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].
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Liu, M., Dobbie, G., Ling, T.W. (2011). What Have We Learnt from Deductive Object-Oriented Database Research?. In: Yu, J.X., Kim, M.H., Unland, R. (eds) Database Systems for Advanced Applications. DASFAA 2011. Lecture Notes in Computer Science, vol 6587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20149-3_3
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