Rule Rewriting Revisited: A Fresh Look at Static Filtering for Datalog and ASP

TL;DR

This paper revisits static filtering for Datalog and ASP, updating the original method with modern features and proposing tractable approximations that significantly improve rule system performance on real-world data.

Contribution

It extends the classical static filtering approach to answer set programming with more general and complex filters, and introduces practical approximations for efficiency.

Findings

Improved logic programs can be achieved with static filtering.

Approximate algorithms provide significant performance gains.

Method applicable to modern Datalog and ASP systems.

Abstract

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.