Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-20149-3_3
Title: What have we learnt from deductive object-oriented database research?
Authors: Liu, M.
Dobbie, G.
Ling, T.W. 
Issue Date: 2011
Source: Liu, M.,Dobbie, G.,Ling, T.W. (2011). What have we learnt from deductive object-oriented database research?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6587 LNCS (PART 1) : 16-21. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-20149-3_3
Abstract: Deductive databases and object-oriented databases (DOOD) are two important extensions of the traditional relational database technology. Deductive databases provide a rule-based language called Datalog¬ (Datalog with negation) that uses function-free Horn clauses with negation to express deductive rules [1], and is a simplified version of the logic programming language Prolog [2]. A deductive database consists of an extensional database and an intensional database. The extensional database (EDB) consists of the relations stored in a relational database whereas the intensional database (IDB) consists of a Datalog¬ program that is a set of deductive rules used to derive relations that are the logical consequences of the program and the extensional database. Datalog¬ is more expressive than pure relational query languages such as relational algebra and relational calculus as it supports recursive deductive rules and recursive queries. Moreover, deductive databases have a firm logical foundation that consists of both model-theoretic semantics in terms of the minimal model [3], the stable model [4], and the well-founded model [5], and proof-theoretic semantics in terms of bottom-up fixpoint semantics [2]. © 2011 Springer-Verlag.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/41275
ISBN: 9783642201486
ISSN: 03029743
DOI: 10.1007/978-3-642-20149-3_3
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

Page view(s)

44
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.