Proceedings

Combining Rewriting and Incremental Materialisation Maintenance for Datalog Programs with Equality

Download BibTeX Cite

Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence. Special Track on Knowledge Representation and Reasoning. Pages 3127-3132.

Abstract

Materialisation precomputes all consequences of a set of facts and a datalog program so that queries can be evaluated directly (i.e., independently from the program). Rewriting optimises materialisation for datalog programs with equality by replacing all equal constants with a single representative; and incremental maintenance algorithms can efficiently update a materialisation for small changes in the input facts. Both techniques are critical to practical applicability of datalog systems; however, we are unaware of an approach that combines rewriting and incremental maintenance. In this paper we present the first such combination, and we show empirically that it can speed up updates by several orders of magnitude compared to using either rewriting or incremental maintenance in isolation.

Boris Motik, Yavor Nenov, Robert Piro, and Ian Horrocks. 2015. Combining Rewriting and Incremental Materialisation Maintenance for Datalog Programs with Equality. In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-15. IJCAI, 3127–3132. https://ijcai.org/proceedings/2015/441
Welcome Future Conferences AI Journal Division IJCAI Proceedings About IJCAI IJCAI Awards