Data Integration: The Relational Logic Approach - download pdf or read online

By Michael Genesereth, Thomas Dietterich, Ronald Brachman

ISBN-10: 1598297414

ISBN-13: 9781598297416

Facts integration is a severe challenge in our more and more interconnected yet necessarily heterogeneous global. there are lots of facts resources to be had in organizational databases and on public info structures just like the world-wide-web. no longer strangely, the resources frequently use diverse vocabularies and diversified information constructions, being created, as they're, via varied humans, at varied instances, for various reasons. The objective of knowledge integration is to supply programmatic and human clients with built-in entry to a number of, heterogeneous information resources, giving each one consumer the appearance of a unmarried, homogeneous database designed for their particular want. the excellent news is that, in lots of circumstances, the information integration procedure should be automatic. This booklet is an creation to the matter of information integration and a rigorous account of 1 of the major ways to fixing this challenge, viz., the relational good judgment technique. Relational good judgment offers a theoretical framework for discussing information integration. additionally, in lots of vital situations, it offers algorithms for fixing the matter in a computationally sensible manner. in lots of respects, relational common sense does for facts integration what relational algebra did for database concept numerous a long time in the past. A spouse website presents interactive demonstrations of the algorithms. desk of Contents: Preface / Interactive version / creation / simple strategies / question Folding / question making plans / grasp Schema administration / Appendix / References / Index / writer Biography

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Extra info for Data Integration: The Relational Logic Approach

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2) The input relations of the program are all relations in D’s signature and have the same arity as in D’s schema. The value of a query Q on a database instance D, written Q(D), is the set of all atoms in the minimal model of Q∪D that mention the query relation. Query Q1 is contained in query Q2 (written Q1≤Q2) if and only if for every database instance D, Q1(D)⊆Q2(D). Query Q1 is equivalent to Q2 if and only if Q1≤Q2 and Q2≤Q1. The problem of determining whether a Datalog program Q is contained in another Datalog program Q is, in general, undecidable.

Query Q1 is contained in query Q2 (written Q1≤Q2) if and only if for every database instance D, Q1(D)⊆Q2(D). Query Q1 is equivalent to Q2 if and only if Q1≤Q2 and Q2≤Q1. The problem of determining whether a Datalog program Q is contained in another Datalog program Q is, in general, undecidable. The problem becomes decidable if Q contains only positive literals and is not recursive. The following is a decision procedure for this case. First, replace all distinct variables in Q by distinct entities, yielding Q.

X ′βγ [ j ]=Z[ j ′] X ′βγ [ j ]=Xβγ [ j] X ′βγ [ j ]=fi(Z) if if If Xβγ [ j ]=Xβ[ j ′] for some j ′∈{1, . . , m} Xβγ [ j ] is a constant Xβγ [ j ]=Xi for some i∈{1, . . , s} Condition φ′ is generated from φ by replacing each constant in φ by the corresponding variable or constant. As a simple example of this method, consider an application in which there is a single disjunctively defined view v with the definition shown below. v(X) :- p(X) v(c) :- q(c) Our job in this case is to find a retrievable query plan that computes values for the relation p.

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Data Integration: The Relational Logic Approach by Michael Genesereth, Thomas Dietterich, Ronald Brachman


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