

On computing minimal and perfect model membership

The computational complexity of a number of problems relating to minimal models of non-Horn deductive databases is considered. In particular, the problem of determining minimal model membership is shown to be NP-complete for non recursive propositional databases. The structure of minimal models is also examined using the notion of a cyclic tree, and methods of determining minimal model membership, minimality of models and compiling the GCWA are presented. The handling of negative premises is also considered using perfect model semantics, and methods for computing perfect model membership are presented.

Knowledge discovery in deductive databases with large deduction results: the first step

Deductive databases have the ability to deduce new facts from a set of existing facts by using a set of rules. They are also useful in the integration of artificial intelligence and databases. However, when recursive rules are involved, the number of deduced facts can become too large to be practically stored, viewed or analyzed. This seriously hinders the usefulness of deductive databases. In order to overcome this problem, we propose four methods to discover characteristic rules from a large number of deduction results without actually having to store all the deduction results. This paper presents the first step in the application of knowledge discovery techniques to deductive databases with large numbers of deduction results.

A cooperative information system to support multi-expert systems development

The development of a multi-expert system (MES) is obviously a team project. This paper shows how database technology can actively and cooperatively support the development and design of a multi-expert system. The proposed solution is based on an Active and Deductive DataBase (ADDB). An extensional database records and maintains information about the project and its evolution. An intensional database, defined by a set of rules, coordinates and controls the activity of the different actors, and models relevant views on the project. This rule base can be fired automatically when an interesting event occurs and consequently either informs the members of the team concerned by this event (beginning of a new step, creation of a new product, modification of a knowledge base, etc.), or triggers another activity, or invokes other tools. This rule base can also be executed explicitly to provide its user (experts, developers, the project manager, etc.) with cooperative information concerning the project. This cooperative information improves the visibility and the intelligibility of the project and knowledge reuse.

Bottom-up computation of the Fitting model for general deductive databases

General logic programs are those that contain both positive and negative subgoals in their clause bodies. For such programs Fitting proposed an elegant 3-valued minimum model semantics that avoids some impracticalities of previous approaches. Here we present a method to compute this Fitting model for deductive databases. We introducepartial relations, which are the semantic objects associated with predicate symbols, and define algebraic operators over them. The first step in our model computation method is to convert the database rules into partial relation definitions involving these operators. The second step is to build the minimum model iteratively. We give algorithms for both steps and show their termination and correctness. We also suggest extensions to our method for computing the well-founded model proposed by van Gelder, Ross and Schlipf.

Hybrid knowledge bases

Deductive databases that interact with, and are accessed by, reasoning agents in the real world (such as logic controllers in automated manufacturing, weapons guidance systems, aircraft landing systems, land-vehicle maneuvering systems, and air-traffic control systems) must have the ability to deal with multiple modes of reasoning. Specifically, the types of reasoning we are concerned with include, among others, reasoning about time, reasoning about quantitative relationships that may be expressed in the form of differential equations or optimization problems, and reasoning about numeric modes of uncertainty about the domain which the database seeks to describe. Such databases may need to handle diverse forms of data structures, and frequently they may require use of the assumption-based nonmonotonic representation of knowledge. A hybrid knowledge base is a theoretical framework capturing all the above modes of reasoning. The theory tightly unifies the constraint logic programming scheme of Jaffar and Lassez (1987), the generalized annotated logic programming theory of Kifer and Subrahmanian (1989), and the stable model semantics of Gelfond and Lifschitz (1988). New techniques are introduced which extend both the work on annotated logic programming and the stable model semantics.

Logic programming: Proceedings of the twelfth international conference on logic programming

13-16 June 1995, Tokyo, Japan ICLP, which is sponsored by the Association for Logic Programming, is one of two major annual international conferences reporting recent research results in logic programming. Logic programming originates from the discovery that a subset of predicate logic could be given a procedural interpretation which was first embodied in the programming language, Prolog. The unique features of logic programming make it appealing for numerous applications in artificial intelligence, computer-aided design and verification, databases, and operations research, and for exploring parallel and concurrent computing. The last two decades have witnessed substantial developments in this field from its foundation to implementation, applications, and the exploration of new language designs.Topics covered : Theoretical Foundations. Higher-Order Logics. Non-Monotonic Reasoning. Programming Methodology. Programming Environments. Extensions to Logic Programming. Constraint Satisfaction. Meta-Programming. Language Design and Constructs. Implementation of Logic Programming Languages. Compilation Techniques. Architectures. Parallelism. Reasoning about Programs. Deductive Databases. Applications.Logic Programming series, Research Reports and Notes

Computation of stable models and its integration with logical query processing

The well-founded semantics and the stable model semantics capture intuitions of the skeptical and credulous semantics in nonmonotonic reasoning, respectively. They represent the two dominant proposals for the declarative semantics of deductive databases and logic programs. However, neither semantics seems to be suitable for all applications. We have developed an efficient implementation of goal-oriented effective query evaluation under the well-founded semantics. It produces a residual program for subgoals that are relevant to a query, which contains facts for true instances and clauses with body literals for undefined instances. We present a simple method of stable model computation that can be applied to the residual program of a query to derive answers with respect to stable models. The method incorporates both forward and backward chaining to propagate the assumed truth values of ground atoms, and derives multiple stable models through backtracking. Users are able to request that only stable models satisfying certain conditions be computed. A prototype has been developed that provides integrated query evaluation under the well-founded semantics, the stable models, and ordinary Prolog execution. We describe the user interface of the prototype and present some experimental results.

