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|Title:||What have we learnt from deductive object-oriented database research?|
|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 , and is a simplified version of the logic programming language Prolog . 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 , the stable model , and the well-founded model , and proof-theoretic semantics in terms of bottom-up fixpoint semantics . © 2011 Springer-Verlag.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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