Deductive Database Technology Research Articles

Deductive systems for BigData integration

The globalization is associated with an increased data to be processed from E-commerce transactions. The specialists are looking for different solutions, such as BigData, Hadoop, Datawarehoues, but it seems that the future is the predicative logic implemented through deductive database technology. It has to be done the swift from imperative languages, to not declaratively languages used for the application development. The deductive databases are very useful in the student teaching programs, too. Thus, the article makes a consistent literature review in the field and shows practical examples of using predicative logic in deductive systems, in order to integrate different kind of data types.

Enhancing context knowledge repositories with justifiable exceptions

Dealing with context dependent knowledge is a well-known area of study that roots in John McCarthy's seminal work. More recently, the Contextualized Knowledge Repository (CKR) framework has been conceived as a logic-based approach in which knowledge bases have a two layered structure, modeled by a global context and a set of local contexts. The global context not only contains the meta-knowledge defining the properties of local contexts, but also holds the global (context independent) object knowledge that is shared by all of the local contexts. In many practical cases, however, it is desirable to leave the possibility to “override” the global object knowledge at the local level: in other words, it is interesting to recognize the pieces of knowledge that can admit exceptional instances in the local contexts that do not need to satisfy the general axiom. To address this need, we present in this paper an extension of CKR in which defeasible axioms can be included in the global context. The latter are verified in the local contexts only for the instances for which no exception to overriding exists, where exceptions require a justification in terms of facts that are provable from the knowledge base. We formally define this semantics and study some semantic and computational properties, where we characterize the complexity of the major reasoning tasks, among them satisfiability testing, instance checking, and conjunctive query answering. Furthermore, we present a translation of extended CKRs with knowledge bases in the Description Logic SROIQ-RL under the novel semantics to datalog programs under the stable model (answer set) semantics. We also present an implementation prototype and examine its scalability with respect to the size of the input CKR and the amount (level) of defeasibility in experiments. Finally, we compare our representation approach with some major formalisms for expressing defeasible knowledge in Description Logics and contextual knowledge representation. Our work adds to the body of results on using deductive database technology such as SQL and datalog in these areas, and provides an expressive formalism (in terms of intrinsic complexity) for exception handling by overriding.

A layered rule-based architecture for approximate knowledge fusion?

In this paper we present a framework for fusing approximate knowledge obtained from various distributed, heterogenous knowledge sources. This issue is substantial in modeling multi-agent systems, where a group of loosely coupled heterogeneous agents cooperate in achieving a common goal. In paper [5] we have focused on defining general mechanism for knowledge fusion. Next, the techniques ensuring tractability of fusing knowledge expressed as a Horn subset of propositional dynamic logic were developed in [13,16]. Propositional logics may seem too weak to be useful in real-world applications. On the other hand, propositional languages may be viewed as sublanguages of first-order logics which serve as a natural tool to define concepts in the spirit of description logics [2]. These notions may be further used to define various ontologies, like e.g. those applicable in the Semantic Web. Taking this step, we propose a framework, in which our Horn subset of dynamic logic is combined with deductive database technology. This synthesis is formally implemented in the framework of HSPDL architecture. The resulting knowledge fusion rules are naturally applicable to real-world data.

ISF: A Visual Formalism for Specifying Interconnection Styles for Software Design

We have developed a framework for specifying high-level software designs. The core of the framework is a very simple visual notation. This notation enables designers to document designs as labelled rectangles and directed edges. In addition to the notation, our framework features a supporting formalism, called ISF (Interconnection Style Formalism). This formalism enables designers to customize the simple design notation by specifying the type of entities, relations, legal configurations of entities and relations, as well as scoping rules of the custom notation. In this paper we present the formal definition of ISF and use ISF to specify two custom design notations. We also describe how ISF specifications, using deductive database technology, are used to generate supporting tools for these custom notations.

Applying deductive database technology to network management

Network Management is essential for successful operation of any communications network. Due to the complexity of modern networks, their management requires application of artificial intelligence based techniques. Two essential aspects of any Network Management system are, a large volume of data, and rules applied to this data. Deductive database systems cater for both. In this paper we examine the suitability of deductive database systems for Network Management application. Fundamentals of Network Management as well as deductive database systems are given. We describe a prototype Network Management system built to test the suitability of deductive database systems for Network Management.

