Datalog and Logic Databases by Sergio Greco

Datalog and Logic Databases by Sergio Greco

By Sergio Greco

Using common sense in databases began 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 big effect at the use of common sense in databases used to be the advance of the sector of good judgment programming. common sense presents a handy formalism for learning classical database difficulties and has the real estate of being declarative, that's, it Read more...

summary: using good judgment in databases begun within the past due Sixties. within the early Nineteen Seventies Codd formalized databases by way of the relational calculus and the relational algebra. a big effect 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 permits one to precise what she desires instead of how you can get it. for a very long time, relational calculus and algebra have been thought of the relational database languages. although, there are easy operations, reminiscent of 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 line with 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 specific queries like computing a graph transitive closure. contemporary years have witnessed a revival of curiosity in Datalog in quite a few rising program domain names reminiscent of facts integration, details extraction, networking, application research, safety, cloud computing, ontology reasoning, and so on. the purpose of this publication is to give the fundamentals of Datalog, a few of its extensions, and up to date purposes to diversified domain names

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T. the Herbrand universe of PD ) of the relation in D for p . Basically, a ground negative literal :A is interpreted as true if A does not belong to D , otherwise it is interpreted as false. X1 ; X2 / 36 4. c; c/: Datalog:sp is strictly more expressive than Datalog. For instance, the difference of two relations can be expressed in Datalog:sp but not in Datalog. Datalog:sp queries can be evaluated in polynomial time (data complexity). , 1988, Chandra and Harel, 1985, Lifschitz, 1988, Van Gelder, 1989].

1995] and Ullman [1988]. A survey of various complexity and expressiveness results for different forms of logic programming can be found in Dantsin et al. [1997, 2001]. , computing the transitive closure of a graph), its expressive power remains limited. In fact, Datalog allows us to express only a proper subset of the queries computable in polynomial time, and only monotonic ones. Simple nonmonotonic queries, such as checking whether the active domain of an input database has an even number of elements or computing the difference of two relations, cannot be expressed.

P /: 1. if H is the head atom and A is a positive body literal, then A belongs to a local stratum lower than or equal to the local stratum of H ; and 2. if H is the head atom and :A is a negative body literal, then A belongs to a local stratum lower than the local stratum of H . We say that P is locally stratified if admits a local stratification. e class of locally stratified Datalog: programs will be also referred to as Datalog:ls . ere is a simple analogy between stratification of Datalog:s programs and local stratification of Datalog:ls programs: the latter treats ground atoms and ground rules in the same way as the former treats predicate symbols and arbitrary rules.

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