Download Declarative Programming and Knowledge Management: by Michael Hanus, Ricardo Rocha (eds.) PDF

By Michael Hanus, Ricardo Rocha (eds.)

This booklet constitutes the court cases of the Kiel Declarative Programming Days, KDPD 2013, unifying the next meetings: the 20 th overseas convention on functions of Declarative Programming and information administration (INAP 2013), the twenty second foreign Workshop on practical and (Constraint) common sense Programming (WFLP 2013) and the twenty seventh Workshop on good judgment Programming (WLP 2013), held in Kiel, Germany, in September 2013. The 15 papers offered have been conscientiously and reviewed and chosen for inclusion during this publication. They conceal the subsequent issues: good judgment programming, constraint challenge fixing, programmable good judgment solvers, sensible programming and constraint programming.

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Extra info for Declarative Programming and Knowledge Management: Declarative Programming Days, KDPD 2013, Unifying INAP, WFLP, and WLP, Kiel, Germany, September 11-13, 2013, Revised Selected Papers

Example text

Let Q(x, y, z) = Enroll(x, y, z). Then, certainS ((I ∪ {M}), Q) = {Enroll(Alex, CSI1390, 80), Enroll(Alex, CSI1790, 80)}. In DE, certain answers of unions of CQs can be evaluated in Logspace by directly posing them over a universal solution [7], and then discarding tuples with null values. The same complexity bound holds in DSE by applying a slightly different algorithm. In fact, certain answers cannot be simply obtained by posing Q on a universal DSE solution J, since J might be incomplete with respect to the implicit data in Σtc .

We define the class of minimal universal DSE solutions which are considered as “best” solutions. A minimal universal DSE solution contains the minimal amount of explicit data required to compute the complete set of explicit and implicit data using a set of target rules Σt . We show that there exists an algorithm to generate a canonical minimal universal DSE solution, with a well-behaved set Σt of target rules, in Logspace. (3) Query Answering. We formally define the set of DSE certain answers for conjunctive queries.

Where i = numericc (f β), and Ci is the ith constructor of T , and vj = decodeTj (aj , β) where Tj is the type of the j-th argument of Ci . As stated, this is a partial function, since any of f, a may be too short. For this section, we assume that abstract values always have enough flags and arguments for decoding, and we defer a discussion of partial decodings to Sect. 4. Allocators for Abstract Values. Since we consider (in this section) finite types only, we restrict to complete allocators: for a type T , a complete allocator is an abstract value a ← A that can represent each element of T : for each e ← T , there is some β such that decodeT (a, β) = e.

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