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.

An introduction to deductive database languages and systems

One of the most fundamental uses of a computer is to store and retrieve information, particularly when there are a large amount of data to be stored, or there are complex manipulations that must be performed on them. There has been a large amount of research on the most efficient techniques to store and retrieve data, and the associated problems now have satisfactory solutions. However, the problem of understanding and interpreting this large amount of information remains, particularly when the amounts of data belong to complex domains, such as those involving mineral exploration and financial analysis. To tackle this problem, a mechanism for reasoning about the stored information is necessary. Such a mechanism must be able to cope with large amounts of information, as well as to perform sophisticated inferences, and to draw the appropriate conclusions. A framework in which these problems may be attacked is given by the field of deductive databases. Deductive databases not only store explicit information in the manner of a relational database, but they also store rules that enable inferences based on the stored data to be made. This area is an outgrowth of the field of logic programming, in which mathematical logic is used to directly model computational concepts. Together with techniques developed for relational databases, this basis in logic means that deductive databases are capable of handling large amounts of information as well as performing reasoning based on that information. There are many application areas for deductive database technology. One area is that of decision support systems. In particular, the exploitation of an organization's resources requires fi~tbniy sufficient information about the current and future status of the resources themselves, but also a way of reasoning effectively about plans for the future. The present generation of decision support systems are severely deficient when it comes to reasoning about future plans. Deductive database technology is an appropriate solution to this problem. Another fruitful application area is that of expert systems. There are many computing applications in which there are large amounts of information, from which the important facts may be distilled by a simple yet tedious analysis. For example, medical analysis and monitoring can generate a large amount of data, and an error can have disastrous consequences. A tool to carefully monitor a patient's condition or to retrieve relevant cases during diagnosis reduces the risk of error in such

DECLARE and SDS: Early efforts to commercialize deductive database technology

The Smart Data System (SDS) and its declarative query language, Declarative Reasoning, represent the first large-scale effort to commercialize deductive database technology. SDS offers the functionality of deductive reasoning in a distributed, heterogeneous database environment. In this article we discuss several interesting aspects of the query compilation and optimization process. The emphasis is on the query execution plan data structure and its transformations by the optimizing rule compiler. Through detailed case studies we demonstrate that efficient and very compact runtime code can be generated. We also discuss our experiences gained from a large pilot application (the MVV-expert) and report on several issues of practical interest in engineering such a complex system, including the migration from Lisp to C. We argue that heuristic knowledge and control should be made an integral part of deductive databases.

Towards a deductive database for small simple groups

This particular study investigates the application of Prolog and the associated technology of deductive databases to the realm of modern algebra. A relatively small, yet diverse, collection of information was chosen for the feasibility study. The 56 non-abelian simple groups of order less than one million have been studied in depth. In print are several tables of information such as minimal generating pairs, presentations, character tables, and maximal subgroups. The information is very heterogeneous in nature, involving formulae, tables, lists, arbitrary precision integers, character strings, irrational numbers, and rules for deducing information from the given facts.While very much a feasibility study, the work to date demonstrates that the Prolog deductive database technology is appropriate. More primitive data types, such as irrational numbers, infinite precision numbers, and tables would improve the efficiency of Prolog in this domain. More work categorizing user queries and incorporating the necessary facts and rules to answer them is required.

Making deductive databases a practical technology: a step forward

Deductive databases provide a formal framework to study rule-based query languages that are extensions of first-order logic. However, deductive database languages and their current implementations do not seem appropriate for improving the development of real applications or even sample of them. Our goal is to make deductive database technology practical. The design and implementation of the RDL1 system, presented in this paper, constitute a step toward this goal. Our approach is based on the integration of a production rule language within a relational database system, the development of a rule-based programming environment and the support of system extensibility using Abstract Data Types. We discuss important practical experience gained during the implementation of the system. Also, comparisons with related work such as LDL, STARBURST and POSTGRES are given.