Datalog and Logic Databases by Sergio Greco

By Sergio Greco

Using common sense in databases all started within the past due Nineteen Sixties. within the early Nineteen Seventies Codd formalized databases when it comes to the relational calculus and the relational algebra. a tremendous impression at the use of good judgment in databases used to be the improvement of the sector of common sense programming. common sense presents a handy formalism for learning classical database difficulties and has the $64000 estate of being declarative, that's, it Read more...

summary: using good judgment in databases began within the overdue Sixties. within the early Nineteen Seventies Codd formalized databases when it comes to the relational calculus and the relational algebra. a tremendous impact at the use of common sense in databases used to be the advance of the sphere of common sense programming. common sense offers a handy formalism for learning classical database difficulties and has the $64000 estate of being declarative, that's, it permits one to specific what she wishes instead of find out how to get it. for a very long time, relational calculus and algebra have been thought of the relational database languages. although, there are easy operations, akin to computing the transitive closure of a graph, which can't be expressed with those languages. Datalog is a declarative question language for relational databases in response to the common sense programming paradigm. one of many peculiarities that distinguishes Datalog from question languages like relational algebra and calculus is recursion, which provides Datalog the aptitude to precise queries like computing a graph transitive closure. fresh years have witnessed a revival of curiosity in Datalog in various rising program domain names akin to info integration, details extraction, networking, software research, defense, cloud computing, ontology reasoning, etc. the purpose of this publication is to offer the fundamentals of Datalog, a few of its extensions, and up to date functions to diversified domain names

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A Datalog program P whose rules define new relations (or “views”) from the database. D is called the Extensional Database (EDB) and P is called the Intensional Database (IDB). We will refer to D simply as database and refer to P as Datalog program. us, predicate symbols are 18 3. DATALOG partitioned into two disjoint sets: base (or EDB or extensional ) and derived (or IDB or intensional ) predicate symbols. e definition of base predicate symbols is stored in D . Base predicate symbols can appear in the body of rules in P but not in the head.

Datalog:sp queries can be evaluated in polynomial time (data complexity). , 1988, Chandra and Harel, 1985, Lifschitz, 1988, Van Gelder, 1989]. Stratified Datalog: programs restrict the manner in which recursion and negation may occur together. First, we need to introduce some additional notions. Let P be a Datalog: program. A partition S1 ; : : : ; Sm of the set of predicate symbols in P , where the Si ’s are called strata, and Sj is lower than Sk if j < k , is a stratification of P iff the following condition holds for every rule in P: 1.

Atoms or negated atoms). Function symbols are not allowed and thus terms appearing in a Datalog: rule are constants and variables. r/. , op 2 f ; >; Ä; <; D; ¤g) and t1 and t2 are terms. 34 4. NEGATION Every rule must be safe. 1 for Datalog is generalized as follows to accommodate negation and comparison atoms. For every Datalog: rule, every variable must be limited in the following sense: • a variable X is limited if it appears in a positive literal of the body whose predicate symbol is not a comparison predicate symbol; • a variable X is limited if it appears in a comparison atom of the form X D c or c D X , where c is a constant; and • a variable X is limited if it appears in a comparison atom of the form X D Y or Y D X , where Y is a limited variable.

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