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Can Datalog Be Approximated?

โœ Scribed by Surajit Chaudhuri; Phokion G. Kolaitis


Book ID
102585823
Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
452 KB
Volume
55
Category
Article
ISSN
0022-0000

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โœฆ Synopsis


In this paper, we investigate whether recursive Datalog predicates can be approximated by finite unions of conjunctive queries. We introduce a quantitative notion of error and examine two types of approximation, namely, absolute approximation and relative approximation. We also stipulate that the approximations obey certain qualitative criteria, namely we require them to be upper envelopes or lower envelopes of the Datalog predicate they approximate. We establish that absolute approximation by finite unions of conjunctive queries is not possible, which means that no unbounded Datalog predicate can be approximated by a finite union of conjunctive queries in such a way that the error is bounded uniformly by the same constant on all finite databases. After this, we examine relative approximations, i.e., approximations that guarantee bounds for the error relative to the size of the Datalog predicate under consideration. Although such approximations exist in some cases, we show that for several large and well-studied classes of unbounded Datalog predicates it is not possible to find finite unions of conjunctive queries that satisfy the aforementioned qualitative criteria and have the property that the relative error of the approximation is bounded by a constant. Finally, we consider first-order approximations and obtain sharp negative results for the approximability of the transitive closure query by first-order queries.


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