A very desirable Datalog extension investigated by many researchers in the
last thirty years consists in allowing the use of the basic SQL aggregates min,
max, count and sum in recursive rules. In this paper, we propose a simple
comprehensive solution that extends the declarative least-fixpoint semantics of
Horn Clauses, along with the optimization techniques used in the bottom-up
implementation approach adopted by many Datalog systems. We start by
identifying a large class of programs of great practical interest in which the
use of min or max in recursive rules does not compromise the declarative
fixpoint semantics of the programs using those rules. Then, we revisit the
monotonic versions of count and sum aggregates proposed in (Mazuran et al.
2013b) and named, respectively, mcount and msum. Since mcount, and also msum on
positive numbers, are monotonic in the lattice of set-containment, they
preserve the fixpoint semantics of Horn Clauses. However, in many applications
of practical interest, their use can lead to inefficiencies, that can be
eliminated by combining them with max, whereby mcount and msum become the
standard count and sum. Therefore, the semantics and optimization techniques of
Datalog are extended to recursive programs with min, max, count and sum, making
possible the advanced applications of superior performance and scalability
demonstrated by BigDatalog (Shkapsky et al. 2016) and Datalog-MC (Yang et al.
2017). This paper is under consideration for acceptance in TPLP.

本文提出一种简单的综合解决方案，该方案扩展了 Horn Clause 的声明性最小固定点语义，以及 Datalog 系统采用的自底向上实现方法中使用的优化技术，以将 Datalog 的语义和优化技术扩展到包括 min、max、count 和 sum 在内的递归程序中，实现了更好的性能和可扩展性。