There has recently been an increasing interest in declarative data analysis,
where analytic tasks are specified using a logical language, and their
implementation and optimisation are delegated to a general-purpose query
engine. Existing declarative languages for data analysis can be formalised as
variants of logic programming equipped with arithmetic function symbols and/or
aggregation, and are typically undecidable. In prior work, the language of
$\mathit{limit\ programs}$ was proposed, which is sufficiently powerful to
capture many analysis tasks and has decidable entailment problem. Rules in this
language, however, do not allow for negation. In this paper, we study an
extension of limit programs with stratified negation-as-failure. We show that
the additional expressive power makes reasoning computationally more demanding,
and provide tight data complexity bounds. We also identify a fragment with
tractable data complexity and sufficient expressivity to capture many relevant
tasks.

研究了带有分层否定的限制程序的扩展，发现它的附加表达能力使推理的计算需求更高，并提供了严格的数据复杂度界限。同时识别出一个具有可操作数据复杂度和足够表达能力以捕捉许多相关任务的片段。

限制 Datalog 程序中的分层否定

Stratified Negation in Limit Datalog Programs

RockIt is a maximum a-posteriori (MAP) query engine for statistical
relational models. MAP inference in graphical models is an optimization problem
which can be compiled to integer linear programs (ILPs). We describe several
advances in translating MAP queries to ILP instances and present the novel
meta-algorithm cutting plane aggregation (CPA). CPA exploits local
context-specific symmetries and bundles up sets of linear constraints. The
resulting counting constraints lead to more compact ILPs and make the symmetry
of the ground model more explicit to state-of-the-art ILP solvers. Moreover,
RockIt parallelizes most parts of the MAP inference pipeline taking advantage
of ubiquitous shared-memory multi-core architectures.
We report on extensive experiments with Markov logic network (MLN) benchmarks
showing that RockIt outperforms the state-of-the-art systems Alchemy, Markov
TheBeast, and Tuffy both in terms of efficiency and quality of results.

RockIt 是一种基于最大后验查询引擎的查询引擎，通过整数线性规划 (ILPs) 的编译实现 MAP 推理，并使用一种名为 cutting plane aggregation (CPA) 的新型元算法。它还利用共享内存多核架构并行化 MAP 推理管道的大部分部分，通过与 Alchemy、Markov TheBeast 和 Tuffy 等状态 - of-the-art 系统的大量实验表明，RockIt 在效率和结果质量方面都优于这些系统。