Static filtering is a data-independent optimisation method for Datalog, which generalises algebraic query rewriting techniques from relational databases. In spite of its early discovery by Kifer and Lozinskii in 1986, the method has been overlooked in recent research and system development, and special cases are being rediscovered independently. We therefore recall the original approach, using updated terminology and more general filter predicates that capture features of modern systems, and we show how to extend its applicability to answer set programming (ASP). The outcome is strictly more general but also more complex than the classical approach: double exponential in general and single exponential even for predicates of bounded arity. As a solution, we propose tractable approximations of the algorithm that can still yield much improved logic programs in typical cases, e.g., it can improve the performance of rule systems over real-world data in the order of magnitude.

翻译：静态过滤是一种独立于数据的Datalog优化方法，它推广了关系数据库中代数查询重写技术。尽管该方法早在1986年就已由Kifer和Lozinskii提出，但在近年来的研究和系统开发中却长期被忽视，其特例反而被独立地重复发现。为此，我们采用更新的术语体系和能捕捉现代系统特征的更广义过滤谓词，重新审视原始方法，并展示如何将其适用性扩展到答案集编程（ASP）领域。相较于经典方法，新方法在严格更一般化的同时也更为复杂：通常具有双指数复杂度，即使在谓词元数受限的情况下仍保持单指数复杂度。针对此问题，我们提出了该算法的可处理近似方案，在典型场景中仍能显著改进逻辑程序——例如可将现实数据规则系统的性能提升一个数量级。