6 research outputs found
Optimizing inequality joins in Datalog with approximated constraint propagation
Datalog systems evaluate joins over arithmetic (in)equalities as a naive generate-and-test of Cartesian products. We exploit aggregates in a source-to-source transformation to reduce the size of Cartesian products and to improve performance. Our approach approximates the well-known propagation technique from Constraint Programming.
Experimental evaluation shows good run time speed-ups on a range of non-recursive as well as recursive programs. Furthermore, our technique improves upon the previously reported in the literature constraint magic set transformation approach
Approximating Constraint Propagation in Datalog
We present a technique exploiting Datalog with aggregates to improve the
performance of programs with arithmetic (in)equalities. Our approach employs a
source-to-source program transformation which approximates the propagation
technique from Constraint Programming. The experimental evaluation of the
approach shows good run time speed-ups on a range of non-recursive as well as
recursive programs. Furthermore, our technique improves upon the previously
reported in the literature constraint magic set transformation approach.Comment: Online Proceedings of the 11th International Colloquium on
Implementation of Constraint LOgic Programming Systems (CICLOPS 2011),
Lexington, KY, U.S.A., July 10, 201
Magic Sets for Disjunctive Datalog Programs
In this paper, a new technique for the optimization of (partially) bound
queries over disjunctive Datalog programs with stratified negation is
presented. The technique exploits the propagation of query bindings and extends
the Magic Set (MS) optimization technique.
An important feature of disjunctive Datalog is nonmonotonicity, which calls
for nondeterministic implementations, such as backtracking search. A
distinguishing characteristic of the new method is that the optimization can be
exploited also during the nondeterministic phase. In particular, after some
assumptions have been made during the computation, parts of the program may
become irrelevant to a query under these assumptions. This allows for dynamic
pruning of the search space. In contrast, the effect of the previously defined
MS methods for disjunctive Datalog is limited to the deterministic portion of
the process. In this way, the potential performance gain by using the proposed
method can be exponential, as could be observed empirically.
The correctness of MS is established thanks to a strong relationship between
MS and unfounded sets that has not been studied in the literature before. This
knowledge allows for extending the method also to programs with stratified
negation in a natural way.
The proposed method has been implemented in DLV and various experiments have
been conducted. Experimental results on synthetic data confirm the utility of
MS for disjunctive Datalog, and they highlight the computational gain that may
be obtained by the new method w.r.t. the previously proposed MS methods for
disjunctive Datalog programs. Further experiments on real-world data show the
benefits of MS within an application scenario that has received considerable
attention in recent years, the problem of answering user queries over possibly
inconsistent databases originating from integration of autonomous sources of
information.Comment: 67 pages, 19 figures, preprint submitted to Artificial Intelligenc