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About 1,260 papers for “Deductive database”

Deductive database theories

John Grant, Jack Minker · The Knowledge Engineering Review · 1989

Abstract This paper surveys a variety of deductive database theories. Such theories differ from one another in the set of axioms and metarules that they allow and use. The following theories are discussed: relational, Horn, and stratified in the text; protected, disjunctive, typed, extended Horn, and normal in the appendix. Connections with programming in terms of the declarative, fixpoint, and procedural semantics are explained. Negation is treated in several different ways: closed world, completed database, and negation as failure. For each theory examples are given and implementation issues are considered.

Cited by 8

Deductive database security

Suzie Barker · Research Portal (King's College London) · 2003

An approach is described for protecting the information in deductive databases from unauthorized retrieval and update requests. The approach we propose combines meta-programming and partial evaluation techniques, has a number of attractive technical results associated with it, and can be used as a basis for efficient implementations of protected forms of deductive database.

Cited by 0

Building a Deductive Database

Miguél Nussbaum, George W. Zobrist · Medical Entomology and Zoology · 1992

This text illustrates the main issues and concepts behind deductive databases through the description of a real system. Both theory and practice combine to advance a pragmatic approach. The book covers all related topics from basic theory to its coupling with a known database management system and its implementation on a commerical multiprocessor. An overview describes the problems related to the field. In the introduction, basic tools and references to related work give the necessary background context. Chapter two slowly begins building the concepts that finally lead to the kern algorithm used throughout the book - mixed top-down, bottom-up computation. Upon completion of the book, the reader should be able to build a deductive database. Implementation problems are exposed and solved and new strategies and algorithms with their performance behaviour are presented. Additionally the reader should also learn the benefits and drawbacks of working with an existing database and the usefulness of a parallel machine.

Cited by 5

Generating Deductive Database Explanations

Sarah Mallet, Mireille Ducassé · 1999

1 Introduction Explaining the behaviour of a program consists of showing an abstraction of its execution. Existing explanation systems for deductive databases [20, 16, 1] show forests of proof trees. Although proof trees are often useful, they are only one possible interesting abstraction. Deductive databases (DDB) have various kinds of users: implementors who develop the deductive part, knowledge engineers who maintain the data, and end-users who query the database. An explanation system for DDB must be useful for all of them. It must therefore be able to generate explanations at several levels of abstraction. A key feature of an explanation system for DDB is therefore its flexibility.

Cited by 0

XSB as a deductive database

Konstantinos Sagonas, Terrance Swift, David S. Warren · ACM SIGMOD Record · 1994

No abstract available.

Cited by 1Open access

A deductive database audit trail

David L. Sallach · 1992

Article A deductive database audit trail Share on Author: David L. Sallach View Profile Authors Info & Claims SAC '92: Proceedings of the 1992 ACM/SIGAPP Symposium on Applied computing: technological challenges of the 1990'sApril 1992 Pages 314–319https://doi.org/10.1145/143559.150704Online:01 April 1992Publication History 3citation749DownloadsMetricsTotal Citations3Total Downloads749Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Cited by 5

The Deductive Database System LDL++

Natraj Arni, KayLiang Ong, Shalom Tsur et al. · 2003

This paper describes the LDL++ system and the research advances that have enabled its design and development. We begin by discussing the new nonmonotonic and nondeterministic constructs that extend the functionality of the LDL++ language, while preserving its model-theoretic and fixpoint semantics. Then, we describe the execution model and the open architecture designed to support these new constructs and to facilitate the integration with existing DBMSs and applications. Finally, we describe the lessons learned by using LDL++ on various tested applications, such as middleware and datamining.

Cited by 0

Architecture of deductive database systems

Carlo Zaniolo · 2002

Deductive databases are coming of age with the emergence of efficient and easy-to-use systems that support queries, reasoning, and application development on databases through declarative logic-based languages. The progress is demonstrated by the completion of prototype systems offering such levels of generality, performance, and robustness that they support well complex application development. An overview of the architectures and techniques of these systems is provided. The main motivations for development of the deductive database systems (DDSs) are: (1) to provide support for advanced database applications, with a focus on expert systems and knowledge-based applications; and (2) to provide better support for traditional database applications by integrating the application development and database queries into one language, thus solving the impedance mismatch problems. The key implementation problems for DDSs pertain to finding efficient executions for the given set of rules and queries. For this purpose, the DDS performs a global analysis of rules. The cornerstone of this analysis is the notion of bound arguments and free arguments of predicates.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 1

\tThe Aditi deductive database system

Jayen Vaghani, Kotagiri Ramamohanarao, David B. Kemp et al. · 2008

Deductive databases generalize relational databases by providing support for recursive views and non-atomic data. Aditi is a deductive database system based on the client-server model: it is inherently multi-user and capable of exploiting parallelism on shared-memory multiprocessors. The back-end uses relational technology for efficiency in the management of disk based data and uses optimization algorithms especially developed for the bottom-up evaluation of logical queries involving recursion. The front-end interacts with the user in a logical language that has more expressive power than relational query languages. We present the structure of Aditi, discuss its components in some detail, and present performance figures.

Cited by 0