Precomputing Datalog evaluation plans in large-scale scenarios
This work addresses performance bottlenecks for researchers and industry experts handling large databases in semantic web applications, though it appears incremental as it builds on existing DLV execution plans.
The paper tackles the problem of efficiently evaluating Datalog queries in large-scale semantic web services by proposing techniques to precompute optimal indexing schemas and body-orderings, resulting in significantly reduced memory usage without compromising efficiency.
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).