On satisfiability, equivalence, and implication problems involving conjunctive queries in database systems

Satisfiability, equivalence, and implication problems involving conjunctive queries are important and widely encountered problems in database management systems. These problems need to be efficiently and effectively solved. In this paper, we consider queries which are conjunctions of the inequalities of the form (X op C), (X op Y), and/or (X op Y+C), where X and Y are two attributes, C is a constant, and op /spl epsiv/ { , /spl ges/}. These types of inequalities are widely used in database systems, since the first type is a selection, the second type is a /spl theta/-join, and the third type is a very popular clause in a deductive database system. The satisfiability, equivalence, and implication problems in the integer domain (for attributes and constants) have been shown to be NP-hard. However, we show that these problems can be solved efficiently in the real domain. The incorporation of the real domain is significant, because the real domain is practically and widely used in a database. Necessary and sufficient conditions and algorithms are presented. A novel concept of the module closure and a set of sound and complete axioms with respect to the module closure are also proposed to infer all correct and necessary inequalities from a given query. The proposed axioms generalize Ullman's axioms (1989) where queries only consist of /spl theta/-joins.

Automatic Classification of Protein Structures with Deductive Database System

Automatic Classification of Protein Structures with Deductive Database System

Space optimization in deductive databases

In the bottom-up evaluation of logic programs and recursively defined views on databases, all generated facts are usually assumed to be stored until the end of the evaluation. Discarding facts during the evaluation, however, can considerably improve the efficiency of the evaluation: the space needed to evaluate the program, the I/O costs, the costs of maintaining and accessing indices, and the cost of eliminating duplicates may all be reduced. Given an evaluation method that is sound, complete, and does not repeat derivation steps, we consider how facts can be discarded during the evaluation without compromising these properties. We show that every such space optimization method has certain components, the first to ensure soundness and completeness, the second to avoid redundancy (i.e., repetition of derivations), and the third to reduce “fact lifetimes” (i.e., the time period for which each fact must be retained during evaluation). We present new techniques based on providing bounds on the number of derivations and uses of facts, and using monotonicity constraints for each of the first two components, and provide novel synchronization techniques for the third component of a space optimization method. We describe how techniques for each of the three components can be combined in practice to obtain a space optimization method for a program. Our results are also of importance in applications such as sequence querying, and in active databases where triggers are defined over multiple “events.”

Competitive Product Development in the Financial Services Industry—A Knowledge‐Based Approach

AbstractThe development of innovative products and the ability to quickly adapt to changes is important for financial institutions in today's highly competitive environment. The information system department has to create the required information technology support (IT support) for these tasks. A model that allows the characterization of bank products by means of parameter sets and integrity constraints that in most cases may be viewed as business rules will be presented. Based on this parameter model a prototype system was developed to verify the approach used. This system also provides support for the areas of risk and yield management and may be used as a basis for systems providing counselling services. The prototype was implemented by coupling PROLOG and a relational database system. Arguments will be presented against the use of object‐oriented database systems and in favour of the currently less popular deductive database approach. The approach presented may be extended such that products related to the life insurance business may also be covered.

Deductive databases for transport engineering

Transport data are typical of many application areas in that they arise from a variety of sources and are used in various ways. Furthermore, important information that is required in practical applications of transport databases is often not stored explicitly, but rather has to be deduced from some that is. It is therefore natural to consider the application in this field of deductive database management systems (DBMSs). These extend traditional DBMSs by permitting the definition of inference rules, default rules, complex data structures and integrity constraints, each of which can be used to provide facilities of substantial practical value. The work described here arose from the application of a novel deductive DBMS called PFL (Persistent Functional Language) to the storage and manipulation of road accident data. Although the primary entity type in such a database is that of an accident, queries are typically based upon the concept of a site. Because site information is not stored explicitly in the database, it must be deduced from auxiliary information that provides some indication of location. The combination of large amounts of data and computationally intensive queries presents extraordinary demands for the database system and has led to the development of new software techniques of high efficiency in both computation and data manipulation.

Extrema Predicates in Deductive Databases

A novel approach is proposed for expressing and computing efficiently a large class of problems, including finding the shortest path in a graph, that were previously considered impervious to an efficient treatment in the declarative framework of logic-based languages. Our approach is based on the use of min and max predicates having a first-order semantics defined using rules with negation in their bodies. We show that, under certain monotonicity conditions, (1) there exists a total well-founded model for these programs expressed using negation, (2) this model can be computed efficiently using a procedure called greedy fixpoint, and (3) programs with min/max goals on recursively defined cost predicates can often be rewritten into more efficient ones by pushing min and max predicates into recursion. The greedy fixpoint evaluation of the program expressing the shortest path problem coincides with Dijkstra′s algorithm, once the finite differencing techniques of seminaive fixpoint are applied.

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.