Magic Sets for Disjunctive Datalog Programs

04/27/2012
by   Mario Alviano, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/27/2022

On Survivorship Bias in MS MARCO

Survivorship bias is the tendency to concentrate on the positive outcome...
research
05/28/2017

Inexpensive Cost-Optimized Measurement Proposal for Sequential Model-Based Diagnosis

In this work we present strategies for (optimal) measurement selection i...
research
03/17/2000

Detecting Unsolvable Queries for Definite Logic Programs

In solving a query, the SLD proof procedure for definite programs someti...
research
05/03/2022

Multi-strategy ensemble binary hunger games search for feature selection

Feature selection is a crucial preprocessing step in the sphere of machi...
research
07/19/2019

Enhancing magic sets with an application to ontological reasoning

Magic sets are a Datalog to Datalog rewriting technique to optimize quer...
research
07/10/2016

Extending Weakly-Sticky Datalog+/-: Query-Answering Tractability and Optimizations

Weakly-sticky (WS) Datalog+/- is an expressive member of the family of D...
research
11/24/2020

Comprehensive and Sensitive Proteogenomics Data Analysis Strategy based on Complementary Multi-Stage Database Search

Proteogenomics provide opportunities for proteomic validation of gene st...

Please sign up or login with your details

Forgot password? Click here to reset