# Precomputing Datalog evaluation plans in large-scale scenarios

## TL;DR

This paper introduces novel techniques for precomputing Datalog evaluation plans that optimize memory usage and execution efficiency in large-scale semantic Web applications.

## Contribution

It proposes new methods for determining optimal indexing schemas and rule orderings, improving memory efficiency without sacrificing performance.

## Key findings

- Memory usage is significantly reduced
- Execution efficiency remains unaffected
- Approach outperforms standard plans in benchmarks

## Abstract

With the more and more growing demand for semantic Web services over large databases, an efficient evaluation of Datalog queries is arousing a renewed interest among researchers and industry experts. In this scenario, to reduce memory consumption and possibly optimize execution times, the paper proposes novel techniques to determine an optimal indexing schema for the underlying database together with suitable body-orderings for the Datalog rules. The new approach is compared with the standard execution plans implemented in DLV over widely used ontological benchmarks. The results confirm that the memory usage can be significantly reduced without paying any cost in efficiency. This paper is under consideration in Theory and Practice of Logic Programming (TPLP).