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.

本文介绍了一种新的技术，通过利用查询绑定的传播并扩展魔法集（MS）优化技术来优化（部分）绑定查询，从而对分层否定性的分离 Datalog 程序进行优化，通过对具体应用进行实验，可以得到该方法带来了显著的计算收益。

关于析取 Datalog 程序的魔法集

Magic Sets for Disjunctive Datalog Programs

Querying over disjunctive ASP with functions is a highly undecidable task in
general. In this paper we focus on disjunctive logic programs with stratified
negation and functions under the stable model semantics (ASP^{fs}). We show
that query answering in this setting is decidable, if the query is finitely
recursive (ASP^{fs}_{fr}). Our proof yields also an effective method for query
evaluation. It is done by extending the magic set technique to ASP^{fs}_{fr}.
We show that the magic-set rewritten program is query equivalent to the
original one (under both brave and cautious reasoning). Moreover, we prove that
the rewritten program is also finitely ground, implying that it is decidable.
Importantly, finitely ground programs are evaluable using existing ASP solvers,
making the class of ASP^{fs}_{fr} queries usable in practice.

该研究论文探讨了具有分层否定和函数的分离逻辑编程的可询问性，并介绍了一种有效的查询评估方法 —— 将魔法集技术扩展到该环境下，并证明了重写程序与原始程序等效。同时，证明了重写程序也是可评估的，这使得该类查询在实践中有